The Gaussian Covariate Method for Variable Selection

[Up] [Top]

Documentation for package ‘gausscov’ version 0.1.7

Help Pages

abcq American Business Cycle
boston Boston data
decode Decodes the number of a subset selected by fasb.R to give the covariates
decomp Decomposes given coded interactions into their component parts
f1st Stepwise selection of covariates
f2st Repeated stepwise selection of covariates
f3st Stepwise selection of covariates
f3sti Selection of covariates with given excluded covariates
fasb Calculates all subsets where each included covariate is significant.
fgeninter Generation of interactions
fgentrig Generation of sine and cosine functions
fgr1st Calculates a dependence graph using Gaussian stepwise selection
fgr2st Calculates an independence graph using repeated stepwise selection
fgrall Calculates a dependence graph using Gaussian all subset selection
flag Calculation of lagged covariates
fnfp Estimates the number of false positives for given dimensions (n,k) and given order statistics nu
fpsired Calculates Hampel's redescending psi function
fpval Calculates the regression coefficients, the P-values and the standard P-values for the chosen subset ind
fr1st Robust stepwise selection of covariates
frasb Robust selection of covariates using Huber's psi-function or Hampel's redescending psi-function based on all subsets
frpval Robust regression using Huber's psi-function or Hampel's three part redescending psi-function providing P-values
fselect Selects the subsets specified by fasb.R and frasb.R.
fundr Converts directed into an undirected graph
fvauto Vector autoregressive approximation
leukemia Leukemia data
mel_temp Melbourne minimum temperature
nufp nufp
redwine Redwine data
snspt Sunspot data