bootstrap by group and calculate statistics - r

Question

I'm trying to bootstrap some model fits and then calculate statistics without having to rerun the models every time. I can do this fine if I calculate r2 inside the first do() but I'd like to know how to access the data.
library(dplyr)
library(tidyr)
library(modelr)
library(purrr)
allmdls <-
mtcars %>%
group_by(cyl) %>%
do({
datsplit=crossv_mc(.,10)
mdls=list(map(datsplit$train, ~glm(hp~disp,data=.,family=gaussian(link='identity'))))
data_frame(datsplit=list(datsplit),mdls)
})
and now something like:
allmdls %>%
by_slice(dmap,.f=map2_dbl(.$mdls,.$datsplit$test,rsquare))
but I get
Error: .y is not a vector (NULL)
or
allmdls %>%
group_by(cyl) %>%
do({
map2_df(.x=.$mdls, .y=.$datsplit, .f=map2_dbl(.x=.x,.y=.y$test,.f=rsquare))
})
Error in map2_dbl(.x = .x, .y = .y$test, .f = rsquare) : object
'.x' not found
I can't seem to get the syntax right.
help?
Thanks
EDIT:
Thanks to #aosmith's comment, I created a somewhat simpler solution:
mtcars %>%
group_by(cyl) %>%
do({
datplit=crossv_mc(.,10) %>%
mutate(mdls=map(train, ~glm(hp~disp,data=.)),
r2=map2_dbl(mdls,test,rsquare)
pctmae=map2_dbl(mdls,test,function(model,data) {mae(model,data)/mean(model$model$hp,na.rm=T)*100})
)
})

Answer 1

One option is to use map2 within mutate. Because you are using lists of lists I ended up with nested map2s to get access to the innermost lists. I pulled the test data out via map(datsplit, "test"), as neither the dollar sign operator nor the extract brackets were working for me.
mutate(allmdls, rsq = map2(mdls, map(datsplit, "test"), ~map2_dbl(.x, .y, rsquare)))
Here is another option that avoids the nested lists all together:
mtcars %>%
split(.$cyl) %>%
map_df(crossv_mc, 10, .id = "cyl") %>%
mutate(models = map(train, ~glm(hp ~ disp, data = .x)),
rsq = map2_dbl(models, test, rsquare))

Answer 2

#aosmith answered my question but here is a simpler solution overall
mtcars %>%
group_by(cyl) %>%
do({
datplit=crossv_mc(.,10) %>%
mutate(mdls=map(train, ~glm(hp~disp,data=.)),
r2=map2_dbl(mdls,test,rsquare)
pctmae=map2_dbl(mdls,test,function(model,data) {mae(model,data)/mean(model$model$hp,na.rm=T)*100})
)
})