https://github.com/cran/robCompositions
Tip revision: b075661d51a7a1ee9b00d94017b8a6be03730ba4 authored by Matthias Templ on 29 April 2009, 00:00:00 UTC
version 1.2.2
version 1.2.2
Tip revision: b075661
impCoda.R
`impCoda` <-
function(x, maxit=10, eps=0.5, method="ltsReg", closed=FALSE,
init="KNN", k=5, dl=rep(0.05, ncol(x)), noise=0.1){
## MT & KH, 1. Version April 2008
## MT 01. August 2008 (modification).
## MT 17. Oktober 2008 (adaption)
## for method pca: classical, mcd, gridMAD
## for regression: lm, ltsReg
## if closed == FALSE, ilr is applied.
if( is.vector(x) ) stop("x must be a matrix or data frame")
stopifnot((method %in% c("ltsReg", "ltsReg2", "classical", "lm", "roundedZero")))
if( k > nrow(x)/4 ) warning("k might be too large")
if(method == "roundedZero") init <- "roundedZero"
xcheck <- x
if(method == "roundedZero"){
x[x==0] <- NA
}
##index of missings / non-missings
w <- is.na(x)
wn <- !is.na(x)
w2 <- apply(x, 1, function(x){
length(which(is.na(x)))
})
if(method == "gmean"){
### mean imputation im Simplex:
geometricmean <- function (x) {
if (any(na.omit(x == 0)))
0
else exp(mean(log(unclass(x)[is.finite(x) & x > 0])))
}
gm <- apply(x, 2, function(x) {
geometricmean(x[complete.cases(x)])
})
xmean <- x
for(i in 1:ncol(x)){
xmean[w[,i], i] <- gm[i]
}
res <- list(xOrig=xcheck, xImp=xmean, criteria=0, iter=0, maxit=maxit, w=length(which(w)), wind=w)
} else if ( method=="meanClosed" ){
xmean <- x
xmean <- impute(xmean)
res <- list(xOrig=xcheck, xImp=xmean, criteria=0, iter=0, maxit=maxit, w=length(which(w)), wind=w)
} else{
##sort the columns of the data according to the amount of missings in the variables
indM <- sort(apply(x,2,function(x) length(which(is.na(x)))),index.return=TRUE,decreasing=TRUE)$ix
cn <- colnames(x)
## first step - replace all NAs with values with 'nearest neighbour' algorithm
#if(init=="NN"){
# library(templdistC)
# x <- templdist.C(x)
#}
if(init=="KNN"){
x <- impKNNa(x, k=k, metric="Aitchison", normknn=TRUE)$xImp #"Aitchison"
}
if(init=="KNNclosed"){
x <- impKNNa(x, k=k, metric="Euclidean")$xImp
}
if(init=="roundedZero"){
x[is.na(x)] <- 0.001
}
#x=acomp(x) #Aitchison compositions (for ilr)
#x2 <- acomp(xcheck) # with missings
##PCA algorithmus
it=0
criteria <- 10000000
error <- rep(0, ncol(x))
###########################################
### start the iteration
##require(StatDA)
##ternary(acomp(x))
#plot(ilr(x[w2==0,]), xlim=c(-5,5), ylim=c(-8,0.5))
#points(ilr(x[w2>0,]), col=gray(0.9), pch=3)
#gr <- seq(0.7,0.3, length.out=8)
while(it <= maxit & criteria >= eps){
xold <- x
it=it+1
for(i in 1:ncol(x)){
#change the first column with that one with the highest amount of NAs
#in the step
xNA=x[,indM[i]]
x1=x[,1]
x[,1]=xNA
x[,indM[i]]=x1
if( closed == FALSE ) xilr=ilr(x) else xilr=x
#apply the PCA algorithm -> ximp
ind <- cbind(w[, indM[i]], rep(FALSE, dim(w)[1]))
if(method=="classical" | method =="mcd" | method == "gridMAD"){
xilr <- impPCA(xilr, indexMiss=ind, eps=1,
indexObs=!ind, method=method)
}
#if( method == "em" ){
# s <- prelim.norm(as.matrix(xilr))
# thetahat <- em.norm(s, showits=FALSE)
# xilr <- imp.norm(s, thetahat, as.matrix(xilr))
#}
#
#if( method == "lls" ){
# xilr <- suppressWarnings(llsImpute(xmiss, 3, verbose = FALSE)@completeObs)
#}
if(method == "ltsReg" | method == "lm"){
#beta=ltsReg(xilr[,1]~xilr[,2],xilr)$coefficients
xilr <- data.frame(xilr)
c1 <- colnames(xilr)[1]
colnames(xilr)[1] <- "V1"
reg1 = get(method)(V1 ~ ., data=xilr)
colnames(xilr)[1] <- c1
##imp= cbind(rep(1, nrow(xilr)), xilr[,-1]) %*% reg1$coef
xilr[w[, indM[i]], 1] <- fitted(reg1)[w[, indM[i]]] ##imp[w[, indM[i]]] ## xilr[w[, indM[i]], 1]
}
if(method == "ltsReg2"){
xilr <- data.frame(xilr)
c1 <- colnames(xilr)[1]
colnames(xilr)[1] <- "V1"
reg1 = ltsReg(V1 ~ ., data=xilr)
imp= as.matrix(cbind(rep(1, nrow(xilr)), xilr[,-1])) %*% reg1$coef
colnames(xilr)[1] <- c1
##imp= cbind(rep(1, nrow(xilr)), xilr[,-1]) %*% reg1$coef
xilr[w[, indM[i]], 1] <- fitted(reg1)[w[, indM[i]]]
error[indM[i]] <- noise*sd(xilr[,1])#sqrt(mad(xilr[,1]))
#+
# rnorm(length(imp[w[, indM[i]]]), 0, sd=0.5*sqrt(mad(xilr[,1])))
# xilr <- data.frame(xilr)
###imp[w[, indM[i]]] + rnorm(length(imp[w[, indM[i]]]), 0, sd=0.5*sqrt(mad(xilr[,1])))
}
if(method == "roundedZero"){
phi <- ilr(cbind(rep(dl[indM[i]], nrow(x)), x[,-1,drop=FALSE]))[,1]
xilr <- data.frame(xilr)
c1 <- colnames(xilr)[1]
colnames(xilr)[1] <- "V1"
reg1 = lm(V1 ~ ., data=xilr)
yhat2 <- predict(reg1, new.data=xilr[,-i])
#colnames(xilr)[1] <- c1
#s <- sd(xilr[,1], na.rm=TRUE)
#ex <- (phi - yhat)/s
#yhat2 <- yhat - s*dnorm(ex)/pnorm(ex)
xilr[w[, indM[i]], 1] <- ifelse(yhat2[w[, indM[i]]] <= phi[w[, indM[i]]], phi[w[, indM[i]]], yhat2[w[, indM[i]]] )
}
#if( method == "rf" ){
# xilr[w[, indM[i]], 1] <- NA
# reg1 <- rfImpute(xilr[,1] ~ xilr[,-1], data=xilr)
# xilr[w[, indM[i]], 1] <- reg1[w[, indM[i]]]
#}
if( closed == FALSE ) x=invilr(xilr) else x=xilr
#return the order of columns
xNA=x[,1]
x1=x[,indM[i]]
x[,1]=x1
x[,indM[i]]=xNA
}
criteria <- sum( ((xold - x)/x)^2, na.rm=TRUE) #sum(abs(as.matrix(xold) - as.matrix(x)), na.rm=TRUE) ## DIRTY: (na.rm=TRUE)
#print(paste(method, ",", it, ",", "criteria=",round(criteria,3)))
if(closed == FALSE) colnames(x) <- colnames(xcheck)
}
if( method == "ltsReg2"){ # finally, add an error for method ltsReg2
for(i in 1:ncol(x)){
xNA=x[,indM[i]]
x1=x[,1]
x[,1]=xNA
x[,indM[i]]=x1
if( closed == FALSE ) xilr=ilr(x) else xilr=x
ind <- cbind(w[, indM[i]], rep(FALSE, dim(w)[1]))
xilr <- data.frame(xilr)
#c1 <- colnames(xilr)[1]
#colnames(xilr)[1] <- "V1"
#reg1 = ltsReg(V1 ~ ., data=xilr)
#imp= as.matrix(cbind(rep(1, nrow(xilr)), xilr[,-1])) %*% reg1$coef
#colnames(xilr)[1] <- c1
xilr[w[, indM[i]], 1] <- xilr[w[, indM[i]], 1] +
rnorm(length(which(w[, indM[i]])), 0, sd=error[indM[i]])
xilr <- data.frame(xilr)
if( closed == FALSE ) x=invilr(xilr) else x=xilr
xNA=x[,1]
x1=x[,indM[i]]
x[,1]=x1
x[,indM[i]]=xNA
}
}
res <- list(xOrig=xcheck, xImp=x, criteria=criteria, iter=it,
maxit=maxit, w=length(which(w)), wind=w)
}
class(res) <- "imp"
invisible(res)
}