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:20200329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20201025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD 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 END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20251006 DTEND;VALUE=DATE:20251011 DTSTAMP:20260419T085157 CREATED:20240613T132140Z LAST-MODIFIED:20241114T124438Z UID:10000460-1759708800-1760140799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 6th\, 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \nTime Zone\nTIME ZONE – GMT – 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 About this course\n The study of animal acoustic signals is a central tool for many fields in behavior\, ecology\, evolution and biodiversity monitoring. The accessibility of recording equipment and growing availability of open-access acoustic libraries provide an unprecedented opportunity to study animal acoustic signals at large temporal\, geographic and taxonomic scales. However\, the diversity of analytical methods and the multidimensionality of these signals posts significant challenges to conduct analyses that can quantify biologically meaningful variation. The recent development of acoustic analysis tools in the R programming environment provides a powerful means for overcoming these challenges\, facilitating the gathering and organization of large acoustic data sets and the use of more elaborated analyses that better fit the studied acoustic signals and associated biological questions. The course will introduce students on the basic concepts in animal acoustic signal research as well as hands-on experience on analytical tools in R. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts of bioacoustics and how animal acoustic signals are analyzed\nGain proficiency in handling and manipulating acoustic data in R\, including working with ‘wave’ objects and other audio formats\nDevelop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R\nImport Raven Pro annotations into R and refine these annotations with warbleR functions\nUnderstand how to quantify the structure of acoustic signals through various approaches\nGain experience in quality control of recordings and annotations\, ensuring data integrity and accuracy\nCompare different methods for quantifying acoustic signal structure and understand the implications of each approach\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students conducting research in bioacoustics\, animal behavior\, ecology\, or related fields\nApplied researchers and analysts in public\, private\, or non-profit organizations who require robust\, reproducible\, and flexible tools for analyzing acoustic data\nCurrent R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis\nWildlife biologists\, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies\nData scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – GMT \nAvailability – 20 places \nDuration – 5 days\, 4 hours per day \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. 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 \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 \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 \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 Monday 6th\n Day 1 – Classes from 13:30 – 17:30Introduction \n– How animal acoustic signals look like?An overview of the variety of acoustic signals produced by animals\, with examples from differentspecies. This includes visualizing sound waves and spectrograms to understand their structureand complexity. \n– Analytical workflow in bioacoustics researchIntroduction to the step-by-step process involved in bioacoustic research\, from recording anddata collection to analysis and interpretation. This session will outline the typical workflow\,emphasizing the importance of each step. \n– Advantages of programmingDiscussion on the benefits of using programming languages like R for bioacoustic analysis\,including reproducibility\, efficiency\, and the ability to handle large datasets. This will highlighthow programming can enhance research capabilities. \nWhat is sound?– Sound as a time seriesExplanation of how sound can be represented as a time series\, with each point in the seriesrepresenting the sound pressure level at a given moment in time. This forms the basis for furtheranalysis and manipulation. \n– Sound as a digital objectDiscussion on the digitization of sound\, including sampling rates\, bit depth\, and the conversion ofanalog sound waves into digital formats that can be analyzed using software. \n– Acoustic data in RIntroduction to handling and analyzing acoustic data in R. This includes importing sound files\,basic data exploration\, and visualization techniques. \n– ‘wave’ object structureExplanation of the ‘wave’ object in R\, its structure\, and the information it contains. This isessential for understanding how to manipulate and analyze sound data in R. \n– ‘wave’ object manipulationsTechniques for manipulating ‘wave’ objects\, including trimming\, concatenating\, and modifyingsound files. Practical exercises will be provided to reinforce these concepts. \n– Additional formatsOverview of other audio file formats (e.g.\, MP3\, FLAC) and how they can be converted and used inR for bioacoustic analysis. \n \n \n \n \n \n Tuesday 7th\n Day 2 – Classes from 13:30 – 17:30 \nBuilding spectrograms– Fourier transformExplanation of the Fourier transform and its application in converting time-domain signals intofrequency-domain representations. This is the foundation for creating spectrograms. \n– Building a spectrogramStep-by-step guide on how to construct spectrograms\, including parameter selection (e.g.\,window size\, overlap) and interpretation of the resulting visual representations. \n– Characteristics and limitationsDiscussion on the strengths and limitations of spectrograms\, including resolution trade-offs andpotential artifacts. Participants will learn to critically evaluate spectrograms. \n– Spectrograms in RPractical session on generating and customizing spectrograms in R using the seewave package.Participants will create spectrograms from their own data.Package seewave \n– Explore\, modify and measure ‘wave’ objectsHands-on exploration of the seewave package\, focusing on functions for modifying andmeasuring 'wave' objects. This includes exercises on filtering\, re-sampling\, and extracting acousticfeatures. \n– Spectrograms and oscillogramsCreating and interpreting both spectrograms and oscillograms in R. Participants will learn tovisualize sound data in different ways to highlight various aspects of the signal. \n– Filtering and re-samplingTechniques for filtering (e.g.\, band-pass\, high-pass) and re-sampling sound files to focus onspecific frequency ranges or standardize sampling rates. \n– Acoustic measurementsUsing the seewave package to perform detailed acoustic measurements\, such as peak frequency\,dominant frequency\, and frequency range. Practical examples will be provided. \n \n \n \n \n \n Wednesday 8th\n Day 3 – Classes from 13:30 – 17:30 \nAnnotations– Introduction to the Raven Pro InterfaceA guided tour of the Raven Pro software\, its main features\, and interface elements. Participantswill learn how to navigate the software efficiently. \n– Introduction to selections and measurementsInstruction on how to make selections within sound files and take basic measurements such asduration and frequency using Raven Pro. \n– Saving\, retrieving\, and exporting selection tablesHow to save\, retrieve\, and export selection tables in Raven Pro for further analysis. This sessionwill cover best practices for data management and organization. \n– Using annotationsTechniques for annotating sound files in Raven Pro\, including the use of labels and notes to marksignificant events or features within the recordings. \nQuantifying acoustic signal structure– Spectro-temporal measurements (spectro_analysis())Introduction to the spectro_analysis() function in R for extracting spectro-temporalmeasurements from audio recordings. Participants will learn to describe acoustic signals in termsof their temporal and spectral characteristics. \n– Parameter descriptionDetailed explanation of key acoustic parameters\, such as duration\, frequency range\, andamplitude\, and how they are used to describe sound signals. \n– Harmonic contentTechniques for analyzing the harmonic content of signals\, including identifying harmonic seriesand measuring harmonic-to-noise ratios. \n– Cepstral coefficients (mfcc_stats())Introduction to Mel-frequency cepstral coefficients (MFCCs) and their use in characterizing thetimbral properties of sound signals. Participants will use the mfcc_stats() function to extractMFCCs. \n– Cross-correlation (cross_correlation())Explanation of cross-correlation techniques for comparing sound signals. Participants will usecross_correlation() to measure the similarity between different recordings. \n– Dynamic time warping (freq_DTW())Introduction to dynamic time warping (DTW) and its application in aligning and comparing time-series data. The freq_DTW() function will be used to compare sound signals. \n– Signal-to-noise ratio (sig2noise())Techniques for calculating the signal-to-noise ratio (SNR) of recordings\, which is crucial forassessing the quality of sound data. \n– Inflections (inflections())Identifying and measuring inflections in sound signals\, which can indicate changes in pitch orother dynamic features. \n– Parameters at other levels (song_analysis())Exploring acoustic parameters at higher hierarchical levels\, such as entire songs or sequences ofvocalizations\, using the song_analysis() function. \n \n \n \n \n \n Thursday 9th\n Day 4 – Classes from 13:30 – 17:30 \nQuality control in recordings and annotations– Create catalogsCompiling catalogs of annotated sound files\, which can be used for further analysis or asreference materials. \n– Check and modify sound file format (check_wavs()\, info_wavs()\, duration_wavs()\,mp32wav() y fix_wavs())Techniques for checking and modifying sound file formats using various functions in R. Thisincludes converting files\, checking file integrity\, and fixing common issues. \n– Tuning spectrogram parameters (tweak_spectro())Adjusting spectrogram parameters to optimize the visualization and analysis of sound signals.Participants will use tweak_spectro() to fine-tune their spectrograms. \n– Double-checking selection tables (check_sels()\, spectrograms()\, full_spectrograms() &catalog())Methods for verifying and refining selection tables\, ensuring that all annotations are accurate andcomprehensive. \n– Re-adjusting selections (tailor_sels())Techniques for re-adjusting selections in response to quality control checks\, ensuring that allannotations are precise and correctly positioned.Characterizing hierarchical levels in acoustic signals \n– Creating ‘song’ spectrograms (full_spectrograms()\, spectrograms())Building spectrograms that represent entire songs or sequences of vocalizations\, providing ahigher-level view of acoustic patterns. \n– ‘Song’ parameters (song_analysis())Measuring and analyzing parameters at the song level\, such as song duration\, number ofelements and element rate\, using the song_analysis() function. \n \n \n \n \n \n Friday 10th\n Day 5 – Classes from 13:30 – 17:30 \nChoosing the right method for quantifying structure– Compare different methods for quantifying structure (compare_methods())Comparing various methods for quantifying acoustic signal structure. Participants will usecompare_methods() to evaluate different approaches.Quantifying acoustic spaces \n– Intro to PhenotypeSpaceIntroduction to the concept of acoustic spaces and the PhenotypeSpace framework\, which allowsfor the visualization and comparison of acoustic diversity. \n– Quantifying space sizeTechniques for measuring the size of acoustic spaces\, which can provide insights into thevariability and complexity of vocalizations. \n– Comparing sub-spacesMethods for comparing different sub-spaces within the overall acoustic space\, allowing for theanalysis of variations between species\, populations\, or other groups. \nEach of these topics will be covered with detailed explanations\, practical examples\, and hands-onexercises to ensure that participants gain a comprehensive understanding of bioacoustics researchusing the R platform. \n \n \n \n \n \n \n Course Instructor\n \n*\nDr. Marcelo Araya Salas\nWorks at – Neuroscience Research Center\, Universidad de Costa Rica \nMarcelo Araya-Salas works at the intersection of scientific programming and evolutionary behavioral ecology\, focusing on the evolution of behavior and the factors influencing it across cultural and evolutionary timescales. His research primarily examines the communication systems of neotropical species using single-species behavioral studies\, comparative phylogenetic methods\, and advanced data analysis techniques. He has developed several computational tools for biological data analysis\, including the R packages warbleR\, Rraven and baRulho which simplify the manipulation of annotated acoustic data and the quantification of structure and degradation of animal sounds. \nResearchGate \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.preprodw.com/course/bioacoustics-data-analysis-biac05/ 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/BIAC02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250311 DTEND;VALUE=DATE:20250314 DTSTAMP:20260419T085157 CREATED:20201008T150900Z LAST-MODIFIED:20241120T125055Z UID:10000326-1741651200-1741910399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, March 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 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 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 Availability – 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 (GMT+0) 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 live sessions attendees will be able 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 We will assume familiarity with general statistical concepts\, linear models\, statistical inference (p-values\, confidence intervals\, etc). Anyone who has taken undergraduate (Bachelor’s) level introductory courses on (applied) statistics can be assumed to have sufficient familiarity with these concepts. \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 \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 \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 16:00 to 19:00 \nTopic 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 Wednesday 12th\n Classes from 16:00 to 19:00 \nTopic 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 Thursday 13th\n Classes from 16:00 to 19:00 \nTopic 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 \nResearchGateGoogleScholarORCIDGitHub URL:https://prstats.preprodw.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ 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/IMMR06R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250217 DTEND;VALUE=DATE:20250222 DTSTAMP:20260419T085157 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:Delivered remotely (United Kingdom)\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 \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:20240422 DTEND;VALUE=DATE:20240426 DTSTAMP:20260419T085157 CREATED:20200327T044645Z LAST-MODIFIED:20240403T125252Z UID:10000304-1713744000-1714089599@prstats.preprodw.com SUMMARY:ONLINE COURSE – Spatio-Ecological Data Analysis using R and Rstudio (SEAR01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, April 22nd\, 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 Spatial ecology 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\, due to both human impact and global change. Particular emphasis will be given to: 1) population ecology: how organisms spread in space and how to study it by point pattern analysis\, 2) community ecology: how communities are structured and how to study such structure by multivariate analysis; 3) monitoring species distributions and their change in space and time by species distribution modelling; 4) 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 species and ecosystems at different spatial scales• be able to report in LaTeX and R Markdown the achieved results \n \n \n \n \n \n Intended Audiences\n This course is aimed at academics and post-graduate students working in spatial ecology \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. 28 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\nCourse packages:– 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 \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 25th – 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– Population Ecology \n[Point Patterns Analysis – Spatial statistics: deriving continuous maps from in-situ data\, principles of autocorrelation and spatial interpolation] \nTuesday 26th – Classes from 09:30 to 17:30 \n– Community ecology[Multivariate analysis in R] \n[Community niche overlap] \n– Remote sensing in R \n[Remotely sensed data visualisation] \nWednesday 27th – Classes from 09:30 to 17:30 \n– Remote sensing in R \n[Spectral indices] \n[Time series] \nThursday 28th – 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[Remotely sensed data classification: land cover maps] \n[Ecosystem variability] \n[Multivariate analysis on remotely sensed data] \nFriday 29th – 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/spatio-ecological-data-analysis-using-r-and-rstudio-sear01/ 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:20240220 DTEND;VALUE=DATE:20240223 DTSTAMP:20260419T085157 CREATED:20200804T125230Z LAST-MODIFIED:20240222T142952Z UID:10000313-1708387200-1708646399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Data visualization with ggplot2 using R and Rstudio (DVGG04) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, March 26th\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 During this course we provide a comprehensive introduction to data visualization in R using ggplot. We begin by providing a brief overview of the general principles data visualization\, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of these cases\, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothing functions to the data\, how to add marginal histograms to the scatterplot\, add labels to points\, and scale each point by the value of a third variable. We then cover some additional plot types that are often related but not identical to those major types covered during the beginning of the course: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. We then consider more fine grained control of the plot by changing 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 create labelled grids of subplots of the kind seen in many published articles. \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 \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n\nThis 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. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n None needed. \n \n \n \n \n \n Assumed computer background\n Some familiarity with R. \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. 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 \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\nTuesday 26th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: What is data visualization. Data visualization is a means to explore and understand our data and should be a major part of any data analysis. Here\, we briefly discuss why data visualization 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 is simply the best. Here\, we briefly introduce the major principles behind how ggplot works\, namely how it is a layered grammar ofgraphics. \nWednesday 27th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \n\nTopic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizing distributions 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\, we will also cover how to provide marginal plots on the scatterplots\, labels to points\, and how to obtain linear and nonlinear smoothing of the plots. \nTopic 5: More plot types. Having already covered the most widely used general purpose plots on Day 1\, we now turn to cover a range of other major plot types: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these are important and widely used types of plots\, and knowing them will expand your repertoire. \n\nThursday 28th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plot styles and layouts. Here\, we will introduce how to modify things like the limits and scales on the axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, and the different types of ggplot themes that are available. \nTopic 7: Plots for publications and presentations: Thus far\, we have primarily focused on data visualization as a means of interactively exploring data. Often\, however\, we also want to present our plots in\, for example\, published articles or in slide presentations. It is simple to save a plot in different file formats\, and then insert them into a document. However\, a much more efficient way of doing this is to use RMarkdown to run the R code and automatically 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 subplots like those found in many scientific articles. \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\nResearchGateGoogleScholarORCIDGitHub \n  URL:https://prstats.preprodw.com/course/data-visualization-with-ggplot2-using-r-and-rstudio-dvgg04/ 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/DVGG02.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240129 DTEND;VALUE=DATE:20240203 DTSTAMP:20260419T085157 CREATED:20230919T155938Z LAST-MODIFIED:20231204T164859Z UID:10000438-1706486400-1706918399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 29th\, 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 – 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 The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata. \nBy the end of the course participants should: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox. \nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R. \nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis. \n \n \n \n \n \n Venue\n Venue – Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Central European Standard Time (CEST) \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: 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 Teaching Format\n \n\nhe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \nAssumed quantitative knowledge \nThe course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. \nAssumed computer background \nAttendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. \nEquipment and software requirements \nA laptop/personal computer with any operating system (Linux\, Windows\, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed. \nhttps://cran.r-project.org/ \nDownload RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n \n \n \n \n \n Assumed computer background\n Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed. \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 \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 \noliverhooker@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 29th\n Classes from 14:00 to 21:00 CET \nDAY 1 \nLECTURE 1 – Intro to INLA \nPRACTICAL 1 – Intro to INLA \nLECTURE 2 – Model fitting with INLA \nPRACTICAL 2 – Model fitting with INLA \nLECTURE 3 – GLMM’s with INLA \nPRACTICAL 3 – GLMM’s with INLA \nQ and A and end of day summary \n \n \n \n \n \n Tuesday 30th\n Classes from 14:00 to 21:00 CET \nDAY 2 \nLECTURE 4 – Spatial Data \nPRACTICAL 4 – Spatial Data \nLECTURE 5 – Spatio-Temporal Data \nPRACTICAL 5 – Spatio-Temporal Data \nLECTURE 6 – Advanced Visualisation \nPRACTICAL 6 – Advanced Visualisation \nQ and A and end of day summary \n \n \n \n \n \n Wednesday 31st\n Classes from 14:00 to 21:00 CET \nDAY 3 \nLECTURE 7 – Spatial Models for Lattice Data \nPRACTICAL 7 – Spatial Models for Lattice Data \nLECTURE 8 – Spatial Models for Continuous Data \nPRACTICAL 8 – Spatial Models for Continuous Data \nLECTURE 9 – Spatial Models for Point Patterns \nPRACTICAL 9 – Spatial Models for Point Patterns \nQ and A and end of day summary \n \n \n \n \n \n Thursday 1st\n Classes from 14:00 to 21:00 CET \nDAY 4 \nLECTURE 10 – Spatio-Temporal Models for Lattice Data \nPRACTICAL 10 – Spatio-Temporal Models for Lattice Data \nLECTURE 11 – Spatio-Temporal Models  for Continuous Data \nPRACTICAL 11 – Spatio-Temporal Models  for Continuous Data \nLECTURE 12 – Spatio-Temporal Models  for Point Patterns \nPRACTICAL 12 – Spatio-Temporal Models  for Point Patterns \nQ and A and end of day summary \n \n \n \n \n \n Friday 2nd\n Classes from 14:00 to 21:00 CET \nDAY 5 \nCase studies\, own data and problem solving. \n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here\n \n\nResearchgate\nGoogle Scholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/spatial-and-spatial-temporal-modelling-using-r-inla-sstm01/ 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/2023/09/Screenshot-2023-09-20-at-14.21.47.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231211 DTEND;VALUE=DATE:20231215 DTSTAMP:20260419T085158 CREATED:20231121T142647Z LAST-MODIFIED:20231204T170316Z UID:10000334-1702252800-1702598399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Data wrangling using R and Rstudio (DWRS03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 11th\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 During this course we provide a comprehensive practical introduction to data wrangling using R. In particular\, we focus on tools provided by R’s tidyverse\, including dplyr\, tidyr\, purrr\, etc. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly\, it can be time consuming\, laborious\, and error-prone. Fortunately\, the tools provided by R’s tidyverse allow us to do data wrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R\, we then cover in detail all the dplyr tools such as select\, filter\, mutate\, etc. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here\, we will consider how to concatenate data frames\, including concatenating all data files in a folder\, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider. \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 \n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \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 \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 \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 \noliverhooker@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\nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: Reading in data. We will begin by reading in data into R using tools such as readr and readxl. Almost all types of data can be read into R\, and here we will consider many of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to contol how data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc. \nTopic 2: Wrangling with dplyr. For the remainder of Day 1\, we will next cover the very powerful dplyr R package. This package 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. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nTopic 2 continued: \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \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 vertical concatenations. Much more powerful are the SQL like “join” operations. Here\, we will consider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will also consider 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 \n \n \n Course Instructor\n \n\n\n\n\nDr. Rafael De Andrade Moral \n\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\n\n  \nResearchGateGoogleScholarORCIDGitHub \n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/data-wrangling-using-r-and-rstudio-dwrs03-2/ 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/DWRS02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230522 DTEND;VALUE=DATE:20230527 DTSTAMP:20260419T085158 CREATED:20230117T164447Z LAST-MODIFIED:20230920T131830Z UID:10000315-1684713600-1685145599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction to Bayesian modelling with INLA (BMIN02) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, May 22nd\, 2023\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics on the INLA methodology as well as practical modelling of different types of data. \nBy the end of the course participants should: \n\nUnderstand the basics of Bayesian inference.\nUnderstand how the INLA method works and its main differences with MCMC methods.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA.\nKnow how to define the different parts of a model with INLA.\nBe able to develop new latent effects not implemented in the R-INLA package.\nKnow how to define new priors not included in the R-INLA package.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects related to data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox. \nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R. \nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis. \n \n \n \n \n \n Venue\n Venue – Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central European Standard Time (CEST) \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: 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 Teaching Format\n \n\nhe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \nAssumed quantitative knowledge \nThe course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. \nAssumed computer background \nAttendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. \nEquipment and software requirements \nA laptop/personal computer with any operating system (Linux\, Windows\, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed. \nhttps://cran.r-project.org/ \n\nDownload RStudio \n\nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n \n \n \n \n \n Assumed computer background\n Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \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 \n \n \n \n \n Monday 22nd\n Classes from 14:00 to 21:00 \nIntroduction to the courseKey concepts related to Bayesian inferenceModels with conjugate priorsIntroduction to Bayesian hierarchical modelsComputational methods for Bayesian inferenceIntroduction to the INLA methodologyFitting generalized linear models with INLA and the R-INLA packageUnderstanding and manipulating the output from model fitting with R-INLA \n \n \n \n \n \n Tuesday 23rd\n Classes from 10:00 – 17:00Fitting generalized linear mixed models with R-INLATypes of latent effects in R-INLAModels with i.i.d. latent effectsFitting multilevel models with R-INLAModels with correlated latent effectsFitting time series models with R-INLA \n \n \n \n \n \n Wednesday 24th\n Classes from 14:00 – 21:00Priors in R-INLASetting priors in R-INLAIntroduction to Penalized Complexity priors (PC-priors)Defining new priors in R-INLASpatially correlated random effectsFitting spatial models with R-INLAVisualizing the output from spatial models and mapping \n \n \n \n \n \n Thursday 25th\n Classes from 14:00 – 21:00Advanced features in R-INLAComputing linear combinations of the latent effectsFitting models with several likelihoodsModels with shared termsAdding linear constraints to the latent effectsImplementing new latent models in R-INLAImputation and missing covariates in R-INLA \n \n \n \n \n \n Friday 26th\n Classes from 14:00 to 21:00 \nCase studies and own data. \n \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n Works at: Universdad de Castilla~La Mancha \n Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference withINLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on his website.\n\n\nResearchgate: https://www.researchgate.net/profile/Virgilio-Gomez-Rubio?ev=hdr_xprf\nGoogle Scholar: https://scholar.google.es/citations?user=OggVQCkAAAAJ&hl=es\nORCID: https://orcid.org/0000-0002-4791-3072\nGitHub: https://github.com/becarioprecario\nHomepage: https://becarioprecario.github.io URL:https://prstats.preprodw.com/course/introduction-to-bayesian-modelling-with-inla-bmin02/ 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/2023/02/book_cover.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20221003 DTEND;VALUE=DATE:20221006 DTSTAMP:20260419T085158 CREATED:20220221T202517Z LAST-MODIFIED:20220926T162226Z UID:10000318-1664755200-1665014399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bioacoustics For Ecologists: Hardware\, Survey design And Data analysis (BIAC03) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, September 20th\, 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \nTime Zone\nTIME ZONE – GMT – 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 About this course\n This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field\, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data. \nBy the end of this 5-day practical course\, attendees will have the capacity to set up and deploy recording devices\, download acoustic data\, how to analyse this data and report the results. \nBioacoustic methods are becoming increasingly recognised as a valuable approach for ecological surveying. Bioacoustics can be used to effectively replace some current techniques whilst increasing the quality of the data collected or can be used in unison to compliment them. They are particularly useful for developing long-term\, permanent datasets that can be independently reviewed\, particularly for rare species with low detectability\, or when working in difficult environments. \nThe course will provide a practical introduction to bioacoustics methods\, with a mix of lectures and practical workshops\, and some optional fieldwork. It will start with a basic introduction to sound and recording theory\, before developing hands-on skills in setting-up and deploying a range of acoustic and ultrasonic audio recorders. Workshops will then cover the download and analysis of audio data\, mainly using Kaleidoscope Pro and Audacity software. The processed audio data will then be analysed and presented using R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). \nExample data sets will mostly cover applications for bat and bird surveys\, as well as the use of Acoustic Indices as biodiversity metrics. If you are working in different areas of ecology using bioacoustics please feel free to contact oliverhooker@prstatistics.com so we can advise if the learning outcomes are transferable to your field of research. \n \n \n \n \n \n Intended Audiences\n This course is suitable for anyone working with bioacoustics from those in academia\, conservation biologists and persons in industry and government. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – GMT \nAvailability – 15 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 1.5 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 \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 \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  \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 Tuesday 20th\n Classes from 09:30 – 17:30 \nSESSION 1 – INTRO TO ACOUSTIC DATA (AND METADATA) \n1. Acoustic Data and Metadata – what does it look like?Data sources – survey methods/approaches\, recorder hardware\, file types etcMetadata recording and systemsCase study examples – terrestrial & freshwater (& marine) \n2. Introduction to spectrogramsVisualizing sound – understanding spectrograms\, identifying speciesBats – peakfreq\, IPI\, max\, min\, duration\, shape etc..Birds – Nathan Pieplow keys – time/frequency characteristics\, song/call shapesMeasuring parameters manually and programatically \n3. Introduction to audio software – for species ID and vocalizationsAnalysis tools for acoustic dataSoftware tools – Kaleidoscope\, Audacity\, R (others: Raven/Lite\, Batscan\, Batsound\, Batscope\, iBatsID\, Analook\, SonoChiro\, Sonobat\, Luscinia\, BirdNet\, MATLAB\, PAMGUARD\, etc)Viewing/listening/measuring\, recognizers\, clusteringManual and automated call detection and ID methodsLimitations and emerging opportunities in acoustic data analysis \n4. Workshop – sound editing\, measuring and management using Audacity \nSESSION 2 – ANALYSING BAT DATA USING KALEIDOSCOPE \n5. Workshop – Kaleidoscope bat ID processing (Paul H-L) \n \n \n \n \n \n Wednesday 21st\n Classes from 09:30 – 17:30 \nSESSION 3 – ANALYSING ACOUSTIC DATA USING R) \n6. Workshop – R (Seewave/Soundecology) (creat/view/analyse spectrograms) \nSESSION 4 – INTERPRETING ACOUSTIC DATA \n7. Data collation\, analysis and interpretationMoving from sound to data to meaning (creating tidy data/metadata and using this)Data and recognizer quality – false positives/negatives and validating auto-IDs…Presence/absenceActivity levelsDistributionTemporal changesPopulation assessments/occupancyLocalizing calls with amplitude levels or microphone arraysIdentifying individualsMention of Soundscapes and Acoustic indices – more on this later \n8. Soundscapes and Acoustic indicesWhat different indicesPros and cons of eachUsing and comparing scores \n9. Example workflows from previous studiesCarlos capercaillie and TBH workBCT/CIEEM guidance on call assessmentOther published research and recommendations \n \n \n \n \n \n Thursday 22nd\n Classes from 09:30 – 17:30 \nSESSION 5 –ACOUSTIC INDICES USING R/KALEIDOSCOPE \n10. Workshop – Kaleidoscope (analyse Acoustic Indices) \n11. Workshop – R (Seewave/Soundecology) (analyse Acoustic Indices) \nSESSION 6 –SPATIAL ACOUSTIC DATA AND COURSE ROUND-UP \n12. Workshop – presenting spatial data using Google Earth and REMtouch kml output – Google EarthCSV outputSpatial analysis with R \n13. Review and roundup/conclusions \n \n \n \n \n \n \n Course Instructor\n \n*\nDr. Carlos Abrahams\nWorks at – Technical Director at Baker Consultants Ltd and Senior Lecturer at Nottingham Trent UniversityTeaches – Bioacoustics for ecologists: Hardware\, Survey design and Data analysis (BIAC) \nCarlos has been working in the practical fields of ecology and nature conservation for over 25 years. Starting his career in nature reserve and countryside management\, he has been an ecological consultant since 2001. Alongside managing a busy consultancy\, undertaking Environmental Impact Assessments for a range of clients\, he is also a part-time lecturer at Nottingham Trent University on the BSc Environmental Biology. Carlos has previously published research on wetland vegetation/management and amphibian habitat selection. However\, after many years of using static and handheld detectors for bat surveys\, he is currently engaged in studying the potential of bioacoustic methods for investigating bird populations\, especially for rare and declining species such as Capercaillie and Nightjar. URL:https://prstats.preprodw.com/course/bioacoustics-for-ecologists-hardware-survey-design-and-data-analysis-biac03/ 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/BIAC02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220620 DTEND;VALUE=DATE:20220623 DTSTAMP:20260419T085158 CREATED:20220218T223151Z LAST-MODIFIED:20220614T232250Z UID:10000354-1655683200-1655942399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bayesian GLM's For Ecologists (BGFE01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, June 20th 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 \n \n \n \n \n \n \n \n \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 – GMT+1 – 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 short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed\, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, attendees will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nApply a 9-step protocol to fitting Bayesian GLMs\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using Bayesian GLMs\n\n  \n \n \n \n \n \n Intended Audiences\n Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R \nApplied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – 30 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 This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n \n \n \n \n \n Assumed quantitative knowledge\n It will be assumed that participants will be familiar with general statistical concepts and fitting GLMs to ecological data. Participants will need experience of performing statistical analysis using R. \n \n \n \n \n \n Assumed computer background\n Experience with performing statistical analyses using R and R Studio will be assumed. \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 \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\nMonday  09:00 – 16:00 \nIntroduction to Bayesian inference \n\nDifference between Bayesian and frequentist approaches\nBayes’ theorem\nA frequentist or Bayesian framework: Which is better?\nFitting Bayesian GLMs\nSteps in fitting a Bayesian GLM\nPriors\nNon-informative priors\nWeakly informative priors\nInformative priors\nThe posterior distribution\nBayesian computational methods\nThe advantages of Bayesian inference\nCriticism of Bayesian inference\n\nData exploration \n\nSix-step data exploration protocol\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\n\nGaussian GLM with INLA \n\nEuropean bitterling territoriality\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\n  \nTuesday  09:00 – 16:00 \nPoisson GLM with INLA \n\nStickleback lateral plate number\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nNegative binomial GLM with INLA \n\nCoral abundance\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nBernoulli GLM with INLA \n\nCuckoo parasitism of reed warbler nests\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\n  \nWednesday 09:00 – 16:00 \nGamma GLM with INLA \n\nStickleback lateral plate number\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nImplementing and assessing Bayesian GLMs \n\nPrior information\nPresenting results of Bayesian GLMs\nReviewing Bayesian GLMs\nMisuse of Bayesian GLMs\nConclusions\n\nDiscussion & questions \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \n Dr. Carl Smith\n \n Teaches:\n\nBayesian GLMs for Ecologists (BGFE01) URL:https://prstats.preprodw.com/course/bayesian-glms-or-ecologists-bgfe01/ 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/BGFE01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220530 DTEND;VALUE=DATE:20220604 DTSTAMP:20260419T085158 CREATED:20220218T222314Z LAST-MODIFIED:20220512T151555Z UID:10000311-1653868800-1654300799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Statistics For Biodiversity And Conservation (SFBC01) This course will be delivered live DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, May 30th 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 \n \n \n \n \n \n \n \n \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 – GMT+1 – 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 The way statistics are used in biology\, and especially ecology\, is changing\, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging. \nAn understanding of statistical modelling is no longer a luxury\, and it is an expectation that postgraduates and post-doctoral researchers\, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software\, such as R\, that permits users to wrangle\, analyse and visualise their data in novel ways. \nThis course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed\, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, you will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nRecognise common problems associated with analysis of ecological data and how to address them\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using GLMs\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\n\n \n \n \n \n \n Intended Audiences\n Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R \nApplied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data \n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – 30 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 This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n \n \n \n \n \n Assumed quantitative knowledge\n It will be assumed that participants have a basic familiarity with general statistical concepts\, linear models\, and statistical inference. Participants may have limited experience of performing statistical analysis using R. \n \n \n \n \n \n Assumed computer background\n Some experience with R and R Studio will be needed to run R script and install R packages\, though guidance will be provided on basic concepts. \n \n \n \n \n \n Equipment and software requirements\n A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \nA full list of required packages will be made available to participants prior to the course. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously\n \n \n \n \n \n \n \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 \n COURSE PROGRAMME\n  \n \n \n \n \n \n \n \n \n \n \n Monday 30th\n Classes from 09:00 to 17:00 \nIntroduction to R and RStudio \n\nGetting started with R and RStudio\nBasic points\nNavigating RStudio\nBasic settings in RStudio\nBasic principles in R\nSetting the working directory\nImporting data\nFunctions and packages in R\n\nData exploration \n\nSix-step data exploration protocol\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\n\nTesting differences between two groups \n\nEuropean hedgehogs\nOutliers\nNormality and homogeneity of the dependent variable\nZeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\nComparing two groups of normal unpaired data: unpaired t-test\nComparing two groups of normal paired data: the paired t-test\nComparing two groups of non-normal unpaired data: the Mann-Whitney test\nComparing two groups of non-normal paired data: the Wilcoxon test\nPresenting results\n\nTesting association between two continuous variables: correlation \n\nBarn owls\nOutliers\nNormality of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationships between variables\nIndependence of variables\nResults of data exploration\nTesting association between two continuous normal variables: Pearson’s correlation\nTesting association between two continuous non-normal variables: Spearmann’s rank correlation\nTesting association between two continuous non-normal variables with small sample size and ties: Kendall’s Tau correlation\nPresenting the results\n\n \n \n \n \n \n Tuesday 31st\n Classes from 09:00 to 17:00 \nModelling two continuous variables with linear regression \n\nNorthern pike length-fecundity relationship\nOutliers\nNormality and homogeneity of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationship between variables\nIndependence of variables\nResults of data exploration\nBivariate linear regression\nModel validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nConclusions from model validation\nData transformation\nRefit linear regression with transformed data\nModel re-validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nModel presentation and interpretation\n\nGaussian General Linear Model (GLM) \n\nDiet of weatherfish in different seasons\nData exploration\nOutliers\nNormality and homogeneity of the variables\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nModel presentation\n\n  \n \n \n \n \n \n Wednesday 1st\n Classes from 09:00 to 17:00 \nModelling two continuous variables with linear regression \n\nNorthern pike length-fecundity relationship\nOutliers\nNormality and homogeneity of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationship between variables\nIndependence of variables\nResults of data exploration\nBivariate linear regression\nModel validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nConclusions from model validation\nData transformation\nRefit linear regression with transformed data\nModel re-validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nModel presentation and interpretation\n\nGaussian General Linear Model (GLM) \n\nDiet of weatherfish in different seasons\nData exploration\nOutliers\nNormality and homogeneity of the variables\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nModel presentation\n\n  \n \n \n \n \n \n Thursday 2nd\n Classes from 09:00 to 17:00 \nPoisson Generalised Linear Model (GLM) \n\nAbundance of freshwater mussels\nData exploration\nOutliers\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nOverdispersion\nModel misfit\nSimulating from the model\nModel presentation\n\nNegative binomial Generalised Linear Model (GLM) \n\nSpecies diversity of chironomids\nData exploration\nOutliers\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nModel fitting\nModel validation\nOverdispersion\nModel presentation\n\n  \n \n \n \n \n \n Friday 3rd\n Classes from 09:00 to 17:00 \nGaussian Generalised Linear Mixed Model (GLMM) \n\nBody condition of European tree frogs\nData exploration\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nRefit model\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nRefit model with random term\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nModel presentation\n\nBayesian inference \n\nIntroduction to Bayesian inference\nEuropean bitterling territoriality\nData exploration\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nIndependence of response variable\nModel fitting\nINLA\nPosterior (marginal) distributions\nComparison with frequentist Gaussian GLM\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nModel presentation\n\n  \n \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \n Dr. Carl Smith\n Senior Lecturer\, Psychology Department\, Nottingham Trent University \n Teaches:\n\nStatistics for biodiversity and conservation (SFBC01)\nBayesian GLMs for Ecologists (BGFE01)\n\nMark 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. \n  URL:https://prstats.preprodw.com/course/statistics-for-biodiversity-and-conservation-sfbc01/ 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/SFBC01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220517T150000 DTEND;TZID=Europe/London:20220517T153000 DTSTAMP:20260419T085158 CREATED:20220221T231747Z LAST-MODIFIED:20220517T124351Z UID:10000333-1652799600-1652801400@prstats.preprodw.com SUMMARY:FREE SEMINAR - Bayesian GLM's For Ecologists (BGFE01S) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Registration is now closed\, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.\nEvent Date \n​\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Bayesian GLM’s for Ecologists”. \n\n\nTime \n\n\n15:00-15:30 GMT+1 \n\n\nSpeaker \n\n\nCourse Instructor Dr. Carl Smith \n\n\nAbout this course \nThis short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed\, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, attendees will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nApply a 9-step protocol to fitting Bayesian GLMs\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using Bayesian GLMs\n\n\nONLINE COURSE – Bayesian GLM’s For Ecologists (BGFE01) 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 Dr. Carl Smith\n Senior Lecturer\, Psychology Department\, Nottingham Trent University \n Teaches:\n\nStatistics for biodiversity and conservation (SFBC01)\nBayesian GLMs for Ecologists (BGFE01)\n\nMark 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. \n  URL:https://prstats.preprodw.com/course/bayesian-glms-for-ecologists-bgfe01s/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BGFE01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220323T170000 DTEND;TZID=Europe/London:20220323T173000 DTSTAMP:20260419T085158 CREATED:20220221T230711Z LAST-MODIFIED:20220512T151922Z UID:10000329-1648054800-1648056600@prstats.preprodw.com SUMMARY:FREE SEMINAR - Statistics For Biodiversity And Conservation (SFBC01S) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nRegistration is now closed\, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Statistics for Biodiversity and Conservation”. \n\n\nTime \n\n\nTBC \n\n\nSpeaker \n\n\nCourse Instructor Dr. Carl Smith and Dr. Mark Warren \n\n\nCourse description \nThe way statistics are used in biology\, and especially ecology\, is changing\, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging. \nAn understanding of statistical modelling is no longer a luxury\, and it is an expectation that postgraduates and post-doctoral researchers\, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software\, such as R\, that permits users to wrangle\, analyse and visualise their data in novel ways. \nThis course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed\, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, you will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nRecognise common problems associated with analysis of ecological data and how to address them\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using GLMs\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Dr. Carl Smith\n Senior Lecturer\, Psychology Department\, Nottingham Trent University \n Teaches:\n\nStatistics for biodiversity and conservation (SFBC01)\nBayesian GLMs for Ecologists (BGFE01)\n\nMark 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. \n  URL:https://prstats.preprodw.com/course/statistics-for-biodiversity-and-conservation-sfbc01s/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SFBC01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20201204 DTEND;VALUE=DATE:20201212 DTSTAMP:20260419T085158 CREATED:20201008T144755Z LAST-MODIFIED:20221019T160608Z UID:10000323-1607040000-1607731199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bayesian hierarchical modelling using R (IBHM05) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nFriday\, December 4th\, 2020\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout\, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors. \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 – GMT – 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 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 \nAvailability – 20 places \nDuration – 3 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n\n\nThere 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. \nAll sessions will be video recorded and made available to all attendees as soon as possible. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this 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. \nAssumed quantitative knowledge \nA basic understanding of regression methods and generalised linear models. \nAssumed computer background \nFamiliarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \nEquipment and software requirements \nA laptop/personal computer with a working version or R\, RStudio\, JAGS and stan installed. All are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/ \n\nDownload RStudio \n\nhttp://mcmc-jags.sourceforge.nethttp://mc-stan.org/ \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \n \n \n \n \n \n Assumed computer background\n Coming soon.. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \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 \n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx 8 hours \nModule 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models \n \n \n \n \n \n Friday 27th November\n Classes from 09:30 to 17:30 \nModule 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models \n \n \n \n \n \n Friday 4th December\n Classes from 09:30 to 17:30 \nModule 5: Hierarchical models vs mixed effects modelsModule 6: Multivariate and multi-layer hierarchical modelsPractical: Advanced examples of hierarchical models \n \n \n \n \n \n Friday 11th December\n Classes from 09:30 to 17:30 \nModule 7: Shrinkage and variable selectionModule 8: Hierarchical models and partial poolingPractical: Shrinkage modelling \n \n \n \n \n \n \n Course Instructor\n\n  \nDr. Antoine Becker-Scarpitta\nWorks at – University of Helsink\nTeaches – 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/bayesian-hierarchical-modelling-using-r-ibhm05/ 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/2022/02/IBHM05R.png GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR