[Rd] Package that does not work until I re write the exactly the same code

[Christophe Genolini]

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< --------------------------