Hello
 
I am trying to use all subsets regression on a test dataset consisting
of 11 trails and 46 potential predictor variables. 
I would like to use Mallow's Cp as a selection criterion. 
The leaps function would provide the required output but does not work
with this many variables (see below).
The alternative function regsubsets should be used, but I am not able to
define the function in such a way that is gives satisfactory results.
 

library(leaps)

data <- read.table('C:/test_plot_sum2.txt', header = TRUE)

attach(data)

nox <- data[,1]

cnt <- data[,2]

vars <- data[,3:48]

 

leaps.setup(x = vars, y = nox, wt = cnt, nvmax = 1)

leaps(x = vars, y = nox, wt = cnt, method = "Cp", nbest = 2, names =
names(vars))

 

Error in leaps(x = vars, y = nox, wt = cnt, method = "Cp", nbest = 2,  :


        leaps does not allow more than 31 variables; use regsubsets()

 

regsubsets(x = vars, y = nox, weights = cnt, method = "seqrep", nbest =
1, names = names(vars), nvmax = 3)

 

1 subsets of each size up to 4

Selection Algorithm: 'sequential replacement'

Warning message: 

37  linear dependencies found in: leaps.setup(x, y, wt = weights, nbest
= nbest, nvmax = nvmax,  

 
Is there someone who could shine a few rays of light on this?
Many thanks for your assistance.
 
Kind regards,
Robin Smit

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