estimate: Estimate parameters

In MInt: Learn Direct Interaction Networks

Description

This function performs iterative conditional modes to obtain maximum a posteriori estimates for β (covariate coefficients), w (latent abundances), and P (the precision matrix).

Usage

estimate(mfit)

Arguments

mfit	- a MInt model object.

Value

A MInt model object with the following attributes:

optim	List containing optimization details
optim$lambda	Value of the L1 penalty used during optimization
data	List containing the raw data
data$design	File path of the design matrix
data$response	File path of the response matrix
data$fmla	Formula used to model each response in terms of the design variables
data$y	Raw numerical data for the response matrix
data$xd	Design matrix in categorical form
data$x	Design matrix in numerical form
param	List containing parameter estimates
param$beta	p-covariates x o-responses matrix of regression coefficients
param$w	n-samples x o-responses matrix of latent abundances
param$P	o-responses x o-responses precision matrix

Examples

x <- system.file("extdata", "x.txt", package="MInt");
y <- system.file("extdata", "y.txt", package="MInt");
m <- mint(y,x,fmla = ~feature1 + feature2)
m <- estimate(m)

Example output

Loading required package: glasso
Loading required package: trust
Loading required package: MASS
Loading required package: testthat
Iteration:  1  max(deltaP):  0.067014  Objective:  -1061.913  Converged:  FALSE 
Iteration:  2  max(deltaP):  0.015160  Objective:  -1061.823  Converged:  FALSE 
Iteration:  3  max(deltaP):  0.003953  Objective:  -1061.813  Converged:  FALSE 
Iteration:  4  max(deltaP):  0.001160  Objective:  -1061.806  Converged:  FALSE 
Iteration:  5  max(deltaP):  0.000396  Objective:  -1061.8  Converged:  FALSE 
Iteration:  6  max(deltaP):  0.000181  Objective:  -1061.793  Converged:  FALSE 
Iteration:  7  max(deltaP):  0.000122  Objective:  -1061.787  Converged:  FALSE 
Iteration:  8  max(deltaP):  0.000114  Objective:  -1061.78  Converged:  FALSE 
Iteration:  9  max(deltaP):  0.000111  Objective:  -1061.774  Converged:  FALSE 
Iteration:  10  max(deltaP):  0.000109  Objective:  -1061.767  Converged:  FALSE 
Iteration:  11  max(deltaP):  0.000108  Objective:  -1061.761  Converged:  FALSE 
Iteration:  12  max(deltaP):  0.000107  Objective:  -1061.755  Converged:  FALSE 
Iteration:  13  max(deltaP):  0.000106  Objective:  -1061.749  Converged:  FALSE 
Iteration:  14  max(deltaP):  0.000105  Objective:  -1061.743  Converged:  FALSE 
Iteration:  15  max(deltaP):  0.000104  Objective:  -1061.737  Converged:  FALSE 
Iteration:  16  max(deltaP):  0.000103  Objective:  -1061.731  Converged:  FALSE 
Iteration:  17  max(deltaP):  0.000102  Objective:  -1061.725  Converged:  FALSE 
Iteration:  18  max(deltaP):  0.000101  Objective:  -1061.719  Converged:  FALSE 
Iteration:  19  max(deltaP):  0.000100  Objective:  -1061.713  Converged:  TRUE 

MInt documentation built on May 2, 2019, 7:24 a.m.