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dsscden <- ## Evaluate conditional density estimate
function (object,y,x) {
## check input
if (!("sscden"%in%class(object))) stop("gss error in dsscden: not a sscden object")
if (!all(sort(object$xnames)==sort(colnames(x))))
stop("gss error in dsscden: mismatched x variable names")
if (length(object$ynames)==1&is.vector(y)) {
y <- data.frame(y)
colnames(y) <- object$ynames
}
if (!all(sort(object$ynames)==sort(colnames(y))))
stop("gss error in dsscden: mismatched y variable names")
if ("sscden1"%in%class(object)) {
qd.pt <- object$rho$env$qd.pt
qd.wt <- object$rho$env$qd.wt
d.qd <- d.sscden1(object,x,qd.pt,scale=FALSE)
int <- apply(d.qd*qd.wt,2,sum)
return(t(t(d.sscden1(object,x,y,scale=FALSE))/int))
}
else {
qd.pt <- object$yquad$pt
qd.wt <- object$yquad$wt
d.qd <- d.sscden(object,x,qd.pt)
int <- apply(d.qd*qd.wt,2,sum)
return(t(t(d.sscden(object,x,y))/int))
}
}
psscden <- ## Compute cdf for univariate density estimate
function(object,q,x) {
if (!("sscden"%in%class(object))) stop("gss error in psscden: not a sscden object")
if (length(object$ynames)!=1) stop("gss error in psscden: y is not 1-D")
if (("sscden1"%in%class(object))&!is.numeric(object$mf[,object$ynames]))
stop("gss error in qssden: y is not continuous")
if ("sscden1"%in%class(object)) ydomain <- object$rho$env$ydomain
else ydomain <- object$ydomain
mn <- min(ydomain[[object$ynames]])
mx <- max(ydomain[[object$ynames]])
order.q <- rank(q)
p <- q <- sort(q)
q.dup <- duplicated(q)
p[q<=mn] <- 0
p[q>=mx] <- 1
qd.hize <- 200
qd <- gauss.quad(2*qd.hize,c(mn,mx))
y.wk <- data.frame(qd$pt)
colnames(y.wk) <- object$ynames
d.qd <- dsscden(object,y.wk,x)
gap <- diff(qd$pt)
g.wk <- gap[qd.hize]/2
for (i in 1:(qd.hize-2)) g.wk <- c(g.wk,gap[qd.hize+i]-g.wk[i])
g.wk <- 2*g.wk
g.wk <- c(g.wk,(mx-mn)/2-sum(g.wk))
gap[qd.hize:1] <- gap[qd.hize+(1:qd.hize)] <- g.wk
brk <- cumsum(c(mn,gap))
kk <- (1:length(q))[q>mn&q<mx]
z <- NULL
for (k in 1:dim(x)[1]) {
d.qd.wk <- d.qd[,k]/sum(d.qd[,k]*qd$wt)
for (i in kk) {
if (q.dup[i]) {
p[i] <- p.dup
next
}
ind <- (1:(2*qd.hize))[qd$pt<q[i]]
if (!length(ind)) {
wk <- d.qd.wk[1]*qd$wt[1]*(q[i]-mn)/gap[1]
}
else {
wk <- sum(d.qd.wk[ind]*qd$wt[ind])
id.mx <- max(ind)
if (q[i]<brk[id.mx+1])
wk <- wk-d.qd.wk[id.mx]*qd$wt[id.mx]*(brk[id.mx+1]-q[i])/gap[id.mx]
else wk <- wk+d.qd.wk[id.mx+1]*qd$wt[id.mx+1]*(q[i]-brk[id.mx+1])/gap[id.mx+1]
}
p[i] <- p.dup <- wk
}
z <- cbind(z,p[order.q])
}
z
}
qsscden <- ## Compute quantiles for univariate density estimate
function(object,p,x) {
if (!("sscden"%in%class(object))) stop("gss error in qsscden: not a sscden object")
if (length(object$ynames)!=1) stop("gss error in qsscden: y is not 1-D")
if (("sscden1"%in%class(object))&!is.numeric(object$mf[,object$ynames]))
stop("gss error in qssden: y is not continuous")
if ("sscden1"%in%class(object)) ydomain <- object$rho$env$ydomain
else ydomain <- object$ydomain
mn <- min(ydomain[[object$ynames]])
mx <- max(ydomain[[object$ynames]])
order.p <- rank(p)
q <- p <- sort(p)
p.dup <- duplicated(p)
q[p<=0] <- mn
q[p>=1] <- mx
qd.hize <- 200
qd <- gauss.quad(2*qd.hize,c(mn,mx))
y.wk <- data.frame(qd$pt)
colnames(y.wk) <- object$ynames
d.qd <- dsscden(object,y.wk,x)
gap <- diff(qd$pt)
g.wk <- gap[qd.hize]/2
for (i in 1:(qd.hize-2)) g.wk <- c(g.wk,gap[qd.hize+i]-g.wk[i])
g.wk <- 2*g.wk
g.wk <- c(g.wk,(mx-mn)/2-sum(g.wk))
gap[qd.hize:1] <- gap[qd.hize+(1:qd.hize)] <- g.wk
brk <- cumsum(c(mn,gap))
kk <- (1:length(p))[p>0&p<1]
z <- NULL
for (k in 1:dim(x)[1]) {
d.qd.wk <- d.qd[,k]/sum(d.qd[,k]*qd$wt)
p.wk <- cumsum(d.qd.wk*qd$wt)
for (i in kk) {
if (p.dup[i]) {
q[i] <- q.dup
next
}
ind <- (1:(2*qd.hize))[p.wk<p[i]]
if (!length(ind)) {
wk <- mn+p[i]/(d.qd.wk[1]*qd$wt[1])*gap[1]
}
else {
id.mx <- max(ind)
wk <- brk[id.mx+1]+(p[i]-p.wk[id.mx])/(d.qd.wk[id.mx+1]*qd$wt[id.mx+1])*gap[id.mx+1]
}
q[i] <- q.dup <- wk
}
z <- cbind(z,q[order.p])
}
z
}
d.sscden <- ## Evaluate conditional density estimate
function (object,x,y) {
## check input
if ("sscden"!=class(object)) stop("gss error in d.sscden: not a sscden object")
if (!all(sort(object$xnames)==sort(colnames(x))))
stop("gss error in d.sscden: mismatched x variable names")
if (!all(sort(object$ynames)==sort(colnames(y))))
stop("gss error in d.sscden: mismatched y variable names")
mf <- object$mf
## exp(eta)
z <- NULL
for (k in 1:dim(x)[1]) {
xy.new <- cbind(x[rep(k,dim(y)[1]),,drop=FALSE],y)
s <- NULL
r <- 0
nu <- nq <- 0
for (label in object$terms$labels) {
vlist <- object$terms[[label]]$vlist
xy <- xy.new[,vlist]
xy.basis <- mf[object$id.basis,vlist]
nphi <- object$terms[[label]]$nphi
nrk <- object$terms[[label]]$nrk
if (nphi) {
phi <- object$terms[[label]]$phi
for (i in 1:nphi) {
nu <- nu + 1
s.wk <- phi$fun(xy,nu=i,env=phi$env)
s <- cbind(s,s.wk)
}
}
if (nrk) {
rk <- object$terms[[label]]$rk
for (i in 1:nrk) {
nq <- nq+1
r.wk <- rk$fun(xy,xy.basis,nu=i,env=rk$env,out=TRUE)
r <- r + 10^object$theta[nq]*r.wk
}
}
}
eta <- exp(cbind(s,r)%*%c(object$d,object$c))
z <- cbind(z,eta)
}
z
}
d.sscden1 <- ## Evaluate conditional density estimate
function (object,x,y,scale=TRUE) {
## check input
if (!("sscden1"%in%class(object))) stop("gss error in d.sscden1: not a sscden1 object")
if (!all(sort(object$xnames)==sort(colnames(x))))
stop("gss error in d.sscden1: mismatched x variable names")
if (!all(sort(object$ynames)==sort(colnames(y))))
stop("gss error in d.sscden1: mismatched y variable names")
mf <- object$mf
## rho
rho <- object$rho$fun(x,y,object$rho$env,outer=TRUE)
## exp(eta)
z <- NULL
for (k in 1:dim(x)[1]) {
xy.new <- cbind(x[rep(k,dim(y)[1]),,drop=FALSE],y)
s <- NULL
r <- 0
nu <- nq <- 0
for (label in object$terms$labels) {
vlist <- object$terms[[label]]$vlist
xy <- xy.new[,vlist]
xy.basis <- mf[object$id.basis,vlist]
nphi <- object$terms[[label]]$nphi
nrk <- object$terms[[label]]$nrk
if (nphi) {
phi <- object$terms[[label]]$phi
for (i in 1:nphi) {
nu <- nu + 1
if (!scale&!(nu%in%object$id.s)) next
s.wk <- phi$fun(xy,nu=i,env=phi$env)
s <- cbind(s,s.wk)
}
}
if (nrk) {
rk <- object$terms[[label]]$rk
for (i in 1:nrk) {
nq <- nq+1
if (!scale&!(nq%in%object$id.r)) next
r.wk <- rk$fun(xy,xy.basis,nu=i,env=rk$env,out=TRUE)
r <- r + 10^object$theta[nq]*r.wk
}
}
}
if (!scale) eta <- exp(cbind(s,r)%*%c(object$d[object$id.s],object$c))
else eta <- exp(cbind(s,r)%*%c(object$d,object$c))*object$scal
z <- cbind(z,eta*rho[k,])
}
z
}
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