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LMERConvenienceFunctions (version 3.0)

Model Selection and Post-Hoc Analysis for (G)LMER Models

Description

The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.

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Version

Install

install.packages('LMERConvenienceFunctions')

Monthly Downloads

568

Version

3.0

License

GPL-2

Maintainer

Antoine Tremblay

Last Published

October 6th, 2020

Functions in LMERConvenienceFunctions (3.0)

bfFixefLMER_F.fnc

Back-fits an LMER model on p-values from ANOVA, llrt, AIC, BIC, relLik.AIC or relLik.BIC.
f

List files and directories in current directory.
cn

List the column names of a data frame in matrix format.
ffRanefLMER.fnc

Forward-fit the random effect structure of an LMER model.
LMERConvenienceFunctions-package

Model Selection and Post-Hoc Analysis for (G)LMER Models
fitLMER.fnc

Back-fit fixed effects and forward-fit random effects of an LMER model.
cdf

Change directory; list files and directories in new directory using function f.
cdup

Change directory one level up.
cd

Change directory.
bfFixefLMER_t.fnc

Back-fits an LMER model on absolute t-value and, optionally, on LLRT.
plotRaw3d.fnc

Visualize raw surface averages (3d)
mcp.fnc

Model criticism plots.
pamer.fnc

ANOVA with upper- and lower-bound p-values and R-sqaured values for LMER.
mcposthoc.fnc

Posthoc analyses for LMER models using parallel capabilities.
plotLMER3d.fnc

Dynamic 3d plot for mer object.
plotDensity3d.fnc

Kernel density estimation for two continuous variables.
relLik

Relative log-likehood.
perSubjectTrim.fnc

Per-subject Trimming of Response Variable.
summary.mcposthoc

Summarize a "mcposthoc" object.
plotLMER.fnc

plot a mer object
romr.fnc

Exclude outliers.