Dlbcl

Dlbcl


Examples
 library("survival")  set.seed(29)  # compute the cutpoint and plot the empirical process   mod <- maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL, smethod="LogRank")  print(mod)  ## Not run:  #   # postscript("statDLBCL.ps", horizontal=F, width=8, height=8) #   pdf("statDLBCL.pdf", width=8, height=8) # ## End(Not run) par(mai=c(1.0196235, 1.0196235, 0.8196973, 0.4198450)) plot(mod, cex.lab=1.6, cex.axis=1.6, xlab="Mean gene expression",lwd=2) ## Not run:  #   dev.off() # ## End(Not run)  # significance of the cutpoint # limiting distribution  maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL,              smethod="LogRank", pmethod="Lau92", iscores=TRUE)  # improved Bonferroni inequality, plot with significance bound  maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL,              smethod="LogRank", pmethod="Lau94", iscores=TRUE)  mod <- maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL, smethod="LogRank",                     pmethod="Lau94", alpha=0.05) plot(mod, xlab="Mean gene expression")  ## Not run:  # #  postscript(file="RNewsStat.ps",horizontal=F, width=8, height=8) #    pdf("RNewsStat.pdf", width=8, height=8) #  # ## End(Not run) par(mai=c(1.0196235, 1.0196235, 0.8196973, 0.4198450)) plot(mod, xlab="Mean gene expression", cex.lab=1.6, cex.axis=1.6) ## Not run:  #   dev.off() # ## End(Not run)  # small sample solution Hothorn & Lausen  maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL,              smethod="LogRank", pmethod="HL")  # normal approximation  maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL,              smethod="LogRank", pmethod="exactGauss", iscores=TRUE,              abseps=0.01)  # conditional Monte-Carlo maxstat.test(Surv(time, cens) ~ MGE, data=DLBCL,              smethod="LogRank", pmethod="condMC", B = 9999)   # survival analysis and plotting like in Alizadeh et al. (2000)    splitGEG <- rep(1, nrow(DLBCL))   DLBCL <- cbind(DLBCL, splitGEG)   DLBCL$splitGEG[DLBCL$GEG == "Activated B-like"] <- 0    plot(survfit(Surv(time, cens) ~ splitGEG, data=DLBCL),        xlab="Survival time in month", ylab="Probability")    text(90, 0.7, "GC B-like")   text(60, 0.3, "Activated B-like")    splitIPI <- rep(1, nrow(DLBCL))   DLBCL <- cbind(DLBCL, splitIPI)   DLBCL$splitIPI[DLBCL$IPI <= 2] <- 0    plot(survfit(Surv(time, cens) ~ splitIPI, data=DLBCL),        xlab="Survival time in month", ylab="Probability")    text(90, 0.7, "Low clinical risk")   text(60, 0.25, "High clinical risk")    # survival analysis using the cutpoint     splitMGE <- rep(1, nrow(DLBCL))   DLBCL <- cbind(DLBCL, splitMGE)   DLBCL$splitMGE[DLBCL$MGE <= mod$estimate] <- 0    ## Not run:  #    # postscript("survDLBCL.ps",horizontal=F, width=8, height=8) #     pdf("survDLBCL.pdf", width=8, height=8) #  #   ## End(Not run)   par(mai=c(1.0196235, 1.0196235, 0.8196973, 0.4198450))    plot(survfit(Surv(time, cens) ~ splitMGE, data=DLBCL),        xlab = "Survival time in month",        ylab="Probability", cex.lab=1.6, cex.axis=1.6, lwd=2)    text(90, 0.9, expression("Mean gene expression" > 0.186), cex=1.6)      text(90, 0.45, expression("Mean gene expression" <= 0.186 ), cex=1.6)       ## Not run:  #     dev.off() #   ## End(Not run) 

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Источник: www.rdocumentation.org


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