https://jonjup.netlify.app/tags/mod3/ Recent content in MOD3 on Amaltheia Hugo -- gohugo.io en-us <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener">CC BY-NC 4.0</a> Thu, 07 Jan 2021 00:00:00 +0000 https://jonjup.netlify.app/post/lecutre-advanced-methods-for-multivariate-analysis/ Thu, 07 Jan 2021 00:00:00 +0000 https://jonjup.netlify.app/post/lecutre-advanced-methods-for-multivariate-analysis/ This post is the penultimate lecture in MOD3: Advanced data analysis given at the University of Koblenz-Landau in the winter semester 20/21. Due to its size it is split into multiple presentations. Each is embedded in this post and you can enter full-screen view by clicking the link above. Introduction mvabund cqo gllvm hmsc copula https://jonjup.netlify.app/post/analyzing-the-anttrait-data-with-boral/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttrait-data-with-boral/ body { text-align: justify} In this post we will, analyze the antTraits data with generalized linear latent variable models fit with the BORAL R package (Hui 2016). Elsewhere on the blog you can find an analysis of the same data using mvabund, gllvm and CAO/CQO. First of, we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-cqo-and-cao/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-cqo-and-cao/ body { text-align: justify} In this script we will analyze the antTraits data with CAO and CQO. Elsewhere on the blog you can find an analysis of the same data using mvabund, boral and gllvm. First of we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. Additionally I will load my custom made residual plot function. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-gllvm/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-gllvm/ body { text-align: justify} In this post we will analyze the antTraits data with generalized linear latent variable models fit with the gllvm R package (Niku et al. 2020). Elsewhere on the blog you can find an analysis of the same data using mvabund and boral. First of we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/references-for-mod3-advances-in-multivariate-statistics/ Wed, 09 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/references-for-mod3-advances-in-multivariate-statistics/ body{ font-size: 10pt; } This documents contains literature that was either cited in the MOD3 Lecture or that I think is helpful to deepen your understanding of the discussed methods. The references are arranged in thematic groups following the same structure as the lecture. References within each group are ordered alphabetically. As some references are pertinent to multiple topics you will find repetitions. In the very end there is an additional section pointing you towards methods or papers that were not discussed but are noteworthy and interesting nonetheless. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-mvabund/ Tue, 08 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-mvabund/ body { text-align: justify} In this post, we will use the mvabund R-package to analyze the antTraits data set. Elsewhere on the blog you can find an analysis of the same data using gllvm, boral and CAO/CQO. Preparing the analysis First we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/species-distribution-model-with-hmsc/ Mon, 26 Oct 2020 00:00:00 +0000 https://jonjup.netlify.app/post/species-distribution-model-with-hmsc/ body { text-align: justify} Introduction In this short example, we will use the Hierarchical Modeling of Species Communities (HMSC, Ovaskainen and Abrego (2020)) approach through its implementation in the HMSC-R package (Tikhonov et al. 2020) to analyze and predict the distribution of the Western jackdaw (Corvus monedula) in Finland. This post works through a univariate example of HMSC - if you are interested in a multi-species example, see here.