vif {VIF} | R Documentation |
vif
selects variables for a linear model. It returns a subset of variables for building a linear model.
vif(y, x, w0 = 0.05, dw = 0.05, subsize = 200, trace = TRUE, mode = c("dense", "sparse"))
y |
the response. |
x |
an optional data frame or matrix containing the variables in the model. |
w0 |
the initial wealth. |
dw |
the incremental wealth attained if a variable is included in the model. |
subsize |
the size of the subsample to approximate the variance inflation factor. |
trace |
logical. if TRUE a list containing current wealth, current test level, absolute t value and p-value for the current variable will be printed out. |
mode |
"dense" or "sparse" , specifying one of the two alpha-investings that should be used. Default is "sparse" . |
A list containing:
select |
the chosen subset of variable. |
modelmatrix |
the model matrix that is ready for fitting a linear model. |
Dongyu Lin dongyu@wharton.upenn.edu
Lin, D., Foster, D.F. and Ungar, L.H. (2009) VIF-Regression: A Fast Regression Algorithm for Large Data. http://stat.wharton.upenn.edu/~dongyu/Papers/VIF.pdf
data(syn); vif.sel <- vif(syn$y, syn$x, trace = FALSE); vif.sel$select; syn$true; data(housingexp); colnames(housingexp$x); vif.sel <- vif(housingexp$y, housingexp$x, w0 = 0.005, dw = 0.005, subsize = 300, trace = FALSE);