loss3: Composite Loss Value for GLM

In zhuwang46/mpath: Regularized Linear Models

Composite Loss Value for GLM

Description

Compute composite loss value

Usage

loss3(y, mu, theta, weights, cfun, family, s, delta)

Arguments

y	response variable values, 0/1 if family=2, or binomial
mu	response prediction of y. If mu is linear predictor, use function loss2 instead
theta	scale parameter for family=4, negative binomial
weights	observation weights, same length as y
cfun	integer from 1-8, concave function as in ccglm_fit
family	integer 2, 3 or 4, convex function binomial, Poisson or negative binomial, respectively
s	tuning parameter of cfun. s > 0 and can be equal to 0 for cfun="tcave".
delta	a small positive number provided by user only if cfun="gcave" and 0 < s <1

Details

For large s values, the loss can be 0 with cfun=2,3,4, or "acave", "bcave", "ccave".

Value

Weighted loss values

Author(s)

Zhu Wang <[e-mail]>

References

Zhu Wang (2020) Unified Robust Estimation, arXiv e-prints, https://arxiv.org/abs/2010.02848

See Also

loss2 ccglm ccglmreg loss2_ccsvm

zhuwang46/mpath documentation built on March 21, 2022, 4:27 a.m.