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kaplan.plot.gg.R
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kaplan.plot.gg.R
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kaplan.plot <- function (x = "TIME", y = "DV", id = "ID", data = NULL, evid = "EVID",
by = NULL, xlab = "Time", ylab = "Survival (%)", object = NULL,
events.to.plot = "All", sim.data = NULL, sim.zip.file = NULL,
VPC = FALSE, nsim.lab = "simNumber", sim.evct.lab = "counter",
probs = c(0.025, 0.975), add.baseline = T, add.last.area = T,
subset = NULL, main = "Default", main.sub = "Default", main.sub.cex = 0.8,
nbins = NULL, real.type = "l", real.lty = 1, real.lwd = 1,
real.col = "blue", real.se = if (!is.null(sim.data)) F else T,
real.se.type = "l", real.se.lty = 2, real.se.lwd = 0.5, real.se.col = "red",
cens.type = "l", cens.lty = 1, cens.col = "black", cens.lwd = 1,
inclZeroWRES = TRUE, onlyfirst = FALSE, samp = NULL, poly.alpha = 1,
poly.fill = "lightgreen", poly.line.col = "darkgreen", poly.lty = 2,
censor.lines = TRUE, ylim = c(-5, 105), cov = NULL, cov.fun = "mean",
# added for ggplot2
facet="grid", facet_dir="vertical",
...)
{
if (is.null(object) && is.null(data))
cat("one of data or object should be defined in function input.")
if (!is.null(data)) {
if (!is.null(subset)) {
on.exit(detach(data))
attach(data, warn.conflicts = F)
data <- data[eval(parse(text = paste("data$", subset))), ]
if (dim(data)[1] == 0)
return(NULL)
}
}
if (!is.null(object)) {
if (!is.null(samp)) {
data <- SData(object, inclZeroWRES, onlyfirst = onlyfirst, subset = subset, samp = samp)
} else {
data <- Data(object, inclZeroWRES, onlyfirst = onlyfirst, subset = subset)
}
}
if (VPC && !is.null(object))
sim.zip.file <- paste("simtab", object@Runno, ".zip",
sep = "")
if (!is.null(sim.zip.file)) {
sim.data <- read.table(unz(sim.zip.file, sub(".zip",
"", sim.zip.file)), skip = 0, header = T)
}
if (!is.null(sim.data)) {
if (!is.null(subset)) {
on.exit(detach(sim.data))
attach(sim.data, warn.conflicts = F)
sim.data <- sim.data[eval(parse(text = paste("sim.data$",
subset))), ]
if (dim(sim.data)[1] == 0)
return(NULL)
}
}
data$counter <- 0
counter <- 0
old.id <- 0
for (i in 1:length(data$counter)) {
new.id <- data[[id]][i]
if (new.id != old.id) {
counter <- 0
}
old.id <- new.id
if (!is.null(data[[evid]])) {
if (data[[evid]][i] == 0)
counter = counter + 1
} else {
counter = counter + 1
}
data$counter[i] = counter
}
events <- max(data$counter)
RTTE = FALSE
if (events > 1)
RTTE = TRUE
max.events <- events
if (!is.null(sim.data)) {
sim.events <- max(sim.data[[sim.evct.lab]])
}
full.data <- data
y.true <- y
if (!is.null(sim.data)) {
full.sim.data <- sim.data
rle.result <- rle(full.sim.data[[id]])
rle.result$values <- 1:length(rle.result$values)
new.id <- inverse.rle(rle.result)
full.sim.data$sim.ID <- new.id
}
by.no <- length(by)
if (!all(is.na(match(events.to.plot, "All")))) {
num.of.plots <- max.events
if (by.no > 0)
num.of.plots <- num.of.plots * by.no
plotList <- vector("list", num.of.plots)
event.list <- 1:max.events
} else {
num.of.plots <- length(events.to.plot)
if (by.no > 0)
num.of.plots <- num.of.plots * by.no
plotList <- vector("list", num.of.plots)
event.list <- events.to.plot
}
plot.num <- 0
for (event.no in event.list) {
if (is.null(by))
by = c(1)
comb.data <- c()
for (by.val in by) {
# by.val = 1 # remove!
if (by.val == 1) {
by.val = NULL
by = NULL
}
tmp.cex <- main.sub.cex
if (is.null(main.sub) | num.of.plots == 1) {
tmp.name <- NULL
} else {
if (!is.na(match(main.sub, "Default"))) {
tmp.name <- paste("Event", event.no, sep = " ")
if (!is.null(by.val))
tmp.name <- paste(tmp.name, by.val, sep = ", ")
}
else {
tmp.name <- main.sub[plot.num + 1]
}
}
data <- full.data
if (!is.null(sim.data))
sim.data <- full.sim.data
data <- subset(data, counter <= event.no)
if (!is.null(data[[evid]])) {
data <- data[data[evid] == 0, ]
}
if (!is.null(sim.data))
sim.data <- subset(sim.data, eval(parse(text = sim.evct.lab)) <= event.no)
data <- data[!duplicated(data[[id]], fromLast = TRUE), ]
if (!is.null(sim.data))
sim.data <- sim.data[!duplicated(sim.data$sim.ID, fromLast = TRUE), ]
data$tmp.event <- 0
data$tmp.event[data[y.true] != 0] = 1
data$tmp.event[data$counter < event.no] = 0
y = "tmp.event"
if (!is.null(sim.data)) {
sim.data$tmp.event <- 0
sim.data$tmp.event[sim.data[y.true] != 0] = 1
sim.data$tmp.event[sim.data[[sim.evct.lab]] < event.no] = 0
}
if (!is.null(by.val)) {
data <- subset(data, eval(parse(text = by.val)))
if (dim(data)[1] == 0)
return(NULL)
if (!is.null(sim.data)) {
sim.data <- subset(sim.data, eval(parse(text = by.val)))
if (dim(sim.data)[1] == 0)
return(NULL)
}
}
S <- Surv(data[, x], data[, y])
f.1 <- survfit(S ~ 1)
a.1 <- summary(f.1)
cov.plot <- FALSE
censored.ids <- FALSE
if (!is.null(cov))
cov.plot <- TRUE
if (cov.plot) {
if (is.factor(data[, cov])) {
cat("\n Transforming", cov, "from levels to numeric\n",
sep = " ")
data[, cov] <- as.numeric(levels(data[, cov]))[data[, cov]]
}
tmp.y.cov <- c()
for (i in 1:length(f.1$time)) {
if (f.1$n.censor[i] > 0 && f.1$n.event[i] == 0) {
tmp.cov <- data[, cov][data[, x] >= f.1$time[i]]
}
else {
tmp.cov <- data[, cov][data[, x] > f.1$time[i]]
}
tmp.y.cov <- c(tmp.y.cov, do.call(cov.fun, list(tmp.cov)))
}
}
if (!is.null(sim.data)) {
times <- sort(unique(sim.data[, x]))
m1 <- matrix(nrow = length(unique(sim.data[, nsim.lab])), ncol = length(times))
if (cov.plot) {
m2 <- m1
}
ii = 0
for (i in unique(sim.data[, nsim.lab])) {
ii = ii + 1
tmp <- sim.data[eval(parse(text = paste("sim.data$", nsim.lab, "==", i))), ]
S.sim <- Surv(tmp[, x], tmp[, y])
f.1.sim <- survfit(S.sim ~ 1)
a.1.sim <- summary(f.1.sim)
tmp.times <- f.1.sim$time
col.index <- match(tmp.times, times)
if (cov.plot) {
if (is.factor(tmp[, cov])) {
cat("\n Transforming", cov, "from levels to numeric\n",
sep = " ")
tmp[, cov] <- as.numeric(levels(tmp[, cov]))[tmp[, cov]]
}
tmp.y.cov.sim.n <- c()
for (j in 1:length(f.1.sim$time)) {
if (f.1.sim$n.censor[j] > 0 && f.1.sim$n.event[j] == 0) {
tmp1.cov <- tmp[, cov][tmp[, x] >= f.1.sim$time[j]]
}
else {
tmp1.cov <- tmp[, cov][tmp[, x] > f.1.sim$time[j]]
}
tmp.y.cov.sim.n <- c(tmp.y.cov.sim.n, do.call(cov.fun, list(tmp1.cov)))
}
}
m1[ii, col.index] <- f.1.sim$surv
if (is.na(m1[ii, 1])) {
m1[ii, 1] <- 1
}
for (j in 2:length(times)) {
if (is.na(m1[ii, j])) {
m1[ii, j] <- m1[ii, j - 1]
}
}
if (cov.plot) {
m2[ii, col.index] <- tmp.y.cov.sim.n
if (is.na(m2[ii, 1])) {
m2[ii, 1] <- do.call(cov.fun, list(tmp[, cov]))
}
for (j in 2:length(times)) {
if (is.na(m2[ii, j])) {
m2[ii, j] <- m2[ii, j - 1]
}
}
}
}
if (cov.plot) {
m1 <- m2
}
if (is.null(nbins)) {
quants <- matrix(nrow = 2, ncol = dim(m1)[2])
time.bin <- times
for (j in 1:dim(quants)[2]) {
quants[, j] <- quantile(m1[, j], probs = probs, na.rm = F)
}
}
else {
remainder.times <- 0
ncomb <- floor(dim(m1)[2]/nbins)
if (ncomb == 0) {
ncomb <- 1
quants <- matrix(nrow = 2, ncol = dim(m1)[2])
time.bin <- c()
}
else {
remainder.times <- dim(m1)[2] - ncomb * nbins
quants <- matrix(nrow = 2, ncol = nbins)
time.bin <- c()
}
k.add.this <- 0
for (j in 1:dim(quants)[2]) {
tmp.bin <- c()
if (remainder.times > 0) {
ncomb.tmp <- ncomb + 1
remainder.times <- remainder.times - 1
}
else {
ncomb.tmp <- ncomb
}
for (k in 1:ncomb.tmp) {
if (k == 1)
time.bin <- c(time.bin, times[k + k.add.this])
if (k + k.add.this > dim(m1)[2])
next
tmp.bin <- c(tmp.bin, m1[, k + k.add.this])
}
k.add.this <- k.add.this + ncomb.tmp
quants[, j] <- quantile(tmp.bin, probs = probs, na.rm = F)
}
}
if (add.baseline) {
tmp.mat <- matrix(nrow = dim(quants)[1], ncol = dim(quants)[2] + 1)
tmp.mat[, 1] <- 1
if (cov.plot)
tmp.mat[, 1] <- do.call(cov.fun, list(sim.data[, cov]))
tmp.mat[, -1] <- quants
tmp.x <- c(0, time.bin)
}
else {
tmp.mat <- quants
tmp.x <- time.bin
}
PI.up <- c()
PI.down <- c()
PI.times <- c()
n.times <- length(tmp.x)
for (i in 1:n.times) {
PI.reps = 2
time.reps = 2
if (i == 1)
time.reps = 1
if (i == n.times)
PI.reps = 1
PI.up <- c(PI.up, rep(tmp.mat[2, i], PI.reps))
PI.down <- c(PI.down, rep(tmp.mat[1, i], PI.reps))
PI.times <- c(PI.times, rep(tmp.x[i], time.reps))
}
if (add.last.area) {
if (tail(tail(PI.times, n = 1)) == tail(times, n = 1)) {
time.pt <- (tail(PI.times, n = 1) - PI.times[1]) * 1.01
}
else {
time.pt <- (tail(times, n = 1) - PI.times[1]) * 1.01
}
PI.times = c(PI.times, time.pt)
PI.up <- c(PI.up, tail(PI.up, n = 1))
PI.down <- c(PI.down, tail(PI.down, n = 1))
}
if (!cov.plot) {
PI.up <- PI.up * 100
PI.down <- PI.down * 100
}
} else {
PI.up = NULL
PI.down = NULL
PI.times = NULL
}
if (add.baseline) {
tmp.y <- c(1, f.1$surv)
tmp.x <- c(0, f.1$time)
tmp.y.upper <- c(1, f.1$upper)
tmp.y.lower <- c(1, f.1$lower)
if (cov.plot)
tmp.y.cov <- c(do.call(cov.fun, list(data[, cov])), tmp.y.cov)
} else {
tmp.y <- c(f.1$surv)
tmp.x <- c(f.1$time)
tmp.y.upper <- c(f.1$upper)
tmp.y.lower <- c(f.1$lower)
}
if (cov.plot) {
tmp.y <- tmp.y.cov
}
real.data <- c()
real.times <- c()
real.data.upper <- c()
real.data.lower <- c()
for (i in 1:length(tmp.y)) {
y.reps = 2
x.reps = 2
if (i == 1)
x.reps = 1
if (i == length(tmp.y))
y.reps = 1
real.data <- c(real.data, rep(tmp.y[i], y.reps))
real.times <- c(real.times, rep(tmp.x[i], x.reps))
real.data.upper <- c(real.data.upper, rep(tmp.y.upper[i], y.reps))
real.data.lower <- c(real.data.lower, rep(tmp.y.lower[i], y.reps))
}
if (!cov.plot) {
real.data <- real.data * 100
real.data.upper <- real.data.upper * 100
real.data.lower <- real.data.lower * 100
}
if (cov.plot) {
if (!is.null(sim.data)) {
ylim <- c(min(range(real.data)[1], range(PI.down)[1]) * 0.9, max(range(real.data)[2], range(PI.up)[2]) * 1.1)
} else {
ylim <- c(min(range(real.data)[1]) * 0.9, max(range(real.data)[2]) * 1.1)
}
real.se <- FALSE
ylab <- paste(cov.fun, cov)
cen.x0 <- f.1$time[f.1$n.censor > 0]
cen.x1 <- f.1$time[f.1$n.censor > 0]
cen.y0 <- tmp.y[match(f.1$time[f.1$n.censor > 0], tmp.x)] - (ylim[2] - ylim[1]) * 0.01
cen.y1 <- tmp.y[match(f.1$time[f.1$n.censor > 0], tmp.x)] + (ylim[2] - ylim[1]) * 0.01
}
as.num <- function(x) { as.numeric(as.character(x)) }
comb.tmp <- na.omit(cbind("time" = real.times,
"obs" = as.num(real.data),
"obs.lower" = as.num(real.data.lower),
"obs.upper" = as.num(real.data.upper),
strata = by.val))
comb.data <- rbind(comb.data, comb.tmp)
} # by list
comb.data <- data.frame(comb.data)
for (i in 1:4) {
comb.data[,i] <- as.num(comb.data[,i])
}
} # event.list
# return(comb.data)
## plotting
default.plot.title <- "Kaplan-Meier plots"
kaplan_gg <- ggplot (comb.data, aes(x=time, y=obs)) +
geom_ribbon(aes(ymin=obs.lower, ymax=obs.upper), fill=rgb(0.5,0.5,0.5,0.5), colour=NA) +
geom_step() +
xlab(xlab) +
ylab(ylab)
if (!is.null(default.plot.title)) {
kaplan_gg <- kaplan_gg + ggtitle(default.plot.title)
}
if (!is.null(by)) {
if (facet == "grid") {
if (facet_dir == "vertical") {
kaplan_gg <- kaplan_gg + facet_grid(strata ~ .)
} else {
kaplan_gg <- kaplan_gg + facet_grid(. ~ strata)
}
} else {
kaplan_gg <- kaplan_gg + facet_wrap(~strata)
}
}
return(kaplan_gg)
# xplot <- xyplot(real.data ~ real.times, main = list(tmp.name,
# cex = tmp.cex), ylim = ylim, xlab = xlab, ylab = ylab,
# real.type = real.type, PI.up = PI.up, PI.down = PI.down,
# PI.times = PI.times, real.data.upper = real.data.upper,
# real.data.lower = real.data.lower, real.se.type = real.se.type,
# real.se.lty = real.se.lty, real.se.col = real.se.col,
# f.1 = f.1, ..., panel = function(x, y, PI.up,
# PI.down, PI.times, real.data.upper, real.data.lower,
# real.se.type = real.se.type, real.type = real.type,
# real.se.lty = real.se.lty, real.se.col = real.se.col,
# f.1 = f.1, ...) {
# if (!is.null(sim.data)) {
# grid.polygon(c(PI.times, rev(PI.times)),
# c(PI.up, rev(PI.down)), default.units = "native",
# gp = gpar(fill = poly.fill, alpha = poly.alpha,
# col = poly.line.col, lty = poly.lty))
# }
# panel.xyplot(x, y, type = real.type, lwd = real.lwd,
# col = real.col, lty = real.lty, ...)
# if (real.se)
# panel.xyplot(x, real.data.upper, type = real.se.type,
# lty = real.se.lty, col = real.se.col, lwd = real.se.lwd,
# ...)
# if (real.se)
# panel.xyplot(x, real.data.lower, type = real.se.type,
# lty = real.se.lty, col = real.se.col, lwd = real.se.lwd,
# ...)
# if (censor.lines) {
# if (any(f.1$n.censor > 0)) {
# if (cov.plot) {
# panel.segments(x0 = cen.x0, y0 = cen.y0,
# x1 = cen.x1, y1 = cen.y1, ...)
# }
# else {
# panel.segments(x0 = c(f.1$time[f.1$n.censor >
# 0]), y0 = c(f.1$surv[f.1$n.censor >
# 0] * 100 - 2), x1 = c(f.1$time[f.1$n.censor >
# 0]), y1 = c(f.1$surv[f.1$n.censor >
# 0] * 100 + 2), type = cens.type, lty = cens.lty,
# col = cens.col, lwd = cens.lwd, ...)
# }
# }
# }
# })
# plot.num <- plot.num + 1
# plotList[[plot.num]] <- xplot
# default.plot.title <- "Kaplan-Meier plots"
# if (cov.plot)
# default.plot.title <- paste(cov.fun, cov)
# if (num.of.plots == 1)
# default.plot.title <- paste("Kaplan-Meier plot for event",
# event.list[1])
# if (num.of.plots == 1 && cov.plot)
# default.plot.title <- paste(cov.fun, cov, "for event",
# event.list[1])
# if (!is.null(object)) {
# plotTitle <- xpose.multiple.plot.title(object = object,
# plot.text = default.plot.title, main = main, ...)
# }
# else {
# if (is.null(main)) {
# plotTitle <- NULL
# }
# else {
# if (!is.na(match(main, "Default"))) {
# plotTitle <- default.plot.title
# if (!is.null(subset)) {
# plotTitle <- paste(plotTitle, "\n[", subset,
# "]", sep = "")
# }
# }
# else {
# plotTitle <- main
# }
# }
# }
# obj <- xpose.multiple.plot(plotList, plotTitle, ...)
#obj <- kaplan_gg
#return(obj)
}
environment(kaplan.plot) <- as.environment("package:xpose4specific")