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R/superbPlot.R
In superb: Summary Plots with Adjusted Error Bars
Defines functions
meanLocalCorrelation
gA
unitaryAlpha
meanCorrelation
mycor
superbPlot
Documented in
superbPlot
######################################################################################
#' @title summary plot of any statistics with adjusted error bars.
#'
#' @md
#'
#' @description The function ``suberbPlot()`` plots standard error or confidence interval for various
#' descriptive statistics under various designs, sampling schemes, population size and purposes,
#' according to the ``suberb`` framework. See \insertCite{cgh21}{superb} for more.
#'
#' @param data Dataframe in wide format
#'
#' @param BSFactors The name of the columns containing the between-subject factor(s)
#' @param WSFactors The name of the within-subject factor(s)
#' @param WSDesign the within-subject design if not a full factorial design (default "fullfactorial")
#' @param variables The dependent variable(s) as strings
#'
#' @param statistic The summary statistic function to use as a string
#' @param errorbar The function that computes the error bar. Should be "CI" or "SE" or
#' any function name if you defined a custom function. Default to "CI"
#' @param gamma The coverage factor; necessary when ``errorbar == "CI"``. Default is 0.95.
#' @param factorOrder Order of factors as shown in the graph (in that order: x axis,
#' groups, horizontal panels, vertical panels)
#'
#' @param adjustments List of adjustments as described below.
#' Default is ``adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none",
#' samplingDesign = "SRS")``
#' @param clusterColumn used in conjunction with samplingDesign = "CRS", indicates which column
#' contains the cluster membership
#'
#' @param showPlot Defaults to TRUE. Set to FALSE if you want the output to be the summary
#' statistics and intervals.
#' @param plotStyle The type of object to plot on the graph. See full list below.
#' Defaults to "bar".
#'
#' @param preprocessfct is a transform (or vector of) to be performed first on data matrix of each group
#' @param postprocessfct is a transform (or vector of)
#'
#' @param ... In addition to the parameters above, superbPlot also accept a number of
#' optional arguments that will be transmitted to the plotting function, such as
#' pointParams (a list of ggplot2 parameters to input inside geoms; see ?geom_bar2) and
#' errorbarParams (a list of ggplot2 parameters for geom_errorbar; see ?geom_errorbar)
#'
#'
#' @return a plot with the correct error bars or a table of those summary statistics.
#' The plot is a ggplot2 object with can be modified with additional declarations.
#'
#'
#' @details The possible adjustements are the following
#' * popsize: Size of the population under study. Defaults to Inf
#' * purpose: The purpose of the comparisons. Defaults to "single".
#' Can be "single", "difference", or "tryon".
#' * decorrelation: Decorrelation method for repeated measure designs.
#' Chooses among the methods "CM", "LM", "CA", "UA", "LDr" (with r an integer) or "none". Defaults to
#' "none". "CA" is correlation-adjusted \insertCite{c19}{superb};
#' "UA" is based on the unitary Alpha method (derived from the Cronbach alpha;
#' see \insertCite{lc22}{superb}).
#' "LDr" is local decorrelation (useful for long time series with autoregressive
#' correlation structures; see \insertCite{cppf24}{superb});
#' .
#' * samplingDesign: Sampling method to obtain the sample. implemented
#' sampling is "SRS" (Simple Randomize Sampling) and "CRS" (Cluster-Randomized Sampling).
#'
#' In version 0.97.5, the layouts for plots are the following:
#' * "bar" Shows the summary statistics with bars and error bars;
#' * "line" Shows the summary statistics with lines connecting the conditions over the first factor;
#' * "point" Shows the summary statistics with isolated points
#' * "pointjitter" Shows the summary statistics along with jittered points depicting the raw data;
#' * "pointjitterviolin" Also adds violin plots to the previous layout
#' * "pointindividualline" Connects the raw data with line along the first factor (which should be a repeated-measure factor)
#' * "raincloud" Illustrates the distribution with a cloud (half_violin_plot) and jittered dots next to it. Looks better when coordinates are flipped ``+coord_flip()``.
#' @md
#'
#' @references
#' \insertAllCited{}
#'
#' @examples
#' ######################################################################
#'
#' # Basic example using a built-in dataframe as data.
#' # By default, the mean is computed and the error bar are 95% confidence intervals
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len")
#'
#' # Example changing the summary statistics to the median and
#' # the error bar to 80% confidence intervals
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len", statistic = "median", errorbar = "CI", gamma = .80)
#'
#' # Example introducing adjustments for pairwise comparisons
#' # and assuming that the whole population is limited to 200 persons
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len",
#' adjustments = list( purpose = "difference", popSize = 200) )
#'
#' # This example adds ggplot directives to the plot produced
#' library(ggplot2)
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len") +
#' xlab("Dose") + ylab("Tooth Growth") +
#' theme_bw()
#'
#' ######################################################################
#'
#' # The following examples are based on repeated measures
#' library(gridExtra)
#' options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages
#'
#' # A simple example: The sleep data
#' # The sleep data are paired data showing the additional time of sleep with
#' # the soporific drugn #1 (("group = 1") and with the soporific drug #2 ("group = 2").
#' # There is 10 participants with two measurements.
#'
#' # sleep is available in long format so we transform it to the in wide format:
#' sleep2 <- reshape(sleep, direction = "wide", idvar = "ID", timevar = "group")
#' sleep2
#'
#' # Makes the plots first without decorrelation:
#' superbPlot(sleep2,
#' WSFactors = "Times(2)",
#' variables = c("extra.1", "extra.2")
#' )
#' # As seen the error bar are very long. Lets take into consideration correlation...
#' # ... with decorrelation (technique Correlation-adjusted CA):
#' superbPlot(sleep2,
#' WSFactors = "Times(2)",
#' variables = c("extra.1", "extra.2"),
#' # only difference:
#' adjustments = list(purpose = "difference", decorrelation = "CA")
#' )
#' # The error bars shortened as the correlation is substantial (r = .795).
#'
#'
#' ######################################################################
#'
#' # Another example: The Orange data
#' # Use the Orange example, but let's define shorter column names...
#' names(Orange) <- c("Tree","age","circ")
#' # ... and turn the data into a wide format using superbToWide:
#' Orange.wide <- superbToWide(Orange, id = "Tree", WSFactors = "age", variable = "circ")
#'
#' # This example contains 5 trees whose diameter (in mm) has been measured at various age (in days):
#' Orange.wide
#'
#' # Makes the plots first without decorrelation:
#' p1 <- superbPlot( Orange.wide, WSFactors = "age(7)",
#' variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
#' adjustments = list(purpose = "difference", decorrelation = "none")
#' ) +
#' xlab("Age level") + ylab("Trunk diameter (mm)") +
#' coord_cartesian( ylim = c(0,250) ) + labs(title="''Standalone'' confidence intervals")
#' # ... and then with decorrelation (technique Correlation-adjusted CA):
#' p2 <- superbPlot( Orange.wide, WSFactors = "age(7)",
#' variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
#' adjustments = list(purpose = "difference", decorrelation = "CA")
#' ) +
#' xlab("Age level") + ylab("Trunk diameter (mm)") +
#' coord_cartesian( ylim = c(0,250) ) + labs(title="Decorrelated confidence intervals")
#'
#' # You can present both plots side-by-side
#' grid.arrange(p1, p2, ncol=2)
#'
#' ######################################################################
#'
######################################################################################
#'
#' @export superbPlot
#' @importFrom lsr wideToLong
#' @importFrom plyr ddply
#' @import ggplot2
#' @importFrom utils capture.output
#
######################################################################################
superbPlot <- function(data,
BSFactors = NULL, # vector of the between-subject factor columns
WSFactors = NULL, # vector of the names of the within-subject factors
WSDesign = "fullfactorial", # or ws levels of each variable if not a full factorial ws design
factorOrder = NULL, # order of the factors for plots
variables, # dependent variable name(s)
statistic = "mean", # descriptive statistics
errorbar = "CI", # content of the error bars
gamma = 0.95, # coverage if confidence intervals
adjustments = list(
purpose = "single", # is "single" or "difference"
popSize = Inf, # is Inf or a specific positive integer
decorrelation = "none", # is "CM", "LM", "CA", "UA", or "none"
samplingDesign = "SRS" # is "SRS" or "CRS" (in which case use clusterColumn)
),
showPlot = TRUE, # show a plot or else summary statistics
plotStyle = "bar", # type of plot (so far, bar, line, point, pointjitter and pointjitterviolin
preprocessfct = NULL, # run preprocessing on the matrix
postprocessfct= NULL, # run post-processing on the matrix
clusterColumn = "", # if samplineScheme = CRS
...
# the following are optional list of graphic directives...
# errorbarParams, # merged into ggplot/geom_errorbar
# pointParams, # merged into ggplot/geom_point
# lineParams, # merged into ggplot/geom_line
# barParams, # merged into ggplot/geom_bar
# etc.
) {
##############################################################################
# STEP 0: Load required libraries
##############################################################################
#require(ggplot2) # all of it
#require(lsr) # only function wideToLong is used
#require(plyr) # only function ddply is used
##############################################################################
# STEP 1: Input validation
##############################################################################
# 1.0: is the data actually data!
data <- as.data.frame(data) # coerce to data.frame if tibble or compatible
if(!(is.data.frame(data)))
stop("superb::ERROR: data is not a data.frame or similar data structure. Exiting...")
# 1.1: missing adjustements
if(is.null(adjustments$purpose)) {adjustments$purpose <- "single"}
if(is.null(adjustments$popSize)) {adjustments$popSize <- Inf}
if(is.null(adjustments$decorrelation)) {adjustments$decorrelation <- "none"}
if(is.null(adjustments$samplingDesign)) {adjustments$samplingDesign <- "SRS"}
# 1.2: unknown adjustments listed
if (!all(names(adjustments) %in% c("purpose","popSize","decorrelation","samplingDesign")))
stop("superb::ERROR: one of the adjustment is unknown. Exiting...")
# 1.3: invalid choice in a list of possible choices
if (is.character(adjustments$popSize)||!(all(adjustments$popSize >0)))
stop("superb::ERROR: popSize should be a positive number or Inf (or a list of these). Exiting...")
if (!(adjustments$purpose %in% c("single","difference","tryon")))
stop("superb::ERROR: Invalid purpose. Did you mean 'difference'? Exiting...")
if (!(substr(adjustments$decorrelation,1,2) %in% c("no","CM","LM","CA","UA","LD")))
stop("superb::ERROR: Invalid decorrelation method. Did you mean 'CM'? Exiting...")
if (substr(adjustments$decorrelation,1,2) == "LD") {
radius <- suppressWarnings(as.integer(substr(adjustments$decorrelation, 3, 100)))
if ((is.na(radius))||(radius<1))
stop("superb::ERROR: radius given to LD not an integer or is smaller than 1. Exiting...")
}
if (!(adjustments$samplingDesign %in% c("SRS","CRS")))
stop("superb::ERROR: Invalid samplingDesign. Did you mean 'SRS'? Exiting...")
# 1.4a: innapropriate choice for between-subject specifications
bsLevels <- dim(unique(data[BSFactors]))[1]
if (!(length(adjustments$popSize) %in% c(1,bsLevels)))
stop("superb::ERROR: popSize is a list whose length does not match the number of groups. Exiting...")
# 1.4b: invalid within-subject factors
if (any(unlist(gregexpr("\\w\\((\\d+)\\)", WSFactors))== -1))
stop("superb::ERROR: One of the repeated-measure factor not properly formed 'name(nlevel)'. Exiting...")
wsMissing <- "DummyWithinSubjectFactor"
wsLevels <- c(1)
if (is.null(WSFactors)) {
WSFactors <- wsMissing
} else {
for (i in 1:length(WSFactors)) {
wsLevels[i] <- as.integer(unlist(strsplit(WSFactors[i], '[()]'))[2])
WSFactors[i] <- unlist(strsplit(WSFactors[i], '[()]'))[1]
}
}
wslevel <- prod(wsLevels)
# 1.4c: setting factorOrder in case it is null
if(is.null(factorOrder)) {
factorOrder <- c(WSFactors, BSFactors)
if (('design' %in% getOption("superb.feedback") ) & (length(factorOrder[factorOrder != wsMissing])) > 1)
message(paste("superb::FYI: The variables will be plotted in that order: ",
paste(factorOrder[factorOrder != wsMissing],collapse=", "),
" (use factorOrder to change).", sep=""))
}
# 1.4d: checking WSFactors based on WSDesign
if (all(WSDesign == "fullfactorial")) {
if (!(length(variables) == wslevel))
stop("superb::ERROR: The number of levels of the within-subject level(s) does not match the number of variables. Exiting...")
if ((wslevel == 1)&&(!(adjustments$decorrelation == "none")))
stop("superb::ERROR: Decorrelation is not to be used when there is no within-subject factors. Exiting...")
} else {
if (!is.list(WSDesign) )
stop("superb::ERROR: the WSdesign is not 'fullfactorial' (default) or a list. Exiting...")
if (length(WSDesign) != length(variables))
stop("superb::ERROR: the WSDesign list is not of the same length as the variable vector. Exiting...")
if (!all(lapply(WSDesign, length)==length(WSFactors)))
stop("superb::ERROR: the WSDesign does not contain vectors of factor levels, one per factor. Exiting...")
}
# 1.5: invalid column names where column names must be listed
if (!(all(variables %in% names(data))))
stop("superb::ERROR: One of the variable column is not found in data. Exiting...")
if (!(all(BSFactors %in% names(data))))
stop("superb::ERROR: One of the BSFactors column is not found in data. Exiting...")
# 1.6: invalid inputs
if (length(factorOrder[factorOrder != wsMissing] ) > 4)
stop("superb::ERROR: Too many factors named on factorOrder. Maximum 4. Exiting...")
if (length(factorOrder[factorOrder != wsMissing]) < length(WSFactors[WSFactors != wsMissing]) + length(BSFactors))
stop("superb::ERROR: Too few factors named on factorOrder. Exiting...")
if ((gamma[1] <0)||(gamma[1]>1))
stop("superb::ERROR: gamma is not within 0 and 1. Exiting...")
if (!is.logical(showPlot))
stop("superb::ERROR: showPlot must be TRUE or FALSE. Exiting...")
# 1.7: align levels and corresponding variables
weird <-"+!+" # to make sure that these characters are not in the column names
if (all(WSDesign == "fullfactorial")) {
combinaisons <- expand.grid(lapply(wsLevels,seq))
} else {
combinaisons <- data.frame(matrix(as.integer(unlist(WSDesign)),nrow=length(WSDesign), byrow = TRUE))
}
newnames <- paste("DV", apply(combinaisons,1,paste,collapse=weird) ,sep=weird)
design <- cbind(combinaisons, variables, newnames)
colnames(design)[1:length(WSFactors)] <- WSFactors
colnames(design)[length(WSFactors)+1] <- "variable"
colnames(design)[length(WSFactors)+2] <- "newvars"
if ( (length(wsLevels)>1) & ('design' %in% getOption("superb.feedback") ) ) {
message("superb::FYI: Here is how the within-subject variables are understood:")
temp = paste0(capture.output(print(design[,c(WSFactors, "variable") ], row.names=FALSE)),collapse="\n")
message(temp)
}
# 1.8: invalid functions
widthfct <- paste(errorbar, statistic, sep = ".")
if (errorbar == "none") { # create a fake function
eval(parse(text=paste(widthfct, "<-function(X) 0",sep="")), envir = globalenv())
}
if ( !(is.stat.function(statistic)) )
stop("superb::ERROR: The function ", statistic, " is not a known descriptive statistic function. Exiting...")
if ( !(is.errorbar.function(widthfct)) )
stop("superb::ERROR: The function ", widthfct, " is not a known function for error bars. Exiting...")
pltfct <- paste("superbPlot", plotStyle, sep = ".")
if ( !(is.superbPlot.function(pltfct)) )
stop("superb::ERROR: The function ", pltfct, " is not a known function for making plots with superbPlot. Exiting...")
# 1.9: if cluster randomized sampling, check that column cluster is set
if (adjustments$samplingDesign == "CRS") {
# make sure that column cluster is defined.
if(!(clusterColumn %in% names(data)))
stop("superb::ERROR: With samplingDesign = \"CRS\", you must specify a valid column with ClusterColumn. Exiting...")
}
# 1.10: is there missing data in the scores?
if (any(is.na(data[,variables])) ) {
message("superb::WARNING: There are misssing data in your dependent variable(s).\n\tsuperb may show empty summaries... consult help(measuresWithMissingData)")
}
# We're clear to go!
runDebug("superb.1", "End of Step 1: Input validation",
c("measure2","design2","BSFactors2","WSFactors2","wsLevels2","wslevel2","factorOrder2","adjustments2"),
list(variables, design, BSFactors, WSFactors, wsLevels, wslevel, factorOrder, adjustments) )
##############################################################################
# STEP 2: Decorrelate repeated-measure variables if needed; apply transforms
##############################################################################
complement <- function(x, U) {U[is.na(pmatch(U,x))]}
x <- c(BSFactors, variables)
U <- names(data)
# keep a copy before transforming the data
# 2022.11.17: sort the columns, bsfactors & wsvariables first
data.untransformed <- data[ c(x, complement(x,U)) ]
data.transformed <- data[ c(x, complement(x,U)) ]
# We do this step for each group and only on columns with repeated measures.
if (adjustments$decorrelation == "CM" || adjustments$decorrelation == "LM") {
data.transformed <- plyr::ddply(data.transformed, .fun = twoStepTransform, .variables= BSFactors, variables)
}
# is LM (pooled standard error) needed?
if (adjustments$decorrelation == "LM") {
data.transformed <- plyr::ddply(data.transformed, .fun = poolSDTransform, .variables= BSFactors, variables)
}
# other custom pre-processing of the data matrix per group
if (!is.null(preprocessfct)) {
for (fct in preprocessfct)
data.transformed <- plyr::ddply(data.transformed, .fun = fct, .variables= BSFactors, variables)
}
# other custom post-processing of the data matrix per group
if (!is.null(postprocessfct)) {
for (fct in postprocessfct)
data.transformed <- plyr::ddply(data.transformed, .fun = fct, .variables= BSFactors, variables)
}
runDebug("superb.2", "End of Step 2: Data post decorrelation",
c("data.transformed2", "newnames2"), list(data.transformed, newnames) )
##############################################################################
# STEP 3: Put data into long format for conveniency
##############################################################################
# replace variable names with names based on design...
temp <- paste("^", variables, "$", collapse="|", sep="")
colnames(data.untransformed)[grep(temp, names(data.untransformed))] = newnames
colnames(data.transformed)[grep(temp, names(data.transformed))] = newnames
# set data to long format using lsr (Navarro, 2015)
data.untransformed.long <- suppressWarnings(lsr::wideToLong(data.untransformed, within = WSFactors, sep = weird))
data.transformed.long <- suppressWarnings(lsr::wideToLong(data.transformed, within = WSFactors, sep = weird))
# New May 11th, 2022, version 0.95.1
as.numeric.factor <- function(x) {strtoi(x)}
data.untransformed.long[WSFactors] <- mapply(
as.numeric.factor, data.untransformed.long[WSFactors])
data.transformed.long[WSFactors] <- mapply(
as.numeric.factor, data.transformed.long[WSFactors])
# if there was no within-subject factor, a dummy had been added
if (WSFactors[1] == wsMissing) {
# removing all traces of the dummy
data.transformed.long[[wsMissing]] <- NULL # remove the column
data.untransformed.long[[wsMissing]] <- NULL # remove the column
WSFactors <- NULL # remove the dummy factor
factorOrder <- factorOrder[ factorOrder != wsMissing]
}
# if the function has an initializer, run it on the long-format data
if (has.init.function(statistic)) {
iname = paste("init",statistic, sep=".")
if (!("none" %in% getOption("superb.feedback")))
message("superb::FYI: Running initializer ", iname)
do.call(iname, list(data.untransformed.long) )
}
runDebug("superb.3", "End of Step 3: Reformat data frame into long format",
c("data.transformed.long2","factorOrder3"), list(data.transformed.long,factorOrder) )
##############################################################################
# STEP 4: Get summary statistics (center, and lowerwidth + upperwidth)
##############################################################################
aggregatefct <- function(subsetOfData) {
params1 <- list( subsetOfData$DV )
if (is.gamma.required(widthfct)) {
paramsV <- list( subsetOfData$DV, gamma = gamma )
} else {
paramsV <- list( subsetOfData$DV )
}
center <- do.call(statistic, params1)
limits <- do.call(widthfct, paramsV)
if (is.interval.function(widthfct)) {
lowerwidth <- ( min(limits) - center)
upperwidth <- ( max(limits) - center )
} else {
lowerwidth <- -limits
upperwidth <- +limits
}
return( c(center=center, lowerwidth=lowerwidth, upperwidth=upperwidth) )
}
### put into FACTORs
summaryStatistics <- plyr::ddply( data.transformed.long, .fun = aggregatefct, .variables = factorOrder )
summaryStatistics[factorOrder] <- lapply(summaryStatistics[factorOrder], as.factor)
data.untransformed.long[factorOrder] <- lapply(data.untransformed.long[factorOrder], as.factor)
runDebug("superb.4", "End of Step 4: Statistics obtained",
c("summaryStatistics2"), list( summaryStatistics) )
##############################################################################
# STEP 5: Get all the adjustments
##############################################################################
# 5.1: Adjust for population size if not infinite
nadj <- if (min(adjustments$popSize) != Inf) {
# Ns the number of subjects per group
Ns <- plyr::ddply(data, .fun = dim, .variables = BSFactors )$V1
# the Ns must be expanded for each repeated measures
Ns <- rep(Ns, length(variables))
sqrt(1 - Ns / adjustments$popSize )
} else {1}
# 5.2: Adjust for purpose if "difference" or "tryon"
padj <- if (adjustments$purpose == "difference") {
sqrt(2)
} else if (adjustments$purpose == "tryon") {
# extension of Tryon, 2001, for heterogeneous variances in between-group design
Ns <- plyr::ddply(data.transformed.long, .fun = dim, .variables = c(WSFactors,BSFactors) )$V1
sds <- suppressWarnings(plyr::ddply(data.transformed.long, .fun = colSDs, .variables = c(WSFactors,BSFactors) )$DV)
es <- sqrt(sum(sds^2/Ns)) / sum(sqrt(sds^2/Ns))
# the es must be expanded for each repeated measures
es <- rep(es, length(variables))
2 * es * sqrt(length(Ns)) / sqrt(2) # 2 because contrary to Tryon, 2001, superb does not want to avoid overlap
} else {1}
# 5.3: Adjust for cluster-randomized sampling
sadj <- if (adjustments$samplingDesign == "CRS") {
ICCs <- plyr::ddply(data, .fun = ShroutFleissICC1, .variables = BSFactors, clusterColumn, variables )
KNSs <- plyr::ddply(data, .fun = getKNs, .variables = BSFactors, clusterColumn )
ICCs <- ICCs[-1:-length(BSFactors)] # drop BSFactors columns
KNSs <- KNSs[-1:-length(BSFactors)] # drop BSFactors columns
ICCsKNNs <- cbind(ICCs, KNSs)
lambdas <- apply(ICCsKNNs, 1, CousineauLaurencelleLambda) # one lambda per group
# the lambdas must be expanded for each repeated measures
lambdas <- rep(lambdas, length(variables))
ICCs <- ICCs$V1 # downcast to a vector for latter display
lambdas
} else {1}
# 5.4: Adjust for correlation if decorrelation == "CA", "UA", ou "LDr"
radj <- if (adjustments$decorrelation == "CA") {
rs <- plyr::ddply(data, .fun = meanCorrelation, .variables = BSFactors, cols = variables)$V1
# the rs must be expanded for each repeated measures
rs <- rep(rs, length(variables))
sqrt(1- rs)
} else if (adjustments$decorrelation == "UA") {
rs <- plyr::ddply(data, .fun = unitaryAlpha, .variables = BSFactors, cols = variables)$V1
# the rs must be expanded for each repeated measures
rs <- rep(rs, length(variables))
sqrt(1- rs)
} else if (substr(adjustments$decorrelation,1,2) == "LD") {
rs <- plyr::ddply(data, .fun = meanLocalCorrelation, .variables = BSFactors, cols = variables, w = radius)
# rs <- unlist(rs[,!is.na(rs)])
rs <- suppressWarnings(as.numeric(unlist(rs))[!is.na(as.numeric(unlist(rs)))])
# the rs is a vector containing one rLD for each measurement
sqrt(1- rs)
} else {1}
# All done: apply the corrections to all the widths
summaryStatistics$lowerwidth <- nadj*padj*sadj*radj*summaryStatistics$lowerwidth
summaryStatistics$upperwidth <- nadj*padj*sadj*radj*summaryStatistics$upperwidth
runDebug("superb.5", "End of Step 5: Getting adjustments",
c("nadj2","padj2","sadj2","radj2","summaryStatistics3"), list(nadj,padj,sadj,radj,summaryStatistics) )
##############################################################################
# STEP 6: Issue feedback information
##############################################################################
if (('warnings' %in% getOption("superb.feedback") ) | ('all' %in% getOption("superb.feedback")) ) {
# 6.1: if option is difference, test heterogeneity of variances
if ((adjustments$purpose == "difference") & (!is.null(BSFactors)) ) {
crit <- data[,BSFactors]
ps <- c()
for (var in variables ) {
out <- split(data[,var], crit )
# 2024.05.30: exlude empty conditions
out <- out[lengths(out)>1]
p <- stats::bartlett.test(out)$p.value
ps <- c(ps, p)
}
if (any(p < .01))
message("superb::ADVICE: Some of the groups' variances are heterogeneous. Consider using purpose=\"tryon\"." )
}
# 6.2: if deccorrelate is CA: show rbar, test Winer
if ((adjustments$decorrelation == "CA")||(adjustments$decorrelation == "UA")||(substr(adjustments$decorrelation,1,2) == "LD")) {
message(paste("superb::FYI: The average correlation per group is ", paste(unique(sprintf("%.4f",mean(round(rs,4)))), collapse=" ")) )
winers <- suppressWarnings(plyr::ddply(data, .fun = "WinerCompoundSymmetryTest", .variables= BSFactors, variables))
winers <- winers[,length(winers)]
if (any(winers<.05, na.rm = TRUE))
message("superb::ADVICE: Some of the groups' data are not compound symmetric. Consider using CM." )
}
# 6.3: if decorrelate is CM or LM: show epsilon, test Winer and Mauchly
if (adjustments$decorrelation %in% c("CM","LM")) {
epsGG <- suppressWarnings(plyr::ddply(data, .fun = "HyunhFeldtEpsilon", .variables= BSFactors, variables))
epsGG <- epsGG[,length(epsGG)]
message(paste("superb::FYI: The HyunhFeldtEpsilon measure of sphericity per group are ", paste(sprintf("%.3f",round(epsGG, 4)), collapse=" ")) )
winers <- suppressWarnings(plyr::ddply(data, .fun = "WinerCompoundSymmetryTest", .variables= BSFactors, variables) )
winers <- winers[,length(winers)]
if (all(winers>.05, na.rm = TRUE))
message("superb::FYI: All the groups' data are compound symmetric. Consider using CA or UA." )
mauchlys <- plyr::ddply(data, .fun = "MauchlySphericityTest", .variables= BSFactors, variables)
mauchlys <- mauchlys[,length(mauchlys)]
if (any(mauchlys<.05, na.rm = TRUE))
message("superb::FYI: Some of the groups' data are not spherical. Use error bars with caution." )
}
# 6.4: if samplingDesign is CRS: print ICC, check that more than 8 clusters
if (adjustments$samplingDesign == "CRS") {
message(paste("superb::FYI: The ICC1 per group are ", paste(sprintf("%.3f",round(ICCs,3)), collapse=" ")) )
}
# 6.5: if objective is tryon: print tryon adjustment
if (adjustments$purpose == "tryon") {
message(paste("superb::FYI: The tryon adjustments per measures are ", paste("Measure ", 1:length(padj), ": ", sprintf("%.4f",round(padj,4)), sep="",collapse=", "), ", all compared to 1.4142.") )
}
}
##############################################################################
# ALL DONE! Output the plot(s) or the summary data
##############################################################################
runDebug("beforeplot", "Kit for testing plotting function", c("ss","factorOrder2","dl"),list(summaryStatistics, factorOrder, data.untransformed.long) )
if (showPlot == TRUE) {
# get the grouping factor
groupingfactor = if(!is.na(factorOrder[2])) {factorOrder[2]} else { NULL}
# get the facet factor(s)
facets <- factorOrder[3:4][!is.na(factorOrder[3:4])]
facets <- paste( c(facets, ".",".")[1:2], collapse="~")
# produce the plot
plot <- do.call( pltfct, list(
summarydata = summaryStatistics,
xfactor = factorOrder[1],
groupingfactor = groupingfactor,
addfactors = facets,
rawdata = data.untransformed.long,
...
))
return(plot)
} else {
# returns a list with summary statistsics as is and rawdata
return( list(summaryStatistics = summaryStatistics, rawData = data.untransformed.long))
}
##############################################################################
# FINISHED! End of function superbPlot
##############################################################################
}
#################################################################################
# Statistics functions: colSDs; meanCorrelation; unitaryAlpha
#################################################################################
mycor <- function(X) {
# a wrapper for cor() that checks if there are constant columns...
rs <- suppressWarnings( cor(X, use = "pairwise.complete.obs") )
if (any(is.na(rs))) {
message("superb::FYI: Some of the measurements are constant. Use CM or use error bars with caution." )
rs[is.na(rs)] <- 1
}
rs
}
meanCorrelation <- function(X, cols) {
# the mean pair-wise correlations from many columns of the dataframe X
rs <- mycor( X[cols] )
rbar <- mean(rs[upper.tri(rs)])
rbar
}
unitaryAlpha <- function(X, cols) {
m <- as.matrix(X[,cols])
k <- dim(m)[2]
V <- var(apply(m, 1, FUN=sum))
S <- sum(apply(m, 2, FUN=var))
(V-S)/((k-1)*S)
}
colSDs = function (x) {
# the equivalent of colMeans for standard deviations
if (is.vector(x)) sd(x)
else if (is.matrix(x)) apply(x, 2, sd)
else if (is.data.frame(x)) apply(x, 2, sd)
else "what the fuck??"
}
##################################################################
# Functions for local decorrelation
##################################################################
# The gaussian kernel
gA <- function(w, mu, dim) {
s1 <- exp(-((1:dim)-mu)^2/(2*w^2))/(w*sqrt(2*pi))
s1[mu] <- 0 # Is a gaussian kernel with a hole a donut?
s1 / sum(s1)
}
meanLocalCorrelation <- function(X, cols, w) {
mat <- mycor( X[cols] )
nrw <- dim(mat)[2]
sapply( 1:nrw,
\(i) sum(mat[i,] * gA(w,i,nrw) )
)
}
# that simple!
##################################################################
# End of suberbPlot.
##################################################################
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Defines functions meanLocalCorrelation gA unitaryAlpha meanCorrelation mycor superbPlot
Documented in superbPlot
######################################################################################
#' @title summary plot of any statistics with adjusted error bars.
#'
#' @md
#'
#' @description The function ``suberbPlot()`` plots standard error or confidence interval for various
#' descriptive statistics under various designs, sampling schemes, population size and purposes,
#' according to the ``suberb`` framework. See \insertCite{cgh21}{superb} for more.
#'
#' @param data Dataframe in wide format
#'
#' @param BSFactors The name of the columns containing the between-subject factor(s)
#' @param WSFactors The name of the within-subject factor(s)
#' @param WSDesign the within-subject design if not a full factorial design (default "fullfactorial")
#' @param variables The dependent variable(s) as strings
#'
#' @param statistic The summary statistic function to use as a string
#' @param errorbar The function that computes the error bar. Should be "CI" or "SE" or
#' any function name if you defined a custom function. Default to "CI"
#' @param gamma The coverage factor; necessary when ``errorbar == "CI"``. Default is 0.95.
#' @param factorOrder Order of factors as shown in the graph (in that order: x axis,
#' groups, horizontal panels, vertical panels)
#'
#' @param adjustments List of adjustments as described below.
#' Default is ``adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none",
#' samplingDesign = "SRS")``
#' @param clusterColumn used in conjunction with samplingDesign = "CRS", indicates which column
#' contains the cluster membership
#'
#' @param showPlot Defaults to TRUE. Set to FALSE if you want the output to be the summary
#' statistics and intervals.
#' @param plotStyle The type of object to plot on the graph. See full list below.
#' Defaults to "bar".
#'
#' @param preprocessfct is a transform (or vector of) to be performed first on data matrix of each group
#' @param postprocessfct is a transform (or vector of)
#'
#' @param ... In addition to the parameters above, superbPlot also accept a number of
#' optional arguments that will be transmitted to the plotting function, such as
#' pointParams (a list of ggplot2 parameters to input inside geoms; see ?geom_bar2) and
#' errorbarParams (a list of ggplot2 parameters for geom_errorbar; see ?geom_errorbar)
#'
#'
#' @return a plot with the correct error bars or a table of those summary statistics.
#' The plot is a ggplot2 object with can be modified with additional declarations.
#'
#'
#' @details The possible adjustements are the following
#' * popsize: Size of the population under study. Defaults to Inf
#' * purpose: The purpose of the comparisons. Defaults to "single".
#' Can be "single", "difference", or "tryon".
#' * decorrelation: Decorrelation method for repeated measure designs.
#' Chooses among the methods "CM", "LM", "CA", "UA", "LDr" (with r an integer) or "none". Defaults to
#' "none". "CA" is correlation-adjusted \insertCite{c19}{superb};
#' "UA" is based on the unitary Alpha method (derived from the Cronbach alpha;
#' see \insertCite{lc22}{superb}).
#' "LDr" is local decorrelation (useful for long time series with autoregressive
#' correlation structures; see \insertCite{cppf24}{superb});
#' .
#' * samplingDesign: Sampling method to obtain the sample. implemented
#' sampling is "SRS" (Simple Randomize Sampling) and "CRS" (Cluster-Randomized Sampling).
#'
#' In version 0.97.5, the layouts for plots are the following:
#' * "bar" Shows the summary statistics with bars and error bars;
#' * "line" Shows the summary statistics with lines connecting the conditions over the first factor;
#' * "point" Shows the summary statistics with isolated points
#' * "pointjitter" Shows the summary statistics along with jittered points depicting the raw data;
#' * "pointjitterviolin" Also adds violin plots to the previous layout
#' * "pointindividualline" Connects the raw data with line along the first factor (which should be a repeated-measure factor)
#' * "raincloud" Illustrates the distribution with a cloud (half_violin_plot) and jittered dots next to it. Looks better when coordinates are flipped ``+coord_flip()``.
#' @md
#'
#' @references
#' \insertAllCited{}
#'
#' @examples
#' ######################################################################
#'
#' # Basic example using a built-in dataframe as data.
#' # By default, the mean is computed and the error bar are 95% confidence intervals
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len")
#'
#' # Example changing the summary statistics to the median and
#' # the error bar to 80% confidence intervals
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len", statistic = "median", errorbar = "CI", gamma = .80)
#'
#' # Example introducing adjustments for pairwise comparisons
#' # and assuming that the whole population is limited to 200 persons
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len",
#' adjustments = list( purpose = "difference", popSize = 200) )
#'
#' # This example adds ggplot directives to the plot produced
#' library(ggplot2)
#' superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
#' variables = "len") +
#' xlab("Dose") + ylab("Tooth Growth") +
#' theme_bw()
#'
#' ######################################################################
#'
#' # The following examples are based on repeated measures
#' library(gridExtra)
#' options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages
#'
#' # A simple example: The sleep data
#' # The sleep data are paired data showing the additional time of sleep with
#' # the soporific drugn #1 (("group = 1") and with the soporific drug #2 ("group = 2").
#' # There is 10 participants with two measurements.
#'
#' # sleep is available in long format so we transform it to the in wide format:
#' sleep2 <- reshape(sleep, direction = "wide", idvar = "ID", timevar = "group")
#' sleep2
#'
#' # Makes the plots first without decorrelation:
#' superbPlot(sleep2,
#' WSFactors = "Times(2)",
#' variables = c("extra.1", "extra.2")
#' )
#' # As seen the error bar are very long. Lets take into consideration correlation...
#' # ... with decorrelation (technique Correlation-adjusted CA):
#' superbPlot(sleep2,
#' WSFactors = "Times(2)",
#' variables = c("extra.1", "extra.2"),
#' # only difference:
#' adjustments = list(purpose = "difference", decorrelation = "CA")
#' )
#' # The error bars shortened as the correlation is substantial (r = .795).
#'
#'
#' ######################################################################
#'
#' # Another example: The Orange data
#' # Use the Orange example, but let's define shorter column names...
#' names(Orange) <- c("Tree","age","circ")
#' # ... and turn the data into a wide format using superbToWide:
#' Orange.wide <- superbToWide(Orange, id = "Tree", WSFactors = "age", variable = "circ")
#'
#' # This example contains 5 trees whose diameter (in mm) has been measured at various age (in days):
#' Orange.wide
#'
#' # Makes the plots first without decorrelation:
#' p1 <- superbPlot( Orange.wide, WSFactors = "age(7)",
#' variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
#' adjustments = list(purpose = "difference", decorrelation = "none")
#' ) +
#' xlab("Age level") + ylab("Trunk diameter (mm)") +
#' coord_cartesian( ylim = c(0,250) ) + labs(title="''Standalone'' confidence intervals")
#' # ... and then with decorrelation (technique Correlation-adjusted CA):
#' p2 <- superbPlot( Orange.wide, WSFactors = "age(7)",
#' variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
#' adjustments = list(purpose = "difference", decorrelation = "CA")
#' ) +
#' xlab("Age level") + ylab("Trunk diameter (mm)") +
#' coord_cartesian( ylim = c(0,250) ) + labs(title="Decorrelated confidence intervals")
#'
#' # You can present both plots side-by-side
#' grid.arrange(p1, p2, ncol=2)
#'
#' ######################################################################
#'
######################################################################################
#'
#' @export superbPlot
#' @importFrom lsr wideToLong
#' @importFrom plyr ddply
#' @import ggplot2
#' @importFrom utils capture.output
#
######################################################################################
superbPlot <- function(data,
BSFactors = NULL, # vector of the between-subject factor columns
WSFactors = NULL, # vector of the names of the within-subject factors
WSDesign = "fullfactorial", # or ws levels of each variable if not a full factorial ws design
factorOrder = NULL, # order of the factors for plots
variables, # dependent variable name(s)
statistic = "mean", # descriptive statistics
errorbar = "CI", # content of the error bars
gamma = 0.95, # coverage if confidence intervals
adjustments = list(
purpose = "single", # is "single" or "difference"
popSize = Inf, # is Inf or a specific positive integer
decorrelation = "none", # is "CM", "LM", "CA", "UA", or "none"
samplingDesign = "SRS" # is "SRS" or "CRS" (in which case use clusterColumn)
),
showPlot = TRUE, # show a plot or else summary statistics
plotStyle = "bar", # type of plot (so far, bar, line, point, pointjitter and pointjitterviolin
preprocessfct = NULL, # run preprocessing on the matrix
postprocessfct= NULL, # run post-processing on the matrix
clusterColumn = "", # if samplineScheme = CRS
...
# the following are optional list of graphic directives...
# errorbarParams, # merged into ggplot/geom_errorbar
# pointParams, # merged into ggplot/geom_point
# lineParams, # merged into ggplot/geom_line
# barParams, # merged into ggplot/geom_bar
# etc.
) {
##############################################################################
# STEP 0: Load required libraries
##############################################################################
#require(ggplot2) # all of it
#require(lsr) # only function wideToLong is used
#require(plyr) # only function ddply is used
##############################################################################
# STEP 1: Input validation
##############################################################################
# 1.0: is the data actually data!
data <- as.data.frame(data) # coerce to data.frame if tibble or compatible
if(!(is.data.frame(data)))
stop("superb::ERROR: data is not a data.frame or similar data structure. Exiting...")
# 1.1: missing adjustements
if(is.null(adjustments$purpose)) {adjustments$purpose <- "single"}
if(is.null(adjustments$popSize)) {adjustments$popSize <- Inf}
if(is.null(adjustments$decorrelation)) {adjustments$decorrelation <- "none"}
if(is.null(adjustments$samplingDesign)) {adjustments$samplingDesign <- "SRS"}
# 1.2: unknown adjustments listed
if (!all(names(adjustments) %in% c("purpose","popSize","decorrelation","samplingDesign")))
stop("superb::ERROR: one of the adjustment is unknown. Exiting...")
# 1.3: invalid choice in a list of possible choices
if (is.character(adjustments$popSize)||!(all(adjustments$popSize >0)))
stop("superb::ERROR: popSize should be a positive number or Inf (or a list of these). Exiting...")
if (!(adjustments$purpose %in% c("single","difference","tryon")))
stop("superb::ERROR: Invalid purpose. Did you mean 'difference'? Exiting...")
if (!(substr(adjustments$decorrelation,1,2) %in% c("no","CM","LM","CA","UA","LD")))
stop("superb::ERROR: Invalid decorrelation method. Did you mean 'CM'? Exiting...")
if (substr(adjustments$decorrelation,1,2) == "LD") {
radius <- suppressWarnings(as.integer(substr(adjustments$decorrelation, 3, 100)))
if ((is.na(radius))||(radius<1))
stop("superb::ERROR: radius given to LD not an integer or is smaller than 1. Exiting...")
}
if (!(adjustments$samplingDesign %in% c("SRS","CRS")))
stop("superb::ERROR: Invalid samplingDesign. Did you mean 'SRS'? Exiting...")
# 1.4a: innapropriate choice for between-subject specifications
bsLevels <- dim(unique(data[BSFactors]))[1]
if (!(length(adjustments$popSize) %in% c(1,bsLevels)))
stop("superb::ERROR: popSize is a list whose length does not match the number of groups. Exiting...")
# 1.4b: invalid within-subject factors
if (any(unlist(gregexpr("\\w\\((\\d+)\\)", WSFactors))== -1))
stop("superb::ERROR: One of the repeated-measure factor not properly formed 'name(nlevel)'. Exiting...")
wsMissing <- "DummyWithinSubjectFactor"
wsLevels <- c(1)
if (is.null(WSFactors)) {
WSFactors <- wsMissing
} else {
for (i in 1:length(WSFactors)) {
wsLevels[i] <- as.integer(unlist(strsplit(WSFactors[i], '[()]'))[2])
WSFactors[i] <- unlist(strsplit(WSFactors[i], '[()]'))[1]
}
}
wslevel <- prod(wsLevels)
# 1.4c: setting factorOrder in case it is null
if(is.null(factorOrder)) {
factorOrder <- c(WSFactors, BSFactors)
if (('design' %in% getOption("superb.feedback") ) & (length(factorOrder[factorOrder != wsMissing])) > 1)
message(paste("superb::FYI: The variables will be plotted in that order: ",
paste(factorOrder[factorOrder != wsMissing],collapse=", "),
" (use factorOrder to change).", sep=""))
}
# 1.4d: checking WSFactors based on WSDesign
if (all(WSDesign == "fullfactorial")) {
if (!(length(variables) == wslevel))
stop("superb::ERROR: The number of levels of the within-subject level(s) does not match the number of variables. Exiting...")
if ((wslevel == 1)&&(!(adjustments$decorrelation == "none")))
stop("superb::ERROR: Decorrelation is not to be used when there is no within-subject factors. Exiting...")
} else {
if (!is.list(WSDesign) )
stop("superb::ERROR: the WSdesign is not 'fullfactorial' (default) or a list. Exiting...")
if (length(WSDesign) != length(variables))
stop("superb::ERROR: the WSDesign list is not of the same length as the variable vector. Exiting...")
if (!all(lapply(WSDesign, length)==length(WSFactors)))
stop("superb::ERROR: the WSDesign does not contain vectors of factor levels, one per factor. Exiting...")
}
# 1.5: invalid column names where column names must be listed
if (!(all(variables %in% names(data))))
stop("superb::ERROR: One of the variable column is not found in data. Exiting...")
if (!(all(BSFactors %in% names(data))))
stop("superb::ERROR: One of the BSFactors column is not found in data. Exiting...")
# 1.6: invalid inputs
if (length(factorOrder[factorOrder != wsMissing] ) > 4)
stop("superb::ERROR: Too many factors named on factorOrder. Maximum 4. Exiting...")
if (length(factorOrder[factorOrder != wsMissing]) < length(WSFactors[WSFactors != wsMissing]) + length(BSFactors))
stop("superb::ERROR: Too few factors named on factorOrder. Exiting...")
if ((gamma[1] <0)||(gamma[1]>1))
stop("superb::ERROR: gamma is not within 0 and 1. Exiting...")
if (!is.logical(showPlot))
stop("superb::ERROR: showPlot must be TRUE or FALSE. Exiting...")
# 1.7: align levels and corresponding variables
weird <-"+!+" # to make sure that these characters are not in the column names
if (all(WSDesign == "fullfactorial")) {
combinaisons <- expand.grid(lapply(wsLevels,seq))
} else {
combinaisons <- data.frame(matrix(as.integer(unlist(WSDesign)),nrow=length(WSDesign), byrow = TRUE))
}
newnames <- paste("DV", apply(combinaisons,1,paste,collapse=weird) ,sep=weird)
design <- cbind(combinaisons, variables, newnames)
colnames(design)[1:length(WSFactors)] <- WSFactors
colnames(design)[length(WSFactors)+1] <- "variable"
colnames(design)[length(WSFactors)+2] <- "newvars"
if ( (length(wsLevels)>1) & ('design' %in% getOption("superb.feedback") ) ) {
message("superb::FYI: Here is how the within-subject variables are understood:")
temp = paste0(capture.output(print(design[,c(WSFactors, "variable") ], row.names=FALSE)),collapse="\n")
message(temp)
}
# 1.8: invalid functions
widthfct <- paste(errorbar, statistic, sep = ".")
if (errorbar == "none") { # create a fake function
eval(parse(text=paste(widthfct, "<-function(X) 0",sep="")), envir = globalenv())
}
if ( !(is.stat.function(statistic)) )
stop("superb::ERROR: The function ", statistic, " is not a known descriptive statistic function. Exiting...")
if ( !(is.errorbar.function(widthfct)) )
stop("superb::ERROR: The function ", widthfct, " is not a known function for error bars. Exiting...")
pltfct <- paste("superbPlot", plotStyle, sep = ".")
if ( !(is.superbPlot.function(pltfct)) )
stop("superb::ERROR: The function ", pltfct, " is not a known function for making plots with superbPlot. Exiting...")
# 1.9: if cluster randomized sampling, check that column cluster is set
if (adjustments$samplingDesign == "CRS") {
# make sure that column cluster is defined.
if(!(clusterColumn %in% names(data)))
stop("superb::ERROR: With samplingDesign = \"CRS\", you must specify a valid column with ClusterColumn. Exiting...")
}
# 1.10: is there missing data in the scores?
if (any(is.na(data[,variables])) ) {
message("superb::WARNING: There are misssing data in your dependent variable(s).\n\tsuperb may show empty summaries... consult help(measuresWithMissingData)")
}
# We're clear to go!
runDebug("superb.1", "End of Step 1: Input validation",
c("measure2","design2","BSFactors2","WSFactors2","wsLevels2","wslevel2","factorOrder2","adjustments2"),
list(variables, design, BSFactors, WSFactors, wsLevels, wslevel, factorOrder, adjustments) )
##############################################################################
# STEP 2: Decorrelate repeated-measure variables if needed; apply transforms
##############################################################################
complement <- function(x, U) {U[is.na(pmatch(U,x))]}
x <- c(BSFactors, variables)
U <- names(data)
# keep a copy before transforming the data
# 2022.11.17: sort the columns, bsfactors & wsvariables first
data.untransformed <- data[ c(x, complement(x,U)) ]
data.transformed <- data[ c(x, complement(x,U)) ]
# We do this step for each group and only on columns with repeated measures.
if (adjustments$decorrelation == "CM" || adjustments$decorrelation == "LM") {
data.transformed <- plyr::ddply(data.transformed, .fun = twoStepTransform, .variables= BSFactors, variables)
}
# is LM (pooled standard error) needed?
if (adjustments$decorrelation == "LM") {
data.transformed <- plyr::ddply(data.transformed, .fun = poolSDTransform, .variables= BSFactors, variables)
}
# other custom pre-processing of the data matrix per group
if (!is.null(preprocessfct)) {
for (fct in preprocessfct)
data.transformed <- plyr::ddply(data.transformed, .fun = fct, .variables= BSFactors, variables)
}
# other custom post-processing of the data matrix per group
if (!is.null(postprocessfct)) {
for (fct in postprocessfct)
data.transformed <- plyr::ddply(data.transformed, .fun = fct, .variables= BSFactors, variables)
}
runDebug("superb.2", "End of Step 2: Data post decorrelation",
c("data.transformed2", "newnames2"), list(data.transformed, newnames) )
##############################################################################
# STEP 3: Put data into long format for conveniency
##############################################################################
# replace variable names with names based on design...
temp <- paste("^", variables, "$", collapse="|", sep="")
colnames(data.untransformed)[grep(temp, names(data.untransformed))] = newnames
colnames(data.transformed)[grep(temp, names(data.transformed))] = newnames
# set data to long format using lsr (Navarro, 2015)
data.untransformed.long <- suppressWarnings(lsr::wideToLong(data.untransformed, within = WSFactors, sep = weird))
data.transformed.long <- suppressWarnings(lsr::wideToLong(data.transformed, within = WSFactors, sep = weird))
# New May 11th, 2022, version 0.95.1
as.numeric.factor <- function(x) {strtoi(x)}
data.untransformed.long[WSFactors] <- mapply(
as.numeric.factor, data.untransformed.long[WSFactors])
data.transformed.long[WSFactors] <- mapply(
as.numeric.factor, data.transformed.long[WSFactors])
# if there was no within-subject factor, a dummy had been added
if (WSFactors[1] == wsMissing) {
# removing all traces of the dummy
data.transformed.long[[wsMissing]] <- NULL # remove the column
data.untransformed.long[[wsMissing]] <- NULL # remove the column
WSFactors <- NULL # remove the dummy factor
factorOrder <- factorOrder[ factorOrder != wsMissing]
}
# if the function has an initializer, run it on the long-format data
if (has.init.function(statistic)) {
iname = paste("init",statistic, sep=".")
if (!("none" %in% getOption("superb.feedback")))
message("superb::FYI: Running initializer ", iname)
do.call(iname, list(data.untransformed.long) )
}
runDebug("superb.3", "End of Step 3: Reformat data frame into long format",
c("data.transformed.long2","factorOrder3"), list(data.transformed.long,factorOrder) )
##############################################################################
# STEP 4: Get summary statistics (center, and lowerwidth + upperwidth)
##############################################################################
aggregatefct <- function(subsetOfData) {
params1 <- list( subsetOfData$DV )
if (is.gamma.required(widthfct)) {
paramsV <- list( subsetOfData$DV, gamma = gamma )
} else {
paramsV <- list( subsetOfData$DV )
}
center <- do.call(statistic, params1)
limits <- do.call(widthfct, paramsV)
if (is.interval.function(widthfct)) {
lowerwidth <- ( min(limits) - center)
upperwidth <- ( max(limits) - center )
} else {
lowerwidth <- -limits
upperwidth <- +limits
}
return( c(center=center, lowerwidth=lowerwidth, upperwidth=upperwidth) )
}
### put into FACTORs
summaryStatistics <- plyr::ddply( data.transformed.long, .fun = aggregatefct, .variables = factorOrder )
summaryStatistics[factorOrder] <- lapply(summaryStatistics[factorOrder], as.factor)
data.untransformed.long[factorOrder] <- lapply(data.untransformed.long[factorOrder], as.factor)
runDebug("superb.4", "End of Step 4: Statistics obtained",
c("summaryStatistics2"), list( summaryStatistics) )
##############################################################################
# STEP 5: Get all the adjustments
##############################################################################
# 5.1: Adjust for population size if not infinite
nadj <- if (min(adjustments$popSize) != Inf) {
# Ns the number of subjects per group
Ns <- plyr::ddply(data, .fun = dim, .variables = BSFactors )$V1
# the Ns must be expanded for each repeated measures
Ns <- rep(Ns, length(variables))
sqrt(1 - Ns / adjustments$popSize )
} else {1}
# 5.2: Adjust for purpose if "difference" or "tryon"
padj <- if (adjustments$purpose == "difference") {
sqrt(2)
} else if (adjustments$purpose == "tryon") {
# extension of Tryon, 2001, for heterogeneous variances in between-group design
Ns <- plyr::ddply(data.transformed.long, .fun = dim, .variables = c(WSFactors,BSFactors) )$V1
sds <- suppressWarnings(plyr::ddply(data.transformed.long, .fun = colSDs, .variables = c(WSFactors,BSFactors) )$DV)
es <- sqrt(sum(sds^2/Ns)) / sum(sqrt(sds^2/Ns))
# the es must be expanded for each repeated measures
es <- rep(es, length(variables))
2 * es * sqrt(length(Ns)) / sqrt(2) # 2 because contrary to Tryon, 2001, superb does not want to avoid overlap
} else {1}
# 5.3: Adjust for cluster-randomized sampling
sadj <- if (adjustments$samplingDesign == "CRS") {
ICCs <- plyr::ddply(data, .fun = ShroutFleissICC1, .variables = BSFactors, clusterColumn, variables )
KNSs <- plyr::ddply(data, .fun = getKNs, .variables = BSFactors, clusterColumn )
ICCs <- ICCs[-1:-length(BSFactors)] # drop BSFactors columns
KNSs <- KNSs[-1:-length(BSFactors)] # drop BSFactors columns
ICCsKNNs <- cbind(ICCs, KNSs)
lambdas <- apply(ICCsKNNs, 1, CousineauLaurencelleLambda) # one lambda per group
# the lambdas must be expanded for each repeated measures
lambdas <- rep(lambdas, length(variables))
ICCs <- ICCs$V1 # downcast to a vector for latter display
lambdas
} else {1}
# 5.4: Adjust for correlation if decorrelation == "CA", "UA", ou "LDr"
radj <- if (adjustments$decorrelation == "CA") {
rs <- plyr::ddply(data, .fun = meanCorrelation, .variables = BSFactors, cols = variables)$V1
# the rs must be expanded for each repeated measures
rs <- rep(rs, length(variables))
sqrt(1- rs)
} else if (adjustments$decorrelation == "UA") {
rs <- plyr::ddply(data, .fun = unitaryAlpha, .variables = BSFactors, cols = variables)$V1
# the rs must be expanded for each repeated measures
rs <- rep(rs, length(variables))
sqrt(1- rs)
} else if (substr(adjustments$decorrelation,1,2) == "LD") {
rs <- plyr::ddply(data, .fun = meanLocalCorrelation, .variables = BSFactors, cols = variables, w = radius)
# rs <- unlist(rs[,!is.na(rs)])
rs <- suppressWarnings(as.numeric(unlist(rs))[!is.na(as.numeric(unlist(rs)))])
# the rs is a vector containing one rLD for each measurement
sqrt(1- rs)
} else {1}
# All done: apply the corrections to all the widths
summaryStatistics$lowerwidth <- nadj*padj*sadj*radj*summaryStatistics$lowerwidth
summaryStatistics$upperwidth <- nadj*padj*sadj*radj*summaryStatistics$upperwidth
runDebug("superb.5", "End of Step 5: Getting adjustments",
c("nadj2","padj2","sadj2","radj2","summaryStatistics3"), list(nadj,padj,sadj,radj,summaryStatistics) )
##############################################################################
# STEP 6: Issue feedback information
##############################################################################
if (('warnings' %in% getOption("superb.feedback") ) | ('all' %in% getOption("superb.feedback")) ) {
# 6.1: if option is difference, test heterogeneity of variances
if ((adjustments$purpose == "difference") & (!is.null(BSFactors)) ) {
crit <- data[,BSFactors]
ps <- c()
for (var in variables ) {
out <- split(data[,var], crit )
# 2024.05.30: exlude empty conditions
out <- out[lengths(out)>1]
p <- stats::bartlett.test(out)$p.value
ps <- c(ps, p)
}
if (any(p < .01))
message("superb::ADVICE: Some of the groups' variances are heterogeneous. Consider using purpose=\"tryon\"." )
}
# 6.2: if deccorrelate is CA: show rbar, test Winer
if ((adjustments$decorrelation == "CA")||(adjustments$decorrelation == "UA")||(substr(adjustments$decorrelation,1,2) == "LD")) {
message(paste("superb::FYI: The average correlation per group is ", paste(unique(sprintf("%.4f",mean(round(rs,4)))), collapse=" ")) )
winers <- suppressWarnings(plyr::ddply(data, .fun = "WinerCompoundSymmetryTest", .variables= BSFactors, variables))
winers <- winers[,length(winers)]
if (any(winers<.05, na.rm = TRUE))
message("superb::ADVICE: Some of the groups' data are not compound symmetric. Consider using CM." )
}
# 6.3: if decorrelate is CM or LM: show epsilon, test Winer and Mauchly
if (adjustments$decorrelation %in% c("CM","LM")) {
epsGG <- suppressWarnings(plyr::ddply(data, .fun = "HyunhFeldtEpsilon", .variables= BSFactors, variables))
epsGG <- epsGG[,length(epsGG)]
message(paste("superb::FYI: The HyunhFeldtEpsilon measure of sphericity per group are ", paste(sprintf("%.3f",round(epsGG, 4)), collapse=" ")) )
winers <- suppressWarnings(plyr::ddply(data, .fun = "WinerCompoundSymmetryTest", .variables= BSFactors, variables) )
winers <- winers[,length(winers)]
if (all(winers>.05, na.rm = TRUE))
message("superb::FYI: All the groups' data are compound symmetric. Consider using CA or UA." )
mauchlys <- plyr::ddply(data, .fun = "MauchlySphericityTest", .variables= BSFactors, variables)
mauchlys <- mauchlys[,length(mauchlys)]
if (any(mauchlys<.05, na.rm = TRUE))
message("superb::FYI: Some of the groups' data are not spherical. Use error bars with caution." )
}
# 6.4: if samplingDesign is CRS: print ICC, check that more than 8 clusters
if (adjustments$samplingDesign == "CRS") {
message(paste("superb::FYI: The ICC1 per group are ", paste(sprintf("%.3f",round(ICCs,3)), collapse=" ")) )
}
# 6.5: if objective is tryon: print tryon adjustment
if (adjustments$purpose == "tryon") {
message(paste("superb::FYI: The tryon adjustments per measures are ", paste("Measure ", 1:length(padj), ": ", sprintf("%.4f",round(padj,4)), sep="",collapse=", "), ", all compared to 1.4142.") )
}
}
##############################################################################
# ALL DONE! Output the plot(s) or the summary data
##############################################################################
runDebug("beforeplot", "Kit for testing plotting function", c("ss","factorOrder2","dl"),list(summaryStatistics, factorOrder, data.untransformed.long) )
if (showPlot == TRUE) {
# get the grouping factor
groupingfactor = if(!is.na(factorOrder[2])) {factorOrder[2]} else { NULL}
# get the facet factor(s)
facets <- factorOrder[3:4][!is.na(factorOrder[3:4])]
facets <- paste( c(facets, ".",".")[1:2], collapse="~")
# produce the plot
plot <- do.call( pltfct, list(
summarydata = summaryStatistics,
xfactor = factorOrder[1],
groupingfactor = groupingfactor,
addfactors = facets,
rawdata = data.untransformed.long,
...
))
return(plot)
} else {
# returns a list with summary statistsics as is and rawdata
return( list(summaryStatistics = summaryStatistics, rawData = data.untransformed.long))
}
##############################################################################
# FINISHED! End of function superbPlot
##############################################################################
}
#################################################################################
# Statistics functions: colSDs; meanCorrelation; unitaryAlpha
#################################################################################
mycor <- function(X) {
# a wrapper for cor() that checks if there are constant columns...
rs <- suppressWarnings( cor(X, use = "pairwise.complete.obs") )
if (any(is.na(rs))) {
message("superb::FYI: Some of the measurements are constant. Use CM or use error bars with caution." )
rs[is.na(rs)] <- 1
}
rs
}
meanCorrelation <- function(X, cols) {
# the mean pair-wise correlations from many columns of the dataframe X
rs <- mycor( X[cols] )
rbar <- mean(rs[upper.tri(rs)])
rbar
}
unitaryAlpha <- function(X, cols) {
m <- as.matrix(X[,cols])
k <- dim(m)[2]
V <- var(apply(m, 1, FUN=sum))
S <- sum(apply(m, 2, FUN=var))
(V-S)/((k-1)*S)
}
colSDs = function (x) {
# the equivalent of colMeans for standard deviations
if (is.vector(x)) sd(x)
else if (is.matrix(x)) apply(x, 2, sd)
else if (is.data.frame(x)) apply(x, 2, sd)
else "what the fuck??"
}
##################################################################
# Functions for local decorrelation
##################################################################
# The gaussian kernel
gA <- function(w, mu, dim) {
s1 <- exp(-((1:dim)-mu)^2/(2*w^2))/(w*sqrt(2*pi))
s1[mu] <- 0 # Is a gaussian kernel with a hole a donut?
s1 / sum(s1)
}
meanLocalCorrelation <- function(X, cols, w) {
mat <- mycor( X[cols] )
nrw <- dim(mat)[2]
sapply( 1:nrw,
\(i) sum(mat[i,] * gA(w,i,nrw) )
)
}
# that simple!
##################################################################
# End of suberbPlot.
##################################################################
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