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DTSTART;TZID=Europe/Lisbon:20260309T100000
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DTSTAMP:20260429T135848
CREATED:20250808T101822Z
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SUMMARY:Statistical Models with R for Biological Sciences
DESCRIPTION:30 hours course \nThe course’s first session is a reminder of linear regression and analysis of variance\, the two statistical techniques that constitute the basis of the most important frames of statistical models. These statistical models (GLM\, GLZ and GAM) will be the content of the next four sessions. \nGLM (General Linear Model) will be studied in the second and third sessions. Due to its robustness and relative simplicity\, this is the most widely used statistical model frame. For this reason\, in the course we spend more time with GLM than with the others modelling frameworks (GLZ\, GAM). Through worked examples will be explained the following topics: I) GLM assumptions\, how to test them and how to solve problems when certain of these assumptions are not met (data transformation for non-normality\, alternative fitting by generalized least squares for variance heteroscedasticity and residual autocorrelation); II) contrast of hypothesis for the main effects and post-hoc tests for the simple effects; III) visualization of model predictions. \nIn the GLM will be explained with particular detail the inclusion of random factors (simple mixed effects models\, hierarchical designs\, and random slopes designs). \nGLZ (Generalized Linear Models) are an extension of GLM for error distributions that could be different than Gaussian. Three worked examples will be shown for different distributions (binomial Poisson and negative binomial\, for over-dispersed data)\, and a simple example including random factors. \nGAM is a statistical model frame that could incorporate any features of GLM and GLZ. But\, in addition\, and considering additional restrictions\, they could incorporate smoothed functions (non-linear but non-parametric) to describe the relationship between the response variable and one or several predictor variables. This statistical model frame has been gaining popularity in different fields of science due to the increase in computer power over the last 20 years. \nAt least a beginner’s level in R language is recommended for this course\, and also some familiarity with basic statistical techniques (regression\, contrast of hypothesis\, ANOVA). \nThe course is open to any level from undergraduate students to senior researchers. It is considered as the “second part” of another course that is regularly taught in CIIMAR entitled Introductory Statistics for Biological Sciences. \nGOALS/SKILLS: \n– Understand the principles of the statistical model frames in order to apply them according to the features of data. \n– Test model assumptions with contrast of hypothesis and diagnosis graphs\, and learn to solve problems associated with this process (data transformation\, alternative fitting techniques\, alternative error distributions\, using non-linear or non-parametric fits). \n– Understand the difference between random and fixed factors and the possible ways to incorporate random factors in the model design. \n– Learn about the different contrast of hypothesis that can be performed in a statistical model (analysis of variance and analysis of deviance for the main effects\, Tukey\, Dunnett\, least significant differences\, etc. for the post-hoc). \n– Learn the most standard methods to select the best model among a series of candidates fit to the same data. \n  \nPrerequisites: Basic knowledge of R and statistics \n  \nINSTRUCTOR \nAldo Barreiro Felpeto is a researcher at Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR) associated to the University of Porto (Porto\, Portugal). His research career has focused in plankton ecology. He defended his Ph.D. dissertation in 2007 in the Department of Ecology at the University of Vigo (Vigo\, Spain) about interactions between zooplankton and toxic phytoplankton species from the Spanish NW Atlantic coast\, southern Baltic sea and southern Tirreno coast. In 2008-2010\, he performed a post-doctorate in the Department of Ecology and Evolutionary Biology at Cornell University (Ithaca\, New York\, USA). Since 2011 he is a researcher at CIIMAR.
URL:https://events.ciimar.up.pt/event/statistical-models-with-r-for-biological-sciences/
LOCATION:Online
CATEGORIES:Researchers
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