My recent research needs to compare different machine learning survival analysis modeling methods. I used the tidymodels package on classification problems and felt very good. I found relevant tutorials on Google, and the website is as follows: Posts /2021/11/survival-analysis-parsnip-adjacent/.
I followed the code of the tutorial exactly, but there is still an error report, I don't know if it is a problem with the package itself. thanks for your answer
rm(list = ls())
options(stringsAsFactors = T)
#> Warning in options(stringsAsFactors = T): 'options(stringsAsFactors = TRUE)' is
#> deprecated and will be disabled
Sys.setenv(LANGUAGE = "en")
library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 4.0.5
#> Warning: package 'ggplot2' was built under R version 4.0.5
#> Warning: package 'tidyr' was built under R version 4.0.5
#> Warning: package 'readr' was built under R version 4.0.5
#> Warning: package 'dplyr' was built under R version 4.0.5
#> Warning: package 'forcats' was built under R version 4.0.5
library(tidymodels)
#> Warning: package 'tidymodels' was built under R version 4.0.5
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
#> Warning: package 'broom' was built under R version 4.0.5
#> Warning: package 'dials' was built under R version 4.0.5
#> Warning: package 'infer' was built under R version 4.0.5
#> Warning: package 'modeldata' was built under R version 4.0.5
#> Warning: package 'recipes' was built under R version 4.0.5
#> Warning: package 'tune' was built under R version 4.0.5
#> Warning: package 'workflows' was built under R version 4.0.5
#> Warning: package 'workflowsets' was built under R version 4.0.5
#> Warning: package 'yardstick' was built under R version 4.0.5
library(censored)
#> Loading required package: survival
#> Warning: package 'survival' was built under R version 4.0.5
library(survival)
# 载入数据集
bladder_train <- bladder[-c(1:3),]
bladder_test <- bladder[1:3,]
cox_spec <- proportional_hazards(penalty = 0.123) %>%
set_engine("glmnet")
f_fit <- fit(cox_spec,
Surv(stop, event) ~ rx + size + number + strata(enum),
data = bladder_train)
#> Error: 'stratifySurv' is not an exported object from 'namespace:glmnet'
f_pred <- predict(f_fit, new_data = bladder_test,
type = "survival", time = seq(0, 20, 1))
#> Error in predict(f_fit, new_data = bladder_test, type = "survival", time = seq(0, : object 'f_fit' not found
f_pred <- f_pred %>%
mutate(id = factor(1:3)) %>%
unnest(cols = .pred)
#> Error in mutate(., id = factor(1:3)): object 'f_pred' not found
f_pred %>%
ggplot(aes(x = .time, y = .pred_survival, col = id)) +
geom_step()
#> Error in ggplot(., aes(x = .time, y = .pred_survival, col = id)): object 'f_pred' not found