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survey.R
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survey.R
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make.formula<-function(names) formula(paste("~",paste(names,collapse="+")))
dimnames.survey.design<-function(x) dimnames(x$variables)
dimnames.svyrep.design<-function(x) dimnames(x$variables)
dimnames.twophase<-function(x) dimnames(x$phase1$sample$variables)
oldsvydesign<-function(ids,probs=NULL,strata=NULL,variables=NULL, fpc=NULL,
data=NULL, nest=FALSE, check.strata=!nest,weights=NULL){
.Deprecated("svydesign")
## less memory-hungry version for sparse tables
interaction<-function (..., drop = TRUE) {
args <- list(...)
narg <- length(args)
if (narg == 1 && is.list(args[[1]])) {
args <- args[[1]]
narg <- length(args)
}
ls<-sapply(args,function(a) length(levels(a)))
ans<-do.call("paste",c(lapply(args,as.character),sep="."))
ans<-factor(ans)
return(ans)
}
na.failsafe<-function(object,...){
if (NCOL(object)==0)
object
else na.fail(object)
}
if(inherits(ids,"formula")) {
mf<-substitute(model.frame(ids,data=data,na.action=na.failsafe))
ids<-eval.parent(mf)
} else if (!is.null(ids))
ids<-na.fail(data.frame(ids))
if(inherits(probs,"formula")){
mf<-substitute(model.frame(probs,data=data,na.action=na.failsafe))
probs<-eval.parent(mf)
}
if(inherits(weights,"formula")){
mf<-substitute(model.frame(weights,data=data,na.action=na.failsafe))
weights<-eval.parent(mf)
} else if (!is.null(weights))
weights<-na.fail(data.frame(weights))
if(!is.null(weights)){
if (!is.null(probs))
stop("Can't specify both sampling weights and probabilities")
else
probs<-1/weights
}
if (!is.null(strata)){
if(inherits(strata,"formula")){
mf<-substitute(model.frame(strata,data=data, na.action=na.failsafe))
strata<-eval.parent(mf)
}
if(is.list(strata))
strata<-na.fail(do.call("interaction", strata))
if (!is.factor(strata))
strata<-factor(strata)
has.strata<-TRUE
} else {
strata<-factor(rep(1,NROW(ids)))
has.strata <-FALSE
}
if (inherits(variables,"formula")){
mf<-substitute(model.frame(variables,data=data,na.action=na.pass))
variables <- eval.parent(mf)
} else if (is.null(variables)){
variables<-data
} else
variables<-data.frame(variables)
if (inherits(fpc,"formula")){
mf<-substitute(model.frame(fpc,data=data,na.action=na.failsafe))
fpc<-eval.parent(mf)
if (length(fpc))
fpc<-fpc[,1]
}
if (is.null(ids) || NCOL(ids)==0)
ids<-data.frame(.id=seq(length=NROW(variables)))
## force subclusters nested in clusters
if (nest && NCOL(ids)>1){
N<-ncol(ids)
for(i in 2:(N)){
ids[,i]<-do.call("interaction", ids[,1:i,drop=TRUE])
}
}
## force clusters nested in strata
if (nest && has.strata && NCOL(ids)){
N<-NCOL(ids)
for(i in 1:N)
ids[,i]<-do.call("interaction", list(strata, ids[,i]))
}
## check if clusters nested in strata
if (check.strata && nest)
warning("No point in check.strata=TRUE if nest=TRUE")
if(check.strata && !is.null(strata) && NCOL(ids)){
sc<-rowSums(table(ids[,1],strata)>0)
if(any(sc>1)) stop("Clusters not nested in strata")
}
## Put degrees of freedom (# of PSUs in each stratum) in object, to
## allow subpopulations
if (NCOL(ids)){
nPSU<-table(strata[!duplicated(ids[,1])])
}
if (!is.null(fpc)){
if (NCOL(ids)>1){
if (all(fpc<1))
warning("FPC is not currently supported for multi-stage sampling")
else
stop("Can't compute FPC from population size for multi-stage sampling")
}
## Finite population correction: specified per observation
if (is.numeric(fpc) && length(fpc)==NROW(variables)){
tbl<-by(fpc,list(strata),unique)
if (any(sapply(tbl,length)!=1))
stop("fpc not constant within strata")
fpc<-data.frame(strata=factor(rownames(tbl),levels=levels(strata)),
N=as.vector(tbl))
}
## Now reduced to fpc per stratum
nstr<-table(strata[!duplicated(ids[[1]])])
if (all(fpc[,2]<=1)){
fpc[,2]<- nstr[match(as.character(fpc[,1]), names(nstr))]/fpc[,2]
} else if (any(fpc[,2]<nstr[match(as.character(fpc[,1]), names(nstr))]))
stop("Over 100% sampling in some strata")
}
## if FPC specified, but no weights, use it for weights
if (is.null(probs) && is.null(weights) && !is.null(fpc)){
pstr<-nstr[match(as.character(fpc[,1]), names(nstr))]/fpc[,2]
probs<-pstr[match(as.character(strata),as.character(fpc[,1]))]
probs<-as.vector(probs)
}
certainty<-rep(FALSE,length(unique(strata)))
names(certainty)<-as.character(unique(strata))
if (any(nPSU==1)){
## lonely PSUs: are they certainty PSUs?
if (!is.null(fpc)){
certainty<- fpc$N < 1.01
names(certainty)<-as.character(fpc$strata)
} else if (all(as.vector(probs)<=1)){
certainty<- !is.na(match(as.character(unique(strata)),as.character(strata)[probs > 0.99]))
names(certainty)<-as.character(unique(strata))
} else {
warning("Some strata have only one PSU and I can't tell if they are certainty PSUs")
}
}
if (is.numeric(probs) && length(probs)==1)
probs<-rep(probs, NROW(variables))
if (length(probs)==0) probs<-rep(1,NROW(variables))
if (NCOL(probs)==1) probs<-data.frame(probs)
rval<-list(cluster=ids)
rval$strata<-strata
rval$has.strata<-has.strata
rval$prob<- apply(probs,1,prod)
rval$allprob<-probs
rval$call<-match.call()
rval$variables<-variables
rval$fpc<-fpc
rval$certainty<-certainty
rval$call<-sys.call()
rval$nPSU<-nPSU
class(rval)<-"survey.design"
rval
}
print.survey.design<-function(x,varnames=FALSE,design.summaries=FALSE,...){
.svycheck(x)
n<-NROW(x$cluster)
if (x$has.strata) cat("Stratified ")
un<-length(unique(x$cluster[,1]))
if(n==un){
cat("Independent Sampling design\n")
is.independent<-TRUE
} else {
cat(NCOL(x$cluster),"- level Cluster Sampling design\n")
nn<-lapply(x$cluster,function(i) length(unique(i)))
cat(paste("With (",paste(unlist(nn),collapse=", "),") clusters.\n",sep=""))
is.independent<-FALSE
}
print(x$call)
if (design.summaries){
cat("Probabilities:\n")
print(summary(x$prob))
if(x$has.strata){
cat("Stratum sizes: \n")
a<-rbind(obs=table(x$strata),
design.PSU=x$nPSU,
actual.PSU=if(!is.independent || !is.null(x$fpc))
table(x$strata[!duplicated(x$cluster[,1])]))
print(a)
}
if (!is.null(x$fpc)){
if (x$has.strata) {
cat("Population stratum sizes (PSUs): \n")
print(x$fpc)
} else {
cat("Population size (PSUs):",x$fpc[,2],"\n")
}
}
}
if (varnames){
cat("Data variables:\n")
print(names(x$variables))
}
invisible(x)
}
"[.survey.design"<-function (x,i, ...){
if (!missing(i)){
if (is.calibrated(x)){
tmp<-x$prob[i,]
x$prob<-rep(Inf, length(x$prob))
x$prob[i,]<-tmp
} else {
x$variables<-"[.data.frame"(x$variables,i,...,drop=FALSE)
x$cluster<-x$cluster[i,,drop=FALSE]
x$prob<-x$prob[i]
x$allprob<-x$allprob[i,,drop=FALSE]
x$strata<-x$strata[i]
}
} else {
x$variables<-x$variables[,...,drop=FALSE]
}
x
}
"[<-.survey.design"<-function(x, ...,value){
if (inherits(value, "survey.design"))
value<-value$variables
x$variables[...]<-value
x
}
dim.survey.design<-function(x){
dim(x$variables)
}
na.fail.survey.design<-function(object,...){
tmp<-na.fail(object$variables,...)
object
}
na.omit.survey.design<-function(object,...){
tmp<-na.omit(object$variables,...)
omit<-attr(tmp,"na.action")
if (length(omit)){
object<-object[-omit,]
object$variables<-tmp
attr(object,"na.action")<-omit
}
object
}
na.exclude.survey.design<-function(object,...){
tmp<-na.exclude(object$variables,...)
exclude<-attr(tmp,"na.action")
if (length(exclude)){
object<-object[-exclude,]
object$variables<-tmp
attr(object,"na.action")<-exclude
}
object
}
update.survey.design<-function(object,...){
dots<-substitute(list(...))[-1]
newnames<-names(dots)
for(j in seq(along=dots)){
object$variables[,newnames[j]]<-eval(dots[[j]],object$variables, parent.frame())
}
object$call<-sys.call(-1)
object
}
subset.survey.design<-function(x,subset,...){
e <- substitute(subset)
r <- eval(e, x$variables, parent.frame())
r <- r & !is.na(r)
x<-x[r,]
x$call<-sys.call(-1)
x
}
summary.survey.design<-function(object,...){
class(object)<-"summary.survey.design"
object
}
print.summary.survey.design<-function(x,...){
y<-x
class(y)<-"survey.design"
print(y,varnames=TRUE,design.summaries=TRUE,...)
}
postStratify.survey.design<-function(design, strata, population, partial=FALSE,...){
if(inherits(strata,"formula")){
mf<-substitute(model.frame(strata, data=design$variables,na.action=na.fail))
strata<-eval.parent(mf)
}
strata<-as.data.frame(strata)
sampletable<-xtabs(I(1/design$prob)~.,data=strata)
sampletable<-as.data.frame(sampletable)
if (inherits(population,"table"))
population<-as.data.frame(population)
else if (is.data.frame(population))
population$Freq <- as.vector(population$Freq) ##allows Freq computed by tapply()
else
stop("population must be a table or dataframe")
if (!all(names(strata) %in% names(population)))
stop("Stratifying variables don't match")
nn<- names(population) %in% names(strata)
if (sum(!nn)!=1)
stop("stratifying variables don't match")
names(population)[which(!nn)]<-"Pop.Freq"
both<-merge(sampletable, population, by=names(strata), all=TRUE)
samplezero <- both$Freq %in% c(0, NA)
popzero <- both$Pop.Freq %in% c(0, NA)
both<-both[!(samplezero & popzero),]
if (any(onlysample<- popzero & !samplezero)){
print(both[onlysample,])
stop("Strata in sample absent from population. This Can't Happen")
}
if (any(onlypop <- samplezero & !popzero)){
if (partial){
both<-both[!onlypop,]
warning("Some strata absent from sample: ignored")
} else {
print(both[onlypop,])
stop("Some strata absent from sample: use partial=TRUE to ignore them.")
}
}
reweight<-both$Pop.Freq/both$Freq
both$label <- do.call("interaction", list(both[,names(strata)]))
designlabel <- do.call("interaction", strata)
index<-match(designlabel, both$label)
attr(index,"oldweights")<-1/design$prob
design$prob<-design$prob/reweight[index]
attr(index,"weights")<-1/design$prob
design$postStrata<-c(design$postStrata,list(index))
## Do we need to iterate here a la raking to get design strata
## and post-strata both balanced?
design$call<-sys.call(-1)
design
}
svyCprod<-function(x, strata, psu, fpc, nPSU, certainty=NULL, postStrata=NULL,
lonely.psu=getOption("survey.lonely.psu")){
x<-as.matrix(x)
n<-NROW(x)
## Remove post-stratum means, which may cut across PSUs
if(!is.null(postStrata)){
for (psvar in postStrata){
if (inherits(psvar, "greg_calibration") || inherits(psvar, "raking"))
stop("rake() and calibrate() not supported for old-style design objects")
psw<-attr(psvar,"weights")
psmeans<-rowsum(x/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar)))
x<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw
}
}
##First collapse over PSUs
if (is.null(strata)){
strata<-rep("1",n)
if (!is.null(nPSU))
names(nPSU)<-"1"
}
else
strata<-as.character(strata) ##can't use factors as indices in for()'
if (is.null(certainty)){
certainty<-rep(FALSE,length(strata))
names(certainty)<-strata
}
if (!is.null(psu)){
x<-rowsum(x, psu, reorder=FALSE)
strata<-strata[!duplicated(psu)]
n<-NROW(x)
}
if (!is.null(nPSU)){
obsn<-table(strata)
dropped<-nPSU[match(names(obsn),names(nPSU))]-obsn
if(sum(dropped)){
xtra<-matrix(0,ncol=NCOL(x),nrow=sum(dropped))
strata<-c(strata,rep(names(dropped),dropped))
if(is.matrix(x))
x<-rbind(x,xtra)
else
x<-c(x,xtra)
n<-NROW(x)
}
} else obsn<-table(strata)
if(is.null(strata)){
x<-t(t(x)-colMeans(x))
} else {
strata.means<-drop(rowsum(x,strata, reorder=FALSE))/drop(rowsum(rep(1,n),strata, reorder=FALSE))
if (!is.matrix(strata.means))
strata.means<-matrix(strata.means, ncol=NCOL(x))
x<- x- strata.means[ match(strata, unique(strata)),,drop=FALSE]
}
p<-NCOL(x)
v<-matrix(0,p,p)
ss<-unique(strata)
for(s in ss){
this.stratum <- strata %in% s
## original number of PSUs in this stratum
## before missing data/subsetting
this.n <-nPSU[match(s,names(nPSU))]
this.df <- this.n/(this.n-1)
if (is.null(fpc))
this.fpc <- 1
else{
this.fpc <- fpc[,2][ fpc[,1]==as.character(s)]
this.fpc <- (this.fpc - this.n)/this.fpc
}
xs<-x[this.stratum,,drop=FALSE]
this.certain<-certainty[names(certainty) %in% s]
## stratum with only 1 design cluster leads to undefined variance
lonely.psu<-match.arg(lonely.psu, c("remove","adjust","fail",
"certainty","average"))
if (this.n==1 && !this.certain){
this.df<-1
if (lonely.psu=="fail")
stop("Stratum ",s, " has only one sampling unit.")
else if (lonely.psu!="certainty")
warning("Stratum ",s, " has only one sampling unit.")
if (lonely.psu=="adjust")
xs<-strata.means[match(s,ss),,drop=FALSE]
} else if (obsn[match(s,names(obsn))]==1 && !this.certain){
## stratum with only 1 cluster left after subsetting
warning("Stratum ",s," has only one PSU in this subset.")
if (lonely.psu=="adjust")
xs<-strata.means[match(s,ss),,drop=FALSE]
}
## add it up
if (!this.certain)
v<-v+crossprod(xs)*this.df*this.fpc
}
if (lonely.psu=="average"){
v<- v/(1-mean(obsn==1 & !certainty))
}
v
}
svymean<-function(x, design,na.rm=FALSE,...){
.svycheck(design)
UseMethod("svymean",design)
}
svymean.survey.design<-function(x,design, na.rm=FALSE,deff=FALSE, influence=FALSE,...){
if (!inherits(design,"survey.design"))
stop("design is not a survey design")
if (inherits(x,"formula")){
## do the right thing with factors
mf<-model.frame(x,design$variables,na.action=na.pass)
xx<-lapply(attr(terms(x),"variables")[-1],
function(tt) model.matrix(eval(bquote(~0+.(tt))),mf))
cols<-sapply(xx,NCOL)
x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols))
scols<-c(0,cumsum(cols))
for(i in 1:length(xx)){
x[,scols[i]+1:cols[i]]<-xx[[i]]
}
colnames(x)<-do.call("c",lapply(xx,colnames))
}
else if(typeof(x) %in% c("expression","symbol"))
x<-eval(x, design$variables)
x<-as.matrix(x)
if (na.rm){
nas<-rowSums(is.na(x))
design<-design[nas==0,]
x<-x[nas==0,,drop=FALSE]
}
pweights<-1/design$prob
psum<-sum(pweights)
average<-colSums(x*pweights/psum)
x<-sweep(x,2,average)
v<-svyCprod(x*pweights/psum,design$strata,design$cluster[[1]], design$fpc,
design$nPSU,design$certainty, design$postStrata)
attr(average,"var")<-v
attr(average,"statistic")<-"mean"
if(influence)
attr(average, "influence")<-x*pweights/psum
class(average)<-"svystat"
if (is.character(deff) || deff){
nobs<-NROW(design$cluster)
vsrs<-svyvar(x,design,na.rm=na.rm)/nobs
vsrs<-vsrs*(psum-nobs)/psum
attr(average, "deff")<-v/vsrs
}
return(average)
}
print.svystat<-function(x,...){
vv<-attr(x,"var")
if (is.matrix(vv))
m<-cbind(x,sqrt(diag(vv)))
else
m<-cbind(x,sqrt(vv))
hasdeff<-!is.null(attr(x,"deff"))
if (hasdeff) {
m<-cbind(m,deff(x))
colnames(m)<-c(attr(x,"statistic"),"SE","DEff")
} else {
colnames(m)<-c(attr(x,"statistic"),"SE")
}
printCoefmat(m)
}
as.data.frame.svystat<-function(x,...){
rval<-data.frame(statistic=coef(x),SE=SE(x))
names(rval)[1]<-attr(x,"statistic")
if (!is.null(attr(x,"deff")))
rval<-cbind(rval,deff=deff(x))
rval
}
coef.svystat<-function(object,...){
attr(object,"statistic")<-NULL
attr(object,"deff")<-NULL
attr(object,"var")<-NULL
unclass(object)
}
vcov.svystat<-function(object,...){
as.matrix(attr(object,"var"))
}
influence.svystat<-function(model,...){
attr(model,"influence")
}
SE.svystat<-function(object,...){
v<-vcov(object)
if (!is.matrix(v) || NCOL(v)==1) sqrt(v) else sqrt(diag(v))
}
deff <- function(object,quietly=FALSE,...) UseMethod("deff")
deff.default <- function(object, quietly=FALSE,...){
rval<-attr(object,"deff")
if (is.null(rval)) {
if(!quietly)
warning("object has no design effect information")
} else rval<-diag(as.matrix(rval))
rval
}
cv<-function(object,...) UseMethod("cv")
cv.default<-function(object, warn=TRUE, ...){
rval<-SE(object)/coef(object)
if (warn && any(coef(object)<0,na.rm=TRUE)) warning("CV may not be useful for negative statistics")
rval
}
svytotal<-function(x,design,na.rm=FALSE,...){
.svycheck(design)
UseMethod("svytotal",design)
}
svytotal.survey.design<-function(x,design, na.rm=FALSE, deff=FALSE,influence=FALSE,...){
if (!inherits(design,"survey.design"))
stop("design is not a survey design")
if (inherits(x,"formula")){
## do the right thing with factors
mf<-model.frame(x,design$variables,na.action=na.pass)
xx<-lapply(attr(terms(x),"variables")[-1],
function(tt) model.matrix(eval(bquote(~0+.(tt))),mf))
cols<-sapply(xx,NCOL)
x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols))
scols<-c(0,cumsum(cols))
for(i in 1:length(xx)){
x[,scols[i]+1:cols[i]]<-xx[[i]]
}
colnames(x)<-do.call("c",lapply(xx,colnames))
} else if(typeof(x) %in% c("expression","symbol"))
x<-eval(x, design$variables)
x<-as.matrix(x)
if (na.rm){
nas<-rowSums(is.na(x))
design<-design[nas==0,]
x<-x[nas==0,,drop=FALSE]
}
N<-sum(1/design$prob)
m <- svymean(x, design, na.rm=na.rm)
total<-m*N
attr(total, "var")<-v<-svyCprod(x/design$prob,design$strata,
design$cluster[[1]], design$fpc,
design$nPSU,design$certainty,design$postStrata)
attr(total,"statistic")<-"total"
if (influence){
attr(total,"influence")<-x/design$prob
}
if (is.character(deff) || deff){
vsrs<-svyvar(x,design)*sum(weights(design)^2)
vsrs<-vsrs*(N-NROW(design$cluster))/N
attr(total,"deff")<-v/vsrs
}
return(total)
}
svyvar<-function(x, design, na.rm=FALSE,...){
.svycheck(design)
UseMethod("svyvar",design)
}
svyvar.survey.design<-function(x, design, na.rm=FALSE,...){
if (inherits(x,"formula"))
x<-model.frame(x,model.frame(design),na.action=na.pass)
else if(typeof(x) %in% c("expression","symbol"))
x<-eval(x, design$variables)
n<-sum((weights(design,"sampling")!=0) & (rowSums(is.na(as.matrix(x)))==0))
xbar<-svymean(x,design, na.rm=na.rm)
if(NCOL(x)==1) {
if(n==1){
v<-NA
attr(v,"statistic")<-NA
attr(v,"var")<-NA
class(v)<-"svystat"
return(v)
}
x<-x-xbar
v<-svymean(x*x*n/(n-1),design, na.rm=na.rm)
attr(v,"statistic")<-"variance"
return(v)
}
x<-t(t(x)-xbar)
p<-NCOL(x)
a<-matrix(rep(x,p),ncol=p*p)
b<-x[,rep(1:p,each=p)]
## Kish uses the n-1 divisor, so it affects design effects
v<-svymean(a*b*n/(n-1),design, na.rm=na.rm)
vv<-matrix(v,ncol=p)
dimnames(vv)<-list(names(xbar),names(xbar))
attr(vv,"var")<-attr(v,"var")
attr(vv,"statistic")<-"variance"
class(vv)<-c("svyvar","svystat")
vv
}
print.svyvar<-function (x, covariance=FALSE, ...)
{
if(!is.matrix(x)) NextMethod()
vv <- attr(x, "var")
if (covariance){
nms<-outer(rownames(x),colnames(x),paste,sep=":")
m<-cbind(as.vector(x), sqrt(diag(vv)))
rownames(m)<-nms
} else{
ii <- which(diag(sqrt(length(x)))>0)
m <- cbind(x[ii], sqrt(diag(vv))[ii])
}
colnames(m) <- c(attr(x, "statistic"), "SE")
printCoefmat(m)
}
as.matrix.svyvar<-function(x,...) unclass(x)
coef.svyratio<-function(object,...,drop=TRUE){
if (!drop) return(object$ratio)
cf<-as.vector(object$ratio)
nms<-as.vector(outer(rownames(object$ratio),colnames(object$ratio),paste,sep="/"))
names(cf)<-nms
cf
}
SE.svyratio<-function(object,...,drop=TRUE){
if(!drop) return(sqrt(object$var))
se<-as.vector(sqrt(object$var))
nms<-as.vector(outer(rownames(object$ratio),colnames(object$ratio),paste,sep="/"))
names(se)<-nms
se
}
svyratio<-function(numerator,denominator, design,...){
.svycheck(design)
UseMethod("svyratio",design)
}
svyratio.survey.design<-function(numerator, denominator, design,...){
if (inherits(numerator,"formula"))
numerator<-model.frame(numerator,design$variables)
else if(typeof(numerator) %in% c("expression","symbol"))
numerator<-eval(numerator, design$variables)
if (inherits(denominator,"formula"))
denominator<-model.frame(denominator,design$variables)
else if(typeof(denominator) %in% c("expression","symbol"))
denominator<-eval(denominator, design$variables)
nn<-NCOL(numerator)
nd<-NCOL(denominator)
all<-cbind(numerator,denominator)
allstats<-svytotal(all,design)
rval<-list(ratio=outer(allstats[1:nn],allstats[nn+1:nd],"/"))
vars<-matrix(ncol=nd,nrow=nn)
for(i in 1:nn){
for(j in 1:nd){
r<-(numerator[,i]-rval$ratio[i,j]*denominator[,j])/sum(denominator[,j]/design$prob)
vars[i,j]<-svyCprod(r*1/design$prob, design$strata, design$cluster[[1]], design$fpc,
design$nPSU, design$certainty,design$postStrata)
}
}
colnames(vars)<-names(denominator)
rownames(vars)<-names(numerator)
rval$var<-vars
rval$call<-sys.call()
class(rval)<-"svyratio"
rval
}
print.svyratio_separate<-function(x,...){
cat("Stratified ratio estimate: ")
if (!is.null(x$call))
print(x$call)
else if (!is.null(attr(x,"call")))
print(attr(x$call))
for(r in x$ratios) {
print(r)
}
invisible(x)
}
print.svyratio<-function(x,...){
cat("Ratio estimator: ")
if (!is.null(x$call))
print(x$call)
else if(!is.null(attr(x,"call")))
print(attr(x,"call"))
cat("Ratios=\n")
print(x$ratio)
cat("SEs=\n")
print(sqrt(x$var))
invisible(NULL)
}
predict.svyratio<-function(object, total, se=TRUE,...){
if (se)
return(list(total=object$ratio*total,se=sqrt(object$var)*total))
else
return(object$ratio*total)
}
predict.svyratio_separate<-function(object, total, se=TRUE,...){
if (length(total)!=length(object$ratios))
stop("Number of strata differ in ratio object and totals.")
if (!is.null(names(total)) && !is.null(levels(object$strata))){
if (!setequal(names(total), levels(object$strata)))
warning("Names of strata differ in ratio object and totals")
else if (!all(names(total)==levels(object$strata))){
warning("Reordering supplied totals to make their names match the ratio object")
total<-total[match(names(total),levels(object$strata))]
}
}
totals<-mapply(predict, object=object$ratios, total=total,se=se,...,SIMPLIFY=FALSE)
if(se){
rval<-totals[[1]]$total
v<-totals[[1]]$se^2
for(ti in totals[-1]) {
rval<-rval+ti$total
v<-v+ti$se^2
}
list(total=rval,se=sqrt(v))
} else {
rval<-totals[[1]]
for (ti in totals[-1]) rval<-rval+ti
rval
}
}
cv.svyratio<-function(object,...){
sqrt(object$var)/object$ratio
}
svytable<-function(formula, design, ...){
UseMethod("svytable",design)
}
svytable.survey.design<-function(formula, design, Ntotal=NULL, round=FALSE,...){
if (!inherits(design,"survey.design")) stop("design must be a survey design")
weights<-1/design$prob
## unstratified or unadjusted
if (length(Ntotal)<=1 || !design$has.strata){
if (length(formula)==3)
tblcall<-bquote(xtabs(I(weights*.(formula[[2]]))~.(formula[[3]]), data=model.frame(design),...))
else
tblcall<-bquote(xtabs(weights~.(formula[[2]]), data=model.frame(design),...))
tbl<-eval(tblcall)
if (!is.null(Ntotal)) {
if(length(formula)==3)
tbl<-tbl/sum(Ntotal)
else
tbl<-tbl*sum(Ntotal)/sum(tbl)
}
if (round)
tbl<-round(tbl)
attr(tbl,"call")<-match.call()
class(tbl)<-c("svytable",class(tbl))
return(tbl)
}
## adjusted and stratified
if (length(formula)==3)
tblcall<-bquote(xtabs(I(weights*.(formula[[2]]))~design$strata[,1]+.(formula[[3]]), data=model.frame(design),...))
else
tblcall<-bquote(xtabs(weights~design$strata[,1]+.(formula[[2]]), data=model.frame(design),...))
tbl<-eval(tblcall)
ss<-match(sort(unique(design$strata[,1])), Ntotal[,1])
dm<-dim(tbl)
layer<-prod(dm[-1])
tbl<-sweep(tbl,1,Ntotal[ss, 2]/apply(tbl,1,sum),"*")
tbl<-apply(tbl, 2:length(dm), sum)
if (round)
tbl<-round(tbl)
class(tbl)<-c("svytable","xtabs", "table")
attr(tbl, "call")<-match.call()
tbl
}
svycoxph<-function(formula,design,subset=NULL,rescale=TRUE,...){
.svycheck(design)
UseMethod("svycoxph",design)
}
svycoxph.survey.design<-function(formula,design, subset=NULL, rescale=TRUE, ...){
subset<-substitute(subset)
subset<-eval(subset, model.frame(design),parent.frame())
if (!is.null(subset))
design<-design[subset,]
if(any(weights(design)<0)) stop("weights must be non-negative")
data<-model.frame(design)
g<-match.call()
g$formula<-eval.parent(g$formula)
g$design<-NULL
g$var<-NULL
g$rescale <- NULL
if (is.null(g$weights))
g$weights<-quote(.survey.prob.weights)
else
g$weights<-bquote(.survey.prob.weights*.(g$weights))
g[[1]]<-quote(coxph)
g$data<-quote(data)
g$subset<-quote(.survey.prob.weights>0)
g$model <- TRUE
##need to rescale weights for stability
## unless the user doesn't want to
if (rescale)
data$.survey.prob.weights<-(1/design$prob)/mean(1/design$prob)
else
data$.survey.prob.weights<-1/design$prob
if (!all(all.vars(formula) %in% names(data)))
stop("all variables must be in design= argument")
g<-with(list(data=data), eval(g))
if (inherits(g, "coxph.penal"))
warning("svycoxph does not support penalised terms")
g$call<-match.call()
g$call[[1]]<-as.name(.Generic)
g$printcall<-sys.call(-1)
g$printcall[[1]]<-as.name(.Generic)
class(g)<-c("svycoxph", class(g))
g$survey.design<-design
nas<-g$na.action
if (length(nas))
design<-design[-nas,]
## if there are betas...
if (length(coef(g))>0){
dbeta.subset<-resid(g,"dfbeta",weighted=TRUE)
if (nrow(design)==NROW(dbeta.subset)){
dbeta<-as.matrix(dbeta.subset)
} else {
dbeta<-matrix(0,ncol=NCOL(dbeta.subset),nrow=nrow(design))
dbeta[is.finite(design$prob),]<-dbeta.subset
}
if (!is.null(g$naive.var)) ## newer versions of survival switch to robust=TRUE with non-integer weights
g$inv.info<-g$naive.var
else
g$inv.info<-g$var
if (inherits(design,"survey.design2"))
g$var<-svyrecvar(dbeta, design$cluster,
design$strata, design$fpc,
postStrata=design$postStrata)
else if (inherits(design, "twophase"))
g$var<-twophasevar(dbeta, design)
else if(inherits(design, "twophase2"))
g$var<-twophase2var(dbeta, design)
else if(inherits(design, "pps"))
g$var<-ppsvar(dbeta,design)
else
g$var<-svyCprod(dbeta, design$strata,
design$cluster[[1]], design$fpc,design$nPSU,
design$certainty,design$postStrata)
g$wald.test<-coef(g)%*%solve(g$var,coef(g))
} else g$var<-matrix(ncol=0,nrow=0)
g$ll<-g$loglik
g$loglik<-NULL
g$rscore<-NULL