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我有一个来自 Rmice包的多重估算模型，其中有很多因子变量。例如：

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)

我想通过针对每次删除一个变量的每个可能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示变量）。手动，我可以这样做：

# 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？对于之前的问题drop1，向我建议了非常有用的功能——现在我想做一些与此完全相同的事情，除了这种情况。mice

可能有用的说明：一个令人讨厌的特性pool.compare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。

4

1 回答 1

4

在按照pool.compare.

因此，使用fake上面的数据 - 调整了类别的数量

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"

模型现在以正确的顺序传递给glm调用。我也替换了glm.mids方法，因为它已被替换with.mids- 请参阅?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

使用这个数据集你会收到很多警告

编辑

你可以把它包装在一个函数中

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)")
于 2014-10-29T07:15:58.537 回答