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ANOVA_in_R_Repeated_Measures_Example.ipynb
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ANOVA_in_R_Repeated_Measures_Example.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RM ANOVA in R using afex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How to Install afex"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"list.of.packages <- c(\"afex\", \"emmeans\")\n",
"new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\n",
"if(length(new.packages)) install.packages(new.packages)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: afex\n",
"Loading required package: lme4\n",
"Loading required package: Matrix\n",
"Registered S3 methods overwritten by 'car':\n",
" method from\n",
" influence.merMod lme4\n",
" cooks.distance.influence.merMod lme4\n",
" dfbeta.influence.merMod lme4\n",
" dfbetas.influence.merMod lme4\n",
"Registered S3 methods overwritten by 'ggplot2':\n",
" method from \n",
" [.quosures rlang\n",
" c.quosures rlang\n",
" print.quosures rlang\n",
"************\n",
"Welcome to afex. For support visit: http://afex.singmann.science/\n",
"- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()\n",
"- Methods for calculating p-values with mixed(): 'KR', 'S', 'LRT', and 'PB'\n",
"- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests\n",
"- NEWS: library('emmeans') now needs to be called explicitly!\n",
"- Get and set global package options with: afex_options()\n",
"- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()\n",
"- For example analyses see: browseVignettes(\"afex\")\n",
"************\n",
"\n",
"Attaching package: 'afex'\n",
"\n",
"The following object is masked from 'package:lme4':\n",
"\n",
" lmer\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>Sub_id</th><th scope=col>rt</th><th scope=col>iv1</th><th scope=col>iv2</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>1 </td><td>1082.9866</td><td>noise </td><td>down </td></tr>\n",
"\t<tr><td>2 </td><td> 938.7997</td><td>noise </td><td>down </td></tr>\n",
"\t<tr><td>3 </td><td>1101.4710</td><td>noise </td><td>down </td></tr>\n",
"\t<tr><td>4 </td><td>1123.0303</td><td>noise </td><td>down </td></tr>\n",
"\t<tr><td>5 </td><td> 938.0516</td><td>noise </td><td>down </td></tr>\n",
"\t<tr><td>6 </td><td> 864.4438</td><td>noise </td><td>down </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" Sub\\_id & rt & iv1 & iv2\\\\\n",
"\\hline\n",
"\t 1 & 1082.9866 & noise & down \\\\\n",
"\t 2 & 938.7997 & noise & down \\\\\n",
"\t 3 & 1101.4710 & noise & down \\\\\n",
"\t 4 & 1123.0303 & noise & down \\\\\n",
"\t 5 & 938.0516 & noise & down \\\\\n",
"\t 6 & 864.4438 & noise & down \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| Sub_id | rt | iv1 | iv2 |\n",
"|---|---|---|---|\n",
"| 1 | 1082.9866 | noise | down |\n",
"| 2 | 938.7997 | noise | down |\n",
"| 3 | 1101.4710 | noise | down |\n",
"| 4 | 1123.0303 | noise | down |\n",
"| 5 | 938.0516 | noise | down |\n",
"| 6 | 864.4438 | noise | down |\n",
"\n"
],
"text/plain": [
" Sub_id rt iv1 iv2 \n",
"1 1 1082.9866 noise down\n",
"2 2 938.7997 noise down\n",
"3 3 1101.4710 noise down\n",
"4 4 1123.0303 noise down\n",
"5 5 938.0516 noise down\n",
"6 6 864.4438 noise down"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"require(afex)\n",
"\n",
"url = 'https://raw.githubusercontent.com/marsja/jupyter/master/Python_ANOVA/rmAOV2way.csv'\n",
"\n",
"df <- read.csv(file=url, header=TRUE, sep=',')\n",
"\n",
"head(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One-Way ANOVA"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anova Table (Type 3 tests)\n",
"\n",
"Response: rt\n",
" Effect df MSE F ges p.value\n",
"1 iv1 1, 59 3652.85 2207.02 *** .95 <.0001\n",
"---\n",
"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1\n"
]
}
],
"source": [
"aov <- aov_ez('Sub_id', 'rt', \n",
" fun_aggregate = mean, df, within = 'iv1')\n",
"print(aov)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Two-Way ANOVA"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anova Table (Type 3 tests)\n",
"\n",
"Response: rt\n",
" Effect df MSE F ges p.value\n",
"1 iv1 1, 59 10958.55 2207.02 *** .87 <.0001\n",
"2 iv2 1.98, 116.79 8871.20 275.41 *** .58 <.0001\n",
"3 iv1:iv2 1.97, 116.37 10668.36 1.87 .01 .16\n",
"---\n",
"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1\n",
"\n",
"Sphericity correction method: GG \n"
]
}
],
"source": [
"aov <- aov_ez('Sub_id', 'rt', fun_aggregate = mean, \n",
" df, within = c('iv1', 'iv2'))\n",
"print(aov)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interaction Plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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qQ45GXSmkihlpZ1q/lDQKRIZGXS6ogUbqms/YiESLHIz6Rbfxls6Zv8+yOSAQlr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\nEoABiARgACIBGPD/AXKKGE15RJq6AAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"afex_plot(aov, x = \"iv1\", trace = \"iv2\",\n",
" error = \"within\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}