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DTSTART;VALUE=DATE:20210201
DTEND;VALUE=DATE:20210213
DTSTAMP:20260419T192157
CREATED:20180924T194838Z
LAST-MODIFIED:20220222T023842Z
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SUMMARY:ONLINE COURSE - Model-based multivariate analysis of abundance data using R (MBMV03)
DESCRIPTION:Delivered remotely (Australia)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, February 1st\, 2021\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\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 provide an introduction to modern multivariate techniques\, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years\, with a focus now on formulating a statistical model to capture key properties of the observed data\, rather than transformation of data using a dissimilarity-based framework. In recent years\, model-based techniques have been developed for hypothesis testing\, identifying indicator species\, ordination\, clustering\, predictive modelling\, and use of species traits as predictors to explain interspecific variation in environmental response.  These techniques are more interpretable than alternatives\, have better statistical properties\, and can be used to address new problems\, such as the prediction of a species’ spatial distribution from its traits alone. \n\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. \n\nTIME ZONE – Australian Eastern Daylight Time – 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				PhD students\, research postgraduates\, and practicing academics as well as persons in industry working with multivariate data\, especially when recorded as presence/absences or some measure of abundance (counts\, biomass\, % cover\, etc). \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – 40 places \nDuration – 10 daysContact hours – Approx. 30 hoursECT’s – Equal to 3 ECT’sLanguage – English \nOther payment options are available please email oliverhooker@prstatistics.com \n			\n				\n				\n				\n				\n				Teaching Format\n				\n\n\nA mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \nAssumed quantitative knowledge \nAn understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \nAssumed computer background \nPrevious experience with data analysis using R is required. 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 and RStudio installed. 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/ \n\nDownload RStudio \n\n \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\nThere will additional Q and A support for people who can follow during real time – this will be from 21:30 to 22:00 EDT \nWEEK 1 \nMonday 1st – Classes from 10:00 to 13:00 EDTRevision of key “Stat 101” messages \nTuesday 2nd – Classes from 10:00 to 13:00 EDTRevision of (univariate) regression analysis: the linear model\, generalised linear model.Main packages: lme4. \nWednesday 3rd – Classes from 10:00 to 13:00 EDTLinear mixed models\, the parametric bootstrap\, permutation tests and the bootstrap.Main packages: lme4\, mvabund. \nThursday 4th – Classes from 10:00 to 13:00 EDTModel selection\, classical multivariate analysis.Main packages: glmnet. \nFriday 5th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: hierarchical models\, key properties\, hypothesis testing.Main packages: mvabund. \nWEEK 2 \nMonday 8th – Classes from 10:00 to 13:00 EDTMultivariate abundance data: design-based inference for dependent data\, indicator species.Main packages: mvabund. \nTuesday 9th – Classes from 10:00 to 13:00 EDTCompositional data\, explaining cross-species patterns using traits.Main packages: mvabund. \nWednesday 10th – Classes from 10:00 to 13:00 EDTClassifying species based on environmental response\, predictive modelsMain packages: Speciesmix\, mvabund\, lme4. \nThursday 11th – Classes from 10:00 to 13:00 EDTModel-based ordination and inferenceMain packages: gllvm. \nFriday 12th – Classes from 10:00 to 13:00 EDTInferring interactions form co-occurrence dataMain packages: gllvm\, ecoCopula. \n\n\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.\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/model-based-multivariate-analysis-of-abundance-data-using-r-mbmv03/
LOCATION:Delivered remotely (Australia)\, Australia
CATEGORIES:Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/09/16-Model-base-multivaraite-analysis-of-abundance-data-using-R-MBMV.jpg
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