Storing data from nested loop in r - r

Question

I need to repeat the sampling procedure of the below loop 1000 times using a second loop.
This is the simplified code i produced for reproducability, the inner loop.
##Number of iterations
N = 8
##Store data from inner loop in vectors
PMSE <- rep(1 , N)
PolynomialDegree <- rep(1, N)
for (I in 1:N){
PolynomialDegree [I] <- I
PMSE [I] <- I*rnorm(1)
}
Now, using a second , outer loop. I want repeat this "sampling procedure" 1000 times and store the data of all those vectors into a single dataframe. Im struggling to write the outer loop and was hoping for some assistance.
This is my attempt with non-reproducable code, I hope it is clear what i am attempting to do.
##Set number of iterations
N <- 8
M <- 1000
##Store data
OUTPUT <- rep(1,M)
##Outer loop starts
for (J in 1:M){
PMSE <- rep(1 , N)
PolynomialDegree <- rep(1, N)
sample <- sample(nrow(tempraindata), floor(nrow(tempraindata)*0.7))
training <- tempraindata[sample,]
testing <- tempraindata[-sample,]
##Inner loop starts
for (I in 1:N){
##Set up linear model with x polynomial of degree I x = year, y = temp
mymodel <- lm(tem ~ poly(Year, degree = I), data = training)
##fit model on testing set and save predictions
predictions <- predict(mymodel, newdata = testing, raw = FALSE)
##define and store PMSE
PMSE[I] <- (1/(nrow(tempraindata)- nrow(training)))*(sum(testing$tem-predictions))^2
PolynomialDegree [I] <- I
} ## End of inner loop
OUTPUT[J] <- ##THIS IS WHERE I WANT TO SAVE THE DATA
} ##End outer loop
I want to store all the data inside OUTPUT and make it a dataframe, if done correctly it should contain 8000 values of PMSE and 8000 values of PolynomialDegree.

Answer 1

Avoid the bookkeeping of initializing vectors and then assigning elements by index. Consider a single sapply (or vapply) passing both iterations to build a matrix of 8,000 elements of the PSME calculations within a 1000 X 8 structure. Every column would then be a model run (or PolynomialDegree) and every row the training/testing data pair.
## Set number of iterations
N <- 8
M <- 1000
## Defined method to generalize process
calc_PSME <- function(M, N) {
## Randomly build training/testing sets
set.seed(M+N) # TO REPRODUCE RANDOM SAMPLES
sample <- sample(nrow(tempraindata), floor(nrow(tempraindata)*0.7))
training <- tempraindata[sample,]
testing <- tempraindata[-sample,]
## Set up linear model with x polynomial of degree I x = year, y = temp
mymodel <- lm(tem ~ poly(Year, degree = N), data = training)
## Fit model on testing set and save predictions
predictions <- predict(mymodel, newdata = testing, raw = FALSE)
## Return single PSME value
(
(1/(nrow(tempraindata)- nrow(training))) *
(sum(testing$tem-predictions)) ^ 2
)
}
# RETURN (1000 X 8) MATRIX WITH NAMED COLUMNS
PSME_matrix <- sapply(1:N, calc_PSME, 1:M)
PSME_matrix <- vapply(1:N, calc_PSME, numeric(M), 1:M)
Should you need a 8,000-row data frame of two columns, consider reshape to long format:
long_df <- reshape(
data.frame(output_matrix),
varying = 1:8,
timevar = "PolynomialDegree",
v.names = "PSME",
ids = NULL,
new.row.names = 1:1E4,
direction = "long"
)