gen_int_beta {HhP} | R Documentation |
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 renmingyang17@mails.ucas.ac.cn.
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