meancoef: meancoef

In SpiceFP: Sparse Method to Identify Joint Effects of Functional Predictors

meancoef

Description

This function can be used to compute the mean of coefficients from different partitions in the context of the spicefp approach.

Usage

meancoef(coef.list, weight)

Arguments

coef.list	list. The second element of the coef_spicefp function outputs. It has the same name as the argument.
weight	a numerical vector of weights with the same length as coef.list.

Details

Here, the fine-mesh coefficients are weighted and a weighted mean is deduced. If the user wishes, he can use as weights the slopes associated with the qualities of the models concerned.

Value

Returns a list of :

weighted_mean

fine-mesh matrix or array with the weighted mean of the coefficients

y.estimated

weighted estimation of X \beta

coefficients.array

An array with all the fine-mesh coefficients that will be used to compute the weighted mean

weight

same as inputs

Examples

##linbreaks: a function allowing to obtain breaks linearly
linbreaks<-function(x,n){
     sort(round(seq(trunc(min(x)),
                ceiling(max(x)+0.001),
                length.out =unlist(n)+1),
            1)
          )
}
# In this example, we will evaluate 2 candidates with 14 temperature
# classes and 15 irradiance classes. The irradiance breaks are obtained
# according to a log scale (logbreaks function) with different alpha
# parameters for each candidate (0.005, 0.01).
## Data and inputs
tpr.nclass=14
irdc.nclass=15
irdc.alpha=c(0.005, 0.01)
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)])

# For the constructed models, only two regularization parameter ratios
# penratios=c(1/25,5) is used. In a real case, more candidates 
# and regularization parameter ratios should be evaluated.
ex_sp<-spicefp(y=FerariIndex_Difference$fi_dif,
              fp1=m.irdc,
              fp2=m.tpr,
              fun1=logbreaks,
              fun2=linbreaks,
              parlists=list(parlist.irdc,
                            parlist.tpr),
              penratios=c(1/25,5),
              appropriate.df=NULL,
              nknots = 100,
              ncores =2,
              write.external.file = FALSE)

## Focus on the 2 best models retained by the AIC criterion at iteration 1
c.mdls <- coef_spicefp(ex_sp, iter_=1, criterion ="AIC_",
                      nmodels=2, ncores = 2,
                      dim.finemesh=c(1000,1000),
                      write.external.file = FALSE)

# meancoef
# Compute the mean of the coefficients of these models
mean.c.mdls<-meancoef(c.mdls$coef.list,
                     weight = c.mdls$Model.parameters$Slope_)
g3<-mean.c.mdls$weighted_mean
g3.x<-as.numeric(rownames(g3))
g3.y<-as.numeric(colnames(g3))


#library(fields)
#plot(c(10,2000),c(15,45),type= "n", axes = FALSE,
#     xlab = "Irradiance (mmol/m2/s - Logarithmic scale)",
#     ylab = "Temperature (deg C)",log = "x")
#rect(min(g3.x),min(g3.y),max(g3.x),max(g3.y), col="black", border=NA)
#image.plot(g3.x,g3.y,g3, horizontal = FALSE,
#           col=designer.colors(256, c("blue","white","red")),
#           add = TRUE)
#axis(1) ; axis(2)

closeAllConnections()

SpiceFP documentation built on June 7, 2023, 5:55 p.m.