nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models
Version 0.3

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

AuthorBaidya Nath Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Date of publication2016-03-10 08:00:33
MaintainerBaidya Nath Mandal <mandal.stat@gmail.com>
LicenseGPL (>= 2)
Version0.3
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("nnlasso")

Popular man pages

cv.nnlasso.poisson: k-fold cross validation for penalized generalized linear...
kfold: k-fold partition of data at random
msefun.normal: Prediction means squared errors for hold out data in cross...
msefun.poisson: Deviances for hold out data in cross validation
nnlasso: Entire regularization path of non-negative penalized...
nnlasso.binomial.lambda: Coefficients of non-negative penalized generalized linear...
nnlasso.normal.lambda: Coefficients of non-negative penalized generalized linear...
See all...

All man pages Function index File listing

Man pages

bars: Error bars
car: The car data
coef.nnlasso: Extract coefficients from a fitted nnlasso object
cv.nnlasso: k-fold cross validation for penalized generalized linear...
cv.nnlasso.binomial: k-fold cross validation for penalized generalized linear...
cv.nnlasso.normal: k-fold cross validation for penalized generalized linear...
cv.nnlasso.poisson: k-fold cross validation for penalized generalized linear...
fold: Particular fold of a data after k fold partition
kfold: k-fold partition of data at random
msefun.binomial: Deviances for hold out data in cross validation
msefun.normal: Prediction means squared errors for hold out data in cross...
msefun.poisson: Deviances for hold out data in cross validation
nnlasso: Entire regularization path of non-negative penalized...
nnlasso.binomial: Entire regularization path of non-negative penalized...
nnlasso.binomial.lambda: Coefficients of non-negative penalized generalized linear...
nnlasso.normal: Entire regularization path of non-negative penalized...
nnlasso.normal.lambda: Coefficients of non-negative penalized generalized linear...
nnlasso.poisson: Entire regularization path of non-negative penalized...
nnlasso.poisson.lambda: Coefficients of non-negative penalized generalized linear...
plot.nnlasso: Plot of regularization path
predict.nnlasso: Prediction of coefficients of a penalized linear regression...

Functions

Files

NAMESPACE
data
data/car.rda
R
R/nnlasso.r
MD5
DESCRIPTION
man
man/cv.nnlasso.Rd
man/nnlasso.binomial.Rd
man/nnlasso.poisson.lambda.Rd
man/kfold.Rd
man/msefun.poisson.Rd
man/nnlasso.normal.lambda.Rd
man/msefun.binomial.Rd
man/nnlasso.normal.Rd
man/cv.nnlasso.poisson.Rd
man/msefun.normal.Rd
man/predict.nnlasso.Rd
man/plot.nnlasso.Rd
man/nnlasso.binomial.lambda.Rd
man/bars.Rd
man/fold.Rd
man/car.Rd
man/nnlasso.poisson.Rd
man/coef.nnlasso.Rd
man/nnlasso.Rd
man/cv.nnlasso.normal.Rd
man/cv.nnlasso.binomial.Rd
nnlasso documentation built on May 19, 2017, 8:39 p.m.

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