How can I create a dataframe which collect the results of a for loop with multilple indexes in R?

I want to obtain from a for loop a dataframe in which i can visualize better the result of the code. I'm in the context of Random survival forest, so I leave an example to explain me better.

library("randomForestSRC")
data(veteran, package = "randomForestSRC")
for(j in 1:3){for(i in 1:2){
  obj<- rfsrc(Surv(time, status) ~ ., data = veteran, 
              ntree = 100, block.size = j, mtry=i, nodesize = 3, samptype = "swr")
  car<-1-get.cindex(obj$yvar[,1], obj$yvar[,2], obj$predicted.oob)
  print(c(car, round(i, 0), j))
}}

the result obtained is the following:

[1] 0.6748275 1.0000000 1.0000000
[1] 0.6877191 2.0000000 1.0000000
[1] 0.6577519 1.0000000 2.0000000
[1] 0.6826303 2.0000000 2.0000000
[1] 0.6614837 1.0000000 3.0000000
[1] 0.6847789 2.0000000 3.0000000

I want to collect this results in a dataframe, in particular i'm interested in the values of "car".

I've firstly tried only with the index j and I've obtained what i want. I have built a dataframe and car:

b<- (1:3)
df<-data.frame(b)
car=0

Then I run the same loop only for the index j and I obtain the dataframe that I want (one column that indicates j and one column for the different values obtained for car) in the subsequent way:

for(j in 1:3){
  obj<- rfsrc(Surv(time, status) ~ ., data = veteran, 
              ntree = 100, block.size = 1, mtry=j, nodesize = 3, samptype = "swr")
  df$car[j]<-1-get.cindex(obj$yvar[,1], obj$yvar[,2], obj$predicted.oob)
}

The same method do not function with i and j indexes together. I hope that someone will give me sone hint to solve the problem.

Having to deal with two indices in loops can be a bit inconvenient,
especially since you’re not after array output. It might be easier to use
Map() with the combinations created with expand.grid(). At the same time
you can swap to using more informative names:

fit_car <- function(mtry, block.size) {
    obj <- rfsrc(
      Surv(time, status) ~ .,
      data = veteran,
      ntree = 100,
      block.size = block.size,
      mtry = mtry,
      nodesize = 3,
      samptype = "swr"
    )
    car <- 1 - get.cindex(obj$yvar[, 1], obj$yvar[, 2], obj$predicted.oob)
    car
}

df <- expand.grid(mtry = 1:2, block.size = 1:3)
df$car <- unlist(Map(fit_car, df$mtry, df$block.size))
df
#>   mtry block.size       car
#> 1    1          1 0.6857967
#> 2    2          1 0.7095443
#> 3    1          2 0.6437295
#> 4    2          2 0.6921294
#> 5    1          3 0.6640846
#> 6    2          3 0.6917901