
The MixedUp Suite
MIXOR, MIXREG, MIXNO and MIXPREG are based on
the collaborative effort of Drs. Donald Hedeker and Robert D. Gibbons
of the University of Illinois at Chicago. We were privileged to be asked
to produce the user interfaces around the computer programs written by
Don Hedeker. The work was supported by the National Institute of Mental
Health and the MacArthur Foundation, and the programs are available free
of charge for download from the MIXOR/MIXREG homepage located at the University of Illinois at Chicago.
While the programs can be run in 'batch mode' using
textual input files, the user interfaces guide you through the definition process and provide filter selections based on current settings with
bounds and type checking. This allows nonexperts to run powerful analyses
on their data. The Mixedup Suite provides mixedeffects regression functionality not available anywhere else — at any price.
Take a look and see if they can help you out.
MIXOR
MIXOR was the first program developed as part
of the Mixedup Suite of applications. MIXOR is a program which provides
estimates for a mixedeffects ordinal probit and logistic regression model.
This model can be used for analysis of clustered or longitudinal ordinal
(and dichotomous) outcome data. For clustered data, the mixedeffects
model does not assume that each observation is independent, but does assume
data within clusters are dependent to some degree. The degree of this
dependency is estimated along with estimates of the usual model parameters,
thus adjusting these effects for the dependency resulting from the clustering
of the data. Similarly, for longitudinal data, the mixedeffects approach
can allow for individualvarying intercepts and slopes across time, and
can estimate the degree to which these timerelated terms vary.
MIXREG
MIXREG is a program that provides estimates for
a mixedeffects regression model (MRM) including autocorrelated errors.
This model can be used for analyses of unbalanced longitudinal data, where
individuals may be measured at different number of timepoints, or even
at different timepoints. Autocorrelated errors of a general form or following
an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also
be used for analysis of clustered data, where the mixedeffects model
does not assume that each observation is independent, but does assume
data within clusters are dependent to some degree. The degree of this
dependency is estimated along with with estimates of usual model parameters,
thus adjusting these effects for the dependency resulting from the clustering
of the data.
MIXNO
MIXNO provides maximum marginal likelihood for
mixedeffects nominal logistic regression analysis. These models can be
used for analysis of correlated nominal response data, for example, data
arising from a clustered design. For clustered data, the mixedeffects
model assumes that data within clusters are dependent. The degree of dependency
is jointly estimated with the usual model parameters, thus adjusting for
dependence resulting from clustering of the data. MIXNO uses marginal
maximum likelihood estimation, utilizing a Fisherscoring solution. For
the scoring solution, the Cholesky factor of the randomeffects variancecovariance
matrix is estimated, along with the effects of model covariates.
MIXPREG
MIXPREG provides maximum marginal likelihood estimates
for mixedeffects Poisson regression analysis. These models can be used
for analysis of correlated count data, for example, data arising from
a clustered design. For clustered data, the mixedeffects model assumes
that data within clusters are dependent. The degree of dependency is jointly
estimated with the usual model parameters, thus adjusting for dependence
resulting from clustering of the data. MIXPREG uses marginal maximum likelihood
estimation, utilizing a NewtonRaphson iterative solution. Specifically,
the Cholesky factor of the randomeffects variancecovariance matrix is
estimated along with the effects of model covariates.
