gfunc

Compute response value to raw prediction such as linear predictor in GLM

regression

classification

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
Wang et al. (2015) <doi:10.1002/bimj.201400143>,
Wang et al. (2016) <doi:10.1177/0962280214530608>,
Wang (2021) <doi:10.1007/s11749-021-00770-2>,
Wang (2024) <doi:10.1111/anzs.12409>.

Zhu Wang

mpath

Regularized Linear Models

gfunc function

<dl> <dt>mu</dt>
<dd>vector of numbers as response value in GLM, for instance, probability estimation if <code>family=2</code></dd> <dt>family</dt>
<dd>integer from 1-4, corresponding to "gaussian", "binomial", "poisson", "negbin", respectively</dd> <dt>epsbino</dt>
<dd>a small positive value for <code>family=2</code> to avoid numeric unstability</dd></dl>

Arguments

Convert response value to raw prediction in GLM — gfunc

<dl>

 <dt>mu</dt>
<dd>vector of numbers as response value in GLM, for instance, probability estimation if <code>family=2</code></dd>

 <dt>family</dt>
<dd>integer from 1-4, corresponding to "gaussian", "binomial", "poisson", "negbin", respectively</dd>

 <dt>epsbino</dt>
<dd>a small positive value for <code>family=2</code> to avoid numeric unstability</dd>

</dl>

gfunc: Convert response value to raw prediction in GLM

Description

Usage

Value

Arguments