L’estimateur de Kaplan-Meier donne la fonction de survie non param\u00e9trique.
Pour l’obtenir sous R on peut utiliser le package survival.<\/p>\n
On se place ici dans un cas tr\u00e8s simple o\u00f9 il n’y a ni censure ni troncature.
Pour bien comprendre le code, je vous conseille vivement de regarder la documentation du package en question!!<\/p>\n
<\/p>\n
#survival analysis
ls()
rm(list=ls())
library(survival)
#on cr\u00e9e un jeu de donn\u00e9es correspondant \u00e0 des dur\u00e9es (\u00e9tudi\u00e9es dans l'analyse de survie)
z<-c(14,14,14,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,23)
d<-data.frame(delay=z)
#on cr\u00e9e une colonne status, ici tous les individus sont \"morts\" pendant l'exp\u00e9rience
# mort au sens de l'analyse de survie
d$status<-1
s<-survfit(Surv(d$delay,d$status)~1)
plot(s,main=\"survival function\")
<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"L’estimateur de Kaplan-Meier donne la fonction de survie non param\u00e9trique.Pour l’obtenir sous R on peut utiliser le package survival. On se place ici dans un cas tr\u00e8s simple o\u00f9 il n’y a ni censure ni troncature.Pour bien comprendre le code, je vous conseille vivement de regarder la documentation du package en question!! #survival analysisls()rm(list=ls())library(survival)#on cr\u00e9e un jeu de donn\u00e9es correspondant \u00e0 des dur\u00e9es (\u00e9tudi\u00e9es dans l’analyse de survie) z<-c(14,14,14,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,23)d<-data.frame(delay=z)#on cr\u00e9e une colonne status, ici tous les individus sont \u00ab\u00a0morts\u00a0\u00bb pendant l’exp\u00e9rience # mort au sens de l’analyse de survie d$status<-1 s<-survfit(Surv(d$delay,d$status)~1)plot(s,main=\u00a0\u00bbsurvival function\u00a0\u00bb)Read More →<\/a><\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","rop_custom_images_group":[],"rop_custom_messages_group":[],"rop_publish_now":"initial","rop_publish_now_accounts":{"twitter_399453572_399453572":""},"rop_publish_now_history":[],"rop_publish_now_status":"pending","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":{"0":"entry","1":"post","2":"publish","3":"author-melen","4":"post-556","6":"format-standard","7":"category-analyse-de-survie"},"acf":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p9O7Sx-8Y","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/comments?post=556"}],"version-history":[{"count":2,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/556\/revisions"}],"predecessor-version":[{"id":4098,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/556\/revisions\/4098"}],"wp:attachment":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/media?parent=556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/categories?post=556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/tags?post=556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}