This is the data analysis textbook used for study programmes at the faculty of BMS at the University of Twente. […] This book is for bachelor students in social, behavioural and management sciences that want to learn how to analyse their data, with the specific aim to answer research questions. The book has a practical take on data analysis: how to do it, how to interpret the results, and how to report the results. All techniques are presented within the framework of linear models: this includes simple and multiple regression models, linear mixed models and generalised linear models. This … Read more →

This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond. […] Michael Clark m-clark.github.io Translator : 김설기 … Read more →

The MumfordBrainStats Mixed Models Series: Companion for the YouTube series […] This is a collection of materials that accompanies a YouTube series on the MumfordBrainStats channel about mixed models. Although I normall focus on material related to neuroimaging, this is for a general audience. Each of these chapters should be understandable without watching the video, but one would probably gain the most by watching the videos as well. The chapter titles indicate which video in the series goes along with that chapter. Not all videos have chapter (yet), since I’m only including chapters with … Read more →

Scripts from the online course on Time Series and Forecasting in R. […] Selecting the model. Due to seasonality involved, simple models will not be able to capture it. We therefore use the seasonal ARIMA and exponential smoothing models. Exponential smoothing models have seasonality built in it by construction. Complex models like mixed models and neural nets will be an overkill. … Read more →

Spatial generalized linear mixed models, Stationary Spatial Gaussian Process, Stan platform, Markov chain Monte Carlo. […] 空间统计的内容非常丰富，主要分为地质统计 （geostatistics）、 离散空间变差 （discrete spatial variation） 和空间点过程 （spatial point processes） 三大块 (Cressie 1993)。 地质统计这个术语最初来自南非的采矿业 (Krige 1951)， 并由 Georges Matheron 及其同事继承和发展，用以预测黄金的矿藏含量和质量。空间广义线性混合效应模型 （Spatial Generalized Linear Mixed Model，简称 SGLMM） 在空间统计中有着广泛的应用，如评估岩心样本石油含量，分析核污染物浓度的空间分布 (Diggle, Tawn, and … Read more →

This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond. […] Michael Clark m-clark.github.io … Read more →