Nothing
## Fit gssanova model
gssanova <- function(formula,family,type=NULL,data=list(),weights,
subset,offset,na.action=na.omit,partial=NULL,
alpha=NULL,nu=NULL,
id.basis=NULL,nbasis=NULL,seed=NULL,random=NULL,
skip.iter=FALSE)
{
if (!(family%in%c("binomial","poisson","Gamma","inverse.gaussian","nbinomial",
"polr","weibull","lognorm","loglogis")))
stop("gss error in gssanova: family not implemented")
if (is.null(alpha)) {
alpha <- 1.4
if (family%in%c("binomial","nbinomial","inverse.gaussian")) alpha <- 1
}
## Obtain model frame and model terms
mf <- match.call()
mf$family <- mf$type <- mf$partial <- NULL
mf$method <- mf$varht <- mf$nu <- NULL
mf$alpha <- mf$id.basis <- mf$nbasis <- mf$seed <- NULL
mf$random <- mf$skip.iter <- NULL
mf[[1]] <- as.name("model.frame")
mf <- eval(mf,parent.frame())
wt <- model.weights(mf)
## Generate sub-basis
nobs <- dim(mf)[1]
if (is.null(id.basis)) {
if (is.null(nbasis)) nbasis <- max(30,ceiling(10*nobs^(2/9)))
if (nbasis>=nobs) nbasis <- nobs
if (!is.null(seed)) set.seed(seed)
id.basis <- sample(nobs,nbasis,prob=wt)
}
else {
if (max(id.basis)>nobs|min(id.basis)<1)
stop("gss error in gssanova: id.basis out of range")
nbasis <- length(id.basis)
}
## Generate terms
term <- mkterm(mf,type)
## Generate random
if (!is.null(random)) {
if (inherits(random,"formula")) random <- mkran(random,data)
}
## Generate s, r, and y
s <- r <- NULL
nq <- 0
for (label in term$labels) {
if (label=="1") {
s <- cbind(s,rep(1,len=nobs))
next
}
x <- mf[,term[[label]]$vlist]
x.basis <- mf[id.basis,term[[label]]$vlist]
nphi <- term[[label]]$nphi
nrk <- term[[label]]$nrk
if (nphi) {
phi <- term[[label]]$phi
for (i in 1:nphi)
s <- cbind(s,phi$fun(x,nu=i,env=phi$env))
}
if (nrk) {
rk <- term[[label]]$rk
for (i in 1:nrk) {
nq <- nq+1
r <- array(c(r,rk$fun(x,x.basis,nu=i,env=rk$env,out=TRUE)),c(nobs,nbasis,nq))
}
}
}
if (is.null(r))
stop("gss error in gssanova: use glm for models with only unpenalized terms")
## Add the partial term
if (!is.null(partial)) {
mf.p <- model.frame(partial,data)
for (lab in colnames(mf.p)) mf[,lab] <- mf.p[,lab]
mt.p <- attr(mf.p,"terms")
lab.p <- labels(mt.p)
matx.p <- model.matrix(mt.p,data)[,-1,drop=FALSE]
if (dim(matx.p)[1]!=dim(mf)[1])
stop("gss error in ssanova: partial data are of wrong size")
matx.p <- scale(matx.p)
center.p <- attr(matx.p,"scaled:center")
scale.p <- attr(matx.p,"scaled:scale")
s <- cbind(s,matx.p)
part <- list(mt=mt.p,center=center.p,scale=scale.p)
}
else part <- lab.p <- NULL
if (qr(s)$rank<dim(s)[2])
stop("gss error in gssanova: unpenalized terms are linearly dependent")
## Prepare the data
if (family=="polr") {
y <- model.response(mf)
if (!is.factor(y))
stop("gss error in gssanova1: need factor response for polr family")
lvls <- levels(y)
if (nlvl <- length(lvls)<3)
stop("gss error in gssanova1: need at least 3 levels to fit polr family")
y <- outer(y,lvls,"==")
}
else y <- model.response(mf,"numeric")
offset <- model.offset(mf)
if (!is.null(offset)) {
term$labels <- c(term$labels,"offset")
term$offset <- list(nphi=0,nrk=0)
}
nu.wk <- list(NULL,FALSE)
if ((family=="nbinomial")&is.vector(y)) nu.wk <- list(NULL,TRUE)
if (family%in%c("weibull","lognorm","loglogis")) {
if (is.null(nu)) nu.wk <- list(nu,TRUE)
else nu.wk <- list(nu,FALSE)
}
## Fit the model
if (nq==1) {
r <- r[,,1]
z <- sspngreg(family,s,r,r[id.basis,],y,wt,offset,alpha,nu.wk,random)
}
else z <- mspngreg(family,s,r,id.basis,y,wt,offset,alpha,nu.wk,random,skip.iter)
## Brief description of model terms
desc <- NULL
for (label in term$labels)
desc <- rbind(desc,as.numeric(c(term[[label]][c("nphi","nrk")])))
if (!is.null(partial)) {
desc <- rbind(desc,matrix(c(1,0),length(lab.p),2,byrow=TRUE))
}
desc <- rbind(desc,apply(desc,2,sum))
if (is.null(partial)) rownames(desc) <- c(term$labels,"total")
else rownames(desc) <- c(term$labels,lab.p,"total")
colnames(desc) <- c("Unpenalized","Penalized")
## Return the results
obj <- c(list(call=match.call(),family=family,mf=mf,terms=term,desc=desc,
alpha=alpha,id.basis=id.basis,partial=part,lab.p=lab.p,
random=random,skip.iter=skip.iter),z)
class(obj) <- c("gssanova","ssanova")
obj
}
## Fit Single Smoothing Parameter Non-Gaussian REGression
sspngreg <- function(family,s,r,q,y,wt,offset,alpha,nu,random)
{
nobs <- nrow(r)
nxi <- ncol(r)
if (!is.null(s)) {
if (is.vector(s)) nnull <- 1
else nnull <- ncol(s)
}
else nnull <- 0
if (!is.null(random)) nz <- ncol(as.matrix(random$z))
else nz <- 0
nxiz <- nxi + nz
nn <- nxiz + nnull
## cv function
cv <- function(lambda) {
if (nu[[2]]) {
la.wk <- lambda[-2]
nu.wk <- list(exp(lambda[2]),FALSE)
}
else {
la.wk <- lambda
nu.wk <- nu
}
if (is.null(random)) q.wk <- 10^(la.wk+theta)*q
else {
q.wk <- matrix(0,nxiz,nxiz)
q.wk[1:nxi,1:nxi] <- 10^(la.wk[1]+theta)*q
q.wk[(nxi+1):nxiz,(nxi+1):nxiz] <-
10^(2*ran.scal)*random$sigma$fun(la.wk[-1],random$sigma$env)
}
alpha.wk <- max(0,log.la0-la.wk[1]-5)*(3-alpha) + alpha
alpha.wk <- min(alpha.wk,3)
z <- ngreg(dc,family,cbind(s,10^theta*r),q.wk,y,wt,offset,nu.wk,alpha.wk)
assign("dc",z$dc,inherits=TRUE)
assign("fit",z[c(1:3,5:10)],inherits=TRUE)
if (family=="polr") assign("nu",z$nu,inherits=TRUE)
z$score
}
cv.wk <- function(lambda) cv.scale*cv(lambda)+cv.shift
## initialization
dc <- rep(0,nn)
tmp <- sum(r^2)
if (is.null(s)) theta <- 0
else theta <- log10(sum(s^2)/nnull/tmp*nxi) / 2
log.la0 <- log10(tmp/sum(diag(q))) + theta
if (!is.null(random)) {
ran.scal <- theta - log10(sum(random$z^2)/nz/tmp*nxi) / 2
r <- cbind(r,10^(ran.scal-theta)*random$z)
}
else ran.scal <- NULL
if (nu[[2]]&is.null(nu[[1]])) {
eta <- rep(0,nobs)
wk <- switch(family,
nbinomial=mkdata.nbinomial(y,eta,wt,offset,NULL),
weibull=mkdata.weibull(y,eta,wt,offset,nu),
lognorm=mkdata.lognorm(y,eta,wt,offset,nu),
loglogis=mkdata.loglogis(y,eta,wt,offset,nu))
nu[[1]] <- wk$nu[[1]]
}
if (family=="polr") {
if (is.null(wt)) P <- apply(y,2,sum)
else P <- apply(y*wt,2,sum)
P <- P/sum(P)
P <- cumsum(P)
nnu <- length(P)-2
dc[1] <- qlogis(P[1])
nu[[1]] <- diff(qlogis(P[-(nnu+2)]))
}
## lambda search
fit <- NULL
la <- log.la0
if (nu[[2]]) la <- c(la, log(nu[[1]]))
if (!is.null(random)) la <- c(la,random$init)
if (length(la)-1) {
counter <- 0
## scale and shift cv
tmp <- abs(cv(la))
cv.scale <- 1
cv.shift <- 0
if (tmp<1&tmp>10^(-4)) {
cv.scale <- 10/tmp
cv.shift <- 0
}
if (tmp<10^(-4)) {
cv.scale <- 10^2
cv.shift <- 10
}
repeat {
zz <- nlm(cv.wk,la,stepmax=1,ndigit=7)
if (zz$code<=3) break
la <- zz$est
counter <- counter + 1
if (counter>=5) {
warning("gss warning in ssanova: iteration for model selection fails to converge")
break
}
}
}
else {
mn0 <- log.la0-6
mx0 <- log.la0+6
repeat {
mn <- max(la-1,mn0)
mx <- min(la+1,mx0)
zz <- nlm0(cv,c(mn,mx))
if ((min(zz$est-mn,mx-zz$est)>=1e-1)||
(min(zz$est-mn0,mx0-zz$est)<1e-1)) break
else la <- zz$est
}
}
## return
jk <- cv(zz$est)
if (nu[[2]]) {
nu.wk <- exp(zz$est[2])
zz$est <- zz$est[-2]
}
else nu.wk <- nu[[1]]
if (is.null(random)) q.wk <- 10^theta*q
else {
q.wk <- matrix(0,nxiz,nxiz)
q.wk[1:nxi,1:nxi] <- 10^theta*q
q.wk[(nxi+1):nxiz,(nxi+1):nxiz] <-
10^(2*ran.scal-zz$est[1])*random$sigma$fun(zz$est[-1],random$sigma$env)
}
se.aux <- regaux(sqrt(fit$w)*s,10^theta*sqrt(fit$w)*r,q.wk,zz$est[1],fit)
c <- fit$dc[nnull+(1:nxi)]
if (nnull) d <- fit$dc[1:nnull]
else d <- NULL
if (nz) b <- 10^(ran.scal)*fit$dc[nnull+nxi+(1:nz)]
else b <- NULL
c(list(theta=theta,ran.scal=ran.scal,c=c,d=d,b=b,nlambda=zz$est[1],
zeta=zz$est[-1],nu=nu.wk),fit[-1],list(se.aux=se.aux))
}
## Fit Multiple Smoothing Parameter Non-Gaussian REGression
mspngreg <- function(family,s,r,id.basis,y,wt,offset,alpha,nu,random,skip.iter)
{
nobs <- nrow(r)
nxi <- ncol(r)
if (!is.null(s)) {
if (is.vector(s)) nnull <- 1
else nnull <- ncol(s)
}
else nnull <- 0
if (!is.null(random)) nz <-ncol(as.matrix(random$z))
else nz <- 0
nxiz <- nxi + nz
nn <- nxiz + nnull
nq <- dim(r)[3]
## cv function
cv <- function(theta) {
if (nu[[2]]) {
the.wk <- theta[-(nq+1)]
nu.wk <- list(exp(theta[nq+1]),FALSE)
}
else {
the.wk <- theta
nu.wk <- nu
}
ind.wk <- theta[1:nq]!=theta.old
if (sum(ind.wk)==nq) {
r.wk0 <- 0
for (i in 1:nq) {
r.wk0 <- r.wk0 + 10^theta[i]*r[,,i]
}
assign("r.wk",r.wk0+0,inherits=TRUE)
assign("theta.old",theta[1:nq]+0,inherits=TRUE)
}
else {
r.wk0 <- r.wk
for (i in (1:nq)[ind.wk]) {
theta.wk <- (10^(theta[i]-theta.old[i])-1)*10^theta.old[i]
r.wk0 <- r.wk0 + theta.wk*r[,,i]
}
}
qq.wk <- r.wk0[id.basis,]
if (is.null(random)) q.wk <- 10^nlambda*qq.wk
else {
r.wk0 <- cbind(r.wk0,10^(ran.scal)*random$z)
q.wk <- matrix(0,nxiz,nxiz)
q.wk[1:nxi,1:nxi] <- 10^nlambda*qq.wk
q.wk[(nxi+1):nxiz,(nxi+1):nxiz] <-
10^(2*ran.scal)*random$sigma$fun(the.wk[-(1:nq)],random$sigma$env)
}
alpha.wk <- max(0,the.wk[1:nq]-log.th0-5)*(3-alpha) + alpha
alpha.wk <- min(alpha.wk,3)
z <- ngreg(dc,family,cbind(s,r.wk0),q.wk,y,wt,offset,nu.wk,alpha.wk)
assign("dc",z$dc,inherits=TRUE)
assign("fit",z[c(1:3,5:10)],inherits=TRUE)
if (family=="polr") assign("nu",z$nu,inherits=TRUE)
z$score
}
cv.wk <- function(theta) cv.scale*cv(theta)+cv.shift
## initialization
theta <- -log10(apply(r[id.basis,,],3,function(x)sum(diag(x))))
r.wk <- 0
for (i in 1:nq) {
r.wk <- r.wk + 10^theta[i]*r[,,i]
}
## theta adjustment
z <- sspngreg(family,s,r.wk,r.wk[id.basis,],y,wt,offset,alpha,nu,random)
if (nu[[2]]|(family=="polr")) nu[[1]] <- z$nu
theta <- theta + z$theta
r.wk <- 0
for (i in 1:nq) {
theta[i] <- 2*theta[i] + log10(t(z$c)%*%r[id.basis,,i]%*%z$c)
r.wk <- r.wk + 10^theta[i]*r[,,i]
}
log.la0 <- log10(sum(r.wk^2)/sum(diag(r.wk[id.basis,])))
log.th0 <- theta-log.la0
## lambda search
z <- sspngreg(family,s,r.wk,r.wk[id.basis,],y,wt,offset,alpha,nu,random)
if (nu[[2]]|(family=="polr")) nu[[1]] <- z$nu
nlambda <- z$nlambda
log.th0 <- log.th0 + z$lambda
theta <- theta + z$theta
if (!is.null(random)) ran.scal <- z$ran.scal
## early return
if (skip.iter) {
z$theta <- theta
return(z)
}
## theta search
dc <- rep(0,nn)
fit <- NULL
theta.old <- theta
if (family=="polr") {
if (is.null(wt)) P <- apply(y,2,sum)
else P <- apply(y*wt,2,sum)
P <- P/sum(P)
P <- cumsum(P)
nnu <- length(P)-2
dc[1] <- qlogis(P[1])
nu[[1]] <- diff(qlogis(P[-(nnu+2)]))
}
if (nu[[2]]) theta <- c(theta, log(nu[[1]]))
if (!is.null(random)) theta <- c(theta,z$zeta)
counter <- 0
r.wk <- 0
for (i in 1:nq) {
r.wk <- r.wk + 10^theta[i]*r[,,i]
}
tmp <- abs(cv(theta))
cv.scale <- 1
cv.shift <- 0
if (tmp<1&tmp>10^(-4)) {
cv.scale <- 10/tmp
cv.shift <- 0
}
if (tmp<10^(-4)) {
cv.scale <- 10^2
cv.shift <- 10
}
repeat {
zz <- nlm(cv.wk,theta,stepmax=1,ndigit=7)
if (zz$code<=3) break
theta <- zz$est
counter <- counter + 1
if (counter>=5) {
warning("gss warning in gssanova: iteration for model selection fails to converge")
break
}
}
## return
jk <- cv(zz$est)
if (nu[[2]]) {
nu.wk <- exp(zz$est[nq+1])
zz$est <- zz$est[-(nq+1)]
}
else nu.wk <- nu[[1]]
r.wk <- 0
for (i in 1:nq) {
r.wk <- r.wk + 10^zz$est[i]*r[,,i]
}
qq.wk <- r.wk[id.basis,]
if (is.null(random)) q.wk <- qq.wk
else {
r.wk <- cbind(r.wk,10^(ran.scal)*random$z)
q.wk <- matrix(0,nxiz,nxiz)
q.wk[1:nxi,1:nxi] <- qq.wk
q.wk[(nxi+1):nxiz,(nxi+1):nxiz] <-
10^(2*ran.scal-nlambda)*random$sigma$fun(zz$est[-(1:nq)],random$sigma$env)
}
se.aux <- regaux(sqrt(fit$w)*s,sqrt(fit$w)*r.wk,q.wk,nlambda,fit)
c <- fit$dc[nnull+(1:nxi)]
if (nnull) d <- fit$dc[1:nnull]
else d <- NULL
if (nz) b <- 10^(ran.scal)*fit$dc[nnull+nxi+(1:nz)]
else b <- NULL
c(list(theta=zz$est[1:nq],c=c,d=d,b=b,nlambda=nlambda,zeta=zz$est[-(1:nq)],nu=nu.wk),
fit[-1],list(se.aux=se.aux))
}
## Non-Gaussian regression with fixed smoothing parameters
ngreg <- function(dc,family,sr,q,y,wt,offset,nu,alpha)
{
nobs <- nrow(sr)
nn <- ncol(sr)
nxi <- nrow(q)
nnull <- nn - nxi
## initialization
cc <- dc[nnull+(1:nxi)]
eta <- as.vector(sr%*%dc)
if (!is.null(offset)) eta <- eta + offset
if ((family=="nbinomial")&is.vector(y)) y <- cbind(y,nu[[1]])
dev <- switch(family,
binomial=dev.resid.binomial(y,eta,wt),
polr=dev.resid.polr(y,eta,wt,nu[[1]]),
nbinomial=dev.resid.nbinomial(y,eta,wt),
poisson=dev.resid.poisson(y,eta,wt),
Gamma=dev.resid.Gamma(y,eta,wt),
inverse.gaussian=dev.resid.inverse.gaussian(y,eta,wt),
weibull=dev.resid.weibull(y,eta,wt,nu[[1]]),
lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]),
loglogis=dev0.resid.loglogis(y,eta,wt,nu[[1]]))
dev <- sum(dev) + t(cc)%*%q%*%cc
## Newton iteration
dc.new <- eta.new <- NULL
dev.line <- function(x) {
assign("dc.new",dc+c(x)*dc.diff,inherits=TRUE)
cc <- dc.new[nnull+(1:nxi)]
eta.wk <- as.vector(sr%*%dc.new)
if (!is.null(offset)) eta.wk <- eta.wk + offset
assign("eta.new",eta.wk,inherits=TRUE)
dev.wk <- switch(family,
binomial=dev.resid.binomial(y,eta.new,wt),
nbinomial=dev.resid.nbinomial(y,eta.new,wt),
polr=dev.resid.polr(y,eta.new,wt,nu[[1]]),
poisson=dev.resid.poisson(y,eta.new,wt),
Gamma=dev.resid.Gamma(y,eta.new,wt),
inverse.gaussian=dev.resid.inverse.gaussian(y,eta.new,wt),
weibull=dev.resid.weibull(y,eta.new,wt,nu[[1]]),
lognorm=dev0.resid.lognorm(y,eta.new,wt,nu[[1]]),
loglogis=dev0.resid.loglogis(y,eta.new,wt,nu[[1]]))
sum(dev.wk) + t(cc)%*%q%*%cc
}
iter <- 0
flag <- 0
flag2 <- 0
repeat {
iter <- iter+1
dat <- switch(family,
binomial=mkdata.binomial(y,eta,wt,offset),
nbinomial=mkdata.nbinomial(y,eta,wt,offset,nu),
polr=mkdata.polr(y,eta,wt,offset,nu[[1]]),
poisson=mkdata.poisson(y,eta,wt,offset),
Gamma=mkdata.Gamma(y,eta,wt,offset),
inverse.gaussian=mkdata.inverse.gaussian(y,eta,wt,offset),
weibull=mkdata.weibull(y,eta,wt,offset,nu),
lognorm=mkdata.lognorm(y,eta,wt,offset,nu),
loglogis=mkdata.loglogis(y,eta,wt,offset,nu))
if (family=="polr") nu[[1]] <- dat$nu
## weighted least squares fit
mumax <- 2*max(abs(t(sr)%*%dat$u+c(rep(0,nnull),q%*%dc[nnull+(1:nxi)])))
w <- as.vector(sqrt(dat$wt))
ywk <- w*dat$ywk
srwk <- w*sr
if (!is.finite(sum(w,ywk,srwk))) {
if (flag) stop("gss error in gssanova: Newton iteration diverges")
dc <- rep(0,nn)
eta <- rep(0,nobs)
if (family=="polr") {
if (is.null(wt)) P <- apply(y,2,sum)
else P <- apply(y*wt,2,sum)
P <- P/sum(P)
P <- cumsum(P)
nnu <- length(P)-2
dc[1] <- qlogis(P[1])
nu[[1]] <- diff(qlogis(P[-(nnu+2)]))
eta <- as.vector(sr%*%dc)
}
if (!is.null(offset)) eta <- eta + offset
dev <- switch(family,
binomial=dev.resid.binomial(y,eta,wt),
nbinomial=dev.resid.nbinomial(y,eta,wt),
polr=dev.resid.polr(y,eta,wt,nu[[1]]),
poisson=dev.resid.poisson(y,eta,wt),
Gamma=dev.resid.Gamma(y,eta,wt),
inverse.gaussian=dev.resid.inverse.gaussian(y,eta,wt),
weibull=dev.resid.weibull(y,eta,wt,nu[[1]]),
lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]),
loglogis=dev0.resid.loglogis(y,eta,wt,nu[[1]]))
dev <- sum(dev)
iter <- 0
flag <- 1
next
}
z <- .Fortran("reg",
as.double(srwk), as.integer(nobs), as.integer(nnull),
as.double(q), as.integer(nxi), as.double(ywk),
as.integer(4),
double(1), double(1), double(1), dc=double(nn),
as.double(.Machine$double.eps),
double(nn*nn), double(nn),
as.integer(c(rep(1,nnull),rep(0,nxi))),
double(max(nobs,nn)), integer(1), integer(1),
PACKAGE="gss")["dc"]
dc.diff <- z$dc-dc
repeat {
dev.new <- dev.line(1)
if (!is.finite(dev.new)) {
dc.diff <- dc.diff/2
next
}
if (!flag2) {
if (dev.new-dev<1e-7*(1+abs(dev))) break
}
zz <- nlm0(dev.line,c(0,1),1e-3)
dev.new <- dev.line(zz$est)
break
}
disc0 <- max((mumax/(1+eta))^2,abs(eta.new-eta)/(1+eta))
disc <- sum(dat$wt*((eta-eta.new)/(1+abs(eta)))^2)/sum(dat$wt)
if (!is.finite(disc)) {
if (flag) stop("gss error in gssanova: Newton iteration diverges")
dc <- rep(0,nn)
eta <- rep(0,nobs)
if (family=="polr") {
if (is.null(wt)) P <- apply(y,2,sum)
else P <- apply(y*wt,2,sum)
P <- P/sum(P)
P <- cumsum(P)
nnu <- length(P)-2
dc[1] <- qlogis(P[1])
nu[[1]] <- diff(qlogis(P[-(nnu+2)]))
eta <- as.vector(sr%*%dc)
}
if (!is.null(offset)) eta <- eta + offset
dev <- switch(family,
binomial=dev.resid.binomial(y,eta,wt),
nbinomial=dev.resid.nbinomial(y,eta,wt),
polr=dev.resid.polr(y,eta,wt,nu[[1]]),
poisson=dev.resid.poisson(y,eta,wt),
Gamma=dev.resid.Gamma(y,eta,wt),
inverse.gaussian=dev.resid.inverse.gaussian(y,eta,wt),
weibull=dev.resid.weibull(y,eta,wt,nu[[1]]),
lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]),
loglogis=dev0.resid.loglogis(y,eta,wt,nu[[1]]))
dev <- sum(dev)
iter <- 0
flag <- 1
next
}
dc <- dc.new
eta <- eta.new
dev <- dev.new
if (min(disc0,disc)<1e-7) break
if (iter<=30) next
if (!flag2) {
flag2 <- 1
iter <- 0
next
}
warning("gss warning in gssanova: Newton iteration fails to converge")
break
}
## calculate cv
dat <- switch(family,
binomial=mkdata.binomial(y,eta,wt,offset),
nbinomial=mkdata.nbinomial(y,eta,wt,offset,nu),
polr=mkdata.polr(y,eta,wt,offset,nu[[1]]),
poisson=mkdata.poisson(y,eta,wt,offset),
Gamma=mkdata.Gamma(y,eta,wt,offset),
inverse.gaussian=mkdata.inverse.gaussian(y,eta,wt,offset),
weibull=mkdata.weibull(y,eta,wt,offset,nu),
lognorm=mkdata.lognorm(y,eta,wt,offset,nu),
loglogis=mkdata.loglogis(y,eta,wt,offset,nu))
if (family=="polr") nu[[1]] <- dat$nu
## weighted least squares fit
w <- as.vector(sqrt(dat$wt))
ywk <- w*dat$ywk
srwk <- w*sr
z <- .Fortran("reg",
as.double(srwk), as.integer(nobs), as.integer(nnull),
as.double(q), as.integer(nxi), as.double(ywk),
as.integer(5),
double(1), double(1), double(1), dc=double(nn),
as.double(.Machine$double.eps),
chol=double(nn*nn), double(nn),
jpvt=as.integer(c(rep(1,nnull),rep(0,nxi))),
hat=double(max(nobs+1,nn)), rkv=integer(1), integer(1),
PACKAGE="gss")[c("dc","chol","jpvt","hat","rkv")]
cv <- switch(family,
binomial=cv.binomial(y,eta,wt,z$hat[1:nobs],alpha),
poisson=cv.poisson(y,eta,wt,z$hat[1:nobs],alpha,sr,q),
Gamma=cv.Gamma(y,eta,wt,z$hat[1:nobs],z$hat[nobs+1],alpha),
inverse.gaussian=cv.inverse.gaussian(y,eta,wt,z$hat[1:nobs],z$hat[nobs+1],alpha),
nbinomial=cv.nbinomial(y,eta,wt,z$hat[1:nobs],alpha),
polr=cv.polr(y,eta,wt,z$hat[1:nobs],nu[[1]],alpha),
weibull=cv.weibull(y,eta,wt,z$hat[1:nobs],nu[[1]],alpha),
lognorm=cv.lognorm(y,eta,wt,z$hat[1:nobs],nu[[1]],alpha),
loglogis=cv.loglogis(y,eta,wt,z$hat[1:nobs],nu[[1]],alpha))
c(z,cv,list(eta=eta,nu=nu))
}
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