BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20280326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20281029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20290325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20291028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20300331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20301027T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20231003 DTEND;VALUE=DATE:20301006 DTSTAMP:20260508T231406 CREATED:20240220T151755Z LAST-MODIFIED:20240221T133040Z UID:10000445-1696291200-1917475199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLMPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre-Recorded \n \n \n \n \n \n \n \n \n \n \n About this course\n This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. The specific models we cover include binary\, binomial\, and categorical logistic regression\, Poisson and negative binomial regression for count variables\, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next\, we introduce the widely used binary logistic regression model\, which is is a regression model for when the outcome variable is binary. Next\, we cover the binomial logistic regression\, and the multinomial case\, which is for modelling outcomes variables that are polychotomous\, i.e.\, have more than two categorically distinct values. We will then cover Poisson regression\, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion\, starting with the quasi-likelihood approach\, then covering the negative binomial and beta-binomial models for counts and discrete proportions\, respectively. Finally\, we will cover zero-inflated Poisson and negative binomial models\, which are for count data with excessive numbers of zero observations. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – NA \nAvailability – NA \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session.\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing.\n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience.  \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Topic 1: The general linear model. We begin by providing an overview of the normal\, as in normal distribution\, general linear model\, including using categorical predictor variables. Although this model is not the focus of the course\, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model\, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression\, implement it using R’s glm\, and then show how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Binomial logistic regression. Here\, we show how the binary logistic regresion can be extended to deal with data on discrete proportions. We will also present alternative link functions to the logit\, such as the probit and complementary log-log links. \n \n \n \n \n \n Day 2\n Topic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression\, categorical logistic regression is also based on an extension of the binary logistic regression case. \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data\, i.e.\, data where the variable denotes the number of times an event has occurred. \n \n \n \n \n \n Day 3\n Topic 6: Overdispersion models. The quasi-likelihood approach for both the Poisson and binomial models. Negative binomial regression. The negative binomial model is\, like the Poisson regression model\, used for unbounded count data\, but it is less restrictive than Poisson regression\, specifically by dealing with overdispersed data. Beta-binomial regression. The beta-binomial model is an overdispersed alternative to the binomial. \nTopic 7: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub \n​ URL:https://prstats.preprodw.com/course/online-course-introduction-to-generalised-linear-models-using-r-and-rstudio-iglmpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IGLM04R.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231024 DTEND;VALUE=DATE:20301027 DTSTAMP:20260508T231406 CREATED:20240220T153455Z LAST-MODIFIED:20240221T132602Z UID:10000446-1698105600-1919289599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction To Mixed Models Using R And Rstudio (IMMRPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nPre-recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n This course provides a comprehensive practical and theoretical introduction to multilevel models\, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models\, but also cover multilevel generalized linear models. Likewise\, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition\, random effects models serve as a solid basis for understanding mixed effects\, i.e. fixed and random effects\, models. In this coverage of random effects\, we will also cover the important concepts of statistical shrinkage in the estimation of effects\, as well as intraclass correlation. We then proceed to cover linear mixed effects models\, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models\, including multilevel models for nested and crossed data data\, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs)\, how to accommodate overdispersion through individual-level random effects\, as well as Bayesian approaches to multilevel levels using the brms R package. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – NA \nAvailability – NA \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \n \n \n \n \n \n Assumed quantitative knowledge\n We will assume familiarity with general statistical concepts\, linear models\, statistical inference (p-values\, confidence intervals\, etc). \n \n \n \n \n \n Assumed computer background\n Minimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands\, how to write code in the RStudio console and script editor\, how to load up data from files\, etc.\n \n \n \n \n \n Equipment and software requirements\n \n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience.  \n\n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Topic 1: Random effects models. The defining feature of multilevel models is that they are models of models. We begin by using a binomial random effects model to illustrate this. Specifically\, we show how multilevel models are models of the variability in models of different clusters or groups of data. \nTopic 2: Normal random effects models. Normal\, as in normal distribution\, random effects models are the key to understanding the more general and widely used linear mixed effects models. Here\, we also cover the key concepts of statistical shrinkage and intraclass correlation. \n \n \n \n \n \n Day 2\n Topic 3: Linear mixed effects models. Next\, we turn to multilevel linear models\, also known as linear mixed effects models. We specifically deal with the cases of varying intercept and/or varying slope linear regression models. \nTopic 4: Multilevel models for nested data. Here\, we will consider multilevel linear models for nested\, as in groups of groups\, data. As an example\, we will look at multilevel linear models applied to data from students within classes that are themselves within different schools\, and where we model the variability of effects across the classes and across the schools. \nTopic 5: Multilevel models for crossed data. In some multilevel models\, each observation occurs in multiple groups\, but these groups are not nested. For example\, animals may be members of different species and in different locations\, but the species are not subsets of locations\, nor vice versa. These are known as crossed or multiclass data structures. \n \n \n \n \n \n Day 4\n Topic 6: Group level predictors. In some multilevel regression models\, predictor variable are sometimes associated with individuals\, and sometimes associated with their groups. In this section\, we consider how to handle these two situations. \nTopic 7: Generalized linear mixed models (GLMMs). Here\, we extend the linear mixed model to the exponential family of distributions and showcase an example using the Poisson GLMM. We also cover how to accommodate overdispersion through individual-level random effects. \nTopic 8: Bayesian multilevel models. All of the models that we have considered can be handled\, often more easily\, using Bayesian models. Here\, we provide an brief introduction to Bayesian models and how to perform examples of the models that we have considered using Bayesian methods and the brms R package. \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/online-course-introduction-to-mixed-models-using-r-and-rstudio-immrpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IMMR06R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240109 DTEND;VALUE=DATE:20300112 DTSTAMP:20260508T231406 CREATED:20240220T154155Z LAST-MODIFIED:20240709T134921Z UID:10000447-1704758400-1894406399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Model selection and model simplification (MSMSPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nPre-recorded \n​\n \n \n \n \n \n \n \n \n \n \n About This Course\n This three day course covers the important and general topics of statistical model building\, model evaluation\, model selection\, model comparison\, model simplification\, and model averaging. These topics are vitally important to almost every type of statistical analysis\, yet these topics are often poorly or incompletely understood. We begin by considering the fundamental issue of how to measure model fit and a model’s predictive performance\, and discuss a wide range of other major model fit measurement concepts like likelihood\, log likelihood\, deviance\, residual sums of squares etc. We then turn to nested model comparison\, particularly in general and generalized linear models\, and their mixed effects counterparts. We then consider the key concept of out-of-sample predictive performance\, and discuss over-fitting or how excellent fits to the observed data can lead to very poor generalization performance. As part of this discussion of out-of-sample generalization\, we introduce leave-one-out cross-validation and Akaike Information Criterion (AIC). We then cover general concepts and methods related to variable selection\, including stepwise regression\, ridge regression\, Lasso\, and elastic nets. Following this\, we turn to model averaging\, which is an arguably always preferable alternative to model selection. Finally\, we cover Bayesian methods of model comparison. Here\, we describe how Bayesian methods allow us to easily compare completely distinct statistical models using a common metric. We also describe how Bayesian methods allow us to fit all the candidate models of potential interest\, including cases were traditional methods fail. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely.\n \n \n \n \n \n Course Details\n Time zone – NA \nAvailability – NA \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n \n \n \n \n \n Assumed computer background\n \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n DAY 1 \nTopic 1: Measuring model fit. In order to introduce the general topic of model evaluation\, selection\, comparison\, etc.\, it is necessary to understand the fundamental issue of how we measure model fit. Here\, the concept of conditional probability of the observed data\, or of future data\, is of vital importance. This is intimately related\, though distinct\, to concept of likelihood and the likelihood function\, which is in turn related to the concept of the log likelihood or deviance of a model. Here\, we also show how these concepts are related to concepts of residual sums of squares\, root mean square error (rmse)\, and deviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested model comparison in general linear models\, generalized linear models\, and their mixed effects (multilevel) counterparts. First\, we precisely define what is meant by a nested model. Then we show how nested model comparison can be accomplished in general linear models with F tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear models\, and mixed effects models\, we can accomplish nested model comparison using deviance based chi-square tests via Wilks’s theorem. \n \n \n \n \n \n Day 2\n DAY 2 \nTopic 3: Out of sample predictive performance: cross validation and information criteria. In the previous sections\, the focus was largely on how well a model fits or predicts the observed data. For reasons that will be discussed in this section\, related to the concept of overfitting\, this can be a misleading and possibly even meaningless means of model evaluation. Here\, we describe how to measure out of sample predictive performance\, which measures how well a model can generalize to new data. This is arguably the gold-standard for evaluating any statistical models. A practical means to measure out of sample predictive performance is cross-validation\, especially leave-one-out cross-validation. Leave-one-out cross-validation can\, in relatively simple models\, be approximated by Akaike Information Criterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how to interpret AIC values\, and describe other related information criteria\, some of which will be used in more detail in later sections. \nTopic 4: Variable selection. Variable selection is a type of nested model comparison. It is also one of the most widely used model selection methods\, and variable selection of some kind is almost always done routinely in all data analysis. Although we will also have discussed variable selection as part of Topic 2 above\, we discuss the topic in more detail here. In particular\, we cover stepwise regression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets. \n \n \n \n \n \n Day 3\n DAY 3 \nTopic 5: Model averaging. Rather than selecting one model from a set of candidates\, it is arguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using information criteria. \nTopic 6: Bayesian model comparison methods. Bayesian methods afford much greater flexibility and extensibility for model building than traditional methods. They also allow us to easily directly compare completely unrelated statistical models of the same data using information criteria such as WAIC and LOOIC. Here\, we will also discuss how Bayesian methods allow us to fit all models of potential interest to us\, including cases where model fitting is computationally intractable using traditional methods (e.g.\, where optimization convergence fails). This allows us therefore to consider all models of potential interest\, rather than just focusing on a limited subset where the traditional fitting algorithms succeed. \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGate\nGoogleScholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/online-course-model-selection-and-model-simplification-msmspr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MSMS03.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240206 DTEND;VALUE=DATE:20300209 DTSTAMP:20260508T231406 CREATED:20240220T160615Z LAST-MODIFIED:20240221T135137Z UID:10000448-1707177600-1896825599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction to Time Series Analysis using R and Rstudio (ITSAPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre-Recorded \n \n \n \n \n \n \n \n \n \n \n About this course\n In this three-day course\, we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas\, such as finance\, meteorology\, ecology\, public policy\, and health. We start by introducing the concepts of time series and stationarity\, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then\, we will introduce benchmark forecasting methods\, namely the naïve (or random walk) method\, mean\, drift\, and seasonal naïve methods. After that\, we will present different exponential smoothing methods (simple\, Holt’s linear method\, and Holt-Winters seasonal method). Finally\, we will cover autoregressive integrated moving-average (or ARIMA) models\, with and without seasonality. If timeallows\, we will introduce regression with ARIMA errors. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in forecasting methods\, and using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – ~NA \nAvailability – NA \nDuration – 3 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \n  \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of R and statistical concepts. Specifically\, linear regression models\, statistical significance\, and hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Section 1: Introductory concepts in time series analysis. White noise\, stationarity\, autocovariance and autocorrelation. \nSection 2: Useful plots in time series analysis. Time plots\, seasonal plots\, autocorrelation plots. Time series decomposition: additive and multiplicative using the fable package in R. \n \n \n \n \n \n Day 2\n Section 3: Benchmark forecasting methods. The naïve\, mean\, drift\, and seasonal naïve methods. \nSection 4: Exponential smoothing. Simple exponential smoothing\, Holt’s linear method\, Holt-Winters seasonal method\, and fable’s general ETS method. \n \n \n \n \n \n Day 3\n Section 5: Autoregressive (AR) and moving-average (MA) models. Unit root tests for stationarity. How to identity the order of an AR(p) or an MA(q) model using autocorrelation and partial autocorrelation plots. \nSection 6: Autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. Automatic order selection for a (seasonal) ARIMA model using fable. Linear regression with ARIMA errors. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub \n  URL:https://prstats.preprodw.com/course/online-course-introduction-to-time-series-analysis-using-r-and-rstudio-itsapr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/02/MDAR-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240325 DTEND;VALUE=DATE:20300102 DTSTAMP:20260508T231406 CREATED:20240709T132655Z LAST-MODIFIED:20240709T132700Z UID:10000465-1711324800-1893542399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Advancing in R (ADVRPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre-Recorded \n \n \n \n \n \n \n \n \n \n \n Course Details\n COURSE DETAILS \nThis course is designed to provide attendees with a comprehensive understanding ofstatistical modelling and its applications in various fields\, such as ecology\, biology\, sociology\,agriculture\, and health. We cover all foundational aspects of modelling\, including all codingaspects\, ranging from data wrangling\, visualisation and exploratory data analysis\, togeneralized linear mixed models\, assessing goodness-of-fit and carrying out modelcomparison. \nData wranglingFor data wrangling\, we focus on tools provided by R's tidyverse. Data wrangling is the art oftaking raw and messy data and formatting and cleaning it so that data analysis andvisualization may be performed on it. Done poorly\, it can be a time consuming\, laborious\,and error-prone. Fortunately\, the tools provided by R's tidyverse allow us to do datawrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequencefor ease and speed with which we analyse data. We start with how to read data of differenttypes into R\, we then cover in detail all the dplyr tools such as select\, filter\, mutate\, andothers. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelinesthat take raw messy data on the one end and return cleaned tidy data on the other. Wethen cover how to perform descriptive or summary statistics on our data using dplyr’sgroup_by and summarise functions. We then turn to combining and merging data. Here\, wewill consider how to concatenate data frames\, including concatenating all data files in afolder\, as well as cover the powerful SQL-like join operations that allow us to mergeinformation in different data frames. The final topic we will consider is how to “pivot” datafrom a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_widerfunctions. \nData visualisationFor visualisation\, we focus on the ggplot2 package. We begin by providing a brief overviewof the general principles data visualization\, and an overview of the general principles behindggplot. We then proceed to cover the major types of plots for visualizing distributions ofunivariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of thesecases\, we will consider how to visualize multiple distributions simultaneously on the sameplot using different colours and "facet" plots. We then turn to the visualization of bivariatedata using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothingfunctions to the data\, how to add marginal histograms to the scatterplot\, add labels topoints\, and scale each point by the value of a third variable. We then cover some additionalplot types that are often related but not identical to those major types covered during thebeginning of the course: frequency polygons\, area plots\, line plots\, uncertainty plots\, violinplots\, and geospatial mapping. We then consider more fine grained control of the plot bychanging axis scales\, axis labels\, axis tick points\, colour palettes\, and ggplot "themes".Finally\, we consider how to make plots for presentations and publications. Here\, we will introduce how to insert plots into documents using RMarkdown\, and also how to createlabelled grids of subplots of the kind seen in many published articles. \nGeneralized linear modelsGeneralized linear models are generalizations of linear regression models for situationswhere the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. Thespecific models we cover include binary\, binomial\, and categorical logistic regression\,Poisson and negative binomial regression for count variables\, as well as extensions foroverdispersed and zero-inflated data. We begin by providing a brief overview of the normalgeneral linear model. Understanding this model is vital for the proper understanding of howit is generalized in generalized linear models. Next\, we introduce the widely used binarylogistic regression model\, which is is a regression model for when the outcome variable isbinary. Next\, we cover the binomial logistic regression\, and the multinomial case\, which isfor modelling outcomes variables that are polychotomous\, i.e.\, have more than twocategorically distinct values. We will then cover Poisson regression\, which is widely used formodelling outcome variables that are counts (i.e the number of times something hashappened). We then cover extensions to accommodate overdispersion\, starting with thequasi-likelihood approach\, then covering the negative binomial and beta-binomial modelsfor counts and discrete proportions\, respectively. Finally\, we will cover zero-inflated Poissonand negative binomial models\, which are for count data with excessive numbers of zeroobservations. \nMixed modelsWe will focus primarily on multilevel linear models\, but also cover multilevel generalizedlinear models. Likewise\, we will also describe Bayesian approaches to multilevel modelling.We will begin by focusing on random effects multilevel models. These models make it clearhow multilevel models are in fact models of models. In addition\, random effects modelsserve as a solid basis for understanding mixed effects\, i.e. fixed and random effects\, models.In this coverage of random effects\, we will also cover the important concepts of statisticalshrinkage in the estimation of effects\, as well as intraclass correlation. We then proceed tocover linear mixed effects models\, particularly focusing on varying intercept and/or varyingslopes regression models. We will then cover further aspects of linear mixed effects models\,including multilevel models for nested and crossed data data\, and group level predictorvariables. Towards the end of the course we also cover generalized linear mixed models(GLMMs)\, how to accommodate overdispersion through individual-level random effects\, aswell as Bayesian approaches to multilevel levels using the brms R package. \nModel selection and model simplificationThroughout the course we consider the fundamental issue of how to measure model fit anda model’s predictive performance\, and discuss a wide range of other major model fitmeasurement concepts like likelihood\, log likelihood\, deviance\, and residual sums ofsquares. We thoroughly explore nested model comparison\, particularly in general andgeneralized linear models\, and their mixed effects counterparts. We discuss out-of-samplegeneralization\, and introduce leave-one-out cross-validation and the Akaike Information Criterion (AIC). We also cover general concepts and methods related to variable selection\,including stepwise regression\, ridge regression\, Lasso\, and elastic nets. Finally\, we turn tomodel averaging\, which may represent a preferable alternative to model selection. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – NA \nAvailability – NA \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at:\n• 10am-12pm\n• 1pm-3pm\n• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nIf some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. For example\, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm\, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing.\n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Day 1 \nTopic 1: Reading in data. We will begin by reading in data into R using tools suchas readr and readxl. Almost all types of data can be read into R\, and here we will considermany of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to controlhow data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc. \nTopic 2: Wrangling with dplyr. We will next cover the very powerful dplyr R package. Thispackage supplies a number of so-called "verbs" — select\, rename\, slice\, filter\, mutate\, arrange\, etc. — each of which focuses on a key data manipulation tools\, such as selecting or changing variables. All of these verbs can be chained together using "pipes" (represented by %>%). Together\, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here\, we will also learn about the key concept of "tidy data"\, which is roughly where each row of a data frame is an observation and each column is a variable. \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used withgreat effect to summarize data using descriptive statistics. \nTopic 4: Merging and joining data frames. There are multiple ways to combine data frames\,with the simplest being "bind" operations\, which are effectively horizontal or verticalconcatenations. Much more powerful are the SQL-like "join" operations. Here\, we willconsider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will alsoconsider how to use purrr to read in and automatically merge large sets of files. \nTopic 5: Pivoting data. Sometimes we need to change data frames from "long" to "wide"formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this. \n \n \n \n \n \n Day 2\n Day 2 \nTopic 1: What is data visualization. Data visualization is a means to explore and understandour data and should be a major part of any data analysis. Here\, we briefly discuss why datavisualization is so important and what the major principles behind it are. \nTopic 2: Introducing ggplot. Though there are many options for visualization in R\, ggplot issimply the best. Here\, we briefly introduce the major principles behind how ggplot works\,namely how it is a layered grammar of graphics.Topic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizingdistributions over single variables: histograms\, density plots\, barplots\, Tukey boxplots. In each case\, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots. \nTopic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data.Here\, in addition to covering how to visualize multiple groups using colours and facets\, wewill also cover how to provide marginal plots on the scatterplots\, labels to points\, and howto obtain linear and nonlinear smoothing of the plots. \nTopic 5: More plot types. Having already covered the most widely used general purposeplots\, we now turn to cover a range of other major plot types: frequency polygons\, areaplots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these areimportant and widely used types of plots\, and knowing them will expand your repertoire. \nTopic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plotstyles and layouts. Here\, we will introduce how to modify things like the limits and scales onthe axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, andthe different types of ggplot themes that are available. \nTopic 7: Plots for publications and presentations. Thus far\, we have primarily focused ondata visualization as a means of interactively exploring data. Often\, however\, we also wantto present our plots in\, for example\, published articles or in slide presentations. It is simpleto save a plot in different file formats\, and then insert them into a document. However\, amuch more efficient way of doing this is to use RMarkdown to run the R code andautomatically insert the resulting figure into a\, for example\, Word document\, pdf document\,html page\, etc. In addition\, here we will also cover how to make labelled grids of subplotslike those found in many scientific articles. \n \n \n \n \n \n Day 3\n Day 3 \nTopic 1: The general linear model. We begin by providing an overview of the normal\, as innormal distribution\, general linear model\, including using categorical predictor variables.Although this model is not the focus of the course\, it is the foundation on which generalizedlinear models are based and so must be understood to understand generalized linearmodels. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logisticregression model\, for use when modelling binary outcome data. We will present theassumed theoretical model behind logistic regression\, implement it using R’s glm\, and thenshow how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Binomial logistic regression. Here\, we show how the binary logistic regression canbe extended to deal with data on discrete proportions. We will also present alternative linkfunctions to the logit\, such as the probit and complementary log-log links. \nTopic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Categorical logistic regression is based on an extension of the binary logistic regression case. \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modellingcount data\, i.e.\, data where the variable denotes the number of times an event has occurred. \n \n \n \n \n \n Day 4\n Day 4 \nTopic 1: Measuring model fit. Here\, the concept of conditional probability of the observeddata\, or of future data\, is of vital importance. This is intimately related\, though distinct\, toconcept of likelihood and the likelihood function\, which is in turn related to the concept ofthe log likelihood or deviance of a model. Here\, we also show how these concepts arerelated to concepts of residual sums of squares\, root mean square error (rmse)\, anddeviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested modelcomparison in general linear models\, generalized linear models\, and their mixed effects(multilevel) counterparts. First\, we precisely define what is meant by a nested model. Thenwe show how nested model comparison can be accomplished in general linear models withF tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linearmodels\, we can accomplish nested model comparison using deviance based chi-square testsvia Wilks’s theorem. \nTopic 3: Overdispersion models. The quasi-likelihood approach for both the Poisson andbinomial models. Negative binomial regression. The negative binomial model is\, like thePoisson regression model\, used for unbounded count data\, but it is less restrictive thanPoisson regression\, specifically by dealing with overdispersed data. Beta-binomialregression. The beta-binomial model is an overdispersed alternative to the binomial. \nTopic 4: Zero inflated models. Zero inflated count data is where there are excessivenumbers of zero counts that can be modelled using either a Poisson or negative binomialmodel. Zero inflated Poisson or negative binomial models are types of latent variablemodels. \nTopic 5: Random effects models. The defining feature of multilevel models is that they aremodels of models. We begin by using a binomial random effects model to illustrate this.Specifically\, we show how multilevel models are models of the variability in models ofdifferent clusters or groups of data. \nTopic 6: Normal random effects models. Normal\, as in normal distribution\, random effectsmodels are the key to understanding the more general and widely used linear mixed effectsmodels. Here\, we also cover the key concepts of statistical shrinkage and intraclasscorrelation. \n \n \n \n \n \n Day 5\n Day 5 \nTopic 1: Out of sample predictive performance: cross validation and information criteria.Here\, we describe how to measure out of sample predictive performance\, which measureshow well a model can generalize to new data. This is arguably the gold-standard forevaluating any statistical models. A practical means to measure out of sample predictiveperformance is cross-validation\, especially leave-one-out cross-validation. Leave-one-outcross-validation can\, in relatively simple models\, be approximated by Akaike InformationCriterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how tointerpret AIC values\, and describe other related information criteria\, some of which will beused in more detail in later sections. \nTopic 2: Linear mixed effects models. Next\, we turn to multilevel linear models\, also knownas linear mixed effects models. We specifically deal with the cases of varying interceptand/or varying slope linear regression models. \nTopic 3: Multilevel models for nested data. Here\, we will consider multilevel linear modelsfor nested\, as in groups of groups\, data. As an example\, we will look at multilevel linearmodels applied to data from students within classes that are themselves within differentschools\, and where we model the variability of effects across the classes and across theschools. \nTopic 4: Multilevel models for crossed data. In some multilevel models\, each observationoccurs in multiple groups\, but these groups are not nested. For example\, animals may bemembers of different species and in different locations\, but the species are not subsets oflocations\, nor vice versa. These are known as crossed or multiclass data structures. \nTopic 5: Group level predictors. In some multilevel regression models\, predictor variable aresometimes associated with individuals\, and sometimes associated with their groups. In thissection\, we consider how to handle these two situations. \nTopic 6: Generalized linear mixed models (GLMMs). Here\, we extend the linear mixed modelto the exponential family of distributions and showcase an example using the PoissonGLMM. We also cover how to accommodate overdispersion through individual-levelrandom effects. \nTopic 7: Bayesian multilevel models. All of the models that we have considered can behandled\, often more easily\, using Bayesian models. Here\, we provide an brief introductionto Bayesian models and how to perform examples of the models that we have consideredusing Bayesian methods and the brms R package. \nTopic 8: Variable selection. Variable selection is a type of nested model comparison. It isalso one of the most widely used model selection methods\, and variable selection of somekind is almost always done routinely in all data analysis. In particular\, we cover stepwiseregression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets.Topic 9: Model averaging. Rather than selecting one model from a set of candidates\, it isarguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using informationcriteria. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/advancing-in-r-advrpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/01/nick-owuor-astro-nic-portraits-wDifg5xc9Z4-unsplash-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250127 DTEND;VALUE=DATE:20250206 DTSTAMP:20260508T231407 CREATED:20230726T154721Z LAST-MODIFIED:20240926T112209Z UID:10000433-1737936000-1738799999@prstats.preprodw.com SUMMARY:ONLINE COURSE - Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 27th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n In this six-day course\, we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas\, such as finance\, meteorology\, ecology\, public policy\, and health. We start by introducing the concepts of time series and stationarity\, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then\, we will introduce benchmark forecasting methods\, namely the naïve (or random walk) method\, mean\, drift\, and seasonal naïve methods. After that\, we will present different exponential smoothing methods (simple\, Holt’s linear method\, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models\, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally\, we will cover Bayesian implementations of time series models and introduce extended models\, such as ARCH\, GARCH and stochastic volatility models\, as well as Brownian motion and Ornstein-Uhlenbeck processes. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in forecasting methods\,and using R for data science or statistics. R is widely used in all areas ofacademic scientific research\, and also widely throughout the public\, andprivate sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – TBC \nDuration – 3 days \nContact hours – Approx. 14 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R.Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. The course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at: 6pm-9pm \nAll sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of R and statistical concepts. Specifically\, linear regression models\, statistical significance\, and hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 27th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nSection 1: Introductory concepts in time series analysis. White noise\, stationarity\, autocovariance and autocorrelation. \nSection 2: Useful plots in time series analysis. Time plots\, seasonal plots\, autocorrelation plots. Time series decomposition: additive and multiplicative using the fable package in R. \n \n \n \n \n \n Tuesday 28th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nSection 3: Time series decomposition: additive and multiplicative using the fable package in R. \nSection 4: Benchmark forecasting methods. The naïve\, mean\, drift\, and seasonal naïve methods. Cross-validation methods for time series analysis. \nTime series plots (Independant practical 1) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Wednesday 29th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nSection 4 (‘ctd) \nSection 5: Exponential smoothing. Simple exponential smoothing\, Holt’s linear method\, Holt-Winters seasonalmethod\, and fable’s general ETS method. \nTime series decomposition and benchmark forecasting methods (Independant practical 2) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Monday 3rd\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 4 \nSection 6: Autoregressive (AR) and moving-average (MA) models. Unit root tests for stationarity. How to identity the order of an AR(p) or an MA(q) model using autocorrelation and partial autocorrelation plots. \nSection 7: Autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. Automatic order selection for a (seasonal) ARIMA model using fable. Linear regression with ARIMA errors. \nExponential smoothing (Independant practical 3) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Tuesday 4th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 5 \nSection 8: Generalized Additive Models (GAMs). An introduction to semi-parametric regression using splines. Incorporating trends and seasonal components of a time series using a GAM. \nSection 9: An introduction to Bayesian modelling. Implementation of random walks\, autoregressive\, and moving average models using JAGS. \nARIMA models (Independant practical 4) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Wednesday 5th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 6 \nSection 10: Modelling the variance as a time series process. Autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models. Stochastic volatility models. \nSection 11: Continuous time models. Brownian motion and Ornstein-Uhlenbeck processes. Fitting continuous time series models using JAGS. \nSection 12: Multivariate time series. Vector autoregression. Simple examples using JAGS. \nGAMs and Bayesian models (Independant practical 5) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGateGoogleScholarORCIDGitHub \n  URL:https://prstats.preprodw.com/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/02/MDAR-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250203 DTEND;VALUE=DATE:20250208 DTSTAMP:20260508T231407 CREATED:20241113T143355Z LAST-MODIFIED:20241114T143930Z UID:10000466-1738540800-1738972799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Vision using Python (MVUP01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 3rd\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n Course Details\n Machine vision has produced many helpful image-processing techniques in several fields\, such as object detection\, classification\, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods\, mainly deep learning\, to solve vision problems. Common problems\, such as classification and localisation\, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification\, focusing on deep neural networks and Convolutional Neural Networks (CNNs)\, including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods\, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind the machine vision ecosystem in Python;\nUnderstand the machine vision pipeline workflow;\nUnderstand the application of standard Python packages such as OpenCV and Tensorflow;\nUnderstand the basic concepts behind Deep Neural Networks;\nUnderstand the basic concepts behind Convolutional Deep Neural Networks;\nUnderstand basic concepts behind Transfer learning;\nHave the confidence to implement basic Machine vision methods using Python;\nHave the confidence to combine basic computer vision and machine learning methods to perform vision tasks;\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to machine vision\nApplied researchers and analysts in public\, private or third-sector organisations who need the reproducibility\, speed and flexibility of a programming language such as Python for machine vision;\nEcologists utilise Python to solve vision-related problems and look to update their knowledge in the machine vision area.\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – TBC \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets\, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \nAll sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and mathematical concepts. Also\, a basic understanding of supervised learning. \n \n \n \n \n \n Assumed computer background\n Day one will cover the basics of Python for the module. However\, some familiarity with any other programming language is welcome \n \n \n \n \n \n Equipment and software requirements\n A laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nhttps://www.python.org/downloads/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 3rd\n Day 1: A Short Course in Python Basics (9:30 – 17:30) \nThis day provides participants with the foundational Python skills required for machine vision tasks. This day is designed for beginners or those needing a refresher in Python programming. \n\nSection 1 (Python Essentials for Machine Vision): This section focuses on Python syntax\, variables\, data types\, conditionals (`if`\, `else`\, `elif`)\, loops (`for`\, `while`)\, and writing reusable code using functions.\nSection 2 (Data Structures and File Handling in Python): Focuses on lists\, dictionaries\, tuples\, sets\, and reading/writing files (e.g.\, CSVs) for data manipulation.\n\n \n \n \n \n \n Tuesday 4th\n Day 2: Fundamentals of Computer Vision (9:30 – 17:30) \nThis day focuses on the theoretical foundations of computer vision\, detailing the main aspects. \n\nSection 3 (Introduction to Computer Vision and Image Processing): This section covers the fundamental structure of an image\, basic image handling techniques\, and an introduction to computer graphics.\nSection 4 (Local Image Descriptors and Feature Mapping): This section explores local image descriptors\, such as the Harris Corner Detector\, and techniques for image-to-image mapping.\n\n \n \n \n \n \n Wednesday 5th\n Day 3: Fundamentals of Deep Learning (9:30 – 17:30) \nThis day focuses on the theoretical foundations of deep learning from Neural Networks to Convolutional Neural Networks (CNNs). \n\nSection 5 (Neural Networks: From Basics to Backpropagation): Introduces artificial neurons and explains how neural networks learn through backpropagation.\nSection 6 (Convolutional Neural Networks (CNNs) for Image Classification): Provides a detailed explanation of CNN architecture\, including convolution layers\, pooling layers\, and fully connected layers.\n\n \n \n \n \n \n Thursday 6th\n Day 4: Understanding the Machine Vision Ecosystem in Python (OpenCV & TensorFlow) (9:30 – 17:30) \nThis day introduces participants to the core libraries used in machine vision tasks. OpenCV is used for image processing\, and TensorFlow is used for building deep learning models. \n\nSection 7 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.\nSection 8 (Image Processing with OpenCV: Filters\, Edge Detection & Contours): Covers basic image manipulation techniques using OpenCV\, including resizing\, cropping\, applying filters (blurring/sharpening)\, edge detection (Canny)\, and contour detection.\n\n \n \n \n \n \n Friday 7th\n Day 5: The Machine Vision Pipeline (9:30 – 17:30) \nParticipants will learn about the end-to-end workflow of a typical machine vision project. \nSection 9 (Preprocessing Images for Deep Learning with OpenCV & TensorFlow): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Vision Pipeline will be used. \nSection 10 (The Complete Machine Vision Pipeline: From Image Capture to Classification): Covers the end-to-end machine vision workflow\, including image capture\, enhancement through preprocessing\, segmentation\, feature extraction\, and classification using machine learning classifiers. \n \n \n \n \n \n \n Course Instructor\n \nDr. Gabriel Palma \nGabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo\, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University\, Ireland\, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling\, machine vision\, machine learning\, and applications to ecology and entomology. His personal webpage can be found here \nResearchGateGoogleScholar \n  URL:https://prstats.preprodw.com/course/machine-vision-using-python-mvup01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2024/11/Screenshot-2024-11-13-at-12.47.27.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250207 DTEND;VALUE=DATE:20250208 DTSTAMP:20260508T231407 CREATED:20220504T113357Z LAST-MODIFIED:20240130T173931Z UID:10000409-1738886400-1738972799@prstats.preprodw.com SUMMARY:Introduction to eco-phylogenetics and comparative analyses using R (ECPHPR) DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded\n \n \n \n \n \n \n \n \n \n \n About This Course\n In this five day course\, we provide an introduction to eco-phylogenetics and comparative analyses using R. We begin by providing an  overview on the use of phylogenies as a tool for evolutionary biologists and modern techniques to deal with large phylogenies and to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover some of the most relevant eco-phylogenetic analyses and provide examples from the community to themacro-ecological scale (day 2-3). Finally\, we introduce a diversity of classic and modern phylogenetic comparative methods to consider the historical relationship of lineages in eco-evolutionary research\, including models of trait evolution\, analysis of clade diversification and the use of phylogenies in spatial distribution models among others (day 4-5). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses.\n \n \n \n \n \n Course Details\n Last Up-Dated – 11:02:2021 \nDuration – Approx. 30 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will\noccur between (UK local time) at:\n• 8:00am-10:00am\n• 11:00pm-13:00pm\n• 14:30pm-16:30pm \nAll sessions will be video recorded and made available to all attendees.\n \n \n \n \n \n Assumed quantative knowledge\n We will assume general familiarity with the very basics of statistics (e.g. summary statistics\, distributions). As this is an introductory course\, no phylogenetic background is required.\n \n \n \n \n \n Assumed computer background\n We will assume general familiarity with R elementary operations (e.g. package sourcing\, data importing and exporting\, object indexing) and some familiarity with programming in R (writing code).\n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 7 Hours \n• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists\, phylogenetic inference (quick overview)\, phylogenies aevolutionary hypotheses. \n• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning\, resolving polytomies\, sticking species). Visualizing large phylogenies. \n• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom\, V.PhyloMaker\, SUNPLIN and randtip R package. \n \n \n \n \n \n \n Day 2\n Approx. 7 Hours \n• Introduction to the eco-phylogenetic framework\, classical conception and posterior modifications. \n• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices\, null models\, applications to biodiversity conservation. \n• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity.\n \n \n \n \n \n Day 3\n Approx. 7 Hours \n• Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses. \n• Spatial phylogenetics. RPD\, RPE and CANEPE analysis. \n• Overview of functional trait ecology. Functional richness\, evenness and divergence.Community weighted means. \n• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology\n \n \n \n \n \n Day 4\n Approx. 7 Hours \n\nThe phylogenetic comparative method\, from independent contrasts to sophisticated modelling.\nAnalyses of phylogenetic signal and models of evolution: rationale\, common- practice\, and new trends.\nCorrelated evolution and ancestral trait reconstruction.\nAnalyses of diversification\, speciation and extinction rates in a geographic context.\n\n\n \n \n \n \n \n Day 5\n Approx. 7 Hours \n\nThe need to account for phylogenetic relationships in models.\nMost common phylogenetic modelling approaches: PGLS\, PGLMM\, BayesianPMM.\nPutting phylogenies in the geography: how to combine phylogenies with species distribution models.\n\n \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \n \n \n \n \n Rafael Molina Venegas \nWorks at: Universidad Autónoma de Madrid \nTeaches: Introduction to eco-phylogenetics and comparative analyses using R \nThe scientific career of Rafael Molina Venegas revolves around three research lines pertaining to (1) the ecological and evolutionary mechanisms that jointly shape species assemblages at the community and macroecological scales\, (2) the development\, improvement\, and assessment of phylogenetic methods\, and (3) the links between biodiversity and human well-being. While these lines represent clearly differentiated research interests\, phylogenetics is a cross-cutting background for all of them. Considering that plants are his true passion in science\, he defines himself as a Phylogenetic Plant Ecologist. \nVisit Website \nGoogle Scholar URL:https://prstats.preprodw.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecphpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250210 DTEND;VALUE=DATE:20250215 DTSTAMP:20260508T231407 CREATED:20241114T114852Z LAST-MODIFIED:20241114T144542Z UID:10000467-1739145600-1739577599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Learning using Python (MLUP01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 10th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n This course comprehensively introduces Machine Learning\, covering theoretical foundations and practical applications. It focuses on crucial machine learning techniques such as supervised and unsupervised learning algorithms\, using Python and popular libraries like Scikit-learn\, TensorFlow\, and Keras. The course emphasises hands-on projects to apply learned concepts to real-world ecological problems. By the end of the course\, participants should: \n\nUnderstand fundamental concepts in machine learning\, including supervised and unsupervised learning.\nBe able to preprocess data for machine learning tasks.\nUnderstand key algorithms for regression\, classification\, clustering\, and dimensionality reduction.\nGain proficiency in building neural networks and deep learning models.\nBe familiar with model selection techniques and hyperparameter tuning.\nHave confidence in deploying machine learning models in production environments.\nBe able to apply machine learning techniques to solve real-world problems through hands-on projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on machine learning projects.\nData scientists and applied researchers in public or private sectors who need to implement machine learning solutions.\nProfessionals looking to integrate machine learning into their workflows or enhance their understanding of AI technologies.\nEcologists looking to understand the basic principles of Machine learning and implement them in their research.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – TBC \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets\, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \nAll sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and mathematical concepts\, such as linear algebra. \n \n \n \n \n \n Assumed computer background\n Day one will cover the basics of Python for the module. However\, some familiarity with any other programming language is welcome. \n \n \n \n \n \n Equipment and software requirements\n A laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nhttps://www.python.org/downloads/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 10th\n Day 1: A Short Course in Python Basics (9:30 – 17:30) \nThis day provides participants with the foundational Python skills required for machine learning tasks. This day is designed for beginners or those needing a refresher in Python programming. \n\nSection 1 (Python Essentials for Machine Learning): This section focuses on Python syntax\, variables\, data types\, conditionals (`if`\, `else`\, `elif`)\, loops (`for`\, `while`)\, and writing reusable code using functions.\nSection 2 (Data Structures and File Handling in Python): Focuses on lists\, dictionaries\, tuples\, sets\, and reading/writing files (e.g.\, CSVs) for data manipulation.\n\n \n \n \n \n \n Tuesday 11th\n Day 2: Fundamentals of Machine Learning (9:30 – 17:30) \nThis day focuses on the theoretical foundations of machine learning\, detailing the application of learning algorithms in preparation for the practical examples in Python. \n\nSection 3 (Introduction to Machine Learning): This section covers the definition of Machine learning\, types of Learning (Supervised\, Unsupervised\, Reinforcement\, Semi-Supervised)\, applications of Machine Learning and an overview of Python libraries for ML (NumPy\, scikit-learn)\nSection 4 (Fundamental learning algorithms): This section explores the available learning algorithms and focuses on their applications. We will also discuss the application of different algorithms with practical examples in Ecology.\n\n \n \n \n \n \n Wednesday 12th\n Day 3: Statistical Learning Theory (9:30 – 17:30) \nThis day focuses on the theoretical foundations of Statistical Learning Theory (SLT) and illustrates their practical implications. \n\nSection 5 (Important Definitions on SLT): In this section\, we will explore the concept of Statistical Learning Theory and its implications for classification tasks in supervised learning settings\, highlighting its importance for machine learning practitioners.\nSection 6 (Practical implications of the SLT): This section provides a detailed explanation of the practical consequences of statistical learning theory based on Vapniks’ findings and using Support Vector Machines as a helpful example in Python\n\n \n \n \n \n \n Thursday 13th\n Day 4: Classification boundaries and the power of Deep Neural networks (9:30 – 17:30) \nThis day introduces participants to the core libraries used in machine learning tasks. scikit-learn is used to implement machine learning algorithms\, and TensorFlow is used to build deep learning models. \n\nSection 7 (Classification with various learning algorithms): Offers a step-by-step guide to building learning algorithms using scikit-learn.\nSection 8 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.\n\n \n \n \n \n \n Friday 14th\n Day 5: The Machine Learning Pipeline (9:30 – 17:30) \nParticipants will learn about the end-to-end workflow of a typical machine learning project using ecological datasets as an illustration. \nSection 9 (Preprocessing data and selecting algorithms): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Learning Pipeline will be used. \nSection 10 (The Complete Machine Learning Pipeline: From Classification to Evaluating Learning): Covers the end-to-end machine learning workflow\, including using the data preprocessed data and creating scikit-learn pipelines to automate critical aspects of the workflow. \n \n \n \n \n \n \n Course Instructor\n \nDr. Gabriel Palma \nGabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo\, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University\, Ireland\, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling\, machine vision\, machine learning\, and applications to ecology and entomology. His personal webpage can be found here \nResearchGateGoogleScholar \n  \n  URL:https://prstats.preprodw.com/course/machine-learning-using-python-mlup01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2024/11/Screenshot-2024-11-13-at-14.55.58.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250211 DTEND;VALUE=DATE:20250214 DTSTAMP:20260508T231407 CREATED:20240613T125347Z LAST-MODIFIED:20250205T170134Z UID:10000452-1739232000-1739491199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, February 11th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees\nthrough the accompanying computer practicals via video link\, so a good internet connection is\nessential. \nTime Zone\nLisbon (Portugal) time\, i.e. UTC / GMT or BST\, depending on time of year (daylight saving time\nfrom last Sunday of March to last Sunday of October). Check online for the time conversion\ncorresponding to the course dates. However\, all sessions will be recorded and made available\,\nallowing attendees from different time zones to follow asynchronously. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n This course focuses on the use of BART (Bayesian Additive Regression Trees) for modellingspecies’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM)\, as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally\, BART allows mapping the uncertainty and credible intervals associated to each local prediction. \nThe course includes a combination of theoretical lectures and hands-on practicals in R\, as well asopen discussions about models and data for SDM applications. The practicals go through acomplete worked example\, from data preparation to model output analysis\, with annotated Rscripts that can be adapted on-the-spot by participants to work on their own species of interest.Along the course\, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations. \n \n \n \n \n \n Intended Audiences\n The course is aimed at students\, researchers and practitioners with an interest in implementing\nbest practices and state-of-the-art methods for modelling species’ distributions or ecological\nniches\, in an automated and reproducible way.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – 18 places \nDuration – 3 days \nContact hours – Approx. 12 hours live\, plus remote assistance via Slack from the first day to the\nweekday after the course. \nECT’s – Equal to 1.5 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n This course runs along 3 days\, each with a 4-hour live online session. Each session is divided into4 parts\, alternating between theoretical lectures and hands-on practicals. Annotated scripts areprovided and instructor assistance is available\, both during the live sessions (on Zoom) andwhenever possible the rest of the day (on Slack)\, until the weekday after the course.Live sessions will be video-recorded\, uploaded to a video hosting website as soon as possible aftereach session\, and remain available for one month after the course. \n \n \n \n \n \n Assumed quantitative knowledge\n Participants should know what species distribution or ecological niche models (SDM / ENM) are\,\nand ideally have some previous experience with the basics. Previous knowledge of Bayesian\nstatistics is not required.\n \n \n \n \n \n Assumed computer background\n Participants should have some previous experience with R\, including package installation and\nbasic data handling\, although commented scripts will be provided for the entire course.\n \n \n \n \n \n Equipment and software requirements\n \nParticipants must use a computer with a good internet connection\, a working recent version or R (and ideally also RStudio)\, and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets\, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 11th\n Classes from 14:00 – 18:00 \nDAY 1– Module 1a: Obtain and process data\, including species presences and environmental variables– Practical– Module 1b: Determine an adequate spatial resolution and extent for modelling– Practical \n \n \n \n \n \n Wednesday 12th\n Classes from 14:00 – 18:00 CET \nDAY 2– Module 2a: Build a species distribution model with BART and obtain predictions of environmentalfavorability\, with credibility intervals and associated uncertainty– Practical– Module 2b: Evaluate and cross-validate the model\, assessing various aspects of predictive ability– Practical \n  \n \n \n \n \n \n Thursday 13th\n Classes from 14:00 – 18:00 CET \nDAY 3 \n– Module 3a: Quantify variable contributions and try out different methods for selecting relevantvariables– Practical– Module 3b: Plot and map the species’ partial response to each variable– Practical \n \n \n \n \n \n \n \n \n Dr. Marcia Barbosa\n \n Márcia is an experienced researcher and instructor in biogeography and macroecology\, particularly in geographic information systems and species distribution modelling. She’s also a reviewer and editor for scientific journals and funding agencies\, and a promoter and developer of free and open-source software implementing transparency\, reproducibility and best practices. You can see her publication list at her website or at Publons/ResearcherID\, Scopus\, ORCID\, Google Scholar\, or ResearchGate. \nResearch Gate\n Google Scholar\n ORCID\n GitHub\nHomepage URL:https://prstats.preprodw.com/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SDMB04.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250217 DTEND;VALUE=DATE:20250222 DTSTAMP:20260508T231407 CREATED:20240530T130225Z LAST-MODIFIED:20240926T113018Z UID:10000458-1739750400-1740182399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 17th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n Course Details\n Course overview: \nEcological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. \nThis course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles\, 2) remotely sensed data gathering and analysis\, 3) monitoring ecosystem change in space and time by remote sensing data. \nThe course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. \nBy the end of the course\, participants will:• be able to create their own projects on monitoring of spatial and temporal changes of ecosystems with remote sensing data• be able to report in LaTeX and R Markdown the achieved results \n \n \n \n \n \n Intended Audiences\n Intended Audience• Practitioners\, students\, academics• People new to R \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central European Time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Theoretical presentations will introduce coding sessions. The whole course is intended to be practical. \n \n \n \n \n \n Assumed quantitative knowledge\n No previous knowledge of R is needed. \n \n \n \n \n \n Assumed computer background\n A basic computer background is needed. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nPackage needed for the course:– imageRy– overlap– spatstat– terra– vegan \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\nMonday 17th – Classes from 09:30 to 17:30 \n– R (intro) \n[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!] \n[Spatial R] \n[Reference systems: introduction to the main coordinate systems] \n– Visualizing data \n[Visualizing multi- e hyper-spectral data] \n  \nTuesday 18th – Classes from 09:30 to 17:30 \n– Spectral indices extracted from satellite imagery \n[Main spectral indices extracted from remote sensing data] \n– Remote sensing data classification \n[Generating land cover maps from remotely sensed data] \n  \nWednesday 19th – Classes from 09:30 to 17:30 \n– Land use change in space and time \n[Analysis ecosystem change in space and time: the case of Mato Grosso] \n[Time series: ice melt in Greenland] \n  \nThursday 20th – Classes from 09:30 to 17:30 \n– External remote sensing data \n[Download and use remote sensing data from internet sources] \n[Downloading and visualising Copernicus data] \n– Image data processing \n[Ecosystem variability] \n[Multivariate analysis on remotely sensed data] \n  \nFriday 21st – Classes from 09:30 to 17:30 \n– Reporting \n[LaTeX for scientific reporting via articles] \n[LaTeX/Beamer for scientific reporting via presentations] \n[R Markdown for scientific reporting via internet pages] \n  \n\n  \n  \n \n \n \n \n \n Course Instructor\n \nDr. Duccio Rocchini\nComing soon… URL:https://prstats.preprodw.com/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01-1.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250225 DTEND;VALUE=DATE:20250228 DTSTAMP:20260508T231407 CREATED:20201010T135502Z LAST-MODIFIED:20241120T124623Z UID:10000328-1740441600-1740700799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, February 25th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Ireland Local Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n Course Details\n COURSE DETAILS \nThis course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. The specific models we cover include binary\, binomial\, and categorical logistic regression\, Poisson and negative binomial regression for count variables\, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next\, we introduce the widely used binary logistic regression model\, which is is a regression model for when the outcome variable is binary. Next\, we cover the binomial logistic regression\, and the multinomial case\, which is for modelling outcomes variables that are polychotomous\, i.e.\, have more than two categorically distinct values. We will then cover Poisson regression\, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion\, starting with the quasi-likelihood approach\, then covering the negative binomial and beta-binomial models for counts and discrete proportions\, respectively. Finally\, we will cover zero-inflated Poisson and negative binomial models\, which are for count data with excessive numbers of zero observations. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nIf some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. For example\, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm\, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).  \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Tuesday 25th\n Classes from 16:00 to 19:00 \nTopic 1: The general linear model. We begin by providing an overview of the normal\, as in normal distribution\, general linear model\, including using categorical predictor variables. Although this model is not the focus of the course\, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model\, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression\, implement it using R’s glm\, and then show how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Binomial logistic regression. Here\, we show how the binary logistic regresion can be extended to deal with data on discrete proportions. We will also present alternative link functions to the logit\, such as the probit and complementary log-log links. \n \n \n \n \n \n Wednesday 26th\n Classes from 16:00 to 19:00 \nTopic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression\, categorical logistic regression is also based on an extension of the binary logistic regression case. \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data\, i.e.\, data where the variable denotes the number of times an event has occurred. \n \n \n \n \n \n Thursday 27th\n Classes from 16:00 to 19:00 \nTopic 6: Overdispersion models. The quasi-likelihood approach for both the Poisson and binomial models. Negative binomial regression. The negative binomial model is\, like the Poisson regression model\, used for unbounded count data\, but it is less restrictive than Poisson regression\, specifically by dealing with overdispersed data. Beta-binomial regression. The beta-binomial model is an overdispersed alternative to the binomial. \nTopic 7: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGateGoogleScholarORCIDGitHub \n​ URL:https://prstats.preprodw.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IGLM04R.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250226 DTEND;VALUE=DATE:20250301 DTSTAMP:20260508T231407 CREATED:20220218T204056Z LAST-MODIFIED:20241216T161521Z UID:10000309-1740528000-1740787199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, February 26th\, 2022\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Eastern Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n This community analytics course is designed for students who have recently started their projects or researchers who are starting using the R ecosystem. During this three-day course\, we will cover the basic concepts of multivariate analysis and their implementation in R. This course is a complement to the PR Statistic offering allowing also beginners and non-programmers to discover the statistical tools needed to analyze an ecological dataset in research\, natural resource management or conservation context. This course is not geared toward any particular taxonomic group or ecological system. \nWe will cover diversity indices\, distance measures and multivariate distance-based methods\, clustering\, classification\, and ordination techniques. We will focus on the concept of the methods and their implementation on R using different R packages. We will use real-world examples to implement analyses\, such as describing patterns along gradients of environmental or anthropogenic disturbances\, quantifying the effects of continuous and discrete predictors\, data mining. The course will consist of lectures\, work on R code scripts\, and exercises for participants. \nPR stats also deliver a more advanced course on analysing community data \nONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) \n \n \n \n \n \n Intended Audiences\n Any researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are interested in learning multivariate statistics. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Eastern Standard Time \nAvailability – 20 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with workshop sessions on R. \n~2 modules per day\, each module consists of ~1h30/2h lecture + coding\, break\, ~1h30/2h exercises + summary/discussion. \nThe schedule can be slightly modified according to the interest of the participants. \nThe course will take place online. All the sessions will be video recorded and made available immediately on a private video hosting website as soon as possible after each 2hr session. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic knowledge of statistics is required. \n \n \n \n \n \n Assumed computer background\n The participants are required to have some previous experience with R and should know the main data types and how to run commands to create basic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).  \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\nThis 3-day course will explores the nature of community data\, and how they are transformed and analysed them in the context of ecological research projects. The course will explore the numerical tools used to describe ecological communities\, with a particular focus on R. \nWednesday 26th – Classes from 09:00-17:00 \n– Classifications (i.e.\, clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram). \nThursday 27th – Classes from 09:00-17:00 \n– Ordinations (i.e.\, unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes). \nFriday 28th – Classes from 09:00-17:00 \n– Canonical ordinations (i.e.\, constrained methods) test hypotheses related to multivariate patterns. \n \n \n \n \n \n Course Instructor\n \n  \n  \n  \nDr. Antoine Becker-Scarpitta\nWorks at – University of HelsinkTeaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03) \nAntoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay\, and from the Natural History Museum of Paris\, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology\, plant ecology and evolution\, linear and multivariate statistics assisted on R. URL:https://prstats.preprodw.com/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/ LOCATION:Delivered remotely (Finland)\, Western European Time\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IMAE01.png GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR