generate_sample

Generates a sample data set for using with BigQuic, the default seed is 1 for 
reproducibility. For high dimensional data, choose p much larger than n.

BigQuic

Use Newton's method, coordinate descent, and METIS clustering
to solve the L1 regularized Gaussian MLE inverse covariance
matrix estimation problem.

Khalid B. Kunji

Big Quadratic Inverse Covariance Estimation

Cho-Jui Hsieh

Matyas A. Sustik

Inderjit S. Dhillon

Pradeep Ravikumar

Tuo Zhao

Xingguo Li

Han Liu

Kathryn Roeder

John Lafferty

Larry Wasserman

George Karypis

Melissa O'Neill

Richard Henderson

generate_sample function

<dl> <dt>n</dt>
<dd>The number of rows in the resulting data set.</dd> <dt>p</dt>
<dd>The number of columns in the resulting data set.</dd> <dt>seed</dt>
<dd>A seed for the random number generator in R.</dd></dl>

Arguments


Generate Sample — generate_sample

<dl>

 <dt>n</dt>
<dd>The number of rows in the resulting data set.</dd>

 <dt>p</dt>
<dd>The number of columns in the resulting data set.</dd>

 <dt>seed</dt>
<dd>A seed for the random number generator in R.</dd>

</dl>

Generate Sample

generate_sample: Generate Sample

Description

Usage

Arguments