HhP.reg {HhP}

Hierarchical Heterogeneity Regression Analysis.

Description

The main function for Transfer learning for tensor graphical models.

Usage

HhP.reg(lambda, whole.data, n, q, p, beta.init,
               merge.all=FALSE, trace=FALSE, selection.sub=FALSE)

Arguments

lambda

The sequences of the tuning parameters (lambda1 and lambda2).

whole.data

The input data analyzed (a list including the response and design matrix).

n

The sample size.

q

The dimension of type 1 features.

p

The dimension of type 2 features.

beta.init

The Initial values of regression coefficients.

merge.all

the logical variable, the default is F.

trace

the logical variable, whether or not to output the number of identified subgroups during the search for parameters.

selection.sub

the logical variable, the default is F.

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