Nothing
##
## PURPOSE: Computation of the preditive univariate conditional densities (given one margin)
## * default method
##
## AUTHOR: Arnost Komarek (LaTeX: Arno\v{s}t Kom\'arek)
## arnost.komarek[AT]mff.cuni.cz
##
## CREATED: 31/05/2009
## 15/03/2017 .C call uses registered routines
##
## FUNCTION: NMixPredCondDensMarg.default (31/05/2009)
##
## ======================================================================
## *************************************************************
## NMixPredCondDensMarg.default
## *************************************************************
##
## Z ~ sum w[j] N(mu[j], Sigma[j])
## It computes univariate conditional densities of X[d] | X[icond], where
## X[d] = scale$shift[d] + scale$scale[d] * Z[d]
##
NMixPredCondDensMarg.default <- function(x, icond, prob, scale, K, w, mu, Li, Krandom=FALSE, ...)
{
thispackage <- "mixAK"
## Dimension of the original normal mixture
if (!is.list(x)) stop("x must be a list")
p <- length(x)
if (p <= 1) stop("length of x must be 2 or more")
LTp <- p * (p + 1)/2
if (p <= 1) stop("not sensible for univariate distribution")
if (icond < 1 | icond > p) stop(paste("icond must be between 1 and ", p, sep=""))
if (is.null(names(x))) names(x) <- paste("x", (1:p), sep="")
## scale
if (missing(scale)) scale <- list(shift=rep(0, p), scale=rep(1, p))
if (!is.list(scale)) stop("scale must be a list")
if (length(scale) != 2) stop("scale must have 2 components")
inscale <- names(scale)
iscale.shift <- match("shift", inscale, nomatch=NA)
iscale.scale <- match("scale", inscale, nomatch=NA)
if (is.na(iscale.shift)) stop("scale$shift is missing")
if (length(scale$shift) == 1) scale$shift <- rep(scale$shift, p)
if (length(scale$shift) != p) stop(paste("scale$shift must be a vector of length ", p, sep=""))
if (is.na(iscale.scale)) stop("scale$scale is missing")
if (length(scale$scale) == 1) scale$scale <- rep(scale$scale, p)
if (length(scale$scale) != p) stop(paste("scale$scale must be a vector of length ", p, sep=""))
if (any(scale$scale <= 0)) stop("all elements of scale$scale must be positive")
## Check chains
Kmax <- max(K)
if (any(K <= 0)) stop("all K's must be positive")
if (Krandom){
M <- length(K)
sumK <- sum(K)
}else{
K <- K[1]
M <- length(w)/K
sumK <- K * M
}
if (length(w) != sumK) stop("incorrect w supplied")
if (length(mu) != p*sumK) stop("incorrect mu supplied")
if (length(Li) != LTp*sumK) stop("incorrect Li supplied")
## Lengths of grids in each margin
n <- sapply(x, length)
if (any(n <= 0)) stop("incorrect x supplied")
## Compute needed space
ldens <- n[icond] + n[icond]*(sum(n[-icond]))
## Adjust grids with respect to scaling
grid <- list()
for (d in 1:p) grid[[d]] <- (x[[d]] - scale$shift[d])/scale$scale[d]
## Pointwise quantiles?
if (missing(prob)){
nquant <- 0
prob <- 0
}else{
nquant <- length(prob)
if (any(prob < 0)) stop("all prob values must be >= 0")
if (any(prob > 1)) stop("all prob values must be <= 1")
}
if (Krandom) stop("not (yet) implemented for random K")
## Compute predictive densities
RES <- .C(C_NMix_PredCondDensCDFMarg,
dens = double(ldens),
qdens = double(ifelse(nquant, ldens * nquant, 1)),
err = integer(1),
calc_dens = as.integer(1),
nquant = as.integer(nquant),
qprob = as.double(prob),
icond = as.integer(icond-1),
y = as.double(unlist(grid)),
p = as.integer(p),
n = as.integer(n),
chK = as.integer(K),
chw = as.double(w),
chmu = as.double(mu),
chLi = as.double(Li),
M = as.integer(M),
PACKAGE=thispackage)
if (RES$err) stop("Something went wrong.")
## Lengths of grids other than x[[icond]]
nmarg <- n[-icond]
## Number of values in RES$dens which preceed values for a specific margin
voor.marg <- cumsum(c(n[icond], nmarg*n[icond]))
voor.marg <- voor.marg[-length(voor.marg)]
## Add also 0 for conditioning margin and sort according indeces of margins
margs <- (1:p)[-icond]
voor <- rep(0, p)
voor[margs] <- voor.marg
## Create resulting object
RET <- list(x=x, icond=icond, dens=list())
for (t in 1:length(x[[icond]])){
RET$dens[[t]] <- list()
for (m0 in (1:p)){
if (m0 == icond){
RET$dens[[t]][[m0]] <- as.numeric(RES$dens[voor[m0] + t])/scale$scale[m0]
next
}
RET$dens[[t]][[m0]] <- as.numeric(RES$dens[(voor[m0] + (t-1)*n[m0] + 1):(voor[m0] + t*n[m0])])/scale$scale[m0]
}
names(RET$dens[[t]]) <- paste(1:p)
}
## Pointwise quantiles
if (nquant){
RET$prob <- prob
for (i in 1:nquant){
qnaam <- paste("q", prob[i]*100, "%", sep="")
RET[[qnaam]] <- list()
for (t in 1:length(x[[icond]])){
RET[[qnaam]][[t]] <- list()
for (m0 in (1:p)){
if (m0 == icond){
RET[[qnaam]][[t]][[m0]] <- as.numeric(RES$qdens[(i-1)*ldens + voor[m0] + t])/scale$scale[m0]
next
}
RET[[qnaam]][[t]][[m0]] <- as.numeric(RES$qdens[((i-1)*ldens + voor[m0] + (t-1)*n[m0] + 1):((i-1)*ldens + voor[m0] + t*n[m0])])/scale$scale[m0]
}
names(RET[[qnaam]][[t]]) <- paste(1:p)
}
}
}
class(RET) <- "NMixPredCondDensMarg"
return(RET)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.