自动比较小鼠glm.mids的嵌套模型 [英] Automatically compare nested models from mice's glm.mids

问题描述

我有一个来自R的mice包的多重插补模型，其中有很多因子变量.例如:

I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example:

library(mice)
library(Hmisc)

# turn all the variables into factors
fake = nhanes
fake$age = as.factor(nhanes$age)
fake$bmi = cut2(nhanes$bmi, g=3) 
fake$chl = cut2(nhanes$chl, g=3) 

head(fake)
  age         bmi hyp       chl
1   1        <NA>  NA      <NA>
2   2 [20.4,25.5)   1 [187,206)
3   1        <NA>   1 [187,206)
4   3        <NA>  NA      <NA>
5   1 [20.4,25.5)   1 [113,187)
6   3        <NA>  NA [113,187)

imput = mice(nhanes)

# big model
fit1 = glm.mids((hyp==2) ~ age + bmi + chl, data=imput, family = binomial)

我想通过针对每个可能一次删除一个变量的嵌套模型测试完整模型，来测试模型中每个整个因子变量的重要性(而不是每个级别的指标变量) .可以手动进行:

I want to test the significance of each entire factor variable in the model (not the indicator variables for each level) by testing the full model against each possible nested model that drops one variable at a time. Manually, I can do:

# small model (no chl)
fit2 = glm.mids((hyp==2) ~ age + bmi, data=imput, family = binomial)

# extract p-value from pool.compare
pool.compare(fit1, fit2)$pvalue

如何为模型中的所有因子变量自动执行此操作?我对向我建议了非常有用的功能drop1>上一个问题-现在，除了mice案例外，我想做一些完全一样的事情.

How can I do this automatically for all the factor variables in my model? The very helpful function drop1 was suggested to me for a previous question -- now I want to do something exactly like that except for the mice case.

可能有用的注释: pool.compare的一个令人讨厌的功能是它似乎希望将较大模型中的额外"变量放在与较小模型共享的变量之后.

Possibly helpful note: An annoying feature of pool.compare is that it appears to want the "extra" variables in the larger model to be placed after the ones that are shared with the smaller model.

推荐答案

在按pool.compare所需的顺序排列预测变量后，您可以使用循环来迭代预测变量的不同组合.

You can use a loop to iterate through the different combinations of predictors, after arranging them in the order required for pool.compare.

因此，请使用上方的fake数据-调整类别数

So using your fake data from above - tweaked the number of categories

library(mice)
library(Hmisc)
# turn all the variables into factors
# turn all the variables into factors
fake <- nhanes
fake$age <- as.factor(nhanes$age)
fake$bmi <- cut2(nhanes$bmi, g=2) 
fake$chl <- cut2(nhanes$chl, g=2) 

# Impute
imput <- mice(fake, seed=1)

# Create models 
# - reduced models with one variable removed
# - full models with extra variables at end of expression
vars <- c("age", "bmi", "chl")

red <- combn(vars, length(vars)-1 , simplify=FALSE)
diffs <- lapply(red, function(i) setdiff(vars, i) )
(full <- lapply(1:length(red), function(i) 
                            paste(c(red[[i]], diffs[[i]]), collapse=" + ")))
#[[1]]
#[1] "age + bmi + chl"

#[[2]]
#[1] "age + chl + bmi"

#[[3]]
#[1] "bmi + chl + age"

(red <- combn(vars, length(vars)-1 , FUN=paste, collapse=" + "))
#[1] "age + bmi" "age + chl" "bmi + chl"

现在，模型以正确的顺序传递给glm调用.我还替换了glm.mids方法，因为它已被with.mids替换-参见?glm.mids

The models are now in the correct order to pass to the glm call. I've also replaced glm.mids method as it has been replaced by with.mids - see ?glm.mids

out <- vector("list", length(red))

for( i in 1:length(red)) {

  redMod <-  with(imput, 
               glm(formula(paste("(hyp==2) ~ ", red[[i]])), family = binomial))

  fullMod <-  with(imput, 
               glm(formula(paste("(hyp==2) ~ ", full[[i]])), family = binomial))

  out[[i]] <- list(predictors = diffs[[i]], 
                   pval = c(pool.compare(fullMod, redMod)$pvalue))
   }

do.call(rbind.data.frame, out)
#    predictors      pval
#2         chl 0.9976629
#21        bmi 0.9985028
#3         age 0.9815831

# Check manually by leaving out chl
mod1 <- with(imput, glm((hyp==2) ~ age + bmi + chl , family = binomial))
mod2 <- with(imput, glm((hyp==2) ~ age + bmi , family = binomial))
pool.compare(mod1, mod2)$pvalue
#         [,1]
#[1,] 0.9976629

使用此数据集，您将收到很多警告

You will get a lot of warnings using this dataset

编辑

您可以将其包装在函数中

You could wrap this in a function

impGlmDrop1 <- function(vars, outcome, Data=imput,  Family="binomial") 
{

  red <- combn(vars, length(vars)-1 , simplify=FALSE)
  diffs <- lapply(red, function(i) setdiff(vars, i))
  full <- lapply(1:length(red), function(i) 
                      paste(c(red[[i]], diffs[[i]]), collapse=" + "))
  red <- combn(vars, length(vars)-1 , FUN=paste, collapse=" + ")

  out <- vector("list", length(red))
  for( i in 1:length(red)) {

  redMod <-  with(Data, 
              glm(formula(paste(outcome, red[[i]], sep="~")), family = Family))
  fullMod <-  with(Data, 
              glm(formula(paste(outcome, full[[i]], sep="~")), family = Family))
  out[[i]] <- list(predictors = diffs[[i]], 
                   pval = c(pool.compare(fullMod, redMod)$pvalue)  )
  }
  do.call(rbind.data.frame, out)
}

# Run
impGlmDrop1(c("age", "bmi", "chl"), "(hyp==2)")

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