sigma_mle

This is an internal function that computes the ith iteration for computing the
 squared imprecision estimates.

 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

sigma_mle function

<dl> <dt>i</dt>
<dd>Iteration i.</dd> <dt>s</dt>
<dd>Variance-covariance N x N matrix.</dd> <dt>sigma2</dt>
<dd>Estimated imprecisions for each method in an N vector</dd> <dt>sigma.mu2</dt>
<dd>Estimated process varinace.</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

Computes the ith iteration for computing the
  squared imprecision estimates. — sigma_mle

<dl>

 <dt>i</dt>
<dd>Iteration i.</dd>

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

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

 <dt>sigma.mu2</dt>
<dd>Estimated process varinace.</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>

sigma_mle: Computes the ith iteration for computing the squared imprecision estimates.

Description

Usage

Value

Arguments

Author

Details

References