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EMSNM
View on CRAN: Click
here
Download and install EMSNM package within the R console
Install from CRAN:
install.packages("EMSNM")
Install from Github:
library("remotes")
install_github("cran/EMSNM")
Install by package version:
library("remotes")
install_version("EMSNM", "1.0")
Attach the package and use:
library("EMSNM")
Maintained by
Linsui Deng
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-25
Latest Update: 2019-04-25
Description:
It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
How to cite:
Linsui Deng (2019). EMSNM: EM Algorithm for Sigmoid Normal Model. R package version 1.0, https://cran.r-project.org/web/packages/EMSNM. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for EMSNM
Functions, R codes and Examples using
the EMSNM R package
Some associated functions: Ccompute . EMSNM-package . EM_parameter_sd . EM_result_sort . EMalgorithm . EMbootstrap . EMsimulation . Ggenerate . Wgenerate . fnorm . softmax . standard . update_eta . update_gamma . weight_matrix .
Some associated R codes: Ccompute.R . EM_parameter_sd.R . EM_result_sort.R . EMalgorithm.R . EMbootstrap.R . EMsimulation.R . Ggenerate.R . Wgenerate.R . fnorm.R . softmax.R . standard.R . update_eta.R . update_gamma.R . weight_matrix.R . Full EMSNM package functions and examples
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