gen_int_beta: Hierarchical Heterogeneity Regression Analysis.

In HhP: Hierarchical Heterogeneity Analysis via Penalization

Hierarchical Heterogeneity Regression Analysis.

Description

The main function for Transfer learning for tensor graphical models.

Usage

gen_int_beta(n, p, q, whole.data, subgroup=c(2,4),
                    ridge = FALSE, gr.init=10, lambda.min=0.0001)

Arguments

n	The sample size.
p	The dimension of type 2 features.
q	The dimension of type 1 features.
whole.data	The input data analyzed (a list including the response and design matrix).
subgroup	When using fmrs to generate initial value, the initial value parameter of fmrs is given. Randomly divide this number of groups into several groups.
ridge	The logical variable, whether or not to yield initial values using ridge regression.
gr.init	The subgroup number of initial values using ridge regression.
lambda.min	The tuning parameter using ridge regression, the default is 0.0001.

Value

A result list.

Author(s)

Mingyang Ren, Qingzhao Zhang, Sanguo Zhang, Tingyan Zhong, Jian Huang, Shuangge Ma. Maintainer: Mingyang Ren [e-mail].

References

Mingyang Ren, Qingzhao Zhang, Sanguo Zhang, Tingyan Zhong, Jian Huang, Shuangge Ma. 2022. Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features. Biometrics, <DOI: 10.1111/biom.13544>.

Examples

library(HhP)
library(Matrix)
library(MASS)
library(fmrs)
data(example.data.reg)
n = example.data.reg$n
q = example.data.reg$q
p = example.data.reg$p

beta.init.list  =  gen_int_beta(n, p, q, example.data.reg)
beta.init  =  beta.init.list$beta.init
lambda  =  genelambda.obo()
result  =  HhP.reg(lambda, example.data.reg, n, q, p, beta.init)
index.list  =  evaluation.sum(n,q,p, result$admmres, result$abic.n,
               result$admmres2, example.data.reg$Beta0, result$bic.var)
index.list$err.s

HhP documentation built on Nov. 23, 2022, 5:07 p.m.