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| friedman.test.with.post.hoc <- function(formu, data, to.print.friedman = T, to.post.hoc.if.signif = T, to.plot.parallel = T, to.plot.boxplot = T, signif.P = .05, color.blocks.in.cor.plot = T, jitter.Y.in.cor.plot =F)
{
# formu is a formula of the shape: Y ~ X | block
# data is a long data.frame with three columns: [[ Y (numeric), X (factor), block (factor) ]]
# Note: This function doesn't handle NA's! In case of NA in Y in one of the blocks, then that entire block should be removed.
# Loading needed packages
if(!require(coin))
{
print("You are missing the package 'coin', we will now try to install it...")
install.packages("coin")
library(coin)
}
if(!require(multcomp))
{
print("You are missing the package 'multcomp', we will now try to install it...")
install.packages("multcomp")
library(multcomp)
}
if(!require(colorspace))
{
print("You are missing the package 'colorspace', we will now try to install it...")
install.packages("colorspace")
library(colorspace)
}
# get the names out of the formula
formu.names <- all.vars(formu)
Y.name <- formu.names[1]
X.name <- formu.names[2]
block.name <- formu.names[3]
if(dim(data)[2] >3) data <- data[,c(Y.name,X.name,block.name)] # In case we have a "data" data frame with more then the three columns we need. This code will clean it from them...
# Note: the function doesn't handle NA's. In case of NA in one of the block T outcomes, that entire block should be removed.
# stopping in case there is NA in the Y vector
if(sum(is.na(data[,Y.name])) > 0) stop("Function stopped: This function doesn't handle NA's. In case of NA in Y in one of the blocks, then that entire block should be removed.")
# make sure that the number of factors goes with the actual values present in the data:
data[,X.name ] <- factor(data[,X.name ])
data[,block.name ] <- factor(data[,block.name ])
number.of.X.levels <- length(levels(data[,X.name ]))
if(number.of.X.levels == 2) { warning(paste("'",X.name,"'", "has only two levels. Consider using paired wilcox.test instead of friedman test"))}
# making the object that will hold the friedman test and the other.
the.sym.test <- symmetry_test(formu, data = data, ### all pairwise comparisons
teststat = "max",
xtrafo = function(Y.data) { trafo( Y.data, factor_trafo = function(x) { model.matrix(~ x - 1) %*% t(contrMat(table(x), "Tukey")) } ) },
ytrafo = function(Y.data){ trafo(Y.data, numeric_trafo = rank, block = data[,block.name] ) }
)
# if(to.print.friedman) { print(the.sym.test) }
if(to.post.hoc.if.signif)
{
if(pvalue(the.sym.test) < signif.P)
{
# the post hoc test
The.post.hoc.P.values <- pvalue(the.sym.test, method = "single-step") # this is the post hoc of the friedman test
# plotting
if(to.plot.parallel & to.plot.boxplot) par(mfrow = c(1,2)) # if we are plotting two plots, let's make sure we'll be able to see both
if(to.plot.parallel)
{
X.names <- levels(data[, X.name])
X.for.plot <- seq_along(X.names)
plot.xlim <- c(.7 , length(X.for.plot)+.3) # adding some spacing from both sides of the plot
if(color.blocks.in.cor.plot)
{
blocks.col <- rainbow_hcl(length(levels(data[,block.name])))
} else {
blocks.col <- 1 # black
}
data2 <- data
if(jitter.Y.in.cor.plot) {
data2[,Y.name] <- jitter(data2[,Y.name])
par.cor.plot.text <- "Parallel coordinates plot (with Jitter)"
} else {
par.cor.plot.text <- "Parallel coordinates plot"
}
# adding a Parallel coordinates plot
matplot(as.matrix(reshape(data2, idvar=X.name, timevar=block.name,
direction="wide")[,-1]) ,
type = "l", lty = 1, axes = FALSE, ylab = Y.name,
xlim = plot.xlim,
col = blocks.col,
main = par.cor.plot.text)
axis(1, at = X.for.plot , labels = X.names) # plot X axis
axis(2) # plot Y axis
points(tapply(data[,Y.name], data[,X.name], median) ~ X.for.plot, col = "red",pch = 4, cex = 2, lwd = 5)
}
if(to.plot.boxplot)
{
# first we create a function to create a new Y, by substracting different combinations of X levels from each other.
subtract.a.from.b <- function(a.b , the.data)
{
the.data[,a.b[2]] - the.data[,a.b[1]]
}
temp.wide <- reshape(data, idvar=X.name, timevar=block.name,
direction="wide") #[,-1]
wide.data <- as.matrix(t(temp.wide[,-1]))
colnames(wide.data) <- temp.wide[,1]
Y.b.minus.a.combos <- apply(with(data,combn(levels(data[,X.name]), 2)), 2, subtract.a.from.b, the.data =wide.data)
names.b.minus.a.combos <- apply(with(data,combn(levels(data[,X.name]), 2)), 2, function(a.b) {paste(a.b[2],a.b[1],sep=" - ")})
the.ylim <- range(Y.b.minus.a.combos)
the.ylim[2] <- the.ylim[2] + max(sd(Y.b.minus.a.combos)) # adding some space for the labels
is.signif.color <- ifelse(The.post.hoc.P.values < .05 , "green", "grey")
boxplot(Y.b.minus.a.combos,
names = names.b.minus.a.combos ,
col = is.signif.color,
main = "Boxplots (of the differences)",
ylim = the.ylim
)
legend("topright", legend = paste(names.b.minus.a.combos, rep(" ; PostHoc P.value:", number.of.X.levels),round(The.post.hoc.P.values,5)) , fill = is.signif.color )
abline(h = 0, col = "blue")
}
list.to.return <- list(Friedman.Test = the.sym.test, PostHoc.Test = The.post.hoc.P.values)
if(to.print.friedman) {print(list.to.return)}
return(list.to.return)
} else {
print("The results where not significant, There is no need for a post hoc test")
return(the.sym.test)
}
}
# Original credit (for linking online, to the package that performs the post hoc test) goes to "David Winsemius", see:
# http://tolstoy.newcastle.edu.au/R/e8/help/09/10/1416.html
} |