如何获得 R 中 k 折交叉验证的每一折的系数、z 分数和 p 值？

Question

我正在使用 glm 执行 5 折交叉验证以执行逻辑回归。这是一个使用内置汽车数据集的可重现示例

library(caret)

data("mtcars")
str(mtcars)


mtcars$vs<-as.factor(mtcars$vs)
df0<-na.omit(mtcars)

set.seed(123) 
train.control <- trainControl(method = "cv", number = 5)
# Train the model
model <- train(vs ~., data = mtcars, method = "glm",
               trControl = train.control)


print(model)

summary(model)

model$resample

confusionMatrix(model)

pred.mod  <- predict(model)
confusionMatrix(data=pred.mod, reference=mtcars$vs)

输出


> print(model)

Generalized Linear Model 

32 samples
10 predictors
 2 classes: '0', '1' 

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 25, 26, 25, 27, 25 
Resampling results:

  Accuracy   Kappa    
  0.9095238  0.8164638

> summary(model)

Call:
NULL

Deviance Residuals: 
       Min          1Q      Median          3Q         Max  
-1.181e-05  -2.110e-08  -2.110e-08   2.110e-08   1.181e-05  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)  8.117e+01  1.589e+07       0        1
mpg          2.451e+00  5.979e+04       0        1
cyl         -3.908e+01  2.947e+05       0        1
disp        -1.927e-02  8.518e+03       0        1
hp           3.129e-01  2.283e+04       0        1
drat        -2.735e+01  9.696e+05       0        1
wt          -1.248e+01  6.437e+05       0        1
qsec         1.565e+01  3.845e+05       0        1
am          -4.562e+01  3.632e+05       0        1
gear        -2.835e+01  5.448e+05       0        1
carb         1.788e+01  2.971e+05       0        1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 4.3860e+01  on 31  degrees of freedom
Residual deviance: 7.2154e-10  on 21  degrees of freedom
AIC: 22

Number of Fisher Scoring iterations: 25

> model$resample
   Accuracy     Kappa Resample
1 0.8571429 0.6956522    Fold1
2 0.8333333 0.6666667    Fold2
3 0.8571429 0.7200000    Fold3
4 1.0000000 1.0000000    Fold4
5 1.0000000 1.0000000    Fold5


> confusionMatrix(model)
Cross-Validated (5 fold) Confusion Matrix 

(entries are percentual average cell counts across resamples)
 
          Reference
Prediction    0    1
         0 50.0  3.1
         1  6.2 40.6
                            
 Accuracy (average) : 0.9062


> pred.mod  <- predict(model)
> confusionMatrix(data=pred.mod, reference=mtcars$vs)
Confusion Matrix and Statistics

          Reference
Prediction  0  1
         0 18  0
         1  0 14
                                     
               Accuracy : 1          
                 95% CI : (0.8911, 1)
    No Information Rate : 0.5625     
    P-Value [Acc > NIR] : 1.009e-08  
                                     
                  Kappa : 1          
                                     
 Mcnemar's Test P-Value : NA         
                                     
            Sensitivity : 1.0000     
            Specificity : 1.0000     
         Pos Pred Value : 1.0000     
         Neg Pred Value : 1.0000     
             Prevalence : 0.5625     
         Detection Rate : 0.5625     
   Detection Prevalence : 0.5625     
      Balanced Accuracy : 1.0000     
                                     
       'Positive' Class : 0

这一切都很好，但我想获得每个折叠的摘要（模型）信息（意味着执行 summary() 时获得的系数、p 值、z 分数等），如果可能，还包括每个折叠的灵敏度和特异性。有人可以帮忙吗？

Answer 1

是个有趣的问题。您正在寻找的值不能直接从 model 对象获得，但可以通过了解训练数据的哪些观察是哪个折叠的一部分来重新计算。如果您在 trainControl 函数中指定 savePredictions = "all"，则可以从 model 中提取此信息。通过对每 k 次折叠的预测，您可以执行以下操作：

#first of all, save all predictions from all folds
set.seed(123) 
train.control <- trainControl(method = "cv", number = 5,savePredictions = 
"all")
# Train the model
model <- train(vs ~., data = mtcars, method = "glm",
           trControl = train.control)

#now we can extract the statistics you are looking for
fold <- unique(pred$Resample)
mystat <- function(model,x){
pred <- model$pred
df <- pred[pred$Resample==x,]
cm <- confusionMatrix(df$pred,df$obs)
control <- trainControl(method = "none")
newdat <- mtcars[pred$rowIndex,]
fit <- train(vs~.,data=newdat,trControl=control)
summ <- summary(model)
z_p <- summ$coefficients[,3:4]
return(list(cm,z_p))
}
stat <- lapply(fold, mystat,model=model)
names(stat) <- fold

请注意，通过在 trainControl 中指定 method="none" 强制 train 将模型拟合到整个训练集，而无需任何重采样或参数调整。在这种形式下，它不是一个漂亮的函数，但它可以做你想做的，你可以随时调整它以使其更通用。

如何获得 R 中 k 折交叉验证的每一折的系数、z 分数和 p 值？

How do I obtain the coefficients, z scores, and p-values for each fold of a k-fold cross validation in R?

r

machine-learning

glm

cross-validation

r-caret