Revision 738b43f6dcce877b85c10f23dafc38e0098bd8fb authored by Daniel Luedecke on 19 February 2014, 11:32:18 UTC, committed by cran-robot on 19 February 2014, 11:32:18 UTC
1 parent 64f2fd6
sjPlotAnova.R
# bind global variables
if(getRversion() >= "2.15.1") utils::globalVariables(c("xv", "lower", "upper", "pv", "p"))
#' @title Plot One-Way-Anova tables
#' @name sjp.aov1
#'
#' @description Plot One-Way-Anova table sum of squares (SS) of each factor level (group)
#' against the dependent variable. The SS of the factor variable against the
#' dependent variable (variance within and between groups) is printed to
#' the model summary.
#' @seealso \link{sju.aov1.levene}
#'
#' @param depVar The dependent variable. Will be used with following formular:
#' \code{aov(depVar ~ grpVar)}
#' @param grpVar The grouping variable, as unordered factor. Will be used with following formular:
#' \code{aov(depVar ~ grpVar)}
#' @param meansums If \code{TRUE}, the values reported are the true group mean values. If \code{FALSE} (default),
#' the values are reported in the standard way, i.e. the values indicate the difference of
#' the group mean in relation to the intercept (reference group).
#' @param type Indicates Whether the group means should be plotted as \code{"dots"} (aka forest plots, default)
#' or as \code{"bars"}.
#' @param hideErrorBars If \code{TRUE}, the error bars that indicate the confidence intervals of the group means are not
#' shown. Only applies if parameter \code{type} is \code{"bars"}. Default value is \code{FALSE}.
#' @param title Diagram's title as string.
#' Example: \code{title=c("my title")}
#' @param titleSize The size of the plot title. Default is 1.3.
#' @param titleColor The color of the plot title. Default is \code{"black"}.
#' @param axisLabels.y Value labels of the grouping variable \code{grpVar} that are used for labelling the
#' grouping variable axis. Passed as vector of strings.
#' Example: \code{axisLabels.y=c("Label1", "Label2", "Label3")}. \cr
#' Note: If you use the \code{\link{sji.SPSS}} function and the \code{\link{sji.getValueLabels}} function, you receive a
#' list object with label string. The labels may also be passed as list object. They will be unlisted and
#' converted to character vector automatically. See examples below. \cr
#' Note: In case \code{type} is \code{"bars"}, the \code{grpVar} will be plotted along
#' the x-axis.
#' @param reverseOrder If \code{TRUE}, the order of the factor categories (groups) is reversed.
#' Default is \code{FALSE}.
#' @param stringIntercept A string that indicates the reference group (intercept), that is appended to
#' the value label of the grouping variable. Default is \code{"(Intercept)"}.
#' @param showAxisLabels.y Whether y axis text (category value) should be shown (use \code{TRUE})
#' or not. Default is \code{TRUE}.
#' @param axisLabelSize The size of value labels in the diagram. Default is 4, recommended values range
#' between 2 and 8.
#' @param axisLabelColor The color of the category labels (predictor labels). Default is a dark grey (grey30).
#' @param axisTitle.x A label for the x axis. Default is \code{NULL}, which means no x-axis title.
#' @param axisTitleColor The color of the x axis label.
#' @param axisTitleSize The size of the x axis label. Default is 1.2.
#' @param axisLimits Defines the range of the axis where the beta coefficients and their confidence intervalls
#' are drawn. By default, the limits range from the lowest confidence interval to the highest one, so
#' the diagram has maximum zoom. Use your own values as 2-value-vector, for instance: \code{limits=c(-0.8,0.8)}.
#' @param valueLabelColor Colour of the values inside the diagram. Only applies, when parameter
#' \code{showValueLabels} is set to \code{TRUE}. Use any valid colour value, e.g. \code{valueLabelColor="grey50"} or
#' \code{valueLabelColor=c("#cc3366")}. Default is \code{"grey20"}.
#' @param valueLabelColorNS Colour of the non significant values inside the diagram.
#' Only applies, when parameter \code{showValueLabels} is set to \code{TRUE}. Use any valid colour value, e.g.
#' \code{valueLabelColor="grey50"} or \code{valueLabelColor=c("#cc3366")}. Default is \code{"grey50"}.
#' @param valueLabelSize Size of the value labels. Drfault is 4.5. Recommended Values range from
#' 2 to 8
#' @param valueLabelAlpha The alpha level (transparancy) of the value labels. Default is 0.8, use
#' any value from 0 to 1.
#' @param axisLabelAngle.x Angle for x-axis-labels, passed as numeric value.
#' @param axisLabelAngle.y Angle for y-axis-labels, passed as numeric value.
#' @param errorBarColor The color of the error bars that indicate the confidence intervalls
#' of the group means. Default is \code{NULL}, which means that if \code{type} is \code{"dots"},
#' the \code{pointColors} value will be used as error bar color. In case \code{type} is \code{"bars"},
#' \code{"black"} will be used as error bar color.
#' @param errorBarWidth The width of the error bar ends. Default is 0.
#' @param errorBarSize The size of the error bar. Default is 0.8.
#' @param errorBarLineType The linetype of error bars. Default is \code{1} (solid line).
#' @param pointColors The colours of the points that indicate the mean-value. \code{pointColors} is a
#' vector with two values: the first indicating groups with positive means and the second
#' indicating negative means. Default is \code{c("#3366a0", "#aa6633")}.
#' @param pointSize The size of the points that indicate the mean-value. Default is 3.
#' @param barColors The colors of the bars in bar charts. Only applies if parameter \code{type} is \code{"bars"}. \code{barColors} is a
#' vector with two values: the first indicating groups with positive means and the second
#' indicating negative means. Default is \code{c("#3366a0", "#aa6633")}.
#' @param barWidth The width of the bars in bar charts. Only applies if parameter \code{type} is \code{"bars"}. Default is 0.5
#' @param barAlpha The alpha value of the bars in bar charts. Only applies if parameter \code{type} is \code{"bars"}. Default is 1
#' @param barOutline If \code{TRUE}, each bar gets a colored outline. Only applies if parameter \code{type} is \code{bars}.
#' Default is \code{FALSE}.
#' @param outlineColor The color of the bar outline. Only applies, if \code{barOutline} is set to \code{TRUE}.
#' Default is black.
#' @param breakTitleAt Wordwrap for diagram title. Determines how many chars of the title are displayed in
#' one line and when a line break is inserted into the title
#' @param breakLabelsAt Wordwrap for diagram labels. Determines how many chars of the category labels are displayed in
#' one line and when a line break is inserted
#' @param gridBreaksAt Sets the breaks on the y axis, i.e. at every n'th position a major
#' grid is being printed. Default is \code{NULL}, so \link{pretty} gridbeaks will be used.
#' @param borderColor User defined color of whole diagram border (panel border).
#' @param axisColor User defined color of axis border (y- and x-axis, in case the axes should have different colors than
#' the diagram border).
#' @param theme Specifies the diagram's background theme. Default (parameter \code{NULL}) is a gray
#' background with white grids.
#' \itemize{
#' \item Use \code{"bw"} for a white background with gray grids
#' \item \code{"classic"} for a classic theme (black border, no grids)
#' \item \code{"minimal"} for a minimalistic theme (no border,gray grids) or
#' \item \code{"none"} for no borders, grids and ticks.
#' }
#' The ggplot-object can be returned with \code{returnPlot} set to \code{TRUE} in order to further
#' modify the plot's theme.
#' @param majorGridColor Specifies the color of the major grid lines of the diagram background.
#' @param minorGridColor Specifies the color of the minor grid lines of the diagram background.
#' @param hideGrid.x If \code{TRUE}, the x-axis-gridlines are hidden. Default if \code{FALSE}.
#' @param hideGrid.y If \code{TRUE}, the y-axis-gridlines are hidden. Default if \code{FALSE}.
#' @param showTickMarks Whether tick marks of axes should be shown or not
#' @param showValueLabels Whether the value labels (mean differences) should be plotted
#' to each dot or not.
#' @param labelDigits The amount of digits for rounding the estimations (see \code{showValueLabels}).
#' Default is 2, i.e. estimators have 2 digits after decimal point.
#' @param showPValueLabels Whether the significance levels of each category/group should be appended
#' to values or not.
#' @param showModelSummary If \code{TRUE} (default), a summary of the anova model with
#' Sum of Squares between groups (ssb), Sum of Squares within groups (ssw), multiple and adjusted
#' R-square and F-Test is printed to the lower right corner
#' of the diagram. Default is \code{TRUE}.
#' @param returnPlot If \code{TRUE}, the ggplot-object with the complete plot will be returned (and not plotted).
#' Default is \code{FALSE}, hence the ggplot object will be plotted, not returned.
#' @return The ggplot-object with the complete plot in case \code{returnPlot} is \code{TRUE}.
#'
#' @examples
#' data(efc)
#' sjp.aov1(efc$c12hour, as.factor(efc$e42dep))
#'
#'
#' data(efc)
#' efc.val <- sji.getValueLabels(efc)
#' efc.var <- sji.getVariableLabels(efc)
#' sjp.aov1(efc$c12hour,
#' as.factor(efc$e42dep),
#' axisLabels.y=efc.val['e42dep'],
#' axisTitle.x=efc.var[['c12hour']])
#'
#' sjp.aov1(efc$c12hour,
#' as.factor(efc$e42dep),
#' axisLabels.y=efc.val['e42dep'],
#' title=efc.var[['c12hour']],
#' type="bars",
#' meansums=TRUE,
#' hideErrorBars=TRUE,
#' theme="minimal",
#' minorGridColor="white",
#' showTickMarks=FALSE,
#' showModelSummary=FALSE,
#' hideGrid.x=TRUE)
#'
#' sjp.aov1(efc$c12hour,
#' as.factor(efc$c172code),
#' axisLabels.y=efc.val['c172code'],
#' title=efc.var[['c12hour']],
#' type="bars",
#' showTickMarks=FALSE,
#' showModelSummary=FALSE,
#' axisLabelAngle.x=90)
#'
#' @import ggplot2
#' @export
sjp.aov1 <- function(depVar,
grpVar,
meansums=FALSE,
type="dots",
hideErrorBars=FALSE,
title=NULL,
titleSize=1.3,
titleColor="black",
axisLabels.y=NULL,
reverseOrder=FALSE,
stringIntercept="(Intercept)",
showAxisLabels.y=TRUE,
axisLabelSize=1.1,
axisLabelColor="gray30",
axisTitle.x=NULL,
axisTitleSize=1.2,
axisTitleColor=c("#444444"),
axisLimits=NULL,
valueLabelColor="grey20",
valueLabelColorNS="grey50",
valueLabelSize=4.5,
valueLabelAlpha=0.8,
axisLabelAngle.x=0,
axisLabelAngle.y=0,
errorBarColor=NULL,
errorBarWidth=0,
errorBarSize=0.8,
errorBarLineType=1,
pointColors=c("#3366a0", "#aa3333"),
pointSize=3,
barColors=c("#3366a0", "#aa3333"),
barWidth=0.5,
barAlpha=1,
barOutline=FALSE,
outlineColor="black",
breakTitleAt=50,
breakLabelsAt=12,
gridBreaksAt=NULL,
borderColor=NULL,
axisColor=NULL,
theme=NULL,
majorGridColor=NULL,
minorGridColor=NULL,
hideGrid.x=FALSE,
hideGrid.y=FALSE,
showTickMarks=TRUE,
showValueLabels=TRUE,
labelDigits=2,
showPValueLabels=TRUE,
showModelSummary=TRUE,
returnPlot=FALSE) {
# --------------------------------------------------------
# unlist labels
# --------------------------------------------------------
# Help function that unlists a list into a vector
unlistlabels <- function(lab) {
dummy <- unlist(lab)
labels <- c()
for (i in 1:length(dummy)) {
labels <- c(labels, as.character(dummy[i]))
}
return (labels)
}
if (!is.null(axisLabels.y)) {
# if labels are lists, unlist
if (is.list(axisLabels.y)) {
axisLabels.y <- unlistlabels(axisLabels.y)
}
# append "intercept" string, to mark the reference category
axisLabels.y[1] <- paste(axisLabels.y[1], stringIntercept)
}
# --------------------------------------------------------
# Parameter check
# --------------------------------------------------------
# --------------------------------------------------------
# Check if grpVar is factor. If not, convert to factor
# --------------------------------------------------------
if (!is.factor(grpVar)) {
grpVar <- as.factor(grpVar)
}
# --------------------------------------------------------
# Check spelling of type-param
# --------------------------------------------------------
if (type=="dot" || type=="d") {
type <- "dots"
}
if (type=="bar" || type=="b") {
type <- "bars"
}
# --------------------------------------------------------
# set geom colors
# --------------------------------------------------------
if (type=="dots") {
geomcols <- pointColors
}
else {
geomcols <- barColors
}
# --------------------------------------------------------
# check whether we colors for error bars. if not, use point color
# in case of dots or "black" in case of bars.
# --------------------------------------------------------
if (is.null(errorBarColor)) {
if (type=="dots") {
errorBarColors <- geomcols
}
else {
errorBarColors <- c("black", "black")
}
}
else {
errorBarColors <- c(errorBarColor, errorBarColor)
}
# --------------------------------------------------------
# check whether we have x-axis title. if not, use standard
# value
# --------------------------------------------------------
if (is.null(axisTitle.x)) {
axisTitle.x <- c("")
}
# --------------------------------------------------------
# check whether bars should have an outline
# --------------------------------------------------------
if (!barOutline) {
outlineColor <- waiver()
}
# check length of diagram title and split longer string at into new lines
# every 50 chars
if (!is.null(title)) {
title <- sju.wordwrap(title, breakTitleAt)
}
# check length of x-axis title and split longer string at into new lines
# every 50 chars
if (!is.null(axisTitle.x)) {
axisTitle.x <- sju.wordwrap(axisTitle.x, breakTitleAt)
}
# check length of x-axis-labels and split longer strings at into new lines
# every 10 chars, so labels don't overlap
if (!is.null(axisLabels.y)) {
axisLabels.y <- sju.wordwrap(axisLabels.y, breakLabelsAt)
}
# ----------------------------
# Calculate one-way-anova. Since we have
# only one group variable, Type of SS does
# not matter.
# ----------------------------
fit <- aov(depVar ~ grpVar)
# coefficients (group mean)
means <- summary.lm(fit)$coefficients[,1]
# p-values of means
means.p <- summary.lm(fit)$coefficients[,4]
# lower confidence intervals of coefficients (group mean)
means.lci <- confint(fit)[,1]
# upper confidence intervals of coefficients (group mean)
means.uci <- confint(fit)[,2]
# ----------------------------
# Check whether true group means should be reported
# or the differences of group means in relation to the
# intercept (reference group). The latter is the default.
# ----------------------------
if (meansums) {
for (i in 2:length(means)) {
means[i] <- means[i]+means[1]
means.lci[i] <- means.lci[i]+means[1]
means.uci[i] <- means.uci[i]+means[1]
}
}
# ----------------------------
# create expression with model summarys. used
# for plotting in the diagram later
# ----------------------------
if (showModelSummary) {
# sum of squares
ss <- summary(fit)[[1]]['Sum Sq']
# multiple r2
r2 <- summary.lm(fit)$r.squared
# adj. r2
r2.adj <- summary.lm(fit)$adj.r.squared
# get F-statistics
fstat <- summary.lm(fit)$fstatistic[1]
# p-value for F-test
pval <- summary(fit)[[1]]['Pr(>F)'][1,1]
# indicate significance level by stars
pan <- c("")
if (pval<=0.005) {
pan <- c("***")
}
else if (pval<=0.01) {
pan <- c("**")
}
else if (pval<=0.05) {
pan <- c("*")
}
# create mathematical term
modsum <- as.character(as.expression(
substitute(italic(SS[B]) == ssb * "," ~~ italic(SS[W]) == ssw * "," ~~ R^2 == mr2 * "," ~~ "adj." * R^2 == ar2 * "," ~~ "F" == f * panval,
list(ssb=sprintf("%.2f", ss[1,]),
ssw=sprintf("%.2f", ss[2,]),
mr2=sprintf("%.3f", r2),
ar2=sprintf("%.3f", r2),
f=sprintf("%.2f", fstat),
panval=pan))))
}
# ----------------------------
# print coefficients and p-values in plot
# ----------------------------
# init data column for p-values
ps <- c(round(means,labelDigits))
# if no values should be shown, clear
# vector now
if (!showValueLabels) {
ps <- rep(c(""), length(ps))
}
# --------------------------------------------------------
# copy p-values into data column
# --------------------------------------------------------
if (showPValueLabels) {
for (i in 1:length(means.p)) {
if (means.p[i]>=0.05) {
}
else if (means.p[i]>=0.01 && means.p[i]<0.05) {
ps[i] <- paste(ps[i], "*")
}
else if (means.p[i]>=0.001 && means.p[i]<0.01) {
ps[i] <- paste(ps[i], "**")
}
else {
ps[i] <- paste(ps[i], "***")
}
}
}
# --------------------------------------------------------
# check whether order of category items should be reversed
# or not
# --------------------------------------------------------
if (reverseOrder) {
catorder <- c(length(means):1)
}
else {
catorder <- c(1:length(means))
}
# --------------------------------------------------------
# create new data.frame, since ggplot requires data.frame as parameter
# The data frame contains means, CI and p-values
# --------------------------------------------------------
df <- data.frame(cbind(
# Append coefficients
means,
# append CI
means.lci,
means.uci,
# append p-value
means.p,
ps,
catorder))
# --------------------------------------------------------
# check if user defined labels have been supplied
# if not, use variable names from data frame
# --------------------------------------------------------
if (is.null(axisLabels.y)) {
axisLabels.y <- row.names(df)
}
# order labels
axisLabels.y <- axisLabels.y[catorder]
# give columns names
names(df) <- c("means", "lower", "upper", "p", "pv", "xv")
df$means <- as.numeric(as.character(df$means))
df$lower <- as.numeric(as.character(df$lower))
df$upper <- as.numeric(as.character(df$upper))
df$p <- as.numeric(as.character(df$p))
df$pv <- as.character(df$pv)
# bind color values to data frame, because we cannot use several
# different color aesthetics in ggplot
df <- cbind(df,
geocol=ifelse(df$means>=0, geomcols[1], geomcols[2]),
labcol=ifelse(df$p<0.05, valueLabelColor, valueLabelColorNS),
errcol=ifelse(df$means>=0, errorBarColors[1], errorBarColors[2]))
# --------------------------------------------------------
# Calculate axis limits. The range is from lowest lower-CI
# to highest upper-CI, or a user-defined range (if "axisLimits"
# is not NULL)
# --------------------------------------------------------
if (is.null(axisLimits)) {
# check whether we have bar chart and error bars hidden
# in this case, the upper limit does not correspond to the
# upper CI, but to the highest OR value
if (type=="bars") {
# if errorbars are hidden, the axis range is defined
# by the mean values
if (hideErrorBars) {
maxval <- max(df$means)+10
minval <- min(df$means)
}
# if errorbars are shown, axis range is defined
# by confidence interval
else {
maxval <- max(df$upper)
minval <- min(df$lower)
}
# if minval is > 0, set it to zero, so we have a proper baseline
if (minval>0) minval <- 0
# if maxval is < 0, set it to zero, so we have a proper baseline
if (maxval<0) maxval <- 0
}
else {
# else we have confindence intervals displayed, so
# the range corresponds to the boundaries given by
# the CI's
maxval <- max(df$upper)
minval <- min(df$lower)
}
if (maxval>0) limfac <- ifelse(abs(maxval)<5, 5, 10)
else limfac <- ifelse(abs(minval)<5, 5, 10)
upper_lim <- ifelse(maxval==0, 0, limfac*ceiling((maxval+1)/limfac))
lower_lim <- ifelse(minval==0, 0, limfac*floor(minval/limfac))
}
else {
lower_lim <- axisLimits[1]
upper_lim <- axisLimits[2]
}
# determine gridbreaks
if (is.null(gridBreaksAt)) {
# gridBreaksAt <- 1
# while ((upper_lim-lower_lim) > (10*gridBreaksAt)) {
# gridBreaksAt <- gridBreaksAt*10
# }
ticks <- pretty(c(lower_lim, upper_lim))
}
else {
ticks <- c(seq(lower_lim, upper_lim, by=gridBreaksAt))
}
# --------------------------------------------------------
# Set theme and default grid colours. grid colours
# might be adjusted later
# --------------------------------------------------------
hideGridColor <- c("white")
if (is.null(theme)) {
ggtheme <- theme_gray()
hideGridColor <- c("gray90")
}
else if (theme=="bw") {
ggtheme <- theme_bw()
}
else if (theme=="classic") {
ggtheme <- theme_classic()
}
else if (theme=="minimal") {
ggtheme <- theme_minimal()
}
else if (theme=="none") {
ggtheme <- theme_minimal()
majorGridColor <- c("white")
minorGridColor <- c("white")
showTickMarks <-FALSE
}
# --------------------------------------------------------
# Set up grid colours
# --------------------------------------------------------
majorgrid <- NULL
minorgrid <- NULL
if (!is.null(majorGridColor)) {
majorgrid <- element_line(colour=majorGridColor)
}
if (!is.null(minorGridColor)) {
minorgrid <- element_line(colour=minorGridColor)
}
hidegrid <- element_line(colour=hideGridColor)
# --------------------------------------------------------
# Set up visibility of tick marks
# --------------------------------------------------------
if (!showTickMarks) {
ggtheme <- ggtheme + theme(axis.ticks = element_blank())
}
if (!showAxisLabels.y) {
axisLabels.y <- c("")
}
# --------------------------------------------------------
# Set up plot padding (margins inside diagram). In case of
# bars, we don't want margins.
# --------------------------------------------------------
if (type=="bars") {
scaley <- scale_y_continuous(limits=c(lower_lim,upper_lim), expand=c(0,0), breaks=ticks, labels=ticks)
}
else {
scaley <- scale_y_continuous(limits=c(lower_lim,upper_lim), breaks=ticks, labels=ticks)
}
# --------------------------------------------------------
# Start plot here!
# --------------------------------------------------------
if (type=="dots") {
anovaplot <- ggplot(df, aes(y=means, x=xv)) +
# print point
geom_point(size=pointSize, colour=df$geocol) +
# and error bar
geom_errorbar(aes(ymin=lower, ymax=upper), colour=df$errcol, size=errorBarSize, width=errorBarWidth, linetype=errorBarLineType) +
# Print p-values. With vertical adjustment, so they don't overlap with the errorbars
geom_text(aes(label=pv, y=means), colour=df$labcol, vjust=-0.8, size=valueLabelSize, alpha=valueLabelAlpha, show_guide=FALSE)
}
# --------------------------------------------------------
# start with bar plots here
# --------------------------------------------------------
else if (type=="bars") {
# check whether we have error bars. if yes, adjust horizontal
# posizion of labels
hlabj <- ifelse(hideErrorBars==FALSE, 1.3, 0.5)
anovaplot <- ggplot(df, aes(y=means, x=xv)) +
# stat-parameter indicates statistics
# stat="bin": y-axis relates to count of variable
# stat="identity": y-axis relates to value of variable
geom_bar(fill=df$geocol, stat="identity", position="identity", width=barWidth, colour=outlineColor, alpha=barAlpha) +
# print value labels and p-values
geom_text(aes(label=pv, y=means), colour=df$labcol, vjust=ifelse(df$means>=0, -1, 1), hjust=hlabj, size=valueLabelSize, alpha=valueLabelAlpha, show_guide=FALSE)
if (hideErrorBars==FALSE) {
anovaplot <- anovaplot +
# print confidence intervalls (error bars)
geom_errorbar(aes(ymin=lower, ymax=upper), colour=df$errcol, width=errorBarWidth, size=errorBarSize, linetype=errorBarLineType)
}
}
# --------------------------------------------------------
# continue with other plot elements
# --------------------------------------------------------
anovaplot <- anovaplot +
# set y-scale-limits, breaks and tick labels
scaley +
# set value labels to x-axis
scale_x_discrete(labels=axisLabels.y, limits=c(1:nrow(df))) +
# flip coordinates
labs(title=title, x=NULL, y=axisTitle.x) +
ggtheme +
# set axes text and
theme(axis.text = element_text(size=rel(axisLabelSize), colour=axisLabelColor),
axis.title = element_text(size=rel(axisTitleSize), colour=axisTitleColor),
axis.text.y = element_text(angle=axisLabelAngle.y),
axis.text.x = element_text(angle=axisLabelAngle.x),
plot.title = element_text(size=rel(titleSize), colour=titleColor))
# --------------------------------------------------------
# Flip coordinates when we have dots
# --------------------------------------------------------
if (type=="dots") {
anovaplot <- anovaplot + coord_flip()
}
# the panel-border-property can only be applied to the bw-theme
if (!is.null(borderColor)) {
if (!is.null(theme) && theme=="bw") {
anovaplot <- anovaplot +
theme(panel.border = element_rect(colour=borderColor))
}
else {
cat("\nParameter 'borderColor' can only be applied to 'bw' theme.\n")
}
}
if (!is.null(axisColor)) {
anovaplot <- anovaplot +
theme(axis.line = element_line(colour=axisColor))
}
if (!is.null(minorgrid)) {
anovaplot <- anovaplot +
theme(panel.grid.minor = minorgrid)
}
if (!is.null(majorgrid)) {
anovaplot <- anovaplot +
theme(panel.grid.major = majorgrid)
}
if (hideGrid.x) {
anovaplot <- anovaplot +
theme(panel.grid.major.x = hidegrid,
panel.grid.minor.x = hidegrid)
}
if (hideGrid.y) {
anovaplot <- anovaplot +
theme(panel.grid.major.y = hidegrid,
panel.grid.minor.y = hidegrid)
}
# check whether modelsummary should be printed
if (showModelSummary) {
# add annotations with model summary
# annotations include intercept-value and model's r-square
if (type=="dots") {
anovaplot <- anovaplot + annotate("text", label=modsum, parse=TRUE, x=-Inf, y=Inf, colour=valueLabelColor, size=valueLabelSize, alpha=valueLabelAlpha, vjust=-0.5, hjust=1.1)
}
else {
anovaplot <- anovaplot + annotate("text", label=modsum, parse=TRUE, x=-Inf, y=Inf, colour=valueLabelColor, size=valueLabelSize, alpha=valueLabelAlpha, vjust=1.2, hjust=-0.02)
}
}
# ---------------------------------------------------------
# Check whether ggplot object should be returned or plotted
# ---------------------------------------------------------
if (returnPlot) {
return(anovaplot)
}
else {
# print plot
print(anovaplot)
}
}
#' @title Plot Levene-Test for One-Way-Anova
#' @name sju.aov1.levene
#'
#' @description Plot results of Levene's Test for Equality of Variances for One-Way-Anova.
#' @seealso \link{sjp.aov1}
#'
#' @param depVar The dependent variable. Will be used with following formular:
#' \code{aov(depVar ~ grpVar)}
#' @param grpVar The grouping variable, as unordered factor. Will be used with following formular:
#' \code{aov(depVar ~ grpVar)}
#'
#' @examples
#' data(efc)
#' sju.aov1.levene(efc$c12hour, as.factor(efc$e42dep))
#'
#' @export
sju.aov1.levene <- function(depVar, grpVar) {
means <- tapply(depVar, grpVar, mean)
depVarNew <- abs(depVar - means[grpVar])
cat("\nLevene's Test for Homogeneity of Variances\n---------------------------------------\n")
fit <- aov(depVarNew ~ grpVar)
print(summary(fit))
pval <- summary(fit)[[1]]['Pr(>F)'][1,1]
# print "summary" of test
cat("\nConclusion:\n")
if (pval>0.05) {
cat("Groups are homogeneous. Everything's fine.\n")
}
else {
cat("Groups are not homogeneous! Consider logarithmic transformation.\n")
}
}
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