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:20260418T154620 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:20260418T154620 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:20260418T154620 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:20260418T154620 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:20260418T154620 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:20250505 DTEND;VALUE=DATE:20280902 DTSTAMP:20260418T154620 CREATED:20220222T031522Z LAST-MODIFIED:20230727T133448Z UID:10000391-1746403200-1851379200@prstats.preprodw.com SUMMARY:Bayesian Data Analysis (BADAPR) 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 Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses\, many researchers do not have opportunities to learn the fundamentals of Bayesian methods\, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods\, both theoretically and practically. We will begin by teaching the fundamental concepts of Bayesian inference and Bayesian modelling\, including how Bayesian methods differ from their classical statistics counterparts\, and show how to do Bayesian data analysis in practice in R. We then provide a solid introduction to Bayesian approaches to these topics using R and the brms package. We begin by covering Bayesian approaches to linear regression. We will then proceed to Bayesian approaches to generalized linear models\, including binary logistic regression\, ordinal logistic regression\, Poisson regression\, zero-inflated models\, etc. Finally\, we will cover Bayesian approaches to multilevel and mixed effects models. Throughout this course\, we will be using\, via the brms package\, Stan based Markov Chain Monte Carlo (MCMC) methods. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science\, including the social sciences\, life sciences\, physical sciences. No prior experience or familiarity with Bayesian statistics is required. \n \n \n \n \n \n Course Details\n Last Up-Dated – 14:01:2022 \nDuration – Approx. 20 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \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. 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 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\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 Approx. 4 Hours \n• Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach\, which is referred to variously as the classical\, or sampling theory based\, or frequentist based approach\, rather than being a specialized or advanced statistics topic. However\, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition\, and a pragmatic blend of both approaches is entirely possible. \n• Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such\, it can be used as a means for performing statistical inference. In this section of the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all of Bayesian data analysis is based on an application of these methods to more complex statistical models\, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases. \n• Topic 3: Bayesian inference in a simple statistical model. In this section\, we will work through a classic statistical inference problem\, namely inferring the number of red marbles in an urn of red and black marbles\, or equivalent problems. This problem is easy to analyse completely with just the use of R\, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function\, prior distributions\, posterior distributions\, maximum a posteriori estimation\, high posterior density intervals\, posterior predictive intervals\, marginal likelihoods\, Bayes factors\, model evaluation of out-of-sample generalization. \n \n \n \n \n \n Day 2\n Approx. 4 Hours \n• Topic 4: Bayesian analysis of normal models. Statistical models based on linear and normal distribution are a mainstay of statistical analyses in general. They encompass models such as linear regression\, Pearson’s correlation\, t-tests\, ANOVA\, ANCOVA\, and so on. In this section\, we will describe how to do Bayesian analysis of normal linear models\, focusing on simple examples. One of the aims of this section is to identify some important and interesting parallels between Bayesian and classical or frequentist analyses. This shows how Bayesian and classical analyses can be seen as ultimately providing two different perspectives on the same problem. \n• Topic 5: The previous section provides a so-called analytical approach to linear and normal models. This is where we can calculate desired quantities and distributions by way of simple formulae. However\, analytical approaches to Bayesian analyses are only possible in a relatively restricted set of cases. On the other hand\, numerical methods\, specifically Markov Chain Monte Carlo (MCMC) methods can be applied to virtually any Bayesian model. In this section\, we will re-perform the analysis presented in the previous section but using MCMC methods. For this\, we will use the brms package in R that provides an exceptionally easy to use interface to Stan. \n  \n \n \n \n \n \n Day 3\n Approx. 4 Hours \n• Topic 6: Bayesian linear models. We begin by covering Bayesian linear regression. For this\, we will use the brm command from the brms package\, and we will compare and contrast the results with the standard lm command. By comparing and contrasting brm with lm we will see all the major similarities and differences between the Bayesian and classical approach to linear regression. We will\, for example\, see how Bayesian inference and model comparison works in practice and how it differs conceptually and practically from inference and model comparison in classical regression. As part of this coverage of linear models\, we will also use categorical predictor variables and explore varying intercept and varying slope linear models. \n  \n \n \n \n \n \n Day 4\n Approx. 4 Hours \n• Topic 7: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that do not always hold in practice. For example\, they assume a normal distribution of the residuals\, and assume homogeneity of variance of this distribution across all values of the predictors. In Bayesian models\, these assumptions are easily relaxed. For example\, we will see how we can easily replace the normal distribution of the residuals with a t-distribution\, which will allow for a regression model that is robust to outliers. Likewise\, we can model the variance of the residuals as being dependent on values of predictor variables. \n• Topic 8: Bayesian generalized linear models. Generalized linear models include models such as logistic regression\, including multinomial and ordinal logistic regression\, Poisson regression\, negative binomial regression\, zero-inflated models\, and other models. Again\, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. In our coverage of this topic\, we will see how powerful Bayesian methods are\, allowing us to easily extend our models in different ways in order to handle a variety of problems and to use assumptions that are most appropriate for the data being modelled. \n  \n \n \n \n \n \n Day 5\n Approx. 4 Hours \n• Topic 9: Multilevel and mixed models. In this section\, we will cover the multilevel and mixed effects variants of the regression models\, i.e. linear\, logistic\, Poisson etc\, that we have covered so far. In general\, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups\, and so on). For this\, we use a wide range of real-world data-sets and problems\, and move between linear\, logistic\, etc.\, models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models\, and how to choose between maximal and minimal models. We will also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical problems (e.g. lack of model convergence) that beset classical approaches. \n  \n \n \n \n \n \n \n \n \n Dr. Mark Andrews\n Works at: Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \nResearchGate URL:https://prstats.preprodw.com/course/bayesian-data-analysis-badapr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BADA01R.png GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR