我想做类似以下的事情:(myData
是一个数据表)
#create some data
myData = data.table(invisible.covariate=rnorm(50),
visible.covariate=rnorm(50),
category=factor(sample(1:3,50, replace=TRUE)),
treatment=sample(0:1,50, replace=TRUE))
myData[,outcome:=invisible.covariate+visible.covariate+treatment*as.integer(category)]
myData[,invisible.covariate:=NULL]
#process it
myData[treatment == 0,untreated.outcome:=outcome]
myData[treatment == 1,treated.outcome:=outcome]
myPredictors = matrix(0,ncol(myData),ncol(myData))
myPredictors[5,] = c(1,1,0,0,0,0)
myPredictors[6,] = c(1,1,0,0,0,0)
myImp = mice(myData,predictorMatrix=myPredictors)
fit1 = with(myImp, lm(treated.outcome ~ category)) #this works fine
for_each_imputed_dataset(myImp, #THIS IS NOT A REAL FUNCTION but I hope you get the idea
function(imputed_data_table) {
imputed_data_table[,treatment.effect:=treated.outcome-untreated.outcome]
})
fit2 = with(myImp, lm(treatment.effect ~ category))
#I want fit2 to be an object similar to fit1
...
我想为每个估算的数据集添加一个计算值,然后使用该计算值进行统计。显然,上面的结构可能不是你会怎么做的。我会对任何解决方案感到满意,无论是在老鼠之前以某种方式准备数据表,在上面勾勒的“fit =”之前的一步,还是在“with”调用中的一些复杂函数。