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CorReg (version 0.15.7)

Linear regression based on linear structure between covariates

Description

Linear regression based on a recursive structural equation model (explicit correlations) found by a MCMC algorithm. It permits to face highly correlated covariates. Variable selection is included (by lasso, elasticnet, etc.). It also provides some graphical tools for basic statistics.

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Version

Install

install.packages('CorReg')

Monthly Downloads

156

Version

0.15.7

License

CeCILL

Maintainer

Clement THERY

Last Published

October 2nd, 2014

Functions in CorReg (0.15.7)

compare_sign

compare signs of the coefficients in two vectors
CorReg-package

Algorithms for regression with correlated covariates
fillmiss

Fill the missing values in the dataset
density_estimation

BIC of estimated marginal gaussian mixture densities
matplot_zone

draws matplot with conditionnal background for easier comparison of curves.
rforge

Upgrades a package to the lastest version on R-forge
Y_generator

Response variable generator with a linear model
showdata

show the missing values of a dataset
readY

a summary-like function
OLS

Ordinary Least Square efficiently computed with SEM for missing values
cleanZR2

To clean Z based on R2
mixture_generator

Gaussiam mixture dataset generator with regression between the covariates
ProbaZ

Probability of Z without knowing the dataset. It also gives the exact number of binary nilpotent matrices of size p.
compare_beta

compare signs of the coefficients in two vectors
recursive_tree

decision tree in a recursive way
Estep

Imputation of missing values knowing alpha (E step of the EM)
Winitial

initialization based on a wheight matrix (correlation or other)
WhoIs

Give the partition implied by a structure
readZ

read the structure and explain it
R2Z

Estimates R2 of each subression
compare_zero

compare 0 values in two vectors
hatB

Estimates B matrix
cleancolZ

clean Z columns (if BIC improved)
CVMSE

Cross validation
BicZcurve

Curve of the BIC for each possible p2 with a fixed Z and truncature of Z
correg

Estimates the response variable using a structure
MSE_loc

simple MSE function
Terminator

Destructing values to have missing ones
cleanZ

clean Z (if BIC improved)
compare_struct

To compare structures (Z)
cleanYtest

Selection method based on p-values (coefficients)
BicZ

Compute the BIC of a given structure
searchZ_sparse

Sparse structure research
confint_coef

plot and give confidence intervals on the coefficients estimated in a model or for proportions
searchZ

MCMC algo to find a structure between the covariates
MSEZ

Computes the MSE on the joint distribution of the dataset