sim_dat


A simulated multivariate data with 30 samples on 15 features.


datasets

This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.

Lin Chen

PEMM

A Penalized EM algorithm incorporating missing-data mechanism

sim_dat function

The data set contains a 30 by 15 matrix, with mean of each feature being -4/15, -3/15 ... to 10/15. Covariance among
different features being 0.1 and the variance for each is 1. This is the complete data without any missingness.

sim_dat: A simulated multivariate data

Description

Arguments

Details