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:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20280326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20281029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20290325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20291028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20300331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20301027T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20240109 DTEND;VALUE=DATE:20300112 DTSTAMP:20260418T204511 CREATED:20240220T154155Z LAST-MODIFIED:20240709T134921Z UID:10000447-1704758400-1894406399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Model selection and model simplification (MSMSPR) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nPre-recorded \n​\n \n \n \n \n \n \n \n \n \n \n About This Course\n This three day course covers the important and general topics of statistical model building\, model evaluation\, model selection\, model comparison\, model simplification\, and model averaging. These topics are vitally important to almost every type of statistical analysis\, yet these topics are often poorly or incompletely understood. We begin by considering the fundamental issue of how to measure model fit and a model’s predictive performance\, and discuss a wide range of other major model fit measurement concepts like likelihood\, log likelihood\, deviance\, residual sums of squares etc. We then turn to nested model comparison\, particularly in general and generalized linear models\, and their mixed effects counterparts. We then consider the key concept of out-of-sample predictive performance\, and discuss over-fitting or how excellent fits to the observed data can lead to very poor generalization performance. As part of this discussion of out-of-sample generalization\, we introduce leave-one-out cross-validation and Akaike Information Criterion (AIC). We then cover general concepts and methods related to variable selection\, including stepwise regression\, ridge regression\, Lasso\, and elastic nets. Following this\, we turn to model averaging\, which is an arguably always preferable alternative to model selection. Finally\, we cover Bayesian methods of model comparison. Here\, we describe how Bayesian methods allow us to easily compare completely distinct statistical models using a common metric. We also describe how Bayesian methods allow us to fit all the candidate models of potential interest\, including cases were traditional methods fail. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely.\n \n \n \n \n \n Course Details\n Time zone – NA \nAvailability – NA \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n \n \n \n \n \n Assumed computer background\n \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\n  \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n DAY 1 \nTopic 1: Measuring model fit. In order to introduce the general topic of model evaluation\, selection\, comparison\, etc.\, it is necessary to understand the fundamental issue of how we measure model fit. Here\, the concept of conditional probability of the observed data\, or of future data\, is of vital importance. This is intimately related\, though distinct\, to concept of likelihood and the likelihood function\, which is in turn related to the concept of the log likelihood or deviance of a model. Here\, we also show how these concepts are related to concepts of residual sums of squares\, root mean square error (rmse)\, and deviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested model comparison in general linear models\, generalized linear models\, and their mixed effects (multilevel) counterparts. First\, we precisely define what is meant by a nested model. Then we show how nested model comparison can be accomplished in general linear models with F tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear models\, and mixed effects models\, we can accomplish nested model comparison using deviance based chi-square tests via Wilks’s theorem. \n \n \n \n \n \n Day 2\n DAY 2 \nTopic 3: Out of sample predictive performance: cross validation and information criteria. In the previous sections\, the focus was largely on how well a model fits or predicts the observed data. For reasons that will be discussed in this section\, related to the concept of overfitting\, this can be a misleading and possibly even meaningless means of model evaluation. Here\, we describe how to measure out of sample predictive performance\, which measures how well a model can generalize to new data. This is arguably the gold-standard for evaluating any statistical models. A practical means to measure out of sample predictive performance is cross-validation\, especially leave-one-out cross-validation. Leave-one-out cross-validation can\, in relatively simple models\, be approximated by Akaike Information Criterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how to interpret AIC values\, and describe other related information criteria\, some of which will be used in more detail in later sections. \nTopic 4: Variable selection. Variable selection is a type of nested model comparison. It is also one of the most widely used model selection methods\, and variable selection of some kind is almost always done routinely in all data analysis. Although we will also have discussed variable selection as part of Topic 2 above\, we discuss the topic in more detail here. In particular\, we cover stepwise regression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets. \n \n \n \n \n \n Day 3\n DAY 3 \nTopic 5: Model averaging. Rather than selecting one model from a set of candidates\, it is arguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using information criteria. \nTopic 6: Bayesian model comparison methods. Bayesian methods afford much greater flexibility and extensibility for model building than traditional methods. They also allow us to easily directly compare completely unrelated statistical models of the same data using information criteria such as WAIC and LOOIC. Here\, we will also discuss how Bayesian methods allow us to fit all models of potential interest to us\, including cases where model fitting is computationally intractable using traditional methods (e.g.\, where optimization convergence fails). This allows us therefore to consider all models of potential interest\, rather than just focusing on a limited subset where the traditional fitting algorithms succeed. \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGate\nGoogleScholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/online-course-model-selection-and-model-simplification-msmspr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MSMS03.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260418T204511 CREATED:20220310T142119Z LAST-MODIFIED:20230727T111252Z UID:10000374-1893456000-1893542399@prstats.preprodw.com SUMMARY:Adapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJPR) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nPre-Recorded\n \n \n \n \n \n \n \n \n \n \n About This Course\n R statistical software is becoming increasingly popular for spatial analysis and mapping. This is partially due to a large number of R packages devoted to applying various spatial methods. These packages\, however\, are being revised\, updated\, or even superseded to allow for better performance\, simpler user interface\, or expanded capabilities. Substantial recent changes in R spatial packages include developing the ‘sf’ package as a successor of ‘sp’\, creation of `terra` as a successor of `raster`\, and establishing the `stars` package. Additionally\, all of these packages were affected by the recent major updates of the PROJ library. In this course\, we will learn to use key packages for the analysis of spatial data\, both vector (‘sf’) and raster (‘terra’)\, and see how they differ from their older counterparts\, ‘sp’ and ‘raster’. Another important aspect of the course will be to understood spatial projections and coordinate systems\, how the recent PROJ changes affect R users\, and how to adjust to them. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind spatial analysis ecosystem in R\nKnow how packages such as sp/rgeos/rgdal/raster differ from their successors sf/terra/star\nBe able to switch from using packages such as sp/rgeos/rgdal/raster to sf/terra/stars\nUnderstood the basic concepts behind spatial projections\, and how PROJ.7 differs from PROJ4\nKnow how to deal with coordinate reference systems in R\nHave the confidence to switch from PROJ4 to PROJ7 (i.e.\, for instance\, adjusting old scripts based on PROJ4)?\n\n \n \n \n \n \n Intended Audiences\n \n\n\nAcademics and post-graduate students working on projects related to spatial data\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 for spatial data analysis\nCurrent R users wanting to update your knowledge\, including switch from using `sp` to `sf`\, and from `raster` to `terra`\n\n\n\nThe course is designed for intermediate R users interested in understanding modern tools for spatial data analysis in R and R beginners who have prior experience with geographic data and other spatial software.\n \n \n \n \n \n Course Details\n Last Up-Dated – 08:12:2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n The course will be a mixture of theoretical and practical. Each concept will be first described and explained\, and next the attendees will exercise the topics using provided data sets.\n \n \n \n \n \n Assumed quantitative knowledge\n Understanding basic GIS concepts\, such as spatial vector\, spatial raster\, coordinate reference systems would be beneficial\, but is not necessary.\n \n \n \n \n \n Assumed computer background\n Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. The experience of using some basic R spatial packages\, such as sp or raster would be beneficial. \nHowever\, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types\, and some programming experience\, the course may also be appropriate for you.\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 \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 7.5 hours \nOverview of spatial analysis ecosystem in R\n• available R packages for spatial analysis in R\n• how do R packages represent spatial objects\, and how are they connected with each other\n• importance of using the more recent R spatial packages\, such as ‘sf’ or ‘terra’\n• main concepts behind map projections (geoids\, datums\, geographic/projected coordinates\, types of projections\, etc.)\n• implementation of these concepts in the PROJ library (used by most R spatial packages)\n• differences between PROJ.4 and its newer versions (e.g. PROJ.7)\nSpatial vector data analysis in R\n• spatial vector data processing & analysis in R\n• read/write/and visualize spatial vector data\n• differences between ‘sp’/’rgdal’/’rgeos’ and ‘sf’\n• moving from ‘sp’ to ‘sf’ for spatial vector data processing & analysis\n• spherical geometry: how this concept was recently implemented in sf\, and what is an impact of this implementation\n \n \n \n \n \n Day 2\n Approx. 7.5 hours \nSpatial raster data analysis in R\n• spatial raster data processing & analysis in R\n• read/write/and visualize spatial raster data\n• differences between ‘raster’ and ‘stars’/’terra’\n• moving from ‘raster’ to ‘terra’ for spatial raster data processing & analysis\n• short overview of package ‘stars’\nCoordinate reference systems\n• how to switch from PROJ.4 to PROJ.7 in R\n• open session: questions from the participants\n \n \n \n \n \n \n \n \n Jakub Nowosad\n Works at: Adam Mickiewicz University \n Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create\, collaborate\, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book. \nResearchGate\nGoogleScholar\nORCID\nLinkedIn\nGitHub\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ\n\n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ URL:https://prstats.preprodw.com/course/adapting-to-the-recent-changes-in-r-spatial-packages-sf-terra-proj-library-projpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PROJ02R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260418T204511 CREATED:20230322T204322Z LAST-MODIFIED:20230727T121901Z UID:10000427-1893456000-1893542399@prstats.preprodw.com SUMMARY:Structural Equation Modelling for Ecologists and Evolutionary Biologists (SEMRPR) DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n The course is a primer on structural equation modelling (SEM) and confirmatory path analysis\, with an emphasis on practical skills and applications to real-world data. \nStructural equation modelling is a rapidly growing technique in ecology and evolution that unites multiple hypotheses in a single causal network. It provides an intuitive graphical representation of relationships among variables\, underpinned by well-described mathematical estimation procedures. Several advances in SEM over the past few years have expanded its utility for typical ecological datasets\, which include count data\, missing observations\, nested or hierarchical designs\, and true non-linear implementations. \nWe will cover the basic philosophy behind SEM\, provide approachable mathematical explanations of the techniques\, and cover recent extensions that better unite the multiple methods of SEM. Along the way\, we will work through many examples from the primary literature using the open-source statistical software R (www.r-project.org). We will draw on two popular R packages for conducting SEM\, including lavaan and piecewiseSEM. \nParticipants are encouraged to bring their own data\, as there will be opportunities throughout the course to plan\, analyze\, and receive feedback on structural equation models.\n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as persons in research or industry working on ecological data. \n \n \n \n \n \n Course Details\n Last up-dated – 10:03:2023 \nDuration – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and mathematics of SEM; practical lectures demonstrating the application to real datasets; computer labs to expand on practical lecture materials. Participants are encouraged to bring their own data and develop their own models. Time will be set aside at the end of each day to work with participants on their models. Datasets will be made available for those who do not have existing data to bring.\n \n \n \n \n \n Assumed quantative knowledge\n Basic knowledge of linear modelling.\n \n \n \n \n \n Assumed computer background\n Proficiency with R programming language\, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g.\, lm()).\n \n \n \n \n \n Equipment and software requirements\n Attendees of the course must use a computer with R/RStudio installed\, as well as the necessary additional R packages. Instructions on how to install the software will be provided before the start of the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/\nDownload RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 7 hours \nIntroduction to SEMModule 1: What is Structural Equation Modeling? Why would I use it?Module 2: Creating multivariate causal modelsModule 3: Fitting piecewise modelsReadings: Grace 2010 (overview)\, Whalen et al. 2013 (example) \n  \n \n \n \n \n \n Day 2\n Approx. 7 hours \nSEM Using LikelihoodModule 4: Fitting Observed Variable models with covariance structures
Module 5: What does it mean to evaluate a multivariate hypothesis?Module 6: Latent Variable models 
Module 7: ANCOVA revisited & NonlinearitiesReadings: Grace & Bollen 2005\, Shipley 2004Optional Reading: Pearl 2012\, Pearl 2009 (causality) \n  \n \n \n \n \n \n Day 3\n Approx. 7 hours \nPiecewise SEMModule 8: Introduction to piecewise approachModule 9: Incorporation of random effects modelsModel 10: Autocorrelation 
Reading: Shipley 2009; Lefcheck 2016 \n  \n \n \n \n \n \n Day 4\n Approx. 7 hours \nAdvanced Topics with Likelihood and Piecewise SEMModule 11: Multigroup models and non-linearitiesModule 12: Composite VariablesModule 13: Phylogenetically-correlated dataModule 14: Prediction using SEMModule 15: How To Reject A Paper That Uses SEMReadings: Grace & Julia 1999\, von Hardenberg & Gonzalez‐Voyer 2013 \n  \n \n \n \n \n \n Day 5\n Approx. 3.5 hours \nOpen Lab and Final Presentations \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Jon Lefcheck\nDr. Jarett Byrnes URL:https://prstats.preprodw.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semrpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SEMR03R.png GEO:53.1423672;-7.6920536 END:VEVENT END:VCALENDAR