<code>mse</code> computes the mean squared error between two numeric vectors

This model fitting tool incorporates cyclic coordinate descent and
majorization-minimization approaches to fit a variety of regression models
found in large-scale observational healthcare data. Implementations focus
on computational optimization and fine-scale parallelization to yield
efficient inference in massive datasets. Please see:
Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.

Marc A Suchard

Cyclops

Cyclic Coordinate Descent for Logistic, Poisson and Survival
Analysis

Marc A. Suchard

Martijn J. Schuemie

Trevor R. Shaddox

Yuxi Tian

Jianxiao Yang

Eric Kawaguchi

Sushil Mittal

Observational Health Data Sciences and Informatics 

Marcus Geelnard

Rutgers University 

R Development Core Team 

mse function

<dl><dt>goldStandard</dt>
<dd>Numeric vector</dd>
<dt>estimates</dt>
<dd>Numeric vector</dd></dl>

Arguments

Mean squared error — mse

<dl>

<dt>goldStandard</dt>
<dd>Numeric vector</dd>


<dt>estimates</dt>
<dd>Numeric vector</dd>

</dl>

Mean squared error

mse: Mean squared error

Description

Usage

Value

Arguments