r语言 - 似然比检验："模型并非全部拟合到相同大小的数据集"

Question

我是一个绝对的R初学者，需要一些帮助来进行单变量分析的似然比测试。这是代码：

#Univariate analysis for conscientiousness (categorical)
fit <- glm(BCS_Bin~Conscientiousness_cat,data=dat,family=binomial)
summary(fit)
#Likelihood ratio test
fit0<-glm(BCS_Bin~1, data=dat, family=binomial)
summary(fit0)
lrtest(fit, fit0)

结果是：

Call:
glm(formula = BCS_Bin ~ Conscientiousness_cat, family = binomial, 
data = dat)
Deviance Residuals: 
Min       1Q   Median       3Q      Max  
-0.8847  -0.8847  -0.8439   1.5016   1.5527  
Coefficients:
Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -0.84933    0.03461 -24.541   <2e-16 ***
Conscientiousness_catLow  0.11321    0.05526   2.049   0.0405 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7962.1  on 6439  degrees of freedom
Residual deviance: 7957.9  on 6438  degrees of freedom
(1963 observations deleted due to missingness)
AIC: 7961.9
Number of Fisher Scoring iterations: 4

和：

Call:
glm(formula = BCS_Bin ~ 1, family = binomial, data = dat)
Deviance Residuals: 
Min       1Q   Median       3Q      Max  
-0.8524  -0.8524  -0.8524   1.5419   1.5419  
Coefficients:
Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.82535    0.02379  -34.69   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 10251  on 8337  degrees of freedom
Residual deviance: 10251  on 8337  degrees of freedom
(65 observations deleted due to missingness)
AIC: 10253
Number of Fisher Scoring iterations: 4

对于我的LRT:

Error in lrtest.default(fit, fit0) : 
models were not all fitted to the same size of dataset

我知道这是因为缺少了不同数量的观测结果？这是因为这是一份大型问卷的数据，与结果变量(身体状况得分/BCS)相比，评估我的预测变量(尽责性)的问题出现了更多的辍学现象。因此，我只是有更多的BCS数据，而不是认真(它也会对我的许多其他变量产生同样的错误)。

Answer 1

为了运行似然比测试，只有截距的模型必须适合与包括Conscientiousness_cat的模型相同的观测值。因此，您需要没有Conscientiousness_cat:缺失值的数据子集

BCS_bin_subset = BCS_bin[complete.cases(BCS_bin[,"Conscientiousness_cat"]), ]

您可以在这一数据子集上运行这两个模型，并且似然比测试应该在没有错误的情况下运行。

在你的情况下，你也可以做：

BCS_bin_subset = BCS_bin[!is.na(BCS_bin$Conscientiousness_cat), ]

然而，当您想要一个数据帧的子集在多个变量之间没有缺失值时，complete.cases很方便。

如果要运行多个模型，另一个更方便但也更复杂的选项是，首先拟合使用BCS_bin中最大数量变量的模型(因为该模型将因缺失而排除最大数量的观测值)，然后使用update函数将该模型更新为变量较少的模型。我们只需要确保update每次使用相同的观测值，我们使用下面定义的包装函数来完成这一操作。下面是一个使用内置mtcars数据帧的示例：

library(lmtest)
dat = mtcars
# Create some missing values in mtcars
dat[1, "wt"] = NA
dat[5, "cyl"] = NA
dat[7, "hp"] = NA
# Wrapper function to ensure the same observations are used for each 
#  updated model as were used in the first model
# From https://stackoverflow.com/a/37341927/496488
update_nested <- function(object, formula., ..., evaluate = TRUE){
update(object = object, formula. = formula., data = object$model, ..., evaluate = evaluate)
}
m1 = lm(mpg ~ wt + cyl + hp, data=dat)
m2 = update_nested(m1, . ~ . - wt)  # Remove wt
m3 = update_nested(m1, . ~ . - cyl) # Remove cyl
m4 = update_nested(m1, . ~ . - wt - cyl) # Remove wt and cyl
m5 = update_nested(m1, . ~ . - wt - cyl - hp) # Remove all three variables (i.e., model with intercept only)
lrtest(m5,m4,m3,m2,m1)