How to create response surface using random forest model in R?

Question

I have a made a rf model in R having six predictors and a response. The predictive model seems to be good enough but we also wanted to generate a response surface for this model.

attach(al_mf)
library(randomForest)
set.seed(1)
rfalloy=randomForest(Mf~.,data=al_mf,mtry=6,importance=TRUE)
rfalloy
rfpred=predict(rfalloy,al_mf$Mf)
rfpred
sse=sum((rfpred-mean(al_mf$Mf))^2)
sse
ssr=sum((rfpred-al_mf$Mf)^2)
ssr
Rsqaure=1-(ssr/(sse+ssr))
Rsqaure
importance(rfalloy)

You've told us what you want to do, but you haven't actually asked a question. Please see [ask]. — ChrisGPT was on strike, Aug 30 '17 at 01:45

score 0 · Accepted Answer · answered Aug 29 '17 at 23:48

At a general level, since you haven't provided too many specifics about exactly what you are looking for in your response surface, here are a few hopefully helpful starting points:

Have you taken a look at rsm? This documentation provides some good use cases for the package.
These in-class notes from a University of New Mexico stats lecture are full of code examples related to response surfaces. Just check out the table of contents and you'll probably find what you're looking for.
This StackOverflow post also provides an example using the rgl package.

How to create response surface using random forest model in R?

1 Answers1