process.var.mle

This is an internal function to compute the process variance.

 htest

N>=3 methods are used to measure each of n items.
The data are used to estimate simultaneously systematic error (bias)
and random error (imprecision). Observed measurements for each method
or device are assumed to be linear functions of the unknown true values
and the errors are assumed normally distributed. Pairwise calibration
curves and plots can be easily generated. Unlike the 'ncb.od' function,
the 'omx' function builds a one-factor measurement error model using 'OpenMx'
and allows missing values, uses full information maximum likelihood to
estimate parameters, and provides both likelihood-based and bootstrapped
confidence intervals for all parameters, in addition to Wald-type intervals.

Richard Bilonick

merror

Accuracy and Precision of Measurements

process.var.mle function

<dl> <dt>sigma2</dt>
<dd>Estimated imprecisions for each method in an N vector.</dd> <dt>s</dt>
<dd>Variance-covariance N x N matrix.</dd> <dt>beta.bars</dt>
<dd>Estimates or hypothesized values for the N betas.</dd> <dt>N</dt>
<dd>No. of methods.</dd> <dt>n</dt>
<dd>No. of items.</dd></dl>

Arguments

Author

Compute process variance. — process.var.mle

<dl>

 <dt>sigma2</dt>
<dd>Estimated imprecisions for each method in an N vector.</dd>

 <dt>s</dt>
<dd>Variance-covariance N x N matrix.</dd>

 <dt>beta.bars</dt>
<dd>Estimates or hypothesized values for the N betas.</dd>

 <dt>N</dt>
<dd>No. of methods.</dd>

 <dt>n</dt>
<dd>No. of items.</dd>

</dl>

process.var.mle: Compute process variance.

Description

Usage

Value

Arguments

Author

Details

References