summary.ctsemMultigroupFit

ctsemMultigroupFit object as generated by <code><a href="/link/ctMultigroupFit?package=ctsem&version=1.1.5.2" rd-options="" data-mini-rdoc="ctsem::ctMultigroupFit">ctMultigroupFit</a></code>

object

character string of subgroup to display summary parameters for. Default of 'show chooser' displays list and lets you select.

group

additional parameters to pass to <code><a href="/link/summary.ctsemFit?package=ctsem&version=1.1.5.2" rd-options="" data-mini-rdoc="ctsem::summary.ctsemFit">summary.ctsemFit</a></code>.

Provides summary details for objects fitted with <code><a href="/link/ctMultigroupFit?package=ctsem&version=1.1.5.2" rd-options="" data-mini-rdoc="ctsem::ctMultigroupFit">ctMultigroupFit</a></code>.

An easily accessible continuous (and discrete) time dynamic
    modelling package for panel and time series data, reliant upon the OpenMx.
    package (http://openmx.psyc.virginia.edu/) for computation. Most dynamic
    modelling approaches to longitudinal data rely on the assumption that time
    intervals between observations are consistent. When this assumption is
    adhered to, the data gathering process is necessarily limited to a specific
    schedule, and when broken, the resulting parameter estimates may be biased
    and reduced in power. Continuous time models are conceptually similar to
    vector autoregressive models (thus also the latent change models popularised
    in a structural equation modelling context), however by explicitly including
    the length of time between observations, continuous time models are freed
    from the assumption that measurement intervals are consistent. This allows:
    data to be gathered irregularly; the elimination of noise and bias due to
    varying measurement intervals; parsimonious structures for complex dynamics.
    The application of such a model in this SEM framework allows full-information
    maximum-likelihood estimates for both N = 1 and N > 1 cases, multiple measured
    indicators per latent process, and the flexibility to incorporate additional
    elements, including individual heterogeneity in the latent process and
    manifest intercepts, and time dependent and independent exogenous covariates.
    Furthermore, due to the SEM implementation we are able to estimate a random
    effects model where the impact of time dependent and time independent predictors
    can be assessed simultaneously, but without the classic problems of random
    effects models assuming no covariance between unit level effects and predictors.

summary.ctsemMultigroupFit: Summary function for ctsemMultigroupFit object

Description

Usage

Arguments

Value