Nothing
#
# ctl.print.R
#
# copyright (c) 2010-2012 - GBIC, Danny Arends, Bruno Tesson and Ritsert C. Jansen
# last modified Oct, 2012
# first written Oct, 2011
#
# Print routines for CTL analysis
#
print.CTLobject <- function(x, ...){
cat("CTLobject summary\n\n")
cat("- Number of scanned phenotypes:", length(x), "\n")
}
print.CTLscan <- function(x, ...){
if(missing(x)) stop("argument 'x' is missing, with no default")
cat("CTLscan summary",attr(x$ctl,"name"),"\n\n")
cat("- Number of background phenotypes:", dim(x$ctl)[2], "\n")
cat("- Number of markers:", dim(x$ctl)[1], "\n")
cat("- Number of permutations:", length(unlist(x$p)), "\n")
invisible(CTLsignificant(x))
}
getPermuteThresholds <-function(x, significance = c(.05,.01,.001), ..., verbose = FALSE){
sorted <- sort(unlist(x))
l <- length(sorted)
values <- NULL
valnames <- NULL
for(x in significance){
if(1/(x) < length(sorted)){
v <- sorted[l*(1-(x))]
values <- c(values,v)
valnames <- c(valnames,paste(x*100,"%"))
if(verbose) cat(x*100,"%\t",v,"\n")
}else{
values <- c(values,NaN)
valnames <- c(valnames,paste(x*100,"%"))
if(verbose) cat(x*100,"%\t",NaN,"\n")
}
}
names(values) <- valnames
values
}
print.CTLpermute <- function(x, ...){
getPermuteThresholds(x, ..., verbose=TRUE)
}
# end of ctl.print.R
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