R/7-fliegen-schiss-plot.R
In stp4/stp25plot: meine plots
Defines functions
extract_pvalue
cordinats
panel.sig.bars
plotSigBars
prism.plots
#' Plot prism-like Plots
#'
#' gestohlen von fifer Dustin Fife
#'
#'
#' @param formula a formula object with the quantitative variable as the response variable (e.g., Var~group).
#' @param data a dataset containing the variables indicated in \code{formula}
#' @param fun what function should be used to indicate the center of the distribution. Defaults to median.
#' @param interquartile Should the interquartile range be plotted? Defaults to TRUE.
#' @param spreadfunc what function should be used to calculate the spread of the distribution? If interquartile=TRUE,
#' this argument will be ignored. The default (when not ignored) is to produce a 95\% confidence interval (1.96*sd(x)/sqrt(n)).
#' @param def.axis Logical. Should the default axes be used?
#' @param jitter Logical. Should the y values be jittered as well?
#' @param add Should the plot be added to an existing plot?
#' @param col What color should the dots be painted? Defaults to gray.
#' @param ... other arguments passed to plot
## @author Dustin Fife
## @seealso \code{\link{boxplot}}, \code{\link{densityPlotR}}, \code{\link{plotSigBars}}
#' @export
## @aliases prismPlots prismplots plots.prism
#' @examples
#'
#'
#' prism.plots(count ~ spray, data = InsectSprays, fun=mean)
#' prism.plots(count ~ spray, data = InsectSprays, fun=median)
prism.plots = function(formula,
data,
fun = median,
interquartile = TRUE,
spreadfunc = function(x) {
return(1.96 * sd(x) / sqrt(length(x)))
},
def.axis = TRUE,
jitter = TRUE,
add = FALSE,
col = "gray",
pch = 16,
...) {
dv = as.character(formula[[2]])
iv = as.character(formula[[3]])
set.seed(1)
#### make a vector of colors if they didn't supply one
col = rep(col, times = nrow(data))
#### resort so variables line up
ord = order(data[, iv])
data = data[ord,]
col = col[ord]
types = unique(data[, iv])
centers = aggregate(formula, data = data, FUN = fun)[, 2]
spread = matrix(nrow = length(types), ncol = 2)
if (interquartile) {
vals = aggregate(formula,
data = data,
FUN = quantile,
probs = c(.25, .75))
spread = vals[, 2]
} else {
ss = aggregate(formula, data = data, FUN = spreadfunc)[, 2]
spread = cbind(centers - ss, centers + ss)
}
data = data[order(data[, iv]),]
un.vals = unique(data[, iv])
iv.vals = rep(NA, times = nrow(data))
for (i in 1:length(iv.vals)) {
rws = which(data[, iv] == un.vals[i])
iv.vals[rws] = i
}
if (jitter) {
iv.vals = jitter(iv.vals)
}
depvar = data[, dv]
labels = list(
xlab = "",
ylab = dv,
ylim = range(depvar, na.rm = T) + c(-.25 * sd(depvar, na.rm = T), .25 *
sd(depvar, na.rm = T)),
xlim = c(.5, (length(types) + .5)),
xaxt = "n",
x = NA,
y = NA
)
args = modifyList(labels, list(x = NA, ...))
##### compute mean (or median)
if (def.axis) {
if (!add) {
do.call("plot", args)
}
points(iv.vals, depvar,
col = col, pch = pch, ...)
axis(1,
at = (1:length(types)),
labels = unique(data[, iv]))
} else {
if (!add) {
do.call("plot", args)
}
points(iv.vals, depvar, col =
col, pch = pch, ...)
}
segments(1:length(unique(data[, iv])) - .2,
centers,
1:length(unique(data[, iv])) + .2,
centers,
lwd = 2,
...)
segments(1:length(unique(data[, iv])),
spread[, 1],
1:length(unique(data[, iv])),
spread[, 2],
lwd = 2,
...)
segments(1:length(unique(data[, iv])) - .05,
spread[, 1],
1:length(unique(data[, iv])) + .05,
spread[, 1],
lwd = 2,
...)
segments(1:length(unique(data[, iv])) - .05,
spread[, 2],
1:length(unique(data[, iv])) + .05,
spread[, 2],
lwd = 2,
...)
}
## Add significance bars to a prism plot, corrected for multiple comparisons either using Tukey's HSD (parametric),
## or Dunn's correction for multiple comparison (non-parametric).
##
## @title Add significance bars to a prism plot
## @param formula a R formula object
## @param data a dataset containing the variables in formula
## @param type either "tukey" or "dunn" indicating which multiple comparison should be used
## @seealso \code{\link{boxplot}}, \code{\link{densityPlotR}}, \code{\link{prism.plots}}
## @export
## @author Dustin Fife
## @examples
## prism.plots(Sepal.Length ~ Species, data = iris, fun=mean)
## plotSigBars(Sepal.Length ~ Species, data = iris, type="tukey")
## @note This function should probably only be used when the number of groups is less than four, otherwise the number
## of pairwise comparisons becomes too large to display.
##
## When p-values are adjusted using Dunn's multiple comparison, this function calls the \code{kruskalmc} function in the
## \code{pgirmess} package. To avoid having to load the entire package, the function was directly copied into the fifer package.
## references Patrick Giraudoux (2013). pgirmess: Data analysis in ecology. R package version 1.5.7.
## http://CRAN.R-project.org/package=pgirmess
#' Der Fliegen-Schiss-Plot
#'
#' @param x Objekt lm oder emmeans
#' @param stars Sternchen oder P-Weret
#' @param shift oben unden oder links rechts
#' @param cex.stars groesse
#' @param ... nicht benutzt
#'
#' @return plot-text
#' @export
#'
#' @examples
#'
#' # kommt noch
#'
plotSigBars <- function(x,
include.stars = TRUE,
shift = TRUE,
cex.stars = .9,
offset = 0.2,
...) {
cntr <- extract_pvalue(x)
if ((length(cntr$p.value) == 0))
return()
cntr$p.value <- cntr$p.value[order(cntr$p.value$p.value), ]
limits <- par("usr")
for (i in seq_len(nrow(cntr$p.value))) {
crt <-
cordinats(i, cntr$p.value, cntr$levels, limits,
shift, include.stars, cex.stars, offset)
segments(crt$blk$x0, crt$blk$y0, crt$blk$x1, crt$blk$y1)
segments(crt$ant1$x0, crt$ant1$y0, crt$ant1$x1, crt$ant1$y1)
segments(crt$ant2$x0, crt$ant2$y0, crt$ant2$x1, crt$ant2$y1)
text(
x = crt$txt$x,
y = crt$txt$y,
labels = crt$txt$labels,
cex = crt$txt$cex,
pos = crt$txt$pos,
offset = offset
)
}
# cntr$p.value
}
#' @rdname plotSigBars
#' @export
#' @examples
#'
#' require(lattice)
#' require(latticeExtra)
#' require(effects)
#'
#'
#' stripplot(
#' DC4 ~ time2,
#' data = dat, ylim=c(-10,70),
#' ylab = "Difference (Absolute Value) [mm]", jitter.data=TRUE,
#' panel = function(x, y, ...) {
#' # panel.conf.int(x, y, ...)
#' panel.stripplot(x,y, pch=19, col="gray50",...)
#' #panel.points(x,y, pch=19,...)
#' #panel.mean(x,y, ...)
#'
#' panel.median(x,y, ...)
#' panel.sig.bars(fit1, include.stars = FALSE, offset = .4)
#' }
#' )
#'
#'
#'
#' fit1 <- lm(DC4 ~ time2, data = dat)
#'
#' p <- stripplot( DC4 ~ time2, data = dat, jitter.data=TRUE,pch=20, col="gray50")
#' p + layer(panel.sig.bars(fit1, include.stars = FALSE, offset = .4) )
#'
#' p2<- plot(effect("time2", fit1), ylim=c(0,60))
#' p2 + layer(panel.sig.bars(fit1, include.stars = FALSE, offset = .4) )
#'
#' #'
#' #'
panel.sig.bars <- function(x,
include.stars = TRUE,
shift = TRUE,
cex.stars = .9,
offset = 0.4,
...) {
cntr <- extract_pvalue(x)
if ((length(cntr$p.value) == 0))
return()
cntr$p.value <- cntr$p.value[order(cntr$p.value$p.value), ]
limits <- current.panel.limits(unit = "native")
limits <- c(limits$xlim, limits$ylim)
for (i in seq_len(nrow(cntr$p.value))) {
crt <-
cordinats(i, cntr$p.value, cntr$levels, limits,
shift, include.stars, cex.stars, offset)
panel.segments(crt$blk$x0, crt$blk$y0, crt$blk$x1, crt$blk$y1, col=1)
panel.segments(crt$ant1$x0, crt$ant1$y0, crt$ant1$x1, crt$ant1$y1, col=1)
panel.segments(crt$ant2$x0, crt$ant2$y0, crt$ant2$x1, crt$ant2$y1, col=1)
panel.text(
x = crt$txt$x,
y = crt$txt$y,
labels = crt$txt$labels,
cex = crt$txt$cex,
pos = crt$txt$pos,
adj = NULL,
offset = offset
)
}
}
cordinats <-
function(i,
cntr,
levs,
limits,
shift,
stars,
cex.stars,
offset) {
xcoords <-
c(which(levs %in% cntr$lhs[i]) , which(levs %in% cntr$rhs[i]))
miny <- limits[3:4]
yheights <- (miny[2] - miny[1]) / 50
if (shift) {
shft <- i * diff(miny) * 0.04
xlengths <- 0
}
else{
shft <- 0
xlengths <- limits
xlengths <- (xlengths[2] - xlengths[1]) / 50
}
### offset so they don't overlap
if (i / 2 == round(i / 2)) {
miny[1] = miny[2] - shft
yheights = -1 * yheights
adj <- c(NA, 0)
pos <- if (stars)
2
else
3
} else{
miny[1] = miny[1] + shft + diff(miny) * 0.04
yheights <- yheights
adj <- c(NA, 1)
pos <- 1
}
if (stars) {
cex.stars <- cex.stars * 1.1
p.text <- cntr$stars[i]
} else{
p.text <- cntr$p[i]
}
y <- miny[1] + yheights
x1 <- xcoords[1] + xlengths
x2 <- xcoords[2] - xlengths
y2 <- miny[1] + 2 * yheights
list(
blk = list(
x0 = x1, y0 = y, x1 = x2, y1 = y
),
ant1 = list(
x0 = x1, y0 = y, x1 = x1, y1 = y2
),
ant2 = list(
x0 = x2, y0 = y, x1 = x2, y1 = y2
),
txt = list(
x = mean(c(xcoords[1], xcoords[2])),
y = y,
labels = p.text,
cex = cex.stars,
pos = pos
)
)
}
#' Title
#'
#' @param x lm, lmer, emm_list
#'
#' @return data.frame
#' @noRd
extract_pvalue <- function(x) {
if (inherits(x, "emm_list")) {
rslt <- as.data.frame(x[[2]])
csp <- strsplit(rslt[[1]], " - ")
rslt <- data.frame(
contrast = rslt[[1]],
lhs = sapply(csp, "[", 1),
rhs = sapply(csp, "[", 2),
p.value = rslt[[ncol(rslt)]]
)
levs <- x[[1]]@levels[[1]]
}
else if (inherits(x, "lmerModLmerTest")) {
rhs <- all.vars(formula(x))[2L]
data <- x@frame
levs <- levels(data[[rhs]])
c1 <- levs[1]
rslt <- summary(x)$coefficients
rslt <- data.frame(
contrast =
paste(c1, "-", gsub(rhs, "", rownames(rslt))),
lhs = c1,
rhs = gsub(rhs, "", rownames(rslt)),
p.value = rslt[, ncol(rslt)]
)[-1,]
}
else {
y <- all.names(formula(x)[3L])[1]
data <- x$model
levs <- levels(data[[y]])
c1 <- levs[1]
rslt <- broom::tidy(x)
rhs = gsub(y, "", rslt[[1]])
if( all(getOption("contrasts") == c( "contr.Treatment","contr.poly" ))){
rhs<- substr(rhs, 4, nchar(rhs)-1)
}
rslt <- data.frame(
contrast =
paste(c1, "-", gsub(y, "", rslt[[1]])),
lhs = c1,
rhs = rhs,
p.value = rslt[[ncol(rslt)]]
)[-1,]
}
#getOption("contrasts") == c("contr.treatment", "contr.poly" )
rslt$p <- stp25stat2:::rndr_P(rslt$p.value)
rslt$stars <- stp25stat2:::rndr_Stars(rslt$p.value)
list(p.value = rslt[which(rslt$p.value <= .1), ],
levels = levs)
}
stp4/stp25plot documentation built on April 3, 2024, 7:11 p.m.
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Defines functions extract_pvalue cordinats panel.sig.bars plotSigBars prism.plots
#' Plot prism-like Plots
#'
#' gestohlen von fifer Dustin Fife
#'
#'
#' @param formula a formula object with the quantitative variable as the response variable (e.g., Var~group).
#' @param data a dataset containing the variables indicated in \code{formula}
#' @param fun what function should be used to indicate the center of the distribution. Defaults to median.
#' @param interquartile Should the interquartile range be plotted? Defaults to TRUE.
#' @param spreadfunc what function should be used to calculate the spread of the distribution? If interquartile=TRUE,
#' this argument will be ignored. The default (when not ignored) is to produce a 95\% confidence interval (1.96*sd(x)/sqrt(n)).
#' @param def.axis Logical. Should the default axes be used?
#' @param jitter Logical. Should the y values be jittered as well?
#' @param add Should the plot be added to an existing plot?
#' @param col What color should the dots be painted? Defaults to gray.
#' @param ... other arguments passed to plot
## @author Dustin Fife
## @seealso \code{\link{boxplot}}, \code{\link{densityPlotR}}, \code{\link{plotSigBars}}
#' @export
## @aliases prismPlots prismplots plots.prism
#' @examples
#'
#'
#' prism.plots(count ~ spray, data = InsectSprays, fun=mean)
#' prism.plots(count ~ spray, data = InsectSprays, fun=median)
prism.plots = function(formula,
data,
fun = median,
interquartile = TRUE,
spreadfunc = function(x) {
return(1.96 * sd(x) / sqrt(length(x)))
},
def.axis = TRUE,
jitter = TRUE,
add = FALSE,
col = "gray",
pch = 16,
...) {
dv = as.character(formula[[2]])
iv = as.character(formula[[3]])
set.seed(1)
#### make a vector of colors if they didn't supply one
col = rep(col, times = nrow(data))
#### resort so variables line up
ord = order(data[, iv])
data = data[ord,]
col = col[ord]
types = unique(data[, iv])
centers = aggregate(formula, data = data, FUN = fun)[, 2]
spread = matrix(nrow = length(types), ncol = 2)
if (interquartile) {
vals = aggregate(formula,
data = data,
FUN = quantile,
probs = c(.25, .75))
spread = vals[, 2]
} else {
ss = aggregate(formula, data = data, FUN = spreadfunc)[, 2]
spread = cbind(centers - ss, centers + ss)
}
data = data[order(data[, iv]),]
un.vals = unique(data[, iv])
iv.vals = rep(NA, times = nrow(data))
for (i in 1:length(iv.vals)) {
rws = which(data[, iv] == un.vals[i])
iv.vals[rws] = i
}
if (jitter) {
iv.vals = jitter(iv.vals)
}
depvar = data[, dv]
labels = list(
xlab = "",
ylab = dv,
ylim = range(depvar, na.rm = T) + c(-.25 * sd(depvar, na.rm = T), .25 *
sd(depvar, na.rm = T)),
xlim = c(.5, (length(types) + .5)),
xaxt = "n",
x = NA,
y = NA
)
args = modifyList(labels, list(x = NA, ...))
##### compute mean (or median)
if (def.axis) {
if (!add) {
do.call("plot", args)
}
points(iv.vals, depvar,
col = col, pch = pch, ...)
axis(1,
at = (1:length(types)),
labels = unique(data[, iv]))
} else {
if (!add) {
do.call("plot", args)
}
points(iv.vals, depvar, col =
col, pch = pch, ...)
}
segments(1:length(unique(data[, iv])) - .2,
centers,
1:length(unique(data[, iv])) + .2,
centers,
lwd = 2,
...)
segments(1:length(unique(data[, iv])),
spread[, 1],
1:length(unique(data[, iv])),
spread[, 2],
lwd = 2,
...)
segments(1:length(unique(data[, iv])) - .05,
spread[, 1],
1:length(unique(data[, iv])) + .05,
spread[, 1],
lwd = 2,
...)
segments(1:length(unique(data[, iv])) - .05,
spread[, 2],
1:length(unique(data[, iv])) + .05,
spread[, 2],
lwd = 2,
...)
}
## Add significance bars to a prism plot, corrected for multiple comparisons either using Tukey's HSD (parametric),
## or Dunn's correction for multiple comparison (non-parametric).
##
## @title Add significance bars to a prism plot
## @param formula a R formula object
## @param data a dataset containing the variables in formula
## @param type either "tukey" or "dunn" indicating which multiple comparison should be used
## @seealso \code{\link{boxplot}}, \code{\link{densityPlotR}}, \code{\link{prism.plots}}
## @export
## @author Dustin Fife
## @examples
## prism.plots(Sepal.Length ~ Species, data = iris, fun=mean)
## plotSigBars(Sepal.Length ~ Species, data = iris, type="tukey")
## @note This function should probably only be used when the number of groups is less than four, otherwise the number
## of pairwise comparisons becomes too large to display.
##
## When p-values are adjusted using Dunn's multiple comparison, this function calls the \code{kruskalmc} function in the
## \code{pgirmess} package. To avoid having to load the entire package, the function was directly copied into the fifer package.
## references Patrick Giraudoux (2013). pgirmess: Data analysis in ecology. R package version 1.5.7.
## http://CRAN.R-project.org/package=pgirmess
#' Der Fliegen-Schiss-Plot
#'
#' @param x Objekt lm oder emmeans
#' @param stars Sternchen oder P-Weret
#' @param shift oben unden oder links rechts
#' @param cex.stars groesse
#' @param ... nicht benutzt
#'
#' @return plot-text
#' @export
#'
#' @examples
#'
#' # kommt noch
#'
plotSigBars <- function(x,
include.stars = TRUE,
shift = TRUE,
cex.stars = .9,
offset = 0.2,
...) {
cntr <- extract_pvalue(x)
if ((length(cntr$p.value) == 0))
return()
cntr$p.value <- cntr$p.value[order(cntr$p.value$p.value), ]
limits <- par("usr")
for (i in seq_len(nrow(cntr$p.value))) {
crt <-
cordinats(i, cntr$p.value, cntr$levels, limits,
shift, include.stars, cex.stars, offset)
segments(crt$blk$x0, crt$blk$y0, crt$blk$x1, crt$blk$y1)
segments(crt$ant1$x0, crt$ant1$y0, crt$ant1$x1, crt$ant1$y1)
segments(crt$ant2$x0, crt$ant2$y0, crt$ant2$x1, crt$ant2$y1)
text(
x = crt$txt$x,
y = crt$txt$y,
labels = crt$txt$labels,
cex = crt$txt$cex,
pos = crt$txt$pos,
offset = offset
)
}
# cntr$p.value
}
#' @rdname plotSigBars
#' @export
#' @examples
#'
#' require(lattice)
#' require(latticeExtra)
#' require(effects)
#'
#'
#' stripplot(
#' DC4 ~ time2,
#' data = dat, ylim=c(-10,70),
#' ylab = "Difference (Absolute Value) [mm]", jitter.data=TRUE,
#' panel = function(x, y, ...) {
#' # panel.conf.int(x, y, ...)
#' panel.stripplot(x,y, pch=19, col="gray50",...)
#' #panel.points(x,y, pch=19,...)
#' #panel.mean(x,y, ...)
#'
#' panel.median(x,y, ...)
#' panel.sig.bars(fit1, include.stars = FALSE, offset = .4)
#' }
#' )
#'
#'
#'
#' fit1 <- lm(DC4 ~ time2, data = dat)
#'
#' p <- stripplot( DC4 ~ time2, data = dat, jitter.data=TRUE,pch=20, col="gray50")
#' p + layer(panel.sig.bars(fit1, include.stars = FALSE, offset = .4) )
#'
#' p2<- plot(effect("time2", fit1), ylim=c(0,60))
#' p2 + layer(panel.sig.bars(fit1, include.stars = FALSE, offset = .4) )
#'
#' #'
#' #'
panel.sig.bars <- function(x,
include.stars = TRUE,
shift = TRUE,
cex.stars = .9,
offset = 0.4,
...) {
cntr <- extract_pvalue(x)
if ((length(cntr$p.value) == 0))
return()
cntr$p.value <- cntr$p.value[order(cntr$p.value$p.value), ]
limits <- current.panel.limits(unit = "native")
limits <- c(limits$xlim, limits$ylim)
for (i in seq_len(nrow(cntr$p.value))) {
crt <-
cordinats(i, cntr$p.value, cntr$levels, limits,
shift, include.stars, cex.stars, offset)
panel.segments(crt$blk$x0, crt$blk$y0, crt$blk$x1, crt$blk$y1, col=1)
panel.segments(crt$ant1$x0, crt$ant1$y0, crt$ant1$x1, crt$ant1$y1, col=1)
panel.segments(crt$ant2$x0, crt$ant2$y0, crt$ant2$x1, crt$ant2$y1, col=1)
panel.text(
x = crt$txt$x,
y = crt$txt$y,
labels = crt$txt$labels,
cex = crt$txt$cex,
pos = crt$txt$pos,
adj = NULL,
offset = offset
)
}
}
cordinats <-
function(i,
cntr,
levs,
limits,
shift,
stars,
cex.stars,
offset) {
xcoords <-
c(which(levs %in% cntr$lhs[i]) , which(levs %in% cntr$rhs[i]))
miny <- limits[3:4]
yheights <- (miny[2] - miny[1]) / 50
if (shift) {
shft <- i * diff(miny) * 0.04
xlengths <- 0
}
else{
shft <- 0
xlengths <- limits
xlengths <- (xlengths[2] - xlengths[1]) / 50
}
### offset so they don't overlap
if (i / 2 == round(i / 2)) {
miny[1] = miny[2] - shft
yheights = -1 * yheights
adj <- c(NA, 0)
pos <- if (stars)
2
else
3
} else{
miny[1] = miny[1] + shft + diff(miny) * 0.04
yheights <- yheights
adj <- c(NA, 1)
pos <- 1
}
if (stars) {
cex.stars <- cex.stars * 1.1
p.text <- cntr$stars[i]
} else{
p.text <- cntr$p[i]
}
y <- miny[1] + yheights
x1 <- xcoords[1] + xlengths
x2 <- xcoords[2] - xlengths
y2 <- miny[1] + 2 * yheights
list(
blk = list(
x0 = x1, y0 = y, x1 = x2, y1 = y
),
ant1 = list(
x0 = x1, y0 = y, x1 = x1, y1 = y2
),
ant2 = list(
x0 = x2, y0 = y, x1 = x2, y1 = y2
),
txt = list(
x = mean(c(xcoords[1], xcoords[2])),
y = y,
labels = p.text,
cex = cex.stars,
pos = pos
)
)
}
#' Title
#'
#' @param x lm, lmer, emm_list
#'
#' @return data.frame
#' @noRd
extract_pvalue <- function(x) {
if (inherits(x, "emm_list")) {
rslt <- as.data.frame(x[[2]])
csp <- strsplit(rslt[[1]], " - ")
rslt <- data.frame(
contrast = rslt[[1]],
lhs = sapply(csp, "[", 1),
rhs = sapply(csp, "[", 2),
p.value = rslt[[ncol(rslt)]]
)
levs <- x[[1]]@levels[[1]]
}
else if (inherits(x, "lmerModLmerTest")) {
rhs <- all.vars(formula(x))[2L]
data <- x@frame
levs <- levels(data[[rhs]])
c1 <- levs[1]
rslt <- summary(x)$coefficients
rslt <- data.frame(
contrast =
paste(c1, "-", gsub(rhs, "", rownames(rslt))),
lhs = c1,
rhs = gsub(rhs, "", rownames(rslt)),
p.value = rslt[, ncol(rslt)]
)[-1,]
}
else {
y <- all.names(formula(x)[3L])[1]
data <- x$model
levs <- levels(data[[y]])
c1 <- levs[1]
rslt <- broom::tidy(x)
rhs = gsub(y, "", rslt[[1]])
if( all(getOption("contrasts") == c( "contr.Treatment","contr.poly" ))){
rhs<- substr(rhs, 4, nchar(rhs)-1)
}
rslt <- data.frame(
contrast =
paste(c1, "-", gsub(y, "", rslt[[1]])),
lhs = c1,
rhs = rhs,
p.value = rslt[[ncol(rslt)]]
)[-1,]
}
#getOption("contrasts") == c("contr.treatment", "contr.poly" )
rslt$p <- stp25stat2:::rndr_P(rslt$p.value)
rslt$stars <- stp25stat2:::rndr_Stars(rslt$p.value)
list(p.value = rslt[which(rslt$p.value <= .1), ],
levels = levs)
}
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