coxModelFrame.coxph(object) 中的错误：在调用 coxph 时设置了无效的对象 x=TRUE
Error in coxModelFrame.coxph(object) : invalid object set x=TRUE in the call to coxph
以下示例适用于正在构建 Cox 比例风险模型并尝试生成预测误差曲线但收到错误说明的任何人：
coxModelFrame.coxph(object) 中的错误：无效的对象
在对 coxph 的调用中设置 x=TRUE。
这是重现错误的代码：
图书馆
library(survival)
library(survminer)
library(pec)
library(Hmisc)
library(rms)
library(riskRegression)
#install.packages("doMC", repos="http://R-Forge.R-project.org")
library(doMC)
数据
#Load and store the data
lcOrig <- read.csv("cancer.csv")
#Replace all the 1's with 0's (censored)
lcOrig$status <- gsub(pattern = "1", replacement = "0", x = lcOrig$status, fixed = TRUE)
#Replace all the 2's with 1's (death)
lcOrig$status <- gsub (pattern = "2", replacement = "1", x = lcOrig$status, fixed = TRUE)
#Do the same thing for sex (0 = Males, 1 = Females)
lcOrig$sex <- gsub(pattern = "1", replacement = "0", x = lcOrig$sex, fixed = TRUE)
lcOrig$sex <- gsub(pattern = "2", replacement = "1", x = lcOrig$sex, fixed = TRUE)
#Change the class of these variables to integer.
lcOrig$status <- as.integer(lcOrig$status)
lcOrig$sex <- as.integer(lcOrig$sex)
lcOrig$ph.ecog <- as.integer(lcOrig$ph.ecog)
#Remove missing values and column with over 20% missing data.
apply(lcOrig, 2, function(x) sum(is.na(x))/length(x))
lcOrig <- lcOrig[, c(1:9, 11)]
lc <- lcOrig[complete.cases(lcOrig), ]
考克斯比例风险
fitform1 <- Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + pat.karno + wt.loss
cox1 <- coxph(fitform1, data = lc)
预测误差曲线
extends <- function(...) TRUE
library("doMC")
registerDoMC()
set.seed(0692)
fitpec1 <- pec(list("CPH" = cox1), data = lc, formula = fitform1, splitMethod = "cv10", B = 5, keep.index = TRUE, keep.matrix = TRUE)
最后一行代码导致以下错误：
coxModelFrame.coxph(object) 中的错误：无效的对象
在对 coxph
的调用中设置 x=TRUE
解决方案
变化：
cox1 <- coxph(fitform1, data = lc)
收件人：
cox1 <- coxph(fitform1, data = lc, x = TRUE)
2 年前这不是一项要求，但现在是。我希望这可以帮助您节省一些时间！
以下示例适用于正在构建 Cox 比例风险模型并尝试生成预测误差曲线但收到错误说明的任何人：
coxModelFrame.coxph(object) 中的错误：无效的对象 在对 coxph 的调用中设置 x=TRUE。
这是重现错误的代码：
图书馆
library(survival)
library(survminer)
library(pec)
library(Hmisc)
library(rms)
library(riskRegression)
#install.packages("doMC", repos="http://R-Forge.R-project.org")
library(doMC)
数据
#Load and store the data
lcOrig <- read.csv("cancer.csv")
#Replace all the 1's with 0's (censored)
lcOrig$status <- gsub(pattern = "1", replacement = "0", x = lcOrig$status, fixed = TRUE)
#Replace all the 2's with 1's (death)
lcOrig$status <- gsub (pattern = "2", replacement = "1", x = lcOrig$status, fixed = TRUE)
#Do the same thing for sex (0 = Males, 1 = Females)
lcOrig$sex <- gsub(pattern = "1", replacement = "0", x = lcOrig$sex, fixed = TRUE)
lcOrig$sex <- gsub(pattern = "2", replacement = "1", x = lcOrig$sex, fixed = TRUE)
#Change the class of these variables to integer.
lcOrig$status <- as.integer(lcOrig$status)
lcOrig$sex <- as.integer(lcOrig$sex)
lcOrig$ph.ecog <- as.integer(lcOrig$ph.ecog)
#Remove missing values and column with over 20% missing data.
apply(lcOrig, 2, function(x) sum(is.na(x))/length(x))
lcOrig <- lcOrig[, c(1:9, 11)]
lc <- lcOrig[complete.cases(lcOrig), ]
考克斯比例风险
fitform1 <- Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + pat.karno + wt.loss
cox1 <- coxph(fitform1, data = lc)
预测误差曲线
extends <- function(...) TRUE
library("doMC")
registerDoMC()
set.seed(0692)
fitpec1 <- pec(list("CPH" = cox1), data = lc, formula = fitform1, splitMethod = "cv10", B = 5, keep.index = TRUE, keep.matrix = TRUE)
最后一行代码导致以下错误： coxModelFrame.coxph(object) 中的错误：无效的对象 在对 coxph
的调用中设置 x=TRUE解决方案
变化：
cox1 <- coxph(fitform1, data = lc)
收件人：
cox1 <- coxph(fitform1, data = lc, x = TRUE)
2 年前这不是一项要求，但现在是。我希望这可以帮助您节省一些时间！