dimnames(x) <- dn 中的错误：“dimnames”的长度； [1] 当我在 R 中运行logistic.display时，不等于数组范围

Question

我正在尝试建立二项式逻辑回归模型。这些是我使用的代码行。我的初始 df 大约是 800000 个观察值。我只是使用 1000 个 obs 来构建模型，而不会使 R studio 运行它变得非常繁重。之前的线路运行没有任何问题。现在，我尝试将这些行应用于我的初始 df，当我运行 stepaic 方法时，我没有得到相同的变量，并且对于logistic.display 函数，我收到错误 "Error in dimnames(x) < ;- dn : 'dimnames' 的长度 [1] 不等于数组范围” 任何人都可以帮忙吗？谢谢

library(epiDisplay)
library(caret)
model <- glm(decision ~., data = train.data,family = "binomial")

step.model <- stepAIC(model, direction = "both", 
                      trace = FALSE)

logistic.display(step.model)

structure(list(id = c(1, 3, 5, 10, 11, 13, 15, 17, 18, 21), 
    var1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), var2 = c(0.1, 
    0.77, 0.75, 0.09, 0.84, 0.52, 0.45, 0.27, 0.71, 0.15), var3 = c("D", 
    "D", "B", "B", "B", "E", "E", "C", "C", "B"), var4 = c(5L, 
    5L, 6L, 7L, 7L, 6L, 6L, 7L, 7L, 7L), var5 = c(0L, 0L, 2L, 
    0L, 0L, 2L, 2L, 0L, 0L, 0L), var6 = c(55L, 55L, 52L, 46L, 
    46L, 38L, 38L, 33L, 33L, 41L), var7 = c(50L, 50L, 50L, 
    50L, 50L, 50L, 50L, 68L, 68L, 50L), var8 = c("B12", "B12", 
    "B12", "B13", "B12", "B14", "B12", "B12", "B13", "B12"), 
    var9 = c("Regular", "Regular", "Diesel", "Diesel", "Diesel", 
    "Regular", "Regular", "Diesel", "Diesel", "Diesel"), var10 = c(1217L, 
    1217L, 54L, 76L, 76L, 3003L, 3003L, 137L, 137L, 60L), var11 = c("R82", 
    "R82", "R22", "R72", "R72", "R31", "R31", "R91", "R91", "R52"
    ), decision = c(1, 1, 1, 1, 0, 1, 1, 0, 1, 1)), row.names = c(NA, 
10L), class = "data.frame")

Answer 1

根据您当前的数据和代码，我得到以下结果：

library(epiDisplay)
library(caret)

model <- glm(decision ~., data = train.data,family = "binomial")

step.model <- stepAIC(model, direction = "both", 
                      trace = FALSE)

logistic.display(step.model)

#> Logistic regression predicting decision 
#>  
#>                   crude OR(95%CI)  adj. OR(95%CI)                P(Wald's test)
#> id (cont. var.)   0.92 (0.7,1.2)   3.85079246964067e+72 (0,Inf)  0.999         
#>                                                                                
#> var2 (cont. var.) 0.23 (0,77.31)   0 (0,Inf)                     0.999         
#>                                                                                
#> var4 (cont. var.) 0 (0,Inf)        0 (0,Inf)                     0.999         
#>                                                                                
#> var6 (cont. var.) 1.1 (0.87,1.38)  7.1846721655706e+38 (0,Inf)   0.999         
#>                                                                                
#>                   P(LR-test)
#> id (cont. var.)   0.014     
#>                             
#> var2 (cont. var.) 0.017     
#>                             
#> var4 (cont. var.) 0.003     
#>                             
#> var6 (cont. var.) 0.017     
#>                             
#> Log-likelihood = 0
#> No. of observations = 10
#> AIC value = 10

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