Wie automatisieren Sie die variable Auswahl in glmnet und cross-Validierung

Lerne ich die Verwendung von glmnet und brnn Pakete. Betrachten Sie den folgenden code:

library(RODBC)
library(brnn)
library(glmnet)
memory.limit(size = 4000)
z <-odbcConnect("mydb") # database with Access queries and tables

# import the data
f5 <- sqlFetch(z,"my_qry")

# head(f5)

# check for 'NA'
sum(is.na(f5))

# choose a 'locn', up to 16 of variable 'locn' are present
f6 <- subset(f5, locn == "mm")
# dim(f6)

# use glmnet to identify possible iv's

training_xnm <- f6[,1:52] # training data
xnm <- as.matrix(training_xnm)
y <- f6[,54] # response

fit.nm <- glmnet(xnm,y, family="binomial", alpha=0.6, nlambda=1000,standardize=TRUE,maxit=100000)
# print(fit.nm)

# cross validation for glmnet to determine a good lambda value
cv.fit.nm <- cv.glmnet(xnm, y)

# have a look at the 'min' and '1se' lambda values
cv.fit.nm$lambda.min
cv.fit.nm$lambda.1se
# returned $lambda.min of 0.002906279, $lambda.1se of 2.587214

# for testing purposes I choose a value between 'min' and '1se'
mid.lambda.nm = (cv.fit.nm$lambda.min + cv.fit.nm$lambda.1se)/2

print(coef(fit.nm, s = mid.lambda.nm)) # 8 iv's retained

# I then manually inspect the data frame and enter the column index for each of the iv's
# these iv's will be the input to my 'brnn' neural nets

cols <- c(1, 3, 6, 8, 11, 20, 25, 38) # column indices of useful iv's

# brnn creation: only one shown but this step will be repeated
# take a 85% sample from data frame
ridxs <- sample(1:nrow(f6), floor(0.85*nrow(f6)) ) # row id's
f6train <- f6[ridxs,] # the resultant data frame of 85%
f6train <-f6train[,cols] # 'cols' as chosen above

# For the 'brnn' phase response is a binary value, 'fin'
# and predictors are the 8 iv's found earlier
out = brnn( fin ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8, data=f6train, neurons=3,normalize=TRUE, epochs=500, verbose=FALSE)
#summary(out)

# see how well the net predicts the training cases
pred <- predict(out)

Das obige Skript läuft OK.

Meine Frage ist: Wie kann ich automatisieren, die oben genannten Skript für verschiedene Werte von locn, dass ist im wesentlichen, wie kann ich das verallgemeinern erste Schritt: cols <- c(1, 3, 6, 8, 11, 20, 25, 38) # column indices of useful iv's. Derzeit kann ich das manuell tun, aber nicht sehen können, wie Sie dies in einer Allgemeinen Art und Weise für verschiedene Werte von locn zum Beispiel

locn.list <- c("am", "bm", "cm", "dm", "em")  
for(j in 1:5) {
this.locn <- locn.list[j]
# run the above script
}