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prevtab <- function(
formula,
data,
meanpop = NULL,
breaks = NULL,
adjust = NULL,
weights = NULL,
subset = NULL,
verbose = FALSE
) {
PF <- parent.frame(1L)
TF <- environment()
checkLexisData(data)
checkPophaz(data, meanpop, haz.name = "meanpop")
meanpop <- data.table(meanpop)
allScales <- attr(data, "time.scales")
oldBreaks <- attr(data, "breaks")
lexis_vars <- c(allScales, "lex.dur", "lex.id", "lex.Cst", "lex.Xst")
lexis_vars <- intersect(names(data), lexis_vars)
meanpop_vars <- setdiff(names(meanpop), "meanpop")
print_vars <- print_vars_tmp <- NULL
adjust_vars <- adjust_vars_tmp <- NULL
all_vars <- unique(intersect(names(data), c(lexis_vars, print_vars, adjust_vars, meanpop_vars)))
## appease R CMD CHECK -------------------------------------------------------
at.risk <- NULL
## subsetting ----------------------------------------------------------------
sb <- substitute(subset)
subset <- evalLogicalSubset(data, substiset = sb, enclos = PF)
## data with only time scales and by variables -------------------------------
data = data[subset, ]
by_data <- usePopFormula(formula, Surv.response = FALSE,
data = data, enclos = PF)
x <- setDT(mget(lexis_vars, as.environment(data)))
if (!is.null(by_data[["print"]])) {
print_vars <- names(by_data[["print"]])
print_vars_tmp <- makeTempVarName(names = all_vars, pre = print_vars)
set(x, j = print_vars_tmp, value = by_data[["print"]])
}
if (!is.null(by_data[["adjust"]])) {
adjust_vars <- names(by_data[["adjust"]])
adjust_vars_tmp <- makeTempVarName(names = all_vars, pre = adjust_vars)
set(x, j = adjust_vars_tmp, value = by_data[["adjust"]])
}
forceLexisDT(x, breaks = oldBreaks, allScales = allScales)
rm("by_data")
x[, c("lex.Cst", "lex.Xst") := 0L]
by_vars <- c(print_vars, adjust_vars)
by_vars_tmp <- c(print_vars_tmp, adjust_vars_tmp)
meanpop_vars_tmp <- makeTempVarName(
names = c(names(x), names(data), names(meanpop)), pre = meanpop_vars
)
meanpop_vars_tmp <- unlist(lapply(seq_along(meanpop_vars), function(i) {
if (!meanpop_vars[i] %in% by_vars) {
return(meanpop_vars_tmp[i])
}
wh <- which(by_vars == meanpop_vars[i])
by_vars_tmp[wh]
}))
lapply(seq_along(meanpop_vars), function(i) {
set(x, j = meanpop_vars_tmp[i], value = data[[meanpop_vars[i]]])
})
## Splitting to ensure breaks exist; also takes copy -------------------------
x <- splitMulti(x, breaks = breaks, drop = TRUE, merge = TRUE)
forceLexisDT(x, breaks = attr(x, "breaks"), allScales = allScales)
newBreaks <- copy(attr(x, "breaks"))
## detect prevalence time scale ----------------------------------------------
prevScale <- detectSurvivalTimeScale(data, eval(formula[[2]], envir = data))
## limit to prevalence time points -------------------------------------------
## since we want prevalence at certain points of time along the prevalence
## time scale, prevScale values not at the breaks are not considered at all.
j <- list(newBreaks[[prevScale]])
names(j) <- prevScale
x <- x[j, on = prevScale, nomatch = 0L]
## aggregate -----------------------------------------------------------------
aggre_vars <- unique(c(print_vars_tmp, adjust_vars_tmp, meanpop_vars_tmp))
print(aggre_vars)
ag <- aggre(x, by = aggre_vars)
ag <- setDT(ag)
setkeyv(
ag, c(aggre_vars, setdiff(aggre_vars, intersect(lexis_vars, print_vars_tmp)))
)
ag[, "n" := cumsum(at.risk), by = eval(aggre_vars)]
ag <- setDT(mget(c(aggre_vars, meanpop_vars_tmp, "n"), as.environment(ag)))
## compute prevalence rates if appropriate -----------------------------------
print(ag)
ag <- merge(ag, meanpop, by = meanpop_vars_tmp)
if (length(c(print_vars_tmp, adjust_vars_tmp))) {
setnames(ag, c(print_vars_tmp, adjust_vars_tmp),
c(print_vars, adjust_vars_tmp))
}
ag <- rate(data = ag, obs = "n", pyrs = "meanpop", print = print_vars,
adjust = adjust_vars, weights = weights)
return(ag)
}
prevtab_ag <- function(
formula,
data,
meanpop = NULL,
adjust = NULL,
weights = NULL,
subset = NULL,
verbose = FALSE
) {
## prevtab(per ~ sex + fot)
}
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