22,675 research outputs found
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
The R package lcmm provides a series of functions to estimate statistical
models based on linear mixed model theory. It includes the estimation of mixed
models and latent class mixed models for Gaussian longitudinal outcomes (hlme),
curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear
multivariate outcomes (multlcmm), as well as joint latent class mixed models
(Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a
time-to-event that can be possibly left-truncated right-censored and defined in
a competing setting. Maximum likelihood esimators are obtained using a modified
Marquardt algorithm with strict convergence criteria based on the parameters
and likelihood stability, and on the negativity of the second derivatives. The
package also provides various post-fit functions including goodness-of-fit
analyses, classification, plots, predicted trajectories, individual dynamic
prediction of the event and predictive accuracy assessment. This paper
constitutes a companion paper to the package by introducing each family of
models, the estimation technique, some implementation details and giving
examples through a dataset on cognitive aging
Model Based Clustering for Mixed Data: clustMD
A model based clustering procedure for data of mixed type, clustMD, is
developed using a latent variable model. It is proposed that a latent variable,
following a mixture of Gaussian distributions, generates the observed data of
mixed type. The observed data may be any combination of continuous, binary,
ordinal or nominal variables. clustMD employs a parsimonious covariance
structure for the latent variables, leading to a suite of six clustering models
that vary in complexity and provide an elegant and unified approach to
clustering mixed data. An expectation maximisation (EM) algorithm is used to
estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is
required. The clustMD model is illustrated by clustering simulated mixed type
data and prostate cancer patients, on whom mixed data have been recorded
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