There are lots of good resources on R available on the internet and I suggest that, if you are serious about learning R, you also look elsewhere.
#Synthesia 10.1 long code how to
This companion is meant to show you how to use R to do the types of analyses covered in “Statistics: Data analysis and modelling.” It is certainly not meant as a complete course on R. This effort will certainly pay off in the end, but it is up to you to decide whether you want to make this investment. However, R is known to have a somewhat steep learning curve, so if you want to learn R, you will have to put in some extra effort (compared to e.g. JASP or SPSS). It is flexible, relatively fast, and has a large number of users and contributors. R is a programming language and environment specifically designed for data analysis. It covers how to perform the analyses discussed in that book, mostly using “base” R and a relatively small selection of add-on packages. This is a companion to the book Statistics: Data analysis and modelling. 10.1.5 Bayesian repeated-measures ANOVA.10 Bayesian hypothesis testing with Bayes Factors.Please ensure that you have met the prerequisites below (e. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Stable represents the most currently tested and supported version of PyTorch.
#Synthesia 10.1 long code install
9.2 Obtaining p-values with afex::mixed Select your preferences and run the install command.9.1.4 Likelihood ratio test with the anova function.9.1.2 Visually assessing model assumptions.9.1 Formulating and estimating linear mixed-effects models with lme4.8.4 Repeated-measures ANOVA with the afex package.8.3 Repeated-measures ANOVA with the car package.8.2.2 Performing a repeated-measures ANOVA with separate models.8.2.1 Computing within-subjects composite scores.8.2 Repeated-measures ANOVA with separate GLMs.7.5 Testing general contrasts with emmeans.7.4 Planned comparisons and post-hoc tests with emmeans.7.2 Formulating, estimating, and testing a factorial ANOVA.6.1 Computing contrast-coded predictors.5.3.2 Investigating the moderated (indirect) effect with a bootstrap test.4.6 Multicollinearity and outlier detection.4.5 Plotting pairwise scatterplots for many variables.4.3 Estimating and testing a multiple regression model.4.1 Estimating an testing a simple regression model.2.3 Hypothesis testing directly with the binomial distribution.1.8 Exploring data: Descriptive statistics.