[R] How to write a loop in R to select multiple regression model and validate it ?
paxkn at nottingham.ac.uk
Wed Jun 5 01:44:12 CEST 2013
I would like to run a loop in R. I have never done this before, so I would be
very grateful for your help !
1. I have a sample set: 25 objects. I would like to draw 1 object from it
and use it as a test set for my future external validation. The remaining 24
objects I would like to use as a training set (to select a model). I would
like to repeat this process until all 25 objects are used as a test set.
2. For each of the training sets I would like to run the following code:
forward <- regsubsets(Y ~.,data = training, method = "forward", nbest=1)
backward <- regsubsets(Y ~.,data = training, method = "backward", nbest=1)
stepwise <- regsubsets(Y ~., data = training, method = "seqrep", nbest=1)
exhaustive <- regsubsets(Y ~.,data = training, method = "forward", nbest=1)
I would like R programme to select the best model (with the highest adjusted
R2) using each of the selection methods, so there are 4 final best models
(e.g. the best model selected with forward selection, the best model
selected with backward selection and so on...).
Afterwards I would like to perform internal cross validation of all 4
selected models and choose 1 out of 4 which has the lowest average mean
squared error (MSE). I used to do it using the code below:
val.daag<-CVlm(df=training, m=1, form.lm=formula(Y ~ X1+X2+X3))
val.daag<-CVlm(df=training, m=1, form.lm=formula(Y ~ X1+X2+X4))
val.daag<-CVlm(df=training, m=1, form.lm=formula(Y ~ X3+X4+X5))
val.daag<-CVlm(df=training, m=1, form.lm=formula(Y ~ X4+X5+X7))
For the best selected model (the lowest MSE) I would like to perform an
external validation on 1 object left on the site at the beginning of the
study (please refer to point 1.).
3. And loop again using different training and test set ....
I hope that you could help me with this.
If you have any suggestions how to select the best model and perform
validation more efficiently, I would be happy to hear about that.
Thank you !
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