mle.se2

This is an internal function that computes squared standard errors for imprecision estimates of the
constant bias model using paired data.

 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

mle.se2 function

<dl> <dt>v</dt>
<dd>Variance-Covariance matrix for the n x N items by methods measurement data.</dd> <dt>r</dt>
<dd>Initial estimates of imprecision, usually Grubbs</dd> <dt>ni</dt>
<dd>No. of items measured</dd></dl>

Arguments

Author

Compute squared standard errors for imprecision estimates for the
constant bias model using paired data. — mle.se2

<dl>

 <dt>v</dt>
<dd>Variance-Covariance matrix for the n x N items by methods measurement data.</dd>

 <dt>r</dt>
<dd>Initial estimates of imprecision, usually Grubbs</dd>

 <dt>ni</dt>
<dd>No. of items measured</dd>

</dl>

mle.se2: Compute squared standard errors for imprecision estimates for the constant bias model using paired data.

Description

Usage

Value

Arguments

Author

Details

References