HhP (version 1.0.0)

gen_int_beta: 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)

Value

A result list.

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.

Author

Mingyang Ren, Qingzhao Zhang, Sanguo Zhang, Tingyan Zhong, Jian Huang, Shuangge Ma. Maintainer: Mingyang Ren .

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

# \donttest{
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
# }