an optional data frame containing the variables in the
model. By default the variables are taken from the
environment from which unifit is called.
family
a GLM family, currently suport gaussian and binomial.
lambda
a user specified tuning sequece. Typical usage is to have the
program compute its own lambda.
nlambda
the number of lambda values, default is 100.
lambda.min.ratio
Smallest value for lambda, as a
fraction of lambda.max. The default depends on the sample
size nobs relative to the number of variables.
output
with values 0 or 1, indicating whether the fitting
process is muted or not.
tune
tuning approach, available methods including AIC, BIC,
GACV, BGACV.
Details
The unilps utilize the class structure of the underlying C++
code and fitted the model with accelerated block-coordinate relaxation algorithm.
Value
An object of classes mvbfit and lps, for which some methods are
available.
See Also
unilps, mvblps
Examples
n <- 100
p <- 4
x <- matrix(rnorm(n * p, 0, 4), n, p)
eta <- x
pr <- exp(eta) / (1+ exp(eta))
res <- rbinom(n, 1, pr)
fit <- unilps(res ~ x - 1, family = 'binomial')
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/unilps.Rd_%03d_medium.png", width=480, height=480)
> ### Name: unilps
> ### Title: univariate model fitting with lasso penalty
> ### Aliases: unilps
>
> ### ** Examples
>
> n <- 100
> p <- 4
> x <- matrix(rnorm(n * p, 0, 4), n, p)
> eta <- x
> pr <- exp(eta) / (1+ exp(eta))
> res <- rbinom(n, 1, pr)
> fit <- unilps(res ~ x - 1, family = 'binomial')
>
>
>
>
>
> dev.off()
null device
1
>