The Gaussian Covariate Method for Variable Selection


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Documentation for package ‘gausscov’ version 0.1.1

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abcq American Business Cycle
boston Boston data
decode Decodes the number of a subset selected by fmch to give the covariates
decomp decompose a given interaction ic into its component parts
dentx Dental data, the 13 covariates, the dependent variable ist denty
denty Dental data, hardness of gold fillings, the corresponding 13 covariates are dentx
f1st Stepwise selection of covariates
f2st Repeated stepwise selection of covariates
fgeninter Generation of interactions
fgentrig generation of sine and cosine functions
fgr1st Calculates an independence graph using stepwise selection
fgr2st Calculates an independence graph using repeated stepwise selection
flag Calculation of lagged covariates
fmch Calculates all subsets where each included covariate is significant.
fpval Calculates the regression coefficients, the P-values and the standard P-values for the chosen subset ind
frmch Robust selection of covariates using Huber's psi-funtion or Hampel's redescending psi-function based on all subsets
frrg Robust regression using Huber's psi-function or Hampel's redescending psi-function without P-values
frrgp Robust regression using Huber's psi-function or Hampel's three part redescending psi-function providing P-values
frst Robust stepwise selection of covariates
fselect Selects the subsets specified by fmch and fmch1.
fsimords Simulates the number of false positives for given dimensions (n,k) and given order statistics nu
lx.original Leukemia data
ly.original Leukemia data
mel_temp Melbourne minimum temperature
redwine Redwine data
snspt Sunspot data