Hi the list,
I am writing a package in S4 and I do not manage to understand a bug.
The "R CMD check" and the "R CMD build" both work. Here is links to
the package (not on CRAN yet for the raison that I explain bellow):
http://christophe.genolini.free.fr/aTelecharger/kml_0.5.zip
http://christophe.genolini.free.fr/aTelecharger/kml_0.5.tar.gz
Then I install the package and I try an example:
--- 8< --------------
library(kml)
dn <- as.cld(gald())
kml(dn)
# XXX ~ Fast KmL ~
# Erreur dans as.vector(x, mode) : argument 'mode' incorrect
--- 8< --------------
So I make some verifications:
--- 8< ----
class(dn)
# [1] "ClusterizLongData"
# attr(,"package")
# [1] "kml"
getMethod("kml","ClusterizLongData")
# Method Definition:
#
# function (Object, nbClusters = 2:6, nbRedrawing = 20, saveFreq = 100,
# maxIt = 200, trajMinSize = 2, print.cal = FALSE, print.traj = FALSE,
# imputationMethod = "copyMean", distance, power = 2, centerMethod
= meanNA,
# startingCond = "allMethods", distanceStartingCond = "euclidean",
# ...)
#{
# nbIdFull <- nrow(Object["traj"])
# ...... [[[The full code is available below]]]
# }
# <environment: namespace:kml>
#
#Signatures:
# Object # target "ClusterizLongData"
# defined "ClusterizLongData"
--- 8< ----
Everything seems fine. The code is correct.
So I copy-and-paste the code that I get with
getMethods("kml","ClusterizLongData") and I affect it to a function
"func". Then I define again the method "kml".
Then I run again the example that does not work before and it works...
Any explanations?
Christophe Genolini
--- 8< --------------------------
###
### Affecting to func the code that
getMethod("kml","ClusterizLongData") delivers
###
func <- function (Object, nbClusters = 2:6, nbRedrawing = 20, saveFreq
= 100,
maxIt = 200, trajMinSize = 2, print.cal = FALSE, print.traj = FALSE,
imputationMethod = "copyMean", distance, power = 2, centerMethod =
meanNA,
startingCond = "allMethods", distanceStartingCond = "euclidean",
...)
{
nbIdFull <- nrow(Object["traj"])
convergenceTime <- 0
noNA <- selectSupTrajMinSize(Object, trajMinSize)
trajNoNA <- Object["traj"][noNA, ]
nbTime <- length(Object["time"])
nbId <- nrow(trajNoNA)
saveCld <- 0
scr <- plotAll(Object, print.cal = print.cal, print.traj = print.traj,
print.sub = FALSE, col = "black", type.mean = "n")
if (length(startingCond) == 1) {
if (startingCond == "allMethods") {
startingCond <- c("maxDist", "randomAll", rep("randomK",
nbRedrawing))[1:nbRedrawing]
}
else {
startingCond <- rep(startingCond, nbRedrawing)
}
}
else {
}
if (missing(distance)) {
distance <- "euclidean"
}
if (is.character(distance)) {
distInt <- pmatch(distance, METHODS)
}
else {
distInt <- NA
}
if (print.traj) {
cat(" ~ Slow KmL ~\n")
fast <- FALSE
screenPlot <- scr[2]
if (!is.na(distInt)) {
distanceSlow <- function(x, y) {
dist(rbind(x, y), method = distance)
}
}
else {
distanceSlow <- distance
}
}
else {
screenPlot <- NA
if (is.na(distInt)) {
cat(" ~ Slow KmL ~\n")
fast <- FALSE
distanceSlow <- distance
}
else {
cat(" ~ Fast KmL ~\n")
fast <- TRUE
}
}
nameObject <- deparse(substitute(Object))
for (iRedraw in 1:nbRedrawing) {
for (iNbClusters in nbClusters) {
saveCld <- saveCld + 1
clustersInit <- partitionInitialise(nbClusters = iNbClusters,
method = startingCond[iRedraw], lengthPart = nbId,
matrixDist = as.matrix(dist(trajNoNA, method =
distanceStartingCond)))
clust <- rep(NA, nbIdFull)
if (fast) {
resultKml <- .C("kml1", as.double(t(trajNoNA)),
iNbInd = as.integer(nbId), iNbTime = as.integer(nbTime),
iNbCluster = as.integer(iNbClusters), maxIt =
as.integer(maxIt),
distance = as.integer(distInt), power =
as.numeric(power),
vClusterAffectation1 =
as.integer(clustersInit["clusters"]),
convergenceTime = as.integer(convergenceTime),
NAOK = TRUE, PACKAGE = "kml")[c(8, 9)]
clust[noNA] <- resultKml[[1]]
}
else {
resultKml <- trajKmlSlow(traj = trajNoNA,
clusterAffectation = clustersInit,
nbId = nbId, nbTime = nbTime, maxIt = maxIt,
screenPlot = scr[2], distance = distanceSlow,
centerMethod = centerMethod, ...)
clust[noNA] <- resultKml[[1]]["clusters"]
}
yPartition <- ordered(partition(nbClusters = iNbClusters,
clusters = clust))
Object["clusters"] <- clusterization(yLongData = as(Object,
"LongData"), xPartition = yPartition, convergenceTime =
resultKml[[2]],
imputationMethod = imputationMethod, startingCondition
= startingCond[iRedraw],
algorithmUsed = "kml")
assign(nameObject, Object, envir = parent.frame())
cat("*")
if (saveCld >= saveFreq) {
save(list = nameObject, file = paste(nameObject,
".Rdata", sep = ""))
saveCld <- 0
cat("\n")
}
else {
}
if (print.cal) {
screen(scr[1])
plotCriterion(Object, all = TRUE)
}
else {
}
}
}
save(list = nameObject, file = paste(nameObject, ".Rdata",
sep = ""))
return(invisible())
}
######
### setting the kml method, using the same code
###
setMethod("kml","ClusterizLongData",func)
#######
### Same example that the one that does not work at the begining of
this mail
###
kml(dn)
--- 8< --------------------------
