Iterate two arguments with map2 (purrr function) - r

Question

I want to calculate all possible predictions with different probabilities of my data with multiple models. The result is a list.
df<-iris
df$y<-sample(0:1,nrow(df),replace=TRUE)
set.seed(101)
#Now Selecting 80% of data as sample from total 'n' rows of the data
sample <- sample.int(n = nrow(df), size = floor(.8*nrow(df)), replace = F)
train <- df[sample, ]
test <- df[-sample, ]
Then i create a logistic model:
full <- glm(y~., data = train, family = "binomial")
min <- glm( y~ 1, data = train, family = "binomial")
backward <- step(full,direction = "backward",trace=0)
forward <- step(min,scope=list(lower=min, upper=full),direction = "forward",trace=0)
model2<- glm(y~Sepal.Length+Sepal.Width , data = train, family = "binomial")
models<-list(backward,forward,model2)
prediction<- lapply(models, function(x){predict(x,newdata=test,type="response")})
First of all i have table with predictions. Then i created a vector with all posible probabilities.
p <- seq(from = 0.1, to = 0.9, by = 0.5)
Problem is i want to apply differents breaks point. I tried with map2 function of purrr package but it doesn't work.
pred = map2(prediction,p, function(x,pi){ifelse(x > pi, 1, 0)})
The problem is:
Error: .x (3) and .y (2) are different lengths
Anyone can help?
I think is best to change apply to sapply, then i will have a data.frame.
prediction<- sapply(models, function(x){predict(x, newdata=test,type="response")},
simplify = T,USE.NAMES = TRUE)
Then i could use pmap function?
thanks
EDIT: I updated with all code.

Answer 1

See if this makes sense:
df<-iris
df$y<-sample(0:1,nrow(df),replace=TRUE)
set.seed(101)
#Now Selecting 80% of data as sample from total 'n' rows of the data
sample <- sample.int(n = nrow(df), size = floor(.8*nrow(df)), replace = F)
train <- df[sample, ]
test <- df[-sample, ]
full <- glm(y~., data = train, family = "binomial")
min <- glm( y~ 1, data = train, family = "binomial")
backward <- step(full,direction = "backward",trace=0)
forward <- step(min,scope=list(lower=min, upper=full),direction = "forward",trace=0)
model2<- glm(y~Sepal.Length+Sepal.Width , data = train, family = "binomial")
models<-list(backward,forward,model2)
prediction<- lapply(models, function(x){predict(x,newdata=test,type="response")})
p <- seq(from = 0.1, to = 0.9, by = 0.5)
combn = cross2(prediction, p)
pred <- map(combn,
function(combination) {
x <- combination[[1]]
pi <- combination[[2]]
ifelse(x > pi, 1, 0)
}
)