pOmegas

takes mcmc posterior samples of omega_t and omega_h
and calculates the prior and posterior probability of the estimate being bigger
or smaller than an arbitrary value

Functionality for reliability estimates. For 'unidimensional' tests:
Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version.
For multidimensional tests: omega_t (total) and omega_h (hierarchical).
The results include confidence and credible intervals, the
probability of a coefficient being larger than a cutoff,
and a check for the factor models, necessary for the omega coefficients.
The method for the Bayesian 'unidimensional' estimates, except for omega_u,
is sampling from the posterior inverse 'Wishart' for the
covariance matrix based measures (see 'Murphy', 2007,
<https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>.
The Bayesian omegas (u, t, and h) are obtained by
'Gibbs' sampling from the conditional posterior distributions of
(1) the single factor model, (2) the second-order factor model, (3) the bi-factor model,
(4) the correlated factor model
('Lee', 2007, <doi:10.1002/9780470024737>).

Julius Pfadt

Bayesrel

Bayesian Reliability Estimation

Julius M. Pfadt

Don van den Bergh

Joris Goosen

pOmegas function

<dl><dt>x</dt>
<dd>A bomegas output object (list)</dd>
<dt>cutoff.t</dt>
<dd>A number indicating the threshold for omega_t</dd>
<dt>cutoff.h</dt>
<dd>A number indicating the threshold for omega_h</dd></dl>

Arguments

prior and posterior probability of omega_t and omega_h being larger than thresholds — pOmegas

<dl>

<dt>x</dt>
<dd>A bomegas output object (list)</dd>


<dt>cutoff.t</dt>
<dd>A number indicating the threshold for omega_t</dd>


<dt>cutoff.h</dt>
<dd>A number indicating the threshold for omega_h</dd>

</dl>

pOmegas: prior and posterior probability of omega_t and omega_h being larger than thresholds

Description

Usage

Arguments

Examples