candidates {SpiceFP}

candidates

Description

The "candidates" function essentially provides the candidate matrices and their characteristics. These candidate matrices can be constructed from 2 or 3 functional predictors.

Usage

candidates(
  fp1,
  fp2,
  fp3 = NULL,
  fun1,
  fun2,
  fun3 = NULL,
  parlists,
  ncores = parallel::detectCores() - 1,
  xcentering = TRUE,
  xscaling = FALSE
)

Arguments

fp1

numerical matrix with in columns observations of one statistical individual to partition. Each column corresponds to the functional predictor observation for one statistical individual. The order of statistical individuals is the same as in fp2. It is assumed that no data are missing and that all functional predictors are observed on an equidistant (time) scale.

fp2

numerical matrix with the same number of columns and rows as fp1. Columns are also observations. The order of statistical individuals is the same as in fp1.

fp3

NULL by default. numerical matrix with the same number of columns and rows as fp1 and fp2. The order of statistical individuals is the same as in fp1 and fp2.

fun1

a function object with 2 arguments. First argument is fp1 and the second is a list of parameters that will help to partition fp1, such as the number of class intervals, etc. For example, the list of parameters for using the logbreaks function is equivalent to list(alpha, J). All arguments to be varied for the creation of different candidate matrices must be stored in the parameter list. The other arguments must be set by default.

fun2

a function object with 2 arguments. First argument is fp2 and the second is a list of parameters.

fun3

NULL by default. Same as fun1 and fun2, a function with 2 arguments fp3 and a list of parameters.

parlists

list of 2 elements when fp3 and fun3 are equal to NULL or of 3 elements when fp3 and fun3 are provided. All elements of parlists are lists that have the same length. Each list contains all the lists of parameters required to create different candidates. The first element of parlists concerns the list of parameters required for fun1, the second element is relative to fun2 and the third to fun3. See Example 2 below.

ncores

numbers of cores that will be used for parallel computation. By default, it is equal to detectCores()-1.

xcentering

TRUE by default. Defined whether or not the variables in the new candidate matrices should be centered.

xscaling

FALSE by default. Defined whether or not the variables in the candidate matrices should be scaled.

Details

The function begins by partitioning each of the functional predictors using the function and associated parameter lists. Once the class intervals are obtained for each predictor, a contingency table is created for each statistical individual. This table counts the components of the observation variable (time for time series). The contingency table is then transformed into a row vector that corresponds to a row of the candidate matrix created. The number of candidate matrices is equal to the length of each element contained in parlists. For a fixed index, the functional predictors (fp1, fp2, fp3), the functions (fun1, fun2, fun3) and the lists of parameters associated to the index in each element of parlists allow to create a single candidate matrix. In addition to constructing the candidate matrices, the function associates with each matrix a vector containing the index and the numbers of class intervals used per predictor.

Value

The function returns a list with:

spicefp.dimension

the dimension of the approach. Equal to 2 if fp3=NULL and 3 if not

candidates

a list that has the same length as the elements of parlists. Each element of this list contains a candidate matrix and a vector with index and the numbers of class intervals used per predictor

fp1, fp2, fp3, fun1, fun2, fun3, parlists, xcentering, xscaling

same as inputs

Examples

##linbreaks: a function allowing to obtain equidistant breaks
linbreaks<-function(x,n){
    sort(round(seq(trunc(min(x)),
         ceiling(max(x)+0.001),
         length.out =unlist(n)+1),
         1)
    )
}

p<-expand.grid(c(12,15),c(15,20))
pl<-list(split(p[,1], seq(nrow(p))),
         split(p[,2], seq(nrow(p))))

# Setting ncores=2 for this example check purpose
test<-candidates(fp1=matrix(rnorm(1000,52,15),ncol=10),
                 fp2=matrix(rpois(1000,50),ncol=10),
                 fun1=linbreaks,
                 fun2=linbreaks,
                 parlists=pl,
                 xcentering = FALSE,
                 xscaling = FALSE,
                 ncores=2)
str(test)
names(test)

# Example 2 from the spiceFP data
tpr.nclass=seq(10,16,2)
irdc.nclass=seq(20,24,2)
irdc.alpha=c(0.01,0.02,0.03)
p2<-expand.grid(tpr.nclass, irdc.alpha, irdc.nclass)
parlist.tpr<-split(p2[,1], seq(nrow(p2)))
parlist.irdc<-split(p2[,2:3], seq(nrow(p2)))
parlist.irdc<-lapply(
  parlist.irdc,function(x){
    list(x[[1]],x[[2]])}
)
m.irdc <- as.matrix(Irradiance[,-c(1)])
m.tpr <- as.matrix(Temperature[,-c(1)])
test2<-candidates(fp1=m.irdc,
                  fp2=m.tpr,
                  fun1=logbreaks,
                  fun2=linbreaks,
                  parlists=list(parlist.irdc,
                                parlist.tpr),
                  xcentering = TRUE,
                  xscaling = FALSE,
                  ncores=2)
length(test2$candidates)
class(test2$candidates)
#View(test2$candidates[[1]][[1]])
dim(test2$candidates[[1]][[1]])
test2$candidates[[1]][[2]]

# Closing the connections for the example check purpose
closeAllConnections()