Script jantung copy
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This document contains R code for analyzing survival data. It loads survival data from a file, fits Kaplan-Meier and Cox proportional hazards models, and generates Kaplan-Meier curves and log-rank test results. Functions are defined to plot single or multiple stratified Kaplan-Meier curves using ggplot. The curves and log-rank test are generated by fitting survival models to treatment groups in the loaded data and summarizing the results.
![ggsurv.m <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def',
cens.col = 'red', lty.est = 1, lty.ci = 2,
cens.shape = 3, back.white = F, xlab = 'Time',
ylab = 'Survival', main = '') {
n <- s$strata
groups <- factor(unlist(strsplit(names
(s$strata), '='))[seq(2, 2*strata, by =
2)])
gr.name <- unlist(strsplit(names(s$strata), '='))[1]
gr.df <- vector('list', strata)
ind <- vector('list', strata)
n.ind <- c(0,n); n.ind <- cumsum(n.ind)
for(i in 1:strata) ind[[i]] <- (n.ind[i]+1):n.ind[i+1]
for(i in 1:strata){
gr.df[[i]] <- data.frame(
time = c(0, s$time[ ind[[i]] ]),
surv = c(1, s$surv[ ind[[i]] ]),
up = c(1, s$upper[ ind[[i]] ]),
low = c(1, s$lower[ ind[[i]] ]),
cens = c(0, s$n.censor[ ind[[i]] ]),
group = rep(groups[i], n[i] + 1))
}
dat <- do.call(rbind, gr.df)
dat.cens <- subset(dat, cens != 0)
pl <- ggplot(dat, aes(x = time, y = surv, group = group)) +
xlab(xlab) + ylab(ylab) + ggtitle(main) +
geom_step(aes(col = group, lty = group))
col <- if(length(surv.col == 1)){
scale_colour_manual(name = gr.name, values = rep(surv.col, strata))
} else{
scale_colour_manual(name = gr.name, values = surv.col)
}
pl <- if(surv.col[1] != 'gg.def'){
pl + col
} else {pl + scale_colour_discrete(name = gr.name)}
line <- if(length(lty.est) == 1){
scale_linetype_manual(name = gr.name, values = rep(lty.est, strata))
} else {scale_linetype_manual(name = gr.name, values = lty.est)}
pl <- pl + line
pl <- if(CI == T) {
if(length(surv.col) > 1 && length(lty.est) > 1){
stop('Either surv.col or lty.est should be of length 1 in order
to plot 95% CI with multiple strata')
}else if((length(surv.col) > 1 | surv.col == 'gg.def')[1]){
pl + geom_step(aes(y = up, color = group), lty = lty.ci) +
geom_step(aes(y = low, color = group), lty = lty.ci)
} else{pl + geom_step(aes(y = up, lty = group), col = surv.col) +
geom_step(aes(y = low,lty = group), col = surv.col)}
} else {pl}
pl <- if(plot.cens == T & length(dat.cens) > 0){
pl + geom_point(data = dat.cens, aes(y = surv), shape = cens.shape,
col = cens.col)](https://image.slidesharecdn.com/scriptjantung-copy-150612114445-lva1-app6891/85/Script-jantung-copy-2-320.jpg)
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Script jantung copy
- 1. datatxt = read.table ("F:Penting!!!03. S2 MATH UGM_TFMSemester 4Bio statistikajantung.txt", header = T) datatxt library(survival) survival<-Surv(datatxt$time, datatxt$status) survival km2=survfit(Surv(time,status) ~treatment, data = datatxt) km2 summary(km2) #interval konfidensi library(ggplot2) #ggplot function ggsurv <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def', cens.col = 'red', lty.est = 1, lty.ci = 2, cens.shape = 3, back.white = F, xlab = 'Time', ylab = 'Survival', main = ''){ library(ggplot2) strata <- ifelse(is.null(s$strata) ==T, 1, length(s$strata)) stopifnot(length(surv.col) == 1 | length(surv.col) == strata) stopifnot(length(lty.est) == 1 | length(lty.est) == strata) ggsurv.s <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def', cens.col = 'red', lty.est = 1, lty.ci = 2, cens.shape = 3, back.white = F, xlab = 'Time', ylab = 'Survival', main = ''){ dat <- data.frame(time = c(0, s$time), surv = c(1, s$surv), up = c(1, s$upper), low = c(1, s$lower), cens = c(0, s$n.censor)) dat.cens <- subset(dat, cens != 0) col <- ifelse(surv.col == 'gg.def', 'black', surv.col) pl <- ggplot(dat, aes(x = time, y = surv)) + xlab(xlab) + ylab(ylab) + ggtitle(main) + geom_step(col = col, lty = lty.est) pl <- if(CI == T | CI == 'def') { pl + geom_step(aes(y = up), color = col, lty = lty.ci) + geom_step(aes(y = low), color = col, lty = lty.ci) } else (pl) pl <- if(plot.cens == T & length(dat.cens) > 0){ pl + geom_point(data = dat.cens, aes(y = surv), shape = cens.shape, col = cens.col) } else if (plot.cens == T & length(dat.cens) == 0){ stop ('There are no censored observations') } else(pl) pl <- if(back.white == T) {pl + theme_bw() } else (pl) pl }
- 2. ggsurv.m <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def', cens.col = 'red', lty.est = 1, lty.ci = 2, cens.shape = 3, back.white = F, xlab = 'Time', ylab = 'Survival', main = '') { n <- s$strata groups <- factor(unlist(strsplit(names (s$strata), '='))[seq(2, 2*strata, by = 2)]) gr.name <- unlist(strsplit(names(s$strata), '='))[1] gr.df <- vector('list', strata) ind <- vector('list', strata) n.ind <- c(0,n); n.ind <- cumsum(n.ind) for(i in 1:strata) ind[[i]] <- (n.ind[i]+1):n.ind[i+1] for(i in 1:strata){ gr.df[[i]] <- data.frame( time = c(0, s$time[ ind[[i]] ]), surv = c(1, s$surv[ ind[[i]] ]), up = c(1, s$upper[ ind[[i]] ]), low = c(1, s$lower[ ind[[i]] ]), cens = c(0, s$n.censor[ ind[[i]] ]), group = rep(groups[i], n[i] + 1)) } dat <- do.call(rbind, gr.df) dat.cens <- subset(dat, cens != 0) pl <- ggplot(dat, aes(x = time, y = surv, group = group)) + xlab(xlab) + ylab(ylab) + ggtitle(main) + geom_step(aes(col = group, lty = group)) col <- if(length(surv.col == 1)){ scale_colour_manual(name = gr.name, values = rep(surv.col, strata)) } else{ scale_colour_manual(name = gr.name, values = surv.col) } pl <- if(surv.col[1] != 'gg.def'){ pl + col } else {pl + scale_colour_discrete(name = gr.name)} line <- if(length(lty.est) == 1){ scale_linetype_manual(name = gr.name, values = rep(lty.est, strata)) } else {scale_linetype_manual(name = gr.name, values = lty.est)} pl <- pl + line pl <- if(CI == T) { if(length(surv.col) > 1 && length(lty.est) > 1){ stop('Either surv.col or lty.est should be of length 1 in order to plot 95% CI with multiple strata') }else if((length(surv.col) > 1 | surv.col == 'gg.def')[1]){ pl + geom_step(aes(y = up, color = group), lty = lty.ci) + geom_step(aes(y = low, color = group), lty = lty.ci) } else{pl + geom_step(aes(y = up, lty = group), col = surv.col) + geom_step(aes(y = low,lty = group), col = surv.col)} } else {pl} pl <- if(plot.cens == T & length(dat.cens) > 0){ pl + geom_point(data = dat.cens, aes(y = surv), shape = cens.shape, col = cens.col)
- 3. } else if (plot.cens == T & length(dat.cens) == 0){ stop ('There are no censored observations') } else(pl) pl <- if(back.white == T) {pl + theme_bw() } else (pl) pl } pl <- if(strata == 1) {ggsurv.s(s, CI , plot.cens, surv.col , cens.col, lty.est, lty.ci, cens.shape, back.white, xlab, ylab, main) } else {ggsurv.m(s, CI, plot.cens, surv.col , cens.col, lty.est, lty.ci, cens.shape, back.white, xlab, ylab, main)} pl } log.rank <-survdiff(Surv(time,status) ~treatment, data = datatxt) log.rank reg.weibul.ida<-survreg(Surv(time,status) ~time+treatment, data = datatxt, dist= "weibul") reg.weibul.ida summary(reg.weibul.ida)