The fence procedure for linear models

This function implements the fence procedure to find the best linear model.

lmfence(mf, cstar, nvmax, adaptive = TRUE, trace = TRUE, force.in = NULL, ...)

Arguments

mf	an object of class lm specifying the full model.
cstar	the boundary of the fence, typically found through bootstrapping.
nvmax	the maximum number of variables that will be be considered in the model.
adaptive	logical. If TRUE the boundary of the fence is given by cstar. Otherwise, it the original (non-adaptive) fence is performed where the boundary is cstar*hat(sigma)_M,tildeM.
trace	logical. If TRUE the function prints out its progress as it iterates up through the dimensions.
force.in	the names of variables that should be forced into all estimated models.
...	further arguments (currently unused)

References

Jiming Jiang, Thuan Nguyen, J. Sunil Rao, A simplified adaptive fence procedure, Statistics & Probability Letters, Volume 79, Issue 5, 1 March 2009, Pages 625-629, http://dx.doi.org/10.1016/j.spl.2008.10.014.

See also

af, glmfence

Other fence: af(), glmfence()

Examples

n = 40 # sample size
beta = c(1,2,3,0,0)
K=length(beta)
set.seed(198)
X = cbind(1,matrix(rnorm(n*(K-1)),ncol=K-1))
e = rnorm(n)
y = X%*%beta + e
dat = data.frame(y,X[,-1])
# Non-adaptive approach (not recommended)
lm1 = lm(y~.,data=dat)
lmfence(lm1,cstar=log(n),adaptive=FALSE)
#> Null model (Not a candidate model) 
#> Model size: 2 (No candidate models found) 
#> Model size: 3 
#>  Candidate model found via leaps. 
#> Exploring other options at this model size. 
#> hatQm: 55.56 ; Upper bound: 59.52 
#> hatQm <= UB: TRUE 
#> y ~ X1 + X2
#> [[1]]
#> y ~ X1 + X2
#>