parKml3d {kml3d} | R Documentation |
~ Function: parKml3d ~
Description
parKml3d
is a constructor of object ParKml
that provide adequate default value for the use of function kml3d
.
Usage
parKml3d(saveFreq = 100, maxIt = 200, imputationMethod = "copyMean",
distanceName = "euclidean3d", power = 2, distance = function() {
}, centerMethod = meanNA, startingCond = "nearlyAll", nbCriterion =100,scale=TRUE)
Arguments
saveFreq |
[numeric] : Long computations can take several
days. So it is possible to save the object ClusterLongData3d
on which works kml3d once in a while. saveFreq
defines the frequency of the saving
process. The ClusterLongData3d is saved every saveFreq
clustering calculations. The object is saved in the file
objectName.Rdata in the curent folder.
|
maxIt |
[numeric] : Set a limit to the number of iteration if
convergence is not reached.
|
imputationMethod |
[character] : the calculation of quality
criterion can not be done if some value are
missing. imputationMethod define the method use to impute the
missing value. See imputation for detail.
|
distanceName |
[character] : name of the
distance used by k-means. If the distanceName is "euclidean3d", a compiled optimized version specificaly design for
joint-trajectories version is used. Otherwise, the function define in
the slot distance is used.
|
power |
[numeric] : If distanceName="minkowski" , this define
the power that will be used.
|
distance |
[numeric <- function(trajA,trajB)] : function that computes the
distance between two trajectories. If no function is specified, the Euclidian
distance with Gower adjustment (to deal with missing value) is
used.
|
centerMethod |
[numeric <-
function(vector(numeric))] : k-means algorithm computes the centers of
each cluster. It is possible to personalize the definition of
"center" by defining a function "centerMethod". This function should
take a vector of numeric as argument and return a single numeric -the
center of the vector-.
|
startingCond |
[character] : specifies the starting
condition. Should be one of "randomAll", "randomK", "maxDist",
"kmeans++", "kmeans+", "kmeans-" or "kmeans–" (see
initializePartition for details). It
also could take two specifics values: "all" stands for
c("maxDist","kmeans-") then an alternance of "kmeans–" and
"randomK" while "nearlyAll" stands for
"kmeans-" then an alternance of "kmeans–" and "randomK".
|
nbCriterion |
[numeric] : set the maximum number of
quality criterion that are display on the graph (since displaying
a high criterion number an slow down the overall process, the
default value is 100).
|
scale |
[logical] : if TRUE, then the data will be
automaticaly scaled (using the function scale with
default values) before the execution of k-means on joint
trajectories. Then the data
will be restore (using the function restoreRealData )
just before the end of the function kml3d . This option
has no effect on kml .
|
Details
parKml3d
is a constructor of object ParKml
that provide adequate default value for the use of function kml3d
.
Value
An object ParKml
.
Examples
### Move to tempdir
wd <- getwd()
setwd(tempdir()); getwd()
### Generation of some data
cld1 <- generateArtificialLongData3d(c(15,15,15))
### Setting two different set of option :
(option1 <- parKml3d())
(option2 <- parKml3d(centerMethod=function(x)median(x,na.rm=TRUE)))
### Running kml. Formaly, the second exemple is 'k-median'
kml3d(cld1,4,1,toPlot="both",parAlgo=option1)
kml3d(cld1,4,1,toPlot="both",parAlgo=option2)
### Go back to current dir
setwd(wd)
[Package
kml3d version 2.4.6.1
Index]