# plot_solutionpaths: MM Algorithm - Plot results of solutionpaths function In gettingtothebottom: Learning Optimization and Machine Learning for Statistics

## Description

`plot_solutionpaths` Function for plotting results of the solutionpaths function

## Usage

 `1` ```plot_solutionpaths(results) ```

## Arguments

 `results` Results from the solutionpaths function

Jocelyn T. Chi

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```# Generate a test matrix seed <- 12345 m <- 100 n <- 100 r <- 3 T <- testmatrix(m,n,r,seed=seed) # Add some noise to the test matrix E <- 0.1*matrix(rnorm(m*n),m,n) A <- T + E # Obtain a vector of unobserved entries temp <- makeOmega(m,n,percent=0.5) omega <- temp\$omega # Remove unobserved entries from test matrix X <- A X[omega] <- NA # Make initial model matrix Z and find initial lambda Z <- matrix(0,m,n) lambda.start <- init.lambda(X,omega) lambdaseq_length=20 tol <- 1e-2 ans <- solutionpaths(A,X,Z,omega,lambda.start,tol=tol, liveupdates=FALSE,lambdaseq_length=lambdaseq_length) # Plot using results from solutionpaths function plot_solutionpaths(ans) ```

gettingtothebottom documentation built on May 29, 2017, 8:28 p.m.