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Repeated stepwise selection of covariates
f2st(y,x,p0=0.01,kmn=0,kmx=0,kex=0,mx=21,lm=9^9,sub=T,inr=T,xinr=F,qq=0)
pv In order the linear approximation, the included covariates, the regression coefficient values, the Gaussian P-values, the standard P-values and the proportional reduction in the sum of squared residuals due to this covariate.
Dependent variable
Covariates
The P-value cut-off
The minimum number of included covariates irrespective of cut-off P-value
The maximum number of included covariates irrespective of cut-off P-value.
The excluded covariates
The maximum number of covariates for an all subset search
The maximum number of linear approximations
Logical if TRUE select the best subset
Logical if TRUE include an intercept
Logical if TRUE intercept already included
The number of covariates to choose from. If qq=0 the number of covariates of x is used.
data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]
a<-f2st(boston[,14],bostint,lm=3)
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