https://github.com/cran/BMA
Tip revision: d0dd78cdafa7874b6786a8d6a9d9990b868be66b authored by Hana Sevcikova on 19 October 2019, 04:20:19 UTC
version 3.18.10
version 3.18.10
Tip revision: d0dd78c
iBMA.surv.R
iBMA.surv<- function(x, ...)
UseMethod("iBMA.surv")
iBMA.surv.data.frame <-
function (x, surv.t, cens, wt = rep(1, nrow(X)), thresProbne0 = 5,
maxNvar = 30, nIter = 100, verbose = FALSE, sorted = FALSE,
factor.type = TRUE, ...)
{
printCGen <- function(printYN) {
printYN <- printYN
return(function(x) if (printYN) cat(paste(paste(x, sep = "",
collapse = " "), "\n", sep = "")))
}
# CF namespcae unlock from https://gist.github.com/wch/3280369
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
inc <- '
/* This is taken from envir.c in the R 2.15.1 source
https://github.com/SurajGupta/r-source/blob/master/src/main/envir.c
*/
#define FRAME_LOCK_MASK (1<<14)
#define FRAME_IS_LOCKED(e) (ENVFLAGS(e) & FRAME_LOCK_MASK)
#define UNLOCK_FRAME(e) SET_ENVFLAGS(e, ENVFLAGS(e) & (~ FRAME_LOCK_MASK))
'
src <- '
if (TYPEOF(env) == NILSXP)
error("use of NULL environment is defunct");
if (TYPEOF(env) != ENVSXP)
error("not an environment");
UNLOCK_FRAME(env);
// Return TRUE if unlocked; FALSE otherwise
SEXP result = PROTECT( Rf_allocVector(LGLSXP, 1) );
LOGICAL(result)[0] = FRAME_IS_LOCKED(env) == 0;
UNPROTECT(1);
return result;
'
unlockEnvironment <- cfunction(signature(env = "environment"),
includes = inc,
body = src)
nsEnv <- asNamespace('BMA')
unlockEnvironment(nsEnv)
nsEnv$glob <- function() {
utils::globalVariables(parent.env(environment()))
}
environment(nsEnv$glob) <- nsEnv
pkgEnv <- as.environment('package:BMA')
unlockEnvironment(pkgEnv)
pkgEnv$glob <- nsEnv$glob
exportEnv <- nsEnv$.__NAMESPACE__.$exports
exportEnv$glob <- c(glob="glob")
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
utils::globalVariables("nastyHack_x.df")
sortX <- function(surv.t, cens, X, wt) {
fitvec <- rep(NA, times = ncol(X))
nastyHack_x.df <- data.frame(X)
scp <- formula(paste("~", paste(colnames(X), sep = "",
collapse = " + ")))
cox.out <- coxph(Surv(surv.t, cens) ~ 1, weights = wt,
method = "breslow", iter.max = 30, data = nastyHack_x.df)
addcox <- add1(cox.out, scope = scp, test = "Chisq",
data = nastyHack_x.df)
fitvec <- addcox[-1, grep("^P.*Chi", names(addcox))]
initial.order <- order(fitvec, decreasing = FALSE)
sortedX <- X[, initial.order]
return(list(sortedX = sortedX, initial.order = initial.order))
}
X <- x
cl <- match.call()
printC <- printCGen(verbose)
if (factor.type == FALSE) {
x.df <- data.frame(X)
X <- model.matrix(terms.formula(~., data = x.df), data = x.df)[,
-1]
}
if (!sorted) {
printC("sorting X")
sorted <- sortX(surv.t, cens, X, wt = wt)
sortedX <- sorted$sortedX
initial.order <- sorted$initial.order
}
else {
sortedX <- X
initial.order <- 1:ncol(sortedX)
}
nVar <- ncol(sortedX)
maxNvar <- min(maxNvar, nVar)
stopVar <- 0
nextVar <- maxNvar + 1
current.probne0 <- rep(0, maxNvar)
maxProbne0 <- rep(0, times = nVar)
nTimes <- rep(0, times = nVar)
currIter <- 0
first.in.model <- rep(NA, times = nVar)
new.vars <- 1:maxNvar
first.in.model[new.vars] <- currIter + 1
iter.dropped <- rep(NA, times = nVar)
currentSet <- NULL
current_state <- list(surv.t = surv.t, cens = cens, sortedX = sortedX,
wt = wt, call = cl, initial.order = initial.order, thresProbne0 = thresProbne0,
maxNvar = maxNvar, nIter = nIter, verbose = verbose,
nVar = nVar, currentSet = currentSet, new.vars = new.vars,
stopVar = stopVar, nextVar = nextVar, current.probne0 = current.probne0,
maxProbne0 = maxProbne0, nTimes = nTimes, currIter = currIter,
first.in.model = first.in.model, iter.dropped = iter.dropped)
class(current_state) <- "iBMA.intermediate.surv"
result <- iBMA.surv.iBMA.intermediate.surv(current_state,
...)
result
}
### this function does a set number of iterations of iBMA, returning an intermediate result unless it is finished,
### in which case it returns a final result
iBMA.surv.iBMA.intermediate.surv<- function (x, nIter = NULL, verbose = NULL, ...)
{
printCGen<- function(printYN)
{
printYN<- printYN
return(function(x) if (printYN) cat(paste(paste(x,sep="", collapse = " "),"\n", sep="")))
}
cs<- x
# check if nIter has been redefined
if (!is.null(nIter)) cs$nIter<- nIter
if (!is.null(verbose)) cs$verbose<- verbose
printC<- printCGen(cs$verbose)
finalIter<- cs$currIter + cs$nIter
### iterate until a final result is produced (cs$stopVar == 1) or nIter more iterations have been done
while (cs$stopVar == 0 && cs$currIter < finalIter)
{
# add in the new variables
nextSet<- c(cs$currentSet, cs$new.vars)
cs$currIter<- cs$currIter + 1
printC(paste("\n\n starting iteration ",cs$currIter," nextVar =",cs$nextVar))
printC("applying bic.surv now")
currentX<- cs$sortedX[,nextSet]
colnames(currentX)<- colnames(cs$sortedX)[nextSet]
ret.bic<- bic.surv(x = currentX, surv.t = cs$surv.t, cens = cs$cens, maxCol = cs$maxNvar + 1, ...)
printC(ret.bic$probne0)
cs$maxProbne0[nextSet]<- pmax(ret.bic$probne0, cs$maxProbne0[nextSet])
cs$nTimes[nextSet]<- cs$nTimes[nextSet] + 1
cs$rmVector <- ret.bic$probne0 < cs$thresProbne0
# adaptive threshold
if (any(cs$rmVector) == FALSE)
{
# no var to swap in!!, increase threshold
currMin <- min (ret.bic$probne0)
printC (paste("no var to swap! Min probne0 = ", currMin, sep=""))
newThresProbne0 <- currMin + 1
printC(paste("new probne0 threshold = ", newThresProbne0, sep=""))
cs$rmVector <- ret.bic$probne0 < newThresProbne0
# check that we do not drop everything!
if (all(cs$rmVector))
cs$rmVector<- c(rep(FALSE, times = length(cs$rmVector)-1), TRUE)
}
# drop the bad ones...
cs$iter.dropped[nextSet[cs$rmVector]]<- cs$currIter
cs$currentSet<- nextSet[!cs$rmVector]
# now if there are more variables to examine add the new set of variables to the current set
if ( cs$nextVar <= cs$nVar)
{
# set up new X
printC ("generating next set of variables")
lastVar<- sum(cs$rmVector) + cs$nextVar - 1
# add in bulk if we are not close to the end of the variables,
if (lastVar <= cs$nVar)
{
cs$new.vars<- cs$nextVar:lastVar
cs$first.in.model[cs$new.vars]<- cs$currIter + 1
cs$nextVar <- lastVar + 1
}
# add in one by one until no variables left
else
{
cs$new.vars<- NULL
for (i in length(cs$rmVector):1)
{
if (cs$rmVector[i] == TRUE && cs$nextVar <= cs$nVar)
{
cs$new.vars<- c(cs$new.vars, cs$nextVar)
cs$first.in.model[cs$nextVar]<- cs$currIter + 1
cs$nextVar <- cs$nextVar + 1
}
}
}
}
else
{
# exhausted all data
cs$stopVar <- 1
cs$new.vars = NULL
}
}
# if we have finished (all variables) do some wrap-up and generate output values
if (cs$stopVar == 1)
{
printC("finished iterating")
currentX<- cs$sortedX[,cs$currentSet]
colnames(currentX)<- colnames(cs$sortedX)[cs$currentSet]
ret.bic<- bic.surv(x = currentX, surv.t = cs$surv.t, cens = cs$cens, maxCol = cs$maxNvar + 1, ...)
output<- cs
output$bma<- ret.bic
output$selected<- cs$currentSet
output$nIterations<- cs$currIter
class(output)<- "iBMA.surv"
}
else
{
output<- cs
class(output)<- "iBMA.intermediate.surv"
}
output
}
iBMA.surv.matrix<- iBMA.surv.data.frame