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mimi (version 0.1.0)

Main Effects and Interactions in Mixed and Incomplete Data

Description

Estimation of main effects and interactions in mixed data sets with missing values. Numeric, binary and count variables are supported. Main effects and interactions are modelled using an exponential family parametric model. Particular examples include the log-linear model for count data and the linear model for numeric data. Estimation is done through a convex program where main effects are assumed sparse and the interactions low-rank. Genevive Robin, Olga Klopp, Julie Josse, <89>ric Moulines, Robert Tibshirani (2018) .

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Install

install.packages('mimi')

Monthly Downloads

197

Version

0.1.0

License

GPL-3

Maintainer

Genevieve Robin

Last Published

January 11th, 2019

Functions in mimi (0.1.0)

acs2016

Excerpt of the 2016 Public Use American Census Survey (Alabama only)
cv.mimi

cv.mimi
irwls.cov

irwls.cov
irwls.lr

irwls.lr
log_factorial

log_factorial
covmatC

covmatC
covmatR

covmatR
wght

wght
mimi

mimi (Main effects and Interactions in Mixed and Incomplete data frames) The method estimates main effects (group effects or effects of covariates) and interactions in mixed data frames with missing values. The results can be used for imputation or interpretation purposes.
armijo.lr

armijo.lr Performs Armijo backtracking line search along a pre-specified search direction
mimi.cov

mimi.cov Compute solution of mimi with covariates effects along regularization path
covmat

covmat
wlra

wlra
mimi.lr

mimi.lr Compute solution of mimi for low-rank model along regularization path
quad_approx

quad_approx
armijo.alpha

armijo.alpha Performs backtracking line search along a pre-specified search direction