bestsetNoise {DAAG} | R Documentation |
Best subset selection applied to completely random noise. This function demonstrates how variable selection techniques in regression can often err in suggesting that more variables be included in a regression model than necessary.
bestsetNoise(m=100, n=40, method="exhaustive", nvmax=3)
m |
the number of observations to be simulated. |
n |
the number of predictor variables in the simulated model. |
method |
Use exhaustive search, or backward selection,
or forward selection, or sequential replacement. |
nvmax |
maximum number of explanatory variables in model. |
A set of n
predictor variables are simulated as independent
standard normal variates, in addition to a response variable which
is also independent of the predictors. The best model with
nvmax
variables is selected using the regsubsets()
function from the leaps package. (The leaps package must be installed
for this function to work.)
bestsetNoise
returns the lm
model object for the "best"
model.
J.H. Maindonald
leaps.out <- try(require(leaps, quietly=TRUE)) leaps.out.log <- is.logical(leaps.out) if ((leaps.out.log==TRUE)&(leaps.out==TRUE)) bestsetNoise(20,6) # `best' 3-variable regression for 20 simulated observations # on 7 unrelated variables (including the response)