https://github.com/cran/rstpm2
Tip revision: e222ff8
pm2-3.R
## extension of ns() to include different boundary derivatives,
## centering and cure
nsx <-
function (x, df = NULL, knots = NULL, intercept = FALSE,
Boundary.knots = range(x),
derivs = if (cure) c(2,1) else c(2,2),
log=FALSE, # deprecated: only used in rstpm2:::stpm2Old
centre = FALSE, cure = FALSE, stata.stpm2.compatible=FALSE)
{
nx <- names(x)
x <- as.vector(x)
nax <- is.na(x)
if (nas <- any(nax))
x <- x[!nax]
if (!missing(Boundary.knots)) {
Boundary.knots <- sort(Boundary.knots)
outside <- (ol <- x < Boundary.knots[1L]) | (or <- x >
Boundary.knots[2L])
}
else outside <- FALSE
if (!missing(df) && missing(knots)) {
nIknots <- df - 1 - intercept + 4 - sum(derivs)
if (nIknots < 0) {
nIknots <- 0
warning("'df' was too small; have used ", 1 + intercept)
}
knots <- if (nIknots > 0) {
knots <- if (!cure)
seq.int(0, 1, length.out = nIknots + 2L)[-c(1L,
nIknots + 2L)]
else c(seq.int(0, 1, length.out = nIknots + 1L)[-c(1L,
nIknots + 1L)], 0.95)
if (!stata.stpm2.compatible)
stats::quantile(x[!outside], knots)
else stats::quantile(x[!outside], round(knots,2), type=2)
}
}
else nIknots <- length(knots)
Aknots <- sort(c(rep(Boundary.knots, 4L), knots))
if (any(outside)) {
basis <- array(0, c(length(x), nIknots + 4L))
if (any(ol)) {
k.pivot <- Boundary.knots[1L]
xl <- cbind(1, x[ol] - k.pivot)
tt <- spline.des(Aknots, rep(k.pivot, 2L), 4, c(0,
1))$design basis[ol, ] <- xl %*% tt } if (any(or)) { k.pivot <- Boundary.knots[2L] xr <- cbind(1, x[or] - k.pivot) tt <- spline.des(Aknots, rep(k.pivot, 2L), 4, c(0, 1))$design
basis[or, ] <- xr %*% tt
}
if (any(inside <- !outside))
basis[inside, ] <- spline.des(Aknots, x[inside],
4)$design } else basis <- spline.des(Aknots, x, 4)$design
const <- splineDesign(Aknots, rep(Boundary.knots, 3-derivs), 4, c(derivs[1]:2, derivs[2]:2))
if (!intercept) {
const <- const[, -1, drop = FALSE]
basis <- basis[, -1, drop = FALSE]
}
qr.const <- qr(t(const))
basis <- as.matrix((t(qr.qty(qr.const, t(basis))))[, -(1L:nrow(const)), drop = FALSE])
n.col <- ncol(basis)
if (nas) {
nmat <- matrix(NA, length(nax), n.col)
nmat[!nax, ] <- basis
basis <- nmat
}
dimnames(basis) <- list(nx, 1L:n.col)
if (centre) {
centreBasis <- nsx(centre,
knots=if (is.null(knots)) numeric(0) else knots,
Boundary.knots=Boundary.knots,
intercept=intercept, derivs=derivs, centre=FALSE, log=log)
oldAttributes <- attributes(basis)
basis <- t(apply(basis,1,function(x) x-centreBasis))
attributes(basis) <- oldAttributes
}
a <- list(degree = 3, knots = if (is.null(knots)) numeric(0) else knots,
Boundary.knots = Boundary.knots, intercept = intercept, derivs=derivs,
centre=centre, log=log)
attributes(basis) <- c(attributes(basis), a)
class(basis) <- c("nsx", "basis", "matrix")
basis
}
makepredictcall.nsx <-
function (var, call)
{
if (as.character(call)[1L] != "nsx")
return(call)
at <- attributes(var)[c("knots", "Boundary.knots", "intercept",
"derivs", "centre", "log")]
xxx <- call[1L:2]
xxx[names(at)] <- at
xxx
}
predict.nsx <-
function (object, newx, ...)
{
if (missing(newx))
return(object)
a <- c(list(x = newx), attributes(object)[c("knots", "Boundary.knots",
"intercept", "derivs", "centre", "log")])
do.call("nsx", a)
}
Shat <- function(obj)
{
## predicted survival for individuals (adjusted for covariates)
newobj = survfit(obj,se.fit=FALSE)
surv = newobj$surv rr = try(predict(obj,type="risk"),silent=TRUE) if (inherits(rr,"try-error")) rr <- 1 surv2 = surv[match(obj$y[,ncol(obj$y)-1],newobj$time)]
return(surv2^rr)
}
replaceCall=function(obj,old,new) {
if (is.atomic(obj) && length(obj)>1)
return(as.call(c(quote(c),lapply(as.list(obj),replaceCall,old,new))))
if (is.name(obj) || is.symbol(obj) || (is.atomic(obj) && length(obj)==1)) {
if (obj==old) return(new)
else return(obj)
}
##   if (length(obj)==1 && length(obj[[1]])==1) {
##     if (obj==old) return(new)
##     else return(obj)
##   }
as.call(lapply(obj,replaceCall,old,new))
}
replaceFormula=function(...) as.formula(replaceCall(...))
## replaceFormula(~f(a+b),quote(f),quote(g))
allCall=function(obj) {
if (is.atomic(obj) && length(obj)==1) return(obj)
if (is.atomic(obj) && length(obj)>1) return(as.call(c(quote(c),as.list(obj))))
if (is.name(obj) || is.symbol(obj)) return(obj)
as.call(lapply(obj,allCall))
}
## allCall(as.call(c(quote(ns),list(df=3,knots=c(1,2)))))[[2]]
vector2call=function(obj) {
if (is.atomic(obj) && length(obj)==1) return(obj)
if (is.atomic(obj) && length(obj)>1) return(as.call(c(quote(c),as.list(obj))))
if (is.name(obj) || is.symbol(obj)) return(obj)
lapply(obj,allCall) # is this correct?
}
## vector2call(list(df=3,knots=c(1,2)))
findSymbol <- function(obj,symbol) {
if (is.symbol(obj) && obj==symbol) TRUE else
if (is.symbol(obj)) FALSE else
if (is.atomic(obj)) FALSE else
Reduce(|,lapply(obj,findSymbol,symbol),FALSE)
}
rhs=function(formula)
if (length(formula)==3) formula[[3]] else formula[[2]]
lhs <- function(formula)
if (length(formula)==3) formula[[2]] else NULL
"rhs<-" = function(formula,value) {
newformula <- formula
newformula[[length(formula)]] <- value
newformula
}
"lhs<-" <- function(formula,value) {
if (length(formula)==2)
as.formula(as.call(c(formula[[1]],value,formula[[2]])))
else {
newformula <- formula
newformula[[2]] <- value
newformula
}
}

## numerically calculate the partial gradient \partial func_j \over \partial x_i
{
h <- .Machine$double.eps^(1/3)*ifelse(abs(x)>1,abs(x),1) temp <- x+h h.hi <- temp-x temp <- x-h h.lo <- x-temp twoeps <- h.hi+h.lo nx <- length(x) ny <- length(func(x,...)) if (ny==0L) stop("Length of function equals 0") df <- if(ny==1L) rep(NA, nx) else matrix(NA, nrow=nx,ncol=ny) for (i in 1L:nx) { hi <- lo <- x hi[i] <- x[i] + h.hi[i] lo[i] <- x[i] - h.lo[i] if (ny==1L) df[i] <- (func(hi, ...) - func(lo, ...))/twoeps[i] else df[i,] <- (func(hi, ...) - func(lo, ...))/twoeps[i] } return(df) } ## numerically calculate the gradient \partial func_i \over \partial x_i ## length(grad(func,x)) == length(func(x)) == length(x) grad1 <- function(func,x,...) { h <- .Machine$double.eps^(1/3)*ifelse(abs(x)>1,abs(x),1)
temp <- x+h
h.hi <- temp-x
temp <- x-h
h.lo <- x-temp
twoeps <- h.hi+h.lo
ny <- length(func(x,...))
if (ny==0L) stop("Length of function equals 0")
(func(x+h, ...) - func(x-h, ...))/twoeps
}
## predict lpmatrix for an lm object
lpmatrix.lm <-
function (object, newdata, na.action = na.pass) {
tt <- terms(object)
if (!inherits(object, "lm"))
warning("calling predict.lm(<fake-lm-object>) ...")
if (missing(newdata) || is.null(newdata)) {
X <- model.matrix(object)
}
else {
Terms <- delete.response(tt)
m <- model.frame(Terms, newdata, na.action = na.action,
xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) X <- model.matrix(Terms, m, contrasts.arg = object$contrasts)
}
X
}
## fun: takes coef as its first argument
## requires: coef() and vcov() on the object
numDeltaMethod <- function(object,fun,...) {
coef <- coef(object)
est <- fun(coef,...)
Sigma <- vcov(object)
## se.est <- as.vector(sqrt(diag(t(gd) %*% Sigma %*% gd)))
se.est <- as.vector(sqrt(colSums(gd* (Sigma %*% gd))))
data.frame(Estimate = est, SE = se.est)
}
"coef<-" <- function (x, value)
UseMethod("coef<-")
predictnl <- function (object, ...)
UseMethod("predictnl")
"coef<-.default" <- function(x,value) {
x$coefficients <- value x } predictnl.default <- function(object,fun,newdata=NULL,...) { ## link=c(I,log,sqrt),invlink=NULL ## link <- match.arg(link) ## if (is.null(invlink)) ## invlink <- switch(deparse(substitute(link)),I=I,log=exp,sqrt=function(x) x^2) if (is.null(newdata) && !is.null(object$data))
newdata <- object$data localf <- function(coef,...) { if ("coefficients" %in% names(object)) { object$coefficients <- coef
} else if ("coef" %in% names(object)) {
object$coef <- coef } else coef(object) <- coef fun(object,...) } numDeltaMethod(object,localf,newdata=newdata,...) } setMethod("predictnl", "mle2", function(object,fun,newdata=NULL,...) { if (is.null(newdata) && !is.null(object@data)) newdata <- object@data localf <- function(coef,...) { object@fullcoef <- coef # changed from predictnl.default() fun(object,...) } numDeltaMethod(object,localf,newdata=newdata,...) }) ## setMethod("predictnl", "mle", function(object,fun,...) ## { ## localf <- function(coef,...) ## { ## object@fullcoef = coef # changed from predictnl.default() ## fun(object,...) ## } ## numDeltaMethod(object,localf,...) ## }) predict.formula <- function(formula,data,newdata,na.action,type="model.matrix") { mf <- match.call(expand.dots = FALSE) type <- match.arg(type) m <- match(c("formula", "data", "na.action"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
mt <- attr(mf, "terms")
xlevels <-.getXlevels(mt, mf)
mfnew <- model.frame(mt, newdata, na.action=na.action, xlev=xlevels)
if (!is.null(cl <- attr(mt, "dataClasses"))) .checkMFClasses(cl, mfnew)
model.matrix(mt, mfnew, contrasts=contrasts)
}
%call+% <- function(left,right) call("+",left,right)
##
bread.stpm2 <- function (x, ...) {
rval <- vcov(x) * nrow(x@y)
dimnames(rval) <- list(names(coef(x)), names(coef(x)))
return(rval)
}
estfun.stpm2 <- function(obj, weighted=FALSE, ...) {
colnames(rr) <- names(coef(obj))
if (weighted)
rr <- rr * obj@weights
rr
}
applyTapplySum <- function(X,index) apply(X, 2, function(col) tapply(col, index, sum))
meat.stpm2 <-
function (x, adjust = FALSE, cluster=NULL, ...)
{
psi <- estfun.stpm2(x, ...)
k <- NCOL(psi)
n <- NROW(psi)
if (!is.null(cluster))
psi <- applyTapplySum(as.matrix(psi),cluster)
rval <- crossprod(as.matrix(psi))/n
rval <- n/(n - k) * rval
rownames(rval) <- colnames(rval) <- colnames(psi)
return(rval)
}
sandwich.stpm2 <-
{
if (is.function(meat.))
meat. <- meat.(x, ...)
n <- NROW(estfun.stpm2(x))
}
incrVar <- function(var,increment=1) {
##var <- deparse(substitute(var))
##function(data) "$<-"(data,var,"$"(data,var)+increment) # FAILS
n <- length(var)
if (n>1 && length(increment)==1)
increment <- rep(increment,n)
function(data) {
for (i in 1:n) {
data[[var[i]]] <- data[[var[i]]] + increment[i]
}
data
}
}
cloglog <- function(x) log(-log(x))
cexpexp <- function(x) exp(-exp(x))
setOldClass("terms")
setClassUnion("listOrNULL",c("list","NULL"))
setClassUnion("nameOrcall",c("name","call"))
setClassUnion("nameOrcallOrNULL",c("name","call","NULL"))
##setClassUnion("numericOrNULL",c("numeric","NULL"))
setOldClass("Surv")
setOldClass("lm")
expit <- function(x) {
ifelse(x==-Inf, 0, ifelse(x==Inf, 1, 1/(1+exp(-x))))
}
logit <- function(p) {
ifelse(p==0, -Inf, ifelse(p==1, Inf, log(p/(1-p))))
} # numerical safety for large values?
## check: weights
##
which.dim <- function (X, silent = TRUE)
{
stopifnot(is.matrix(X))
silent <- as.logical(silent)[1]
qr.X <- qr(X, tol = 1e-07, LAPACK = FALSE)
if (qr.X$rank == ncol(X)) return(TRUE) if (!silent) message(gettextf("design is column rank deficient so dropping %d coef", ncol(X) - qr.X$rank))
return(qr.X$pivot[1:qr.X$rank])
}

H=function(eta) exp(eta),
gradh=function(eta,etaD,obj) obj$XD*exp(eta)+obj$X*etaD*exp(eta),
gradH=function(eta,obj) obj$X*exp(eta)) link.PO <- list(link=function(S) -logit(S), ilink=function(eta) expit(-eta), H=function(eta) log(1+exp(eta)), h=function(eta,etaD) etaD*exp(eta)*expit(-eta), gradh=function(eta,etaD,obj) { etaD*exp(eta)*obj$X*expit(-eta) -
exp(2*eta)*obj$X*etaD*expit(-eta)^2 + exp(eta)*obj$XD*expit(-eta)
},
gradH=function(eta,obj) obj$X*exp(eta)*expit(-eta)) link.probit <- list(link=function(S) -qnorm(S), ilink=function(eta) pnorm(-eta), H=function(eta) -log(pnorm(-eta)), h=function(eta,etaD) dnorm(eta)/pnorm(-eta)*etaD, gradh=function(eta,etaD,obj) { -eta*obj$X*dnorm(eta)*etaD/pnorm(-eta) +
obj$X*dnorm(eta)^2/pnorm(-eta)^2*etaD + dnorm(eta)/pnorm(-eta)*obj$XD
},
gradH=function(eta,obj) obj$X*dnorm(eta)/pnorm(-eta)) link.AH <- list(link=function(S) -log(S), ilink=function(eta) exp(-eta), h=function(eta,etaD) etaD, H=function(eta) eta, gradh=function(eta,etaD,obj) obj$XD,
gradH=function(eta,obj) obj$X) link.AO <- function(theta) { # Aranda-Ordaz if (theta==0) { return(link.PH) } else { list(link = function(S) log((S^(-theta)-1)/theta), ilink = function(eta) exp(-log(theta*exp(eta)+1)/theta), H = function(eta) log(theta*exp(eta)+1)/theta, h = function(eta,etaD) exp(eta)*etaD/(theta*exp(eta)+1), gradH = function(eta,obj) exp(eta)*obj$X/(1+theta*exp(eta)),
eta <- as.vector(eta)
((theta*exp(2*eta)+exp(eta))*obj$XD+exp(eta)*etaD*obj$X) /
(theta*exp(eta)+1)^2
})
}
}

setClass("stpm2", representation(xlevels="list",
contrasts="listOrNULL",
terms="terms",
logli="function",
## weights="numericOrNULL",
lm="lm",
timeVar="character",
time0Var="character",
timeExpr="nameOrcall",
time0Expr="nameOrcallOrNULL",
delayed="logical",
interval="logical",
frailty="logical",
model.frame="list",
call.formula="formula",
x="matrix",
xd="matrix",
termsd="terms",
Call="call",
y="Surv",
args="list"
),
contains="mle2")
stpm2 <- function(formula, data, smooth.formula = NULL, smooth.args = NULL,
df = 3, cure = FALSE, logH.args = NULL, logH.formula = NULL,
tvc = NULL, tvc.formula = NULL,
control = list(parscale = 1, maxit = 300), init = NULL,
coxph.strata = NULL, weights = NULL, robust = FALSE, baseoff = FALSE,
bhazard = NULL, timeVar = "", time0Var = "", use.gr = TRUE,
reltol=1.0e-8, trace = 0,
frailty = !is.null(cluster) & !robust, cluster = NULL, logtheta=-6, nodes=9, RandDist=c("Gamma","LogN"), recurrent = FALSE,
adaptive = TRUE, maxkappa = 1e3, Z = ~1,
contrasts = NULL, subset = NULL, ...) {
RandDist <- match.arg(RandDist)
optimiser <- match.arg(optimiser)
use.gr <- TRUE # old code
## logH.formula and logH.args are deprecated
if (!is.null(smooth.formula) && is.null(logH.formula))
logH.formula <- smooth.formula
if (!is.null(smooth.args) && is.null(logH.args))
logH.args <- smooth.args
## parse the event expression
eventInstance <- eval(lhs(formula),envir=data)
stopifnot(length(lhs(formula))>=2)
eventExpr <- lhs(formula)[[length(lhs(formula))]]
delayed <- length(lhs(formula))>=4 # indicator for multiple times (cf. strictly delayed)
surv.type <- attr(eventInstance,"type")
if (surv.type %in% c("interval2","left","mstate"))
stop("stpm2 not implemented for Surv type ",surv.type,".")
counting <- attr(eventInstance,"type") == "counting"
interval <- attr(eventInstance,"type") == "interval"
timeExpr <- lhs(formula)[[if (delayed) 3 else 2]] # expression
if (timeVar == "")
timeVar <- all.vars(timeExpr)
## set up the formulae
if (is.null(logH.formula) && is.null(logH.args)) {
logH.args$df <- df if (cure) logH.args$cure <- cure
}
if (is.null(logH.formula))
logH.formula <- as.formula(call("~",as.call(c(quote(nsx),call("log",timeExpr),
vector2call(logH.args)))))
if (is.null(tvc.formula) && !is.null(tvc)) {
tvc.formulas <-
lapply(names(tvc), function(name)
call(":",
as.name(name),
as.call(c(quote(nsx),
call("log",timeExpr),
vector2call(if (cure) list(cure=cure,df=tvc[[name]]) else list(df=tvc[[name]])
)))))
if (length(tvc.formulas)>1)
tvc.formulas <- list(Reduce(%call+%, tvc.formulas))
tvc.formula <- as.formula(call("~",tvc.formulas[[1]]))
}
if (!is.null(tvc.formula)) {
rhs(logH.formula) <- rhs(logH.formula) %call+% rhs(tvc.formula)
}
if (baseoff)
rhs(logH.formula) <- rhs(tvc.formula)
full.formula <- formula
rhs(full.formula) <- rhs(formula) %call+% rhs(logH.formula)
##
## set up the data
## ensure that data is a data frame
## data <- get_all_vars(full.formula, data) # but this loses the other design information
## restrict to non-missing data (assumes na.action=na.omit)
.include <- apply(model.matrix(formula, data, na.action = na.pass), 1, function(row) !any(is.na(row))) &
!is.na(eval(eventExpr,data)) & !is.na(eval(timeExpr,data))
data <- data[.include, , drop=FALSE]
##
## parse the function call
Call <- match.call()
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset", "contrasts", "weights"),
names(mf), 0L)
mf <- mf[c(1L, m)]
##
## get variables
time <- eval(timeExpr, data, parent.frame())
time0Expr <- NULL # initialise
if (delayed) {
time0Expr <- lhs(formula)[[2]]
if (time0Var == "")
time0Var <- all.vars(time0Expr)
time0 <- eval(time0Expr, data, parent.frame())
}
event <- eval(eventExpr,data)
event <- event > min(event) ## ???
## setup for initial values
if (!interval) {
## Cox regression
coxph.call <- mf
coxph.call[[1L]] <- as.name("coxph")
coxph.strata <- substitute(coxph.strata)
if (!is.null(coxph.strata)) {
coxph.formula <- formula
rhs(coxph.formula) <- rhs(formula) %call+% call("strata",coxph.strata)
coxph.call$formula <- coxph.formula } coxph.call$model <- TRUE
coxph.obj <- eval(coxph.call, envir=parent.frame())
y <- model.extract(model.frame(coxph.obj),"response")
data$logHhat <- pmax(-18,link$link(Shat(coxph.obj)))
}
if (interval) {
## survref regression
survreg.call <- mf
survreg.call[[1L]] <- as.name("survreg")
survreg.obj <- eval(survreg.call, envir=parent.frame())
weibullShape <- 1/survreg.obj$scale weibullScale <- predict(survreg.obj) y <- model.extract(model.frame(survreg.obj),"response") data$logHhat <- pmax(-18,link$link(pweibull(time,weibullShape,weibullScale,lower.tail=FALSE))) } ## ## initial values and object for lpmatrix predictions lm.call <- mf lm.call[[1L]] <- as.name("lm") lm.formula <- full.formula lhs(lm.formula) <- quote(logHhat) # new response lm.call$formula <- lm.formula
dataEvents <- data[event,]
if (interval)
dataEvents <- data
lm.call$data <- quote(dataEvents) # events only lm.obj <- eval(lm.call) if (is.null(init)) { init <- coef(lm.obj) } ## ## set up mf and wt mt <- terms(lm.obj) mf <- model.frame(lm.obj) ## wt <- model.weights(lm.obj$model)
wt <- if (is.null(substitute(weights))) rep(1,nrow(data)) else eval(substitute(weights),data,parent.frame())
##
## XD matrix
lpfunc <- function(delta,fit,dataset,var) {
dataset[[var]] <- dataset[[var]]+delta
lpmatrix.lm(fit,dataset)
}
##
bhazard <- substitute(bhazard)
bhazard <- if (is.null(bhazard)) rep(0,nrow(data)) else eval(bhazard,data,parent.frame())
## initialise values specific to either delayed entry or interval-censored
ind0 <- FALSE
map0 <- 0L
which0 <- 0
wt0 <- 0
ttype <- 0
transX <- function(X, data) X
transXD <- function(XD) XD
if (!interval) { # surv.type %in% c("right","counting")
X <- lpmatrix.lm(lm.obj,data)
datat0 <- data
datat0[[timeVar]] <- 0
index0 <- which.dim(X - lpmatrix.lm(lm.obj,datat0))
transX <- function(X, data) {
datat0 <- data
datat0[[timeVar]] <- 0
Xt0 <- lpmatrix.lm(lm.obj,datat0)
(X - Xt0)[, index0, drop=FALSE]
}
transXD <- function(XD) XD[, index0, drop=FALSE]
init <- init[index0]
}
X <- transX(X,data)
XD <- transXD(matrix(XD,nrow=nrow(X)))
X1 <- matrix(0,nrow(X),ncol(X))
X0 <- matrix(0,1,ncol(X))
if (delayed && all(time0==0)) delayed <- FALSE # CAREFUL HERE: delayed redefined
if (delayed) {
ind0 <- time0>0
map0 <- vector("integer",nrow(X))
map0[ind0] <- as.integer(1:sum(ind0))
map0[!ind0] <- NaN
##which0 <- which(ind0)
which0 <- 1:nrow(X)
which0[!ind0] <- NaN
data0 <- data[ind0,,drop=FALSE] # data for delayed entry times
data0[[timeVar]] <- data0[[time0Var]]
X0 <- transX(lpmatrix.lm(lm.obj, data0), data0)
wt0 <- wt[ind0]
rm(data0)
}
} else { ## interval-censored
## ttime <- eventInstance[,1]
## ttime2 <- eventInstance[,2]
ttype <- eventInstance[,3]
X1 <- transX(lpmatrix.lm(lm.obj,data),data)
data0 <- data
data0[[timeVar]] <- data0[[time0Var]]
X <- transX(lpmatrix.lm(lm.obj, data0), data0)
XD <- transXD(matrix(XD,nrow=nrow(X)))
X0 <- matrix(0,nrow(X),ncol(X))
rm(data0)
}
if (frailty) {
init <- c(init,logtheta=logtheta)
}
if (!is.null(control) && "parscale" %in% names(control)) {
if (length(control$parscale)==1) control$parscale <- rep(control$parscale,length(init)) if (is.null(names(control$parscale)))
names(control$parscale) <- names(init) } parscale <- rep(if (is.null(control$parscale)) 1 else control$parscale,length=length(init)) names(parscale) <- names(init) args <- list(init=init,X=X,XD=XD,bhazard=bhazard,wt=wt,event=ifelse(event,1,0),time=time, delayed=delayed, interval=interval, X0=X0, wt0=wt0, X1=X1, parscale=parscale, reltol=reltol, kappa=1, trace = trace, oldcluster=cluster, cluster=if(!is.null(cluster)) as.vector(unclass(factor(cluster))) else NULL, map0 = map0 - 1L, ind0 = ind0, which0 = which0 - 1L, link=link.type, ttype=ttype, RandDist=RandDist, optimiser=optimiser, type=if (frailty && RandDist=="Gamma") "stpm2_gamma_frailty" else if (frailty && RandDist=="LogN") "stpm2_normal_frailty" else "stpm2", recurrent = recurrent, return_type="optim", transX=transX, transXD=transXD, maxkappa=maxkappa, Z.formula = Z, thetaAO = theta.AO) if (frailty) { rule <- fastGHQuad::gaussHermiteData(nodes) args$gauss_x <- rule$x args$gauss_w <- rule$w args$adaptive <- adaptive
args$Z <- model.matrix(Z, data) if (ncol(args$Z)>1) stop("Current implementation only allows for a single random effect")
args$Z <- as.vector(args$Z)
}
negll <- function(beta) {
localargs <- args
localargs$return_type <- "objective" localargs$init <- beta
return(.Call("model_output", localargs, package="rstpm2"))
}
localargs <- args
localargs$init <- beta localargs$return_type <- "gradient"
return(.Call("model_output", localargs, package="rstpm2"))
}
sapply(1:length(beta), function(i) {
betau <- betal <- beta
betau[i] <- beta[i]+eps
betal[i] <- beta[i]-eps
(negll(betau)-negll(betal))/2/eps
})
}
logli <- function(beta) {
localargs <- args
localargs$init <- beta localargs$return_type <- "li"
return(.Call("model_output", localargs, package="rstpm2"))
}
## MLE
fit <- .Call("model_output", args, package="rstpm2")
args$init <- coef <- as.vector(fit$coef)
args$kappa.final <- fit$kappa
hessian <- fit$hessian names(coef) <- rownames(hessian) <- colnames(hessian) <- names(init) mle2 <- if (use.gr) mle2(negll, coef, vecpar=TRUE, control=control, gr=gradnegll, ..., eval.only=TRUE) else mle2(negll, coef, vecpar=TRUE, control=control, ..., eval.only=TRUE) mle2@vcov <- if (!inherits(vcov <- try(solve(hessian)), "try-error")) vcov else matrix(NA,length(coef), length(coef)) mle2@details$convergence <- fit$fail # fit$itrmcd
out <- new("stpm2",
call = mle2@call,
call.orig = mle2@call,
coef = mle2@coef,
fullcoef = mle2@fullcoef,
vcov = mle2@vcov,
min = mle2@min,
details = mle2@details,
minuslogl = mle2@minuslogl,
method = mle2@method,
data = data,
formula = mle2@formula,
optimizer = "optim",
xlevels = .getXlevels(mt, mf),
##contrasts = attr(X, "contrasts"),
contrasts = contrasts,
logli = logli,
##weights = weights,
Call = Call,
terms = mt,
model.frame = mf,
lm = lm.obj,
timeVar = timeVar,
time0Var = time0Var,
timeExpr = timeExpr,
time0Expr = time0Expr,
delayed = delayed,
interval = interval,
frailty = frailty,
call.formula = formula,
x = X,
xd = XD,
termsd = mt, # wrong!
y = y,
args=args)
if (robust) # kludge
out@vcov <- sandwich.stpm2(out, cluster=cluster)
return(out)
}
## summary.mle is not exported from bbmle
.__C__summary.mle2 <- bbmle:::.__C__summary.mle2 # hack suggested from http://stackoverflow.com/questions/28871632/how-to-resolve-warning-messages-metadata-object-not-found-spatiallinesnull-cla
setClass("summary.stpm2", representation(frailty="logical",theta="list",wald="matrix"), contains="summary.mle2")
## setAs("summary.stpm2", "summary.mle2",
##       function(from,to) new("summary.mle2", call=from@call, coef=from@call, m2logL=from@m2logL))
## setMethod("show", "stpm2", function(object) show(as(object,"mle2")))
setMethod("summary", "stpm2",
function(object) {
newobj <- as(summary(as(object,"mle2")),"summary.stpm2")
newobj@frailty <- object@frailty
if (object@frailty) {
coef <- coef(newobj)
theta <- exp(coef[nrow(coef),1])
se.logtheta <- coef[nrow(coef),2]
se.theta <- theta*se.logtheta
test.statistic <- (1/se.logtheta)^2
p.value <- pchisq(test.statistic,df=1,lower.tail=FALSE)/2
newobj@theta <- list(theta=theta, se.theta=se.theta, p.value=p.value)
} else newobj@theta <- list()
newobj@wald <- matrix(NA,1,1) # needed by summary.pstpm2
newobj })
setMethod("show", "summary.stpm2",
function(object) {
show(as(object,"summary.mle2"))
if (object@frailty)
cat(sprintf("\ntheta=%g\tse=%g\tp=%g\n",
object@theta$theta,object@theta$se.theta,object@theta$p.value)) }) setMethod("predictnl", "stpm2", function(object,fun,newdata=NULL,link=c("I","log","cloglog","logit"),...) { link <- match.arg(link) invlinkf <- switch(link,I=I,log=exp,cloglog=cexpexp,logit=expit) linkf <- eval(parse(text=link)) if (is.null(newdata) && !is.null(object@data)) newdata <- object@data localf <- function(coef,...) { object@fullcoef = coef linkf(fun(object,...)) } dm <- numDeltaMethod(object,localf,newdata=newdata,...) out <- invlinkf(data.frame(Estimate=dm$Estimate,
lower=dm$Estimate-1.96*dm$SE,
upper=dm$Estimate+1.96*dm$SE))
## cloglog switches the bounds
out <- data.frame(Estimate=out$Estimate,lower=out$upper,upper=out$lower) return(out) }) ## setMethod("predict", "stpm2", function(object,newdata=NULL, type=c("surv","cumhaz","hazard","density","hr","sdiff","hdiff","loghazard","link","meansurv","meansurvdiff","odds","or","margsurv","marghaz","marghr","meanhaz"), grid=FALSE,seqLength=300, se.fit=FALSE,link=NULL,exposed=incrVar(var),var,...) { type <- match.arg(type) ## exposed is a function that takes newdata and returns the revised newdata ## var is a string for a variable that defines a unit change in exposure local <- function (object, newdata=NULL, type="surv", exposed) { args <- object@args tt <- object@terms link <- object@link if (is.null(newdata)) { ##mm <- X <- model.matrix(object) # fails (missing timevar) X <- object@x XD <- object@xd ##y <- model.response(object@model.frame) y <- object@y time <- as.vector(y[,ncol(y)-1]) } else { lpfunc <- function(delta,fit,data,var) { data[[var]] <- data[[var]]+delta lpmatrix.lm(fit,data) } X <- args$transX(lpmatrix.lm(object@lm, newdata), newdata)
XD <- args$transXD(matrix(XD,nrow=nrow(X))) ## resp <- attr(Terms, "variables")[attr(Terms, "response")] ## similarly for the derivatives if (type %in% c("hazard","hr","sdiff","hdiff","loghazard","or","marghaz","marghr")) { ## how to elegantly extract the time variable? ## timeExpr <- ## lhs(object@call.formula)[[length(lhs(object@call.formula))-1]] time <- eval(object@timeExpr,newdata) ## } ## if (object@delayed && !object@interval) { ## newdata0 <- newdata ## newdata0[[object@timeVar]] <- newdata[[object@time0Var]] ## X0 <- lpmatrix.lm(object@lm, newdata0) ## ## XD0 <- grad(lpfunc,0,object@lm,newdata,object@timeVar) ## ## XD0 <- matrix(XD0,nrow=nrow(X0)) ## } if (type %in% c("hr","sdiff","hdiff","meansurvdiff","or","marghr")) { if (missing(exposed)) stop("exposed needs to be specified for type in ('hr','sdiff','hdiff','meansurvdiff','or','marghr')") newdata2 <- exposed(newdata) X2 <- args$transX(lpmatrix.lm(object@lm, newdata2), newdata2)
XD2 <- args$transXD(matrix(XD2,nrow=nrow(X))) } } beta <- coef(object) if (object@frailty) { theta <- exp(beta[length(beta)]) beta <- beta[-length(beta)] gauss_x <- object@args$gauss_x
gauss_w <- object@args$gauss_w Z <- model.matrix(args$Z.formula, newdata)
if (ncol(Z)>1) stop("Current implementation only allows for a single random effect")
Z <- as.vector(Z)
}
eta <- as.vector(X %*% beta)
S <- link$ilink(eta) h <- link$h(eta,etaD)
if (any(h<0)) warning(sprintf("Predicted hazards less than zero (n=%i).",sum(h<0)))
H = link$H(eta) Sigma = vcov(object) if (type=="link") { return(eta) } if (type=="cumhaz") { ## if (object@delayed) { ## eta0 <- as.vector(X0 %*% beta) ## etaD0 <- as.vector(XD0 %*% beta) ## H0 <- link$H(eta0)
##     return(H - H0)
## }
## else
return(H)
}
if (type=="density")
return (S*h)
if (type=="surv") {
return(S)
}
if (type=="odds") { # delayed entry?
return((1-S)/S)
}
if (type=="sdiff")
return(link$ilink(as.vector(X2 %*% beta)) - S) if (type=="hazard") { return(h) } if (type=="loghazard") { return(log(h)) } if (type=="hdiff") { eta2 <- as.vector(X2 %*% beta) etaD2 <- as.vector(XD2 %*% beta) h2 <- link$h(eta2,etaD2)
return(h2 - h)
}
if (type=="hr") {
eta2 <- as.vector(X2 %*% beta)
h2 <- link$h(eta2,etaD2) return(h2/h) } if (type=="or") { S2 <- link$ilink(as.vector(X2 %*% beta))
return((1-S2)/S2/((1-S)/S))
}
if (type=="meansurv") {
return(mean(S))
}
if (type=="meanhaz") {
return(sum(h*S)/sum(S))
}
if (type=="meansurvdiff") {
eta2 <- as.vector(X2 %*% beta)
S2 <- link$ilink(eta2) return(mean(S2-S)) } if (type=="margsurv") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
if (object@args$RandDist=="Gamma") return((1+theta*H)^(-1/theta)) if (object@args$RandDist=="LogN") {
return(sapply(1:length(gauss_x),
function(i) link$ilink(eta+Z*sqrt(theta)*gauss_x[i])) %*% gauss_w / sum(gauss_w)) } } if (type=="marghaz") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
if (object@args$RandDist=="Gamma") { margsurv <- (1+theta*H)^(-1/theta) return(h*margsurv^theta) } if (object@args$RandDist=="LogN") {
return(sapply(1:length(gauss_x),
function(i) link$h(eta+Z*sqrt(theta)*gauss_x[i],etaD)) %*% gauss_w / sum(gauss_w)) } } if (type=="marghr") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
eta2 <- as.vector(X2 %*% beta)
if (object@args$RandDist=="Gamma") { H2 <- link$H(eta2)
h2 <- link$h(eta2,etaD2) margsurv <- (1+theta*H)^(-1/theta) marghaz <- h*margsurv^theta margsurv2 <- (1+theta*H2)^(-1/theta) marghaz2 <- h2*margsurv2^theta } if (object@args$RandDist=="LogN") {
marghaz <- sapply(1:length(gauss_x),
function(i) as.vector(link$h(eta+Z*sqrt(theta)*gauss_x[i],etaD))) %*% gauss_w / sum(gauss_w) marghaz2 <- sapply(1:length(gauss_x), function(i) as.vector(link$h(eta2+Z*sqrt(theta)*gauss_x[i],etaD2))) %*%
gauss_w / sum(gauss_w)
}
return(marghaz2/marghaz)
}
}
##debug(local)
if (is.null(newdata) && type %in% c("hr","sdiff","hdiff","meansurvdiff","or","marghr"))
stop("Prediction using type in ('hr','sdiff','hdiff','meansurvdiff','or','marghr') requires newdata to be specified.")
if (grid) {
Y <- object@y
event <- Y[,ncol(Y)]==1 | object@interval
time <- object@data[[object@timeVar]]
eventTimes <- time[event]
X <- seq(min(eventTimes),max(eventTimes),length=seqLength)[-1]
data.x <- data.frame(X)
names(data.x) <- object@timeVar
newdata <- merge(newdata,data.x)
}
pred <- if (!se.fit) {
local(object,newdata,type=type,exposed=exposed,
...)
}
else {
exposed=exposed,...)
}
attr(pred,"newdata") <- newdata
##if (grid) cbind(newdata,as.data.frame(pred)) else pred
return(pred)
})
##%c% <- function(f,g) function(...) g(f(...)) # function composition
setMethod("plot", signature(x="stpm2", y="missing"),
function(x,y,newdata,type="surv",
xlab=NULL,ylab=NULL,line.col=1,ci.col="grey",lty=par("lty"),
var=NULL,exposed=incrVar(var),...) {
y <- predict(x,newdata,type=if (type=="fail") "surv" else type,var=var,exposed=exposed,
grid=!(x@timeVar %in% names(newdata)), se.fit=ci)
if (type=="fail") y <- if (ci) data.frame(Estimate=1-y$Estimate, lower=1-y$upper, upper=1-y$lower) else data.frame(Estimate=1-y) if (is.null(xlab)) xlab <- deparse(x@timeExpr) if (is.null(ylab)) ylab <- switch(type,hr="Hazard ratio",hazard="Hazard",surv="Survival",density="Density", sdiff="Survival difference",hdiff="Hazard difference",cumhaz="Cumulative hazard", loghazard="log(hazard)",link="Linear predictor",meansurv="Mean survival", meansurvdiff="Difference in mean survival",odds="Odds",or="Odds ratio", margsurv="Marginal survival",marghaz="Marginal hazard",marghr="Marginal hazard ratio", haz="Hazard",fail="Failure", meanhaz="Mean hazard") xx <- attr(y,"newdata") xx <- eval(x@timeExpr,xx) # xx[,ncol(xx)] if (!add) matplot(xx, y, type="n", xlab=xlab, ylab=ylab, ...) if (ci) { polygon(c(xx,rev(xx)), c(y[,2],rev(y[,3])), col=ci.col, border=ci.col) lines(xx,y[,1],col=line.col,lty=lty) } else lines(xx,y,col=line.col,lty=lty) if (rug) { Y <- x@y eventTimes <- Y[Y[,ncol(Y)]==1,ncol(Y)-1] rug(eventTimes,col=line.col) } return(invisible(y)) }) if (FALSE) { lpfunc <- function(delta,fit,dataset,var) { dataset[[var]] <- dataset[[var]]+delta lpmatrix.lm(fit,dataset) } XD <- grad(lpfunc,0,lm.obj,data,timeVar) XD <- matrix(XD,nrow=nrow(X)) # XD <- grad1(lpfunc,data[[timeVar]]) } derivativeDesign <- function (functn, lower = -1, upper = 1, rule = NULL, ...) { pred <- if (length(list(...)) && length(formals(functn)) > 1) function(x) functn(x, ...) else functn if (is.null(rule)) rule <- ## gaussquad::legendre.quadrature.rules(20)[[20]] data.frame(x = c(0.993128599185095, 0.963971927277914, 0.912234428251326, 0.839116971822219, 0.746331906460151, 0.636053680726515, 0.510867001950827, 0.37370608871542, 0.227785851141646, 0.0765265211334977, -0.0765265211334974, -0.227785851141645, -0.373706088715418, -0.510867001950827, -0.636053680726516, -0.746331906460151, -0.839116971822219, -0.912234428251326, -0.963971927277913, -0.993128599185094), w = c(0.0176140071391522, 0.040601429800387, 0.0626720483341092, 0.0832767415767053, 0.101930119817241, 0.11819453196152, 0.131688638449176, 0.14209610931838, 0.149172986472603, 0.152753387130726, 0.152753387130726, 0.149172986472603, 0.142096109318381, 0.131688638449175, 0.11819453196152, 0.10193011981724, 0.0832767415767068, 0.0626720483341075, 0.0406014298003876, 0.0176140071391522)) lambda <- (upper - lower)/(2) mu <- (lower + upper)/(2) x <- lambda * rule$x + mu
w <- rule$w eps <- .Machine$double.eps^(1/8)
X0 <- pred(x)
X1 <- (-pred(x+2*eps)+8*pred(x+eps)-8*pred(x-eps)+pred(x-2*eps))/12/eps
X2 <- (-pred(x+2*eps)/12+4/3*pred(x+eps)-5/2*pred(x)+4/3*pred(x-eps)-pred(x-2*eps)/12)/eps/eps
X3 <- (-pred(x+3*eps)/8+pred(x+2*eps)-13/8*pred(x+eps)+
13/8*pred(x-eps)-pred(x-2*eps)+pred(x-3*eps)/8)/eps/eps/eps
return(list(x=x,w=w,lambda=lambda,X0=X0,X1=X1,X2=X2,X3=X3))
}
smootherDesign <- function(gamobj,data,parameters = NULL) {
d <- data[1,,drop=FALSE] ## how to get mean prediction values, particularly for factors?
makepred <- function(var,inverse) {
function(value) {
d <- d[rep(1,length(value)),]
d[[var]] <- inverse(value)
predict(gamobj,newdata=d,type="lpmatrix")
}
}
smoother.names <- sapply(gamobj$smooth, function(obj) obj$term)
lapply(1:length(gamobj$smooth), function(i) { smoother <- gamobj$smooth[[i]]
if (is.null(parameters)) {
var <- smoother$term stopifnot(var %in% names(data)) transform <- I inverse <- I } else { j <- match(smoother$term,names(parameters))
stopifnot(!is.na(j))
var <- parameters[[j]]$var transform <- parameters[[j]]$transform
inverse <- parameters[[j]]$inverse } pred <- makepred(var,inverse) derivativeDesign(pred, lower=transform(min(data[[var]])), upper=transform(max(data[[var]]))) }) } ## TODO: If we transform a smoother (e.g. log(time)), we can use information on ## (i) the variable name, (ii) the transform and (iii) the inverse transform. ## penalised stpm2 setOldClass("gam") setClass("pstpm2", representation(xlevels="list", contrasts="listOrNULL", terms="terms", logli="function", gam="gam", timeVar="character", time0Var="character", timeExpr="nameOrcall", time0Expr="nameOrcallOrNULL", like="function", model.frame="list", fullformula="formula", delayed="logical", frailty="logical", x="matrix", xd="matrix", termsd="terms", Call="call", y="Surv", sp="numeric", nevent="numeric", link="list", edf="numeric", edf_var="numeric", df="numeric", args="list"), contains="mle2") pstpm2 <- function(formula, data, smooth.formula = NULL, smooth.args = NULL, logH.args = NULL, tvc = NULL, control = list(parscale = 1, maxit = 300), init = NULL, coxph.strata = NULL, coxph.formula = NULL, weights = NULL, robust = FALSE, bhazard = NULL, timeVar = "", time0Var = "", sp=NULL, use.gr = TRUE, criterion=c("GCV","BIC"), penalty = c("logH","h"), smoother.parameters = NULL, alpha=if (is.null(sp)) switch(criterion,GCV=1,BIC=1) else 1, sp.init=1, trace = 0, link.type=c("PH","PO","probit","AH","AO"), theta.AO=0, optimiser=c("BFGS","NelderMead","Nlm"), recurrent = FALSE, frailty=!is.null(cluster) & !robust, cluster = NULL, logtheta=-6, nodes=9,RandDist=c("Gamma","LogN"), adaptive=TRUE, maxkappa = 1e3, Z = ~1, reltol = list(search = 1.0e-10, final = 1.0e-10, outer=1.0e-5),outer_optim=1, contrasts = NULL, subset = NULL, ...) { link.type <- match.arg(link.type) link <- switch(link.type,PH=link.PH,PO=link.PO,probit=link.probit,AH=link.AH,AO=link.AO(theta.AO)) RandDist <- match.arg(RandDist) optimiser <- match.arg(optimiser) ## logH.args is deprecated if (!is.null(smooth.args) && is.null(logH.args)) logH.args <- smooth.args ## set up the data ## ensure that data is a data frame temp.formula <- formula if (!is.null(smooth.formula)) rhs(temp.formula) <-rhs(temp.formula) %call+% rhs(smooth.formula) raw.data <- data ## data <- get_all_vars(temp.formula, raw.data) criterion <- match.arg(criterion) penalty <- match.arg(penalty) ## ## parse the function call Call <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "data", "subset", "contrasts", "weights"), names(mf), 0L) mf <- mf[c(1L, m)] ## ## parse the event expression eventExpr <- lhs(formula)[[length(lhs(formula))]] eventInstance <- eval(lhs(formula),envir=data) stopifnot(length(lhs(formula))>=2) delayed <- length(lhs(formula))>=4 surv.type <- attr(eventInstance,"type") if (surv.type %in% c("interval2","left","mstate")) stop("stpm2 not implemented for Surv type ",surv.type,".") interval <- attr(eventInstance,"type") == "interval" timeExpr <- lhs(formula)[[if (delayed) 3 else 2]] # expression if (timeVar == "") timeVar <- all.vars(timeExpr) ## restrict to non-missing data (assumes na.action=na.omit) .include <- apply(model.matrix(formula, data, na.action = na.pass), 1, function(row) !any(is.na(row))) & !is.na(eval(eventExpr,data)) & !is.na(eval(timeExpr,data)) data <- data[.include, , drop=FALSE] ### REPLACEMENT ### ## we can now evaluate over data time <- eval(timeExpr, data, parent.frame()) time0Expr <- NULL # initialise if (delayed) { time0Expr <- lhs(formula)[[2]] if (time0Var == "") time0Var <- all.vars(time0Expr) time0 <- eval(time0Expr, data, parent.frame()) } event <- eval(eventExpr,data,parent.frame()) event <- event > min(event) nevent <- sum(event) ## ## set up the formulae if (is.null(smooth.formula) && is.null(logH.args)) { logH.args$k <- -1
}
if (is.null(smooth.formula))
smooth.formula <- as.formula(call("~",as.call(c(quote(s),call("log",timeExpr),
vector2call(logH.args)))))
if (!is.null(tvc)) {
tvc.formulas <-
lapply(names(tvc), function(name)
call(":",
as.name(name),
as.call(c(quote(s),
call("log",timeExpr),
vector2call(list(k=tvc[[name]]))))))
if (length(tvc.formulas)>1)
tvc.formulas <- list(Reduce(%call+%, tvc.formulas))
tvc.formula <- as.formula(call("~",tvc.formulas[[1]]))
rhs(smooth.formula) <- rhs(smooth.formula) %call+% rhs(tvc.formula)
}
full.formula <- formula
rhs(full.formula) <- rhs(smooth.formula)
}
else{
rhs(full.formula) <- rhs(formula) %call+% rhs(smooth.formula)
}
##
left <- deparse(substitute(formula))
tf <- terms.formula(smooth.formula, specials = c("s", "te"))
terms <- attr(tf, "term.labels")
right <- paste0(terms, collapse = "+")
fullformula <- as.formula(paste0(left, "+", right), env = parent.frame())
if (!interval) {
## Cox regression
coxph.call <- mf
coxph.call[[1L]] <- as.name("coxph")
coxph.strata <- substitute(coxph.strata)
coxph.call$data <- quote(coxph.data) coxph.data <- data if (!is.null(coxph.formula)) { coxph.formula2 <- coxph.call$formula
rhs(coxph.formula2) <- rhs(formula) %call+% rhs(coxph.formula)
coxph.call$formula <- coxph.formula2 } if (!is.null(coxph.strata)) { coxph.formula2 <- coxph.call$formula
rhs(coxph.formula2) <- rhs(formula) %call+% call("strata",coxph.strata)
coxph.call$formula <- coxph.formula2 } coxph.call$model <- TRUE
## coxph.obj <- eval(coxph.call, envir=parent.frame())
coxph.obj <- eval(coxph.call, coxph.data)
y <- model.extract(model.frame(coxph.obj),"response")
data$logHhat <- pmax(-18,link$link(Shat(coxph.obj)))
}
if (interval) {
## survref regression
survreg.call <- mf
survreg.call[[1L]] <- as.name("survreg")
survreg.obj <- eval(survreg.call, envir=parent.frame())
weibullShape <- 1/survreg.obj$scale weibullScale <- predict(survreg.obj) y <- model.extract(model.frame(survreg.obj),"response") data$logHhat <- pmax(-18,link$link(pweibull(time,weibullShape,weibullScale,lower.tail=FALSE))) } ## ## initial values and object for lpmatrix predictions gam.call <- mf gam.call[[1L]] <- as.name("gam") gam.formula <- full.formula lhs(gam.formula) <- quote(logHhat) # new response gam.call$formula <- gam.formula
gam.call$sp <- sp if (is.null(sp) && !is.null(sp.init) && (length(sp.init)>1 || sp.init!=1)) gam.call$sp <- sp.init
dataEvents <- data[event,]
if (interval)
dataEvents <- data
gam.call$data <- quote(dataEvents) # events only gam.obj <- eval(gam.call) ## re-run gam if sp.init==1 (default) if (is.null(sp) && !is.null(sp.init) && length(sp.init)==1 && sp.init==1) { sp.init <- gam.call$sp <- rep(sp.init,length=length(gam.obj$sp)) gam.obj <- eval(gam.call) } ## ## set up X, mf and wt mt <- terms(gam.obj) mf <- model.frame(gam.obj) wt <- if (is.null(substitute(weights))) rep(1,nrow(data)) else eval(substitute(weights),data,parent.frame()) lpfunc <- function(x,...) { newdata <- data newdata[[timeVar]] <- x predict(gam.obj,newdata,type="lpmatrix") } ## bhazard <- substitute(bhazard) bhazard <- if (is.null(bhazard)) rep(0,nrow(data)) else eval(bhazard,data,parent.frame()) ## initialise values specific to either delayed entry or interval-censored ind0 <- FALSE map0 <- 0L which0 <- 0 wt0 <- 0 ttype <- 0 transX <- function(X, data) X transXD <- function(XD) XD ## browser() smooth <- gam.obj$smooth
if (!interval) { # surv.type %in% c("right","counting")
X <- predict(gam.obj,data,type="lpmatrix")
datat0 <- data
datat0[[timeVar]] <- 0
index0 <- which.dim(X - predict(gam.obj, datat0, type="lpmatrix"))
smooth <- lapply(smooth, function(smoothi) {
Sindex <- which((1:ncol(X) %in% index0)[smoothi$first.para:smoothi$last.para])
para <- range(which((1:ncol(X) %in% smoothi$first.para:smoothi$last.para)[index0]))
smoothi$S[[1]] <- smoothi$S[[1]][Sindex,Sindex]
smoothi$first.para <- para[1] smoothi$last.para <- para[2]
smoothi
})
transX <- function(X, data) {
datat0 <- data
datat0[[timeVar]] <- 0
Xt0 <- predict(gam.obj, datat0, type="lpmatrix")
(X - Xt0)[, index0, drop=FALSE]
}
transXD <- function(XD) XD[, index0, drop=FALSE]
## init <- init[index0]
}
X <- transX(X,data)
XD <- transXD(matrix(XD,nrow=nrow(X)))
X1 <- matrix(0,nrow(X),ncol(X))
X0 <- matrix(0,1,ncol(X))
if (delayed && all(time0==0)) delayed <- FALSE # CAREFUL HERE: delayed redefined
if (delayed) {
ind0 <- time0>0
map0 <- vector("integer",nrow(X))
map0[ind0] <- as.integer(1:sum(ind0))
map0[!ind0] <- NaN
## which0 <- which(ind0)
which0 <- 1:nrow(X)
which0[!ind0] <- NaN
data0 <- data[ind0,,drop=FALSE] # data for delayed entry times
data0[[timeVar]] <- data0[[time0Var]]
X0 <- transX(predict(gam.obj,data0,type="lpmatrix"), data0)
wt0 <- wt[ind0]
rm(data0)
}
} else { ## interval-censored
## ttime <- eventInstance[,1]
## ttime2 <- eventInstance[,2]
ttype <- eventInstance[,3]
X1 <- transX(predict(gam.obj,data,type="lpmatrix"), data)
data0 <- data
data0[[timeVar]] <- data0[[time0Var]]
lpfunc <- function(x,...) {
newdata <- data0
newdata[[timeVar]] <- x
predict(gam.obj,newdata,type="lpmatrix")
}
X <- transX(predict(gam.obj,data0,type="lpmatrix"), data0)
XD <- transXD(matrix(XD,nrow=nrow(X)))
X0 <- matrix(0,nrow(X),ncol(X))
rm(data0)
}
## initial values
if (is.null(init)) {
init <- coef(gam.obj)
}
init <- init[index0]
}
if (frailty) {
init <- c(init,logtheta=logtheta)
}
## smoothing parameters
## cases:
##  (1) sp fixed
##  (2) sp.init
##  (3) use GAM
if (no.sp <- is.null(sp)) {
sp <- if(is.null(gam.obj$full.sp)) gam.obj$sp else gam.obj$full.sp if (!is.null(sp.init)) sp <- sp.init } if (!is.null(control) && "parscale" %in% names(control)) { if (length(control$parscale)==1)
control$parscale <- rep(control$parscale,length(init))
if (is.null(names(control$parscale))) names(control$parscale) <- names(init)
} else {
if(is.null(control))
control <- list()
control$parscale <- rep(1,length(init)) names(control$parscale) <- names(init)
}
args <- list(init=init,X=X,XD=XD,bhazard=bhazard,wt=wt,event=ifelse(event,1,0),time=time,
delayed=delayed, interval=interval, X0=X0, wt0=wt0, X1=X1, parscale=control$parscale, smooth=if(penalty == "logH") smooth else design, sp=sp, reltol_search=reltol$search, reltol=reltol$final, reltol_outer=reltol$outer, trace=trace,
kappa=1.0,outer_optim=outer_optim,
alpha=alpha,criterion=switch(criterion,GCV=1,BIC=2),
oldcluster=cluster, cluster=if(!is.null(cluster)) as.vector(unclass(factor(cluster))) else NULL,
map0 = map0 - 1L, ind0 = ind0, which0=which0 - 1L, link = link.type,
penalty = penalty, ttype=ttype, RandDist=RandDist, optimiser=optimiser,
type=if (frailty && RandDist=="Gamma") "pstpm2_gamma_frailty" else if (frailty && RandDist=="LogN") "pstpm2_normal_frailty" else "pstpm2", recurrent = recurrent, maxkappa=maxkappa,
transX=transX, transXD=transXD, Z.formula = Z, thetaAO = theta.AO,
return_type="optim")
if (frailty) {
args$gauss_x <- rule$x
args$gauss_w <- rule$w
args$adaptive <- adaptive args$Z <- model.matrix(Z, data)
if (ncol(args$Z)>1) stop("Current implementation only allows for a single random effect") args$Z <- as.vector(args$Z) } ## penalty function pfun <- function(beta,sp) { sum(sapply(1:length(gam.obj$smooth),
function(i) {
smoother <- gam.obj$smooth[[i]] betai <- beta[smoother$first.para:smoother$last.para] sp[i]/2 * betai %*% smoother$S[[1]] %*% betai
}))
}
negllsp <- function(beta,sp) {
localargs <- args
localargs$sp <- sp localargs$init <- beta
localargs$return_type <- "objective" negll <- .Call("model_output", localargs, package="rstpm2") localargs$return_type <- "feasible"
feasible <- .Call("model_output", localargs, package="rstpm2")
attr(negll,"feasible") <- feasible
return(negll)
}
negll0sp <- function(beta,sp) {
localargs <- args
localargs$sp <- sp localargs$init <- beta
localargs$return_type <- "objective0" negll <- .Call("model_output", localargs, package="rstpm2") localargs$return_type <- "feasible"
feasible <- .Call("model_output", localargs, package="rstpm2")
attr(negll,"feasible") <- feasible
return(negll)
}
## unused?
dpfun <- function(beta,sp) {
deriv <- beta*0
for (i in 1:length(gam.obj$smooth)) { smoother <- gam.obj$smooth[[i]]
ind <- smoother$first.para:smoother$last.para
deriv[ind] <- sp[i] * smoother$S[[1]] %*% beta[ind] } return(deriv) } if (penalty == "h") { ## a current limitation is that the hazard penalty needs to extract the variable names from the smoother objects (e.g. log(time) will not work) stopifnot(sapply(gam.obj$smooth,function(obj) obj$term) %in% names(data) || !is.null(smoother.parameters)) ## new penalty using the second derivative of the hazard design <- smootherDesign(gam.obj,data,smoother.parameters) pfun <- function(beta,sp) { sum(sapply(1:length(design), function(i) { obj <- design[[i]] s0 <- as.vector(obj$X0 %*% beta)
s1 <- as.vector(obj$X1 %*% beta) s2 <- as.vector(obj$X2 %*% beta)
s3 <- as.vector(obj$X3 %*% beta) h2 <- (s3+3*s1*s2+s1^3)*exp(s0) sp[i]/2*obj$lambda*sum(obj$w*h2^2) })) } dpfun <- function(beta,sp) { if (frailty) beta <- beta[-length(beta)] deriv <- beta*0 for (i in 1:length(design)) { obj <- design[[i]] s0 <- as.vector(obj$X0 %*% beta)
s1 <- as.vector(obj$X1 %*% beta) s2 <- as.vector(obj$X2 %*% beta)
s3 <- as.vector(obj$X3 %*% beta) h2 <- (s3+3*s1*s2+s1^3)*exp(s0) dh2sq.dbeta <- 2*h2*(exp(s0)*(obj$X3+3*(obj$X1*s2+obj$X2*s1)+3*s1^2*obj$X1)+h2*obj$X0)
deriv <- deriv + sp[i]*obj$lambda*colSums(obj$w*dh2sq.dbeta)
}
deriv
}
}
localargs <- args
localargs$init <- beta localargs$return_type <- "gradient"
.Call("model_output", localargs, package="rstpm2")
}
localargs <- args
localargs$init <- beta localargs$return_type <- "gradient0"
.Call("model_output", localargs, package="rstpm2")
}
logli <- function(beta) {
localargs <- args
localargs$init <- beta localargs$return_type <- "li"
return(.Call("model_output", localargs, package="rstpm2"))
}
like <- function(beta) {
eta <- as.vector(X %*% beta)
h <- link$h(eta,etaD) + bhazard H <- link$H(eta)
ll <- sum(wt[event]*log(h[event])) - sum(wt*H)
if (delayed) {
eta0 <- as.vector(X0 %*% beta)
## etaD0 <- as.vector(XD0 %*% beta)
ll <- ll + sum(wt0*link$H(eta0)) } return(ll) } if (no.sp && !is.null(sp.init)) { if(!is.null(gam.obj$full.sp)) gam.obj$sp <- gam.obj$full.sp
value <- NULL
while(is.na(value <- negllsp(init,gam.obj$sp)) || !attr(value,"feasible")) { gam.call$sp <- gam.obj$sp * 5 if (no.sp) sp <- gam.call$sp
## Unresolved: should we change sp.init if the initial values are not feasible?
gam.obj <- eval(gam.call)
if(!is.null(gam.obj$full.sp)) gam.obj$sp <- gam.obj$full.sp init <- coef(gam.obj) if (frailty) init <- c(init,logtheta=logtheta) if (all(gam.obj$sp > 1e5)) break
## stop("Initial values not valid and revised sp>1e5")
}
args$sp <- gam.obj$sp
} else args$sp <- sp # ### Using exterior penalty method for nonlinear constraints: h(t)>=0 or increasing logH(t) # ### Some initial values should be outside the feasible region # while(all(XD%*%init>=0)){ # init <- init+0.001 # } # ### Check initial value # if(any(XD%*%init<=0)) { # cat("Some initial values are exactly outside the feasible region of this problem","\n") # } ## MLE args$return_type <- if (!no.sp) { # fixed sp as specified
args$return_type <- "optim_fixed" } else if (length(sp)>1) { "optim_multivariate" } else { "optim_first" } fit <- .Call("model_output", args, package = "rstpm2") fit$coef <- as.vector(fit$coef) fit$sp <- as.vector(fit$sp) names(fit$coef) <- names(init)
args$init <- init <- fit$coef
args$sp <- sp <- fit$sp
edf <- fit$edf edf_var<- as.vector(fit$edf_var)
names(edf_var) <- sapply(gam.obj$smooth,"[[","label") names(fit$coef) <- rownames(fit$hessian) <- colnames(fit$hessian) <- names(init)
args$kappa.final <- fit$kappa
negll <- function(beta) negllsp(beta,sp)
mle2 <- if (use.gr) {
} else mle2(negll,init,vecpar=TRUE, control=control, eval.only=TRUE, ...)
mle2@details$hessian <- fit$hessian
mle2@vcov <- solve(fit$hessian) mle2@details$convergence <- 0
out <- new("pstpm2",
call = mle2@call,
call.orig = mle2@call,
coef = mle2@coef,
fullcoef = mle2@fullcoef,
vcov = mle2@vcov,
min = mle2@min,
details = mle2@details,
minuslogl = mle2@minuslogl,
method = mle2@method,
optimizer = "optim", # mle2@optimizer
data = data, # mle2@data, which uses as.list()
formula = mle2@formula,
xlevels = .getXlevels(mt, mf),
##contrasts = attr(X, "contrasts"),
contrasts = NULL, # wrong!
logli = logli,
##weights = weights,
Call = Call,
terms = mt,
model.frame = mf,
gam = gam.obj,
timeVar = timeVar,
time0Var = time0Var,
timeExpr = timeExpr,
time0Expr = time0Expr,
like = like,
fullformula = fullformula,
delayed=delayed,
frailty = frailty,
x = X,
xd = XD,
termsd = mt, # wrong!
y = y,
sp = sp,
nevent=nevent,
edf=edf,
edf_var=edf_var,
df=edf,
args=args)
if (robust) # kludge
out@vcov <- sandwich.stpm2(out, cluster=cluster)
return(out)
}
## Could this inherit from summary.stpm2?
setClass("summary.pstpm2", representation(pstpm2="pstpm2",frailty="logical",theta="list",wald="matrix"), contains="summary.mle2")
setMethod("summary", "pstpm2",
function(object) {
newobj <- as(summary(as(object,"mle2")),"summary.pstpm2")
newobj@pstpm2 <- object
newobj@frailty <- object@frailty
if (object@frailty) {
coef <- coef(newobj)
theta <- exp(coef[nrow(coef),1])
se.logtheta <- coef[nrow(coef),2]
se.theta <- theta*se.logtheta
test.statistic <- (1/se.logtheta)^2
p.value <- pchisq(test.statistic,df=1,lower.tail=FALSE)/2
newobj@theta <- list(theta=theta, se.theta=se.theta, p.value=p.value)
} else newobj@theta <- list()
vcov1 <- vcov(object)
coef1 <- coef(object)
## Wald test for the smoothers
wald <- t(sapply(names(object@edf_var), function(name) {
i <- grep(name,colnames(vcov1),fixed=TRUE)
statistic <- as.vector(coef1[i] %*% solve(vcov1[i,i]) %*% coef1[i])
edf <- object@edf_var[name]
c(statistic=statistic,ncoef=length(i),edf=edf,p.value=pchisq(statistic, edf, lower.tail=FALSE))
}))
colnames(wald) <- c("Wald statistic","Number of coef","Effective df","P value")
newobj@wald <- wald
newobj })
setMethod("show", "summary.pstpm2",
function(object) {
show(as(object,"summary.mle2"))
cat(sprintf("\nEffective df=%g\n",object@pstpm2@edf))
printCoefmat(object@wald)
if (object@frailty)
cat(sprintf("\ntheta=%g\tse=%g\tp=%g\n",
object@theta$theta,object@theta$se.theta,object@theta$p.value)) }) setMethod("AICc", "pstpm2", function (object, ..., nobs=NULL, k=2) { L <- list(...) if (length(L)) { L <- c(list(object),L) if (is.null(nobs)) { nobs <- sapply(L,nobs) } if (length(unique(nobs))>1) stop("nobs different: must have identical data for all objects") val <- sapply(L, AICc, nobs=nobs, k=k) df <- sapply(L,attr,"edf") data.frame(AICc=val,df=df) } else { df <- attr(object,"edf") if (is.null(nobs)) nobs <- object@nevent c(-2*logLik(object)+k*df+k*df*(df+1)/(nobs-df-1)) } }) setMethod("qAICc", "pstpm2", function (object, ..., nobs = NULL, dispersion = 1, k = 2) { L <- list(...) if (length(L)) { L <- c(list(object),L) if (is.null(nobs)) { nobs <- sapply(L,nobs) } if (length(unique(nobs))>1) stop("nobs different: must have identical data for all objects") val <- sapply(L, qAICc, nobs=nobs,dispersion=dispersion,k=k) df <- sapply(L,attr,"edf") data.frame(qAICc=val,df=df) } else { df <- attr(object,"edf") if (is.null(nobs)) nobs <- object@nevent c(-2*logLik(object)/dispersion+k*df+k*df*(df+1)/(nobs-df-1)) } }) setMethod("qAIC", "pstpm2", function (object, ..., dispersion = 1, k = 2) { L <- list(...) if (length(L)) { L <- c(list(object),L) if (is.null(nobs)) { nobs <- sapply(L,nobs) } if (length(unique(nobs))>1) stop("nobs different: must have identical data for all objects") val <- sapply(L, qAIC, dispersion=dispersion, k=k) df <- sapply(L,attr,"edf") data.frame(qAICc=val,df=df) } else { df <- attr(object,"edf") c(-2*logLik(object)/dispersion+k*df) } }) setMethod("AIC", "pstpm2", function (object, ..., k = 2) { L <- list(...) if (length(L)) { L <- c(list(object),L) if (!all(sapply(L,class)=="pstpm2")) stop("all objects in list must be class pstpm2") val <- sapply(L,AIC,k=k) df <- sapply(L,attr,"edf") data.frame(AIC=val,df=df) } else -2 * as.numeric(logLik(object)) + k * attr(object, "edf") }) setMethod("BIC", "pstpm2", function (object, ..., nobs = NULL) { L <- list(...) if (length(L)) { L <- c(list(object),L) if (!all(sapply(L,class)=="pstpm2")) stop("all objects in list must be class pstpm2") val <- sapply(L,BIC,nobs=nobs) df <- sapply(L,attr,"edf") data.frame(BIC=val,df=df) } else { if (is.null(nobs)) nobs <- object@nevent -2 * as.numeric(logLik(object)) + log(nobs) * attr(object, "edf") } }) ## Revised from bbmle: ## changed the calculation of the degrees of freedom in the third statement of the .local function setMethod("anova", signature(object="pstpm2"), function (object, ..., width = getOption("width"), exdent = 10) { mlist <- c(list(object), list(...)) mnames <- sapply(sys.call(sys.parent())[-1], deparse) ltab <- as.matrix(do.call("rbind", lapply(mlist, function(x) { c(Tot Df = x@edf, Deviance = -2 * logLik(x)) # changed to x@edf }))) terms = sapply(mlist, function(obj) { if (is.null(obj@formula) || obj@formula == "") { mfun <- obj@call$minuslogl
mfun <- paste("[", if (is.name(mfun)) {
as.character(mfun)
}
else {
"..."
}, "]", sep = "")
paste(mfun, ": ", paste(names(obj@coef), collapse = "+"),
sep = "")
}
else {
as.character(obj@formula)
}
})
mterms <- paste("Model ", 1:length(mnames), ": ", mnames,
", ", terms, sep = "")
mterms <- strwrapx(mterms, width = width, exdent = exdent,
wordsplit = "[ \n\t]")
heading <- paste("Likelihood Ratio Tests", paste(mterms,
collapse = "\n"), sep = "\n")
ltab <- cbind(ltab, Chisq = abs(c(NA, diff(ltab[, "Deviance"]))),
Df = abs(c(NA, diff(ltab[, "Tot Df"]))))
ltab <- cbind(ltab, Pr(>Chisq) = c(NA, pchisq(ltab[,
"Chisq"][-1], ltab[, "Df"][-1], lower.tail = FALSE)))
rownames(ltab) <- 1:nrow(ltab)
class(ltab) <- "anova"
ltab
})

setMethod("predictnl", "pstpm2",
{
if (is.null(newdata) && !is.null(object@data))
newdata <- object@data
localf <- function(coef,...)
{
object@fullcoef = coef
}
dm <- numDeltaMethod(object,localf,newdata=newdata,...)
out <- invlinkf(data.frame(Estimate=dm$Estimate, lower=dm$Estimate-1.96*dm$SE, upper=dm$Estimate+1.96*dm$SE)) ## cloglog switches the bounds if (link=="cloglog") out <- data.frame(Estimate=out$Estimate,lower=out$upper,upper=out$lower)
return(out)
})
##
setMethod("predict", "pstpm2",
function(object,newdata=NULL,
grid=FALSE,seqLength=300,
{
type <- match.arg(type)
## exposed is a function that takes newdata and returns the revised newdata
## var is a string for a variable that defines a unit change in exposure
local <-  function (object, newdata=NULL, type="surv", exposed)
{
args <- object@args
tt <- object@terms
if (is.null(newdata)) {
##mm <- X <- model.matrix(object) # fails (missing timevar)
X <- object@x
XD <- object@xd
##y <- model.response(object@model.frame)
y <- object@y
time <- as.vector(y[,ncol(y)-1])
}
else {
X <- args$transX(predict(object@gam, newdata, type="lpmatrix"), newdata) ## lpfunc <- function(delta,fit,data,var) { ## data[[var]] <- data[[var]]+delta ## predict(fit,data,type="lpmatrix") ## } ## XD <- grad(lpfunc,0,object@gam,newdata,object@timeVar) ## XD <- matrix(XD,nrow=nrow(X)) lpfunc <- function(x,...) { newdata2 <- newdata newdata2[[object@timeVar]] <- x predict(object@gam,newdata2,type="lpmatrix") } XD <- args$transXD(grad1(lpfunc,newdata[[object@timeVar]]))
## resp <- attr(Terms, "variables")[attr(Terms, "response")]
## similarly for the derivatives
## if (object@delayed) {
##   newdata0 <- newdata
##   newdata0[[object@timeVar]] <- newdata[[object@time0Var]]
##   X0 <- predict(object@gam, newdata0,type="lpmatrix")
##   ## XD0 <- matrix(XD0,nrow=nrow(X0))
## }
if (type %in% c("hazard","hr","sdiff","hdiff","loghazard","meansurvdiff","or",
"marghaz","marghr")) {
time <- eval(object@timeExpr,newdata)
##
}
if (type %in% c("hr","sdiff","hdiff","meansurvdiff","or","marghr")) {
if (missing(exposed))
stop("exposed needs to be specified for type in ('hr','sdiff','hdiff','meansurvdiff','or','marghr')")
newdata2 <- exposed(newdata)
lpfunc <- function(x,...) {
newdata3 <- newdata2
newdata3[[object@timeVar]] <- x
predict(object@gam,newdata3,type="lpmatrix")
}
X2 <- args$transX(predict(object@gam, newdata2, type="lpmatrix"), newdata2) XD2 <- args$transXD(grad1(lpfunc,newdata2[[object@timeVar]]))
## XD2 <- matrix(XD2,nrow=nrow(X))
}
}
beta <- coef(object)
if (object@frailty) {
theta <- exp(beta[length(beta)])
beta <- beta[-length(beta)]
gauss_x <- object@args$gauss_x gauss_w <- object@args$gauss_w
Z <- model.matrix(args$Z.formula, newdata) if (ncol(Z)>1) stop("Current implementation only allows for a single random effect") Z <- as.vector(Z) } eta <- as.vector(X %*% beta) etaD <- as.vector(XD %*% beta) S <- link$ilink(eta)
h <- link$h(eta,etaD) if (any(h<0)) warning(sprintf("Predicted hazards less than zero (n=%i).",sum(h<0))) H = link$H(eta)
Sigma = vcov(object)
if (type=="link") { # delayed entry?
return(eta)
}
if (type=="density")
return (S*h)
if (type=="cumhaz") { # delayed entry?
return(H)
}
if (type=="surv") { # delayed entry?
return(S)
}
if (type=="odds") { # delayed entry?
return((1-S)/S)
}
if (type=="sdiff")
return(link$ilink(as.vector(X2 %*% beta)) - S) if (type=="or") { S2 <- link$ilink(as.vector(X2 %*% beta))
return((1-S2)/S2/((1-S)/S))
}
if (type=="hazard") {
return(h)
}
if (type=="loghazard") {
return(log(h))
}
if (type=="hdiff") {
eta2 <- as.vector(X2 %*% beta)
h2 <- link$h(eta2,etaD2) return(h2 - h) } if (type=="hr") { eta2 <- as.vector(X2 %*% beta) etaD2 <- as.vector(XD2 %*% beta) h2 <- link$h(eta2,etaD2)
return(h2/h)
}
if (type=="meansurv") {
return(mean(S))
}
if (type=="meanhaz") {
return(sum(h*S)/sum(S))
}
if (type=="meansurvdiff") {
eta2 <- as.vector(X2 %*% beta)
S2 <- link$ilink(eta2) return(mean(S2-S)) } if (type=="margsurv") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
if (object@args$RandDist=="Gamma") return((1+theta*H)^(-1/theta)) if (object@args$RandDist=="LogN") {
return(sapply(1:length(gauss_x),
function(i) link$ilink(eta+Z*sqrt(theta)*gauss_x[i])) %*% gauss_w / sum(gauss_w)) } } if (type=="marghaz") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
if (object@args$RandDist=="Gamma") { margsurv <- (1+theta*H)^(-1/theta) return(h*margsurv^theta) } if (object@args$RandDist=="LogN") {
return(sapply(1:length(gauss_x),
function(i) link$h(eta+Z*sqrt(theta)*gauss_x[i],etaD)) %*% gauss_w / sum(gauss_w)) } } if (type=="marghr") { stopifnot(object@frailty && object@args$RandDist %in% c("Gamma","LogN"))
eta2 <- as.vector(X2 %*% beta)
if (object@args$RandDist=="Gamma") { H2 <- link$H(eta2)
h2 <- link$h(eta2,etaD2) margsurv <- (1+theta*H)^(-1/theta) marghaz <- h*margsurv^theta margsurv2 <- (1+theta*H2)^(-1/theta) marghaz2 <- h2*margsurv2^theta } if (object@args$RandDist=="LogN") {
marghaz <- sapply(1:length(gauss_x),
function(i) as.vector(link$h(eta+Z*sqrt(theta)*gauss_x[i],etaD))) %*% gauss_w / sum(gauss_w) marghaz2 <- sapply(1:length(gauss_x), function(i) as.vector(link$h(eta2+Z*sqrt(theta)*gauss_x[i],etaD2))) %*%
gauss_w / sum(gauss_w)
}
return(marghaz2/marghaz)
}
}
##debug(local)
if (is.null(newdata) && type %in% c("hr","sdiff","hdiff","meansurvdiff","or","marghr"))
stop("Prediction using type in ('hr','sdiff','hdiff','meansurvdiff','or','marghr') requires newdata to be specified.")
if (grid) {
Y <- object@y
event <- Y[,ncol(Y)]==1
time <- object@data[[object@timeVar]]
eventTimes <- time[event]
X <- seq(min(eventTimes),max(eventTimes),length=seqLength)[-1]
data.x <- data.frame(X)
names(data.x) <- object@timeVar
newdata <- merge(newdata,data.x)
}
pred <- if (!se.fit) {
local(object,newdata,type=type,exposed=exposed,
...)
}
else {
exposed=exposed,...)
}
attr(pred,"newdata") <- newdata
##if (grid) cbind(newdata,as.data.frame(pred)) else pred
return(pred)
})
##%c% <- function(f,g) function(...) g(f(...)) # function composition
## to do:
## (*) Stata-compatible knots
setMethod("plot", signature(x="pstpm2", y="missing"),
function(x,y,newdata,type="surv",
xlab=NULL,ylab=NULL,line.col=1,ci.col="grey",lty=par("lty"),
lwd=par("lwd"),
var=NULL,...) {
y <- predict(x,newdata,type=if (type=="fail") "surv" else type,var=var,exposed=exposed,
grid=!(x@timeVar %in% names(newdata)), se.fit=TRUE)
if (type=="fail") y <- if (ci) data.frame(Estimate=1-y$Estimate, lower=1-y$upper, upper=1-y$lower) else data.frame(Estimate=y) if (is.null(xlab)) xlab <- deparse(x@timeExpr) if (is.null(ylab)) ylab <- switch(type,hr="Hazard ratio",hazard="Hazard",surv="Survival",density="Density", sdiff="Survival difference",hdiff="Hazard difference",cumhaz="Cumulative hazard", loghazard="log(hazard)",link="Linear predictor",meansurv="Mean survival", meansurvdiff="Difference in mean survival",odds="Odds",or="Odds ratio", margsurv="Marginal survival",marghaz="Marginal hazard", marghr="Marginal hazard ratio", haz="Hazard", fail="Failure", meanhaz="Mean hazard") xx <- attr(y,"newdata") xx <- eval(x@timeExpr,xx) # xx[,ncol(xx)] if (!add) matplot(xx, y, type="n", xlab=xlab, ylab=ylab, ...) if (ci) { polygon(c(xx,rev(xx)), c(y[,2],rev(y[,3])), col=ci.col, border=ci.col) lines(xx,y[,1],col=line.col,lty=lty,lwd=lwd) } else lines(xx,y,col=line.col,lty=lty,lwd=lwd) if (rug) { Y <- x@y eventTimes <- Y[Y[,ncol(Y)]==1,ncol(Y)-1] rug(eventTimes,col=line.col) } return(invisible(y)) }) ## sandwich variance estimator (from the sandwich package) ## coeftest.stpm2 <- ## function (x, vcov. = NULL, df = NULL, ...) ## { ## est <- coef(x) ## if (is.null(vcov.)) ## se <- vcov(x) ## else { ## if (is.function(vcov.)) ## se <- vcov.(x) ## else se <- vcov. ## } ## se <- sqrt(diag(se)) ## if (!is.null(names(est)) && !is.null(names(se))) { ## anames <- names(est)[names(est) %in% names(se)] ## est <- est[anames] ## se <- se[anames] ## } ## tval <- as.vector(est)/se ## pval <- 2 * pnorm(abs(tval), lower.tail = FALSE) ## cnames <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)") ## mthd <- "z" ## rval <- cbind(est, se, tval, pval) ## colnames(rval) <- cnames ## class(rval) <- "coeftest" ## attr(rval, "method") <- paste(mthd, "test of coefficients") ## return(rval) ## } ## weights.stpm2 <- ## function (object, ...) ## { ## wts <- object@weights ## if (is.null(wts)) ## wts ## else napredict(object@na.action, wts) ## } ## copy of bbmle:::strwrapx strwrapx <- function (x, width = 0.9 * getOption("width"), indent = 0, exdent = 0, prefix = "", simplify = TRUE, parsplit = "\n[ \t\n]*\n", wordsplit = "[ \t\n]") { if (!is.character(x)) x <- as.character(x) indentString <- paste(rep.int(" ", indent), collapse = "") exdentString <- paste(rep.int(" ", exdent), collapse = "") y <- list() plussplit = function(w) { lapply(w, function(z) { plusloc = which(strsplit(z, "")[[1]] == "+") plussplit = apply(cbind(c(1, plusloc + 1), c(plusloc, nchar(z, type = "width"))), 1, function(b) substr(z, b[1], b[2])) plussplit }) } z <- lapply(strsplit(x, parsplit), function(z) { lapply(strsplit(z, wordsplit), function(x) unlist(plussplit(x))) }) for (i in seq_along(z)) { yi <- character(0) for (j in seq_along(z[[i]])) { words <- z[[i]][[j]] nc <- nchar(words, type = "w") if (any(is.na(nc))) { nc0 <- nchar(words) nc[is.na(nc)] <- nc0[is.na(nc)] } if (any(nc == 0)) { zLenInd <- which(nc == 0) zLenInd <- zLenInd[!(zLenInd %in% (grep("\\.$",
words) + 1))]
if (length(zLenInd) > 0) {
words <- words[-zLenInd]
nc <- nc[-zLenInd]
}
}
if (length(words) == 0) {
yi <- c(yi, "", prefix)
next
}
currentIndex <- 0
lowerBlockIndex <- 1
upperBlockIndex <- integer(0)
lens <- cumsum(nc + 1)
first <- TRUE
maxLength <- width - nchar(prefix, type = "w") -
indent
while (length(lens) > 0) {
k <- max(sum(lens <= maxLength), 1)
if (first) {
first <- FALSE
maxLength <- maxLength + indent - exdent
}
currentIndex <- currentIndex + k
if (nc[currentIndex] == 0)
upperBlockIndex <- c(upperBlockIndex, currentIndex -
1)
else upperBlockIndex <- c(upperBlockIndex, currentIndex)
if (length(lens) > k) {
if (nc[currentIndex + 1] == 0) {
currentIndex <- currentIndex + 1
k <- k + 1
}
lowerBlockIndex <- c(lowerBlockIndex, currentIndex +
1)
}
if (length(lens) > k)
lens <- lens[-(1:k)] - lens[k]
else lens <- NULL
}
nBlocks <- length(upperBlockIndex)
s <- paste(prefix, c(indentString, rep.int(exdentString,
nBlocks - 1)), sep = "")
for (k in (1:nBlocks)) {
s[k] <- paste(s[k], paste(words[lowerBlockIndex[k]:upperBlockIndex[k]],
collapse = " "), sep = "")
}
s = gsub("\\+ ", "+", s)
yi <- c(yi, s, prefix)
}
y <- if (length(yi))
c(y, list(yi[-length(yi)]))
else c(y, "")
}
if (simplify)
y <- unlist(y)
y
}