R 中带有插补数据集的精细和灰色模型

Question

I have a long (vertically stacked) dataset containing 10 imputations (variable "imputation" identifies imputation number). The imputation was done in SAS but I would like to calculate some c-statistics using R.

I know how to calculate c-stats using cindex function and FGR function for one imputed dataset. I am not sure how I will repeat this in the vertically stacked dataset. I tried to use "with" function but no luck.

Here are my codes:

fgr.model <- FGR(Hist(time, outcome) ~ x1 + x2 + x3, data=mydata1, cause=1)

cscore <- cindex(list(fgr.model), forumula=Hist(time, outcome)~1,
          cens.model="marginal", data=mydata1, eval.time=c(1826), cause=1)

How to calculate c-stats using cindex function and FGR function in the vertically stacked dataset?

Answer 1

Based on details in the comments: I understood that you need to calculate stats on a partition of the original dataframe mydata1 --ie, you need to select only the rows that correspond to one "imputation" (this is the name you use, are you referring with a wrong word to an.. 'input session'? just curious)

First you have to create a new dataframe containing only the data for one "imputation", in the following examples we consider the operation for the imputation number 4. There are different ways to make the job done.

First way that works if columns names are right:

mydata1portion = mydata1[mydata1$ImputationNo==4,]

Second that works if position/order of columns are right:

mydata1portion = mydata1[mydata1$V1==4,]

Third way in the case the dataframe imputations/rows are ordered.

mydata1portion = mydata1[(100*(4-1)+1):(100*4),]

The first two you use the value for the column ImputationNo in order to filter the dataframe, in the last one you cut the dataframe based on the position of the rows.

Finally you can calculate the stats on the obtained mydata1portion and NOT on the full mydata1 .