R :Fitting survival trees with time-varying covariates in RandomForestSRC2019 Community Moderator ElectionFitting a fully parametric proportional hazard model with time-varying covariates in RAdding time varying covariates to survival data using 'tmerge' in 'survival' packageSurvival analysis on left truncated data with ipredbagg or pecCforestCox PH modeling and prediction with time varying dependencyHow to find the accuracy for Survival Analysis using RandomForestSRC package in RInterval censored data: Cox proportional hazard and surival difference in RHow to calculate median survival in randomForestSRCSurvival analysis with time varying covariates using coxph in RDeviance residuals diagnostics on a Cox model with 13 independent covariates - any way I can get plots for each and every covariate?time-varying covariates for Survival analysis
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R :Fitting survival trees with time-varying covariates in RandomForestSRC
2019 Community Moderator ElectionFitting a fully parametric proportional hazard model with time-varying covariates in RAdding time varying covariates to survival data using 'tmerge' in 'survival' packageSurvival analysis on left truncated data with ipredbagg or pecCforestCox PH modeling and prediction with time varying dependencyHow to find the accuracy for Survival Analysis using RandomForestSRC package in RInterval censored data: Cox proportional hazard and surival difference in RHow to calculate median survival in randomForestSRCSurvival analysis with time varying covariates using coxph in RDeviance residuals diagnostics on a Cox model with 13 independent covariates - any way I can get plots for each and every covariate?time-varying covariates for Survival analysis
In the package LTRCtrees one can fit a decision tree to the special format of the Surv function :Surv(time, time2, event) as per below example
set.seed(0)
library(survival)
library(LTRCtrees)
## Create the start-stop-event triplet needed for coxph and LTRC trees
first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject
last <- c(first[-1], TRUE) #last id
time1 <- with(pbcseq, ifelse(first, 0, day))
time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0)))
event <- with(pbcseq, ifelse(last, status, 0))
event <- 1*(event==2)
pbcseq$time1 <- time1
pbcseq$time2 <- time2
pbcseq$event <- event
## Fit the Cox model and LTRC trees with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCIT.fit <- LTRCIT(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCART.fit <- LTRCART(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
Is it possible to use the same function on a Random Forest using the RandomForestSRC library
library(randomForestSRC)
RF.fit <- rfsrc(Surv(time1, time2, event) ~ age + sex + log(bili), data=pbcseq, nsplit = 3, ntree = 100, importance = TRUE)
Which generates an error:
Error in parseFormula(formula, data, ytry) :
Survival formula incorrectly specified.
r random-forest survival-analysis
asked Mar 7 at 12:27
aieduaiedu
306
add a comment |
In the package LTRCtrees one can fit a decision tree to the special format of the Surv function :Surv(time, time2, event) as per below example
set.seed(0)
library(survival)
library(LTRCtrees)
## Create the start-stop-event triplet needed for coxph and LTRC trees
first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject
last <- c(first[-1], TRUE) #last id
time1 <- with(pbcseq, ifelse(first, 0, day))
time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0)))
event <- with(pbcseq, ifelse(last, status, 0))
event <- 1*(event==2)
pbcseq$time1 <- time1
pbcseq$time2 <- time2
pbcseq$event <- event
## Fit the Cox model and LTRC trees with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCIT.fit <- LTRCIT(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCART.fit <- LTRCART(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
Is it possible to use the same function on a Random Forest using the RandomForestSRC library
library(randomForestSRC)
RF.fit <- rfsrc(Surv(time1, time2, event) ~ age + sex + log(bili), data=pbcseq, nsplit = 3, ntree = 100, importance = TRUE)
Which generates an error:
Error in parseFormula(formula, data, ytry) :
Survival formula incorrectly specified.
r random-forest survival-analysis
asked Mar 7 at 12:27
aieduaiedu
306
add a comment |
In the package LTRCtrees one can fit a decision tree to the special format of the Surv function :Surv(time, time2, event) as per below example
set.seed(0)
library(survival)
library(LTRCtrees)
## Create the start-stop-event triplet needed for coxph and LTRC trees
first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject
last <- c(first[-1], TRUE) #last id
time1 <- with(pbcseq, ifelse(first, 0, day))
time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0)))
event <- with(pbcseq, ifelse(last, status, 0))
event <- 1*(event==2)
pbcseq$time1 <- time1
pbcseq$time2 <- time2
pbcseq$event <- event
## Fit the Cox model and LTRC trees with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCIT.fit <- LTRCIT(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCART.fit <- LTRCART(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
Is it possible to use the same function on a Random Forest using the RandomForestSRC library
library(randomForestSRC)
RF.fit <- rfsrc(Surv(time1, time2, event) ~ age + sex + log(bili), data=pbcseq, nsplit = 3, ntree = 100, importance = TRUE)
Which generates an error:
Error in parseFormula(formula, data, ytry) :
Survival formula incorrectly specified.
r random-forest survival-analysis
asked Mar 7 at 12:27
aieduaiedu
306
In the package LTRCtrees one can fit a decision tree to the special format of the Surv function :Surv(time, time2, event) as per below example
set.seed(0)
library(survival)
library(LTRCtrees)
## Create the start-stop-event triplet needed for coxph and LTRC trees
first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject
last <- c(first[-1], TRUE) #last id
time1 <- with(pbcseq, ifelse(first, 0, day))
time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0)))
event <- with(pbcseq, ifelse(last, status, 0))
event <- 1*(event==2)
pbcseq$time1 <- time1
pbcseq$time2 <- time2
pbcseq$event <- event
## Fit the Cox model and LTRC trees with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCIT.fit <- LTRCIT(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCART.fit <- LTRCART(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
Is it possible to use the same function on a Random Forest using the RandomForestSRC library
library(randomForestSRC)
RF.fit <- rfsrc(Surv(time1, time2, event) ~ age + sex + log(bili), data=pbcseq, nsplit = 3, ntree = 100, importance = TRUE)
Which generates an error:
Error in parseFormula(formula, data, ytry) :
Survival formula incorrectly specified.
r random-forest survival-analysis
r random-forest survival-analysis
asked Mar 7 at 12:27
aieduaiedu
306
asked Mar 7 at 12:27
aieduaiedu
306
asked Mar 7 at 12:27
aieduaiedu
306
asked Mar 7 at 12:27
aieduaiedu
306
asked Mar 7 at 12:27
aieduaiedu
306
306
add a comment |
add a comment |
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