mgdmm {depmix} | R Documentation |
Multi group model specification
Description
mgdmm
creates an object of class mgd
, a
multi-group model, from a given model of either class dmm
or
class mixdmm
or lists of these.
Usage
mgdmm(dmm,ng=1,modname=NULL,trans=FALSE,obser=FALSE,init=FALSE,conpat=NULL)
## S3 method for class 'mgd'
summary(object, specs=FALSE, precision=3, se=NULL, ...)
Arguments
modname |
A character string with the name of the model, good when fitting many models. Components of mixture models keep their own names. Names are printed in the summary. Boring default names are provided. |
dmm |
Object (or list of objects) of class |
ng |
Number of groups for a multigroup model. |
trans , obser , init |
Logical arguments specify whether
|
conpat |
Can be used to specify general linear constraints.
See |
precision |
Precision sets the number of digits to be printed in the summary functions. |
se |
Vector with standard errors, these are passed on from the summary.fit function if and when ses are available. |
specs , ... |
Internal use. |
object |
An object of class |
Details
The function mgdmm
can be used to define an mgd
-model or
multi group dmm
. Its default behavior is to create ng
copies of the dmm
argument, thereby providing identical starting
values for each group's model. If the dmm
argument is a list of
models of length ng
, the starting values of those models will be
used instead. This may save quite some cpu time when fitting large
models by providing the parameter values of separately fitted models as
starting values. Currently, depmix
does not automatically
generate starting values for multi group models.
Value
mgdmm
returns an object of class mgd
which contains all the
fields of an object of class dmm
and the following extra:
ng |
|
mixmod |
|
itemtypes |
See above. |
npars , freepars , pars , fixed , A , bl , bu |
The same as above but now for the combined model, here npars equals the sum of npars of the component models plus the mixing proportions. |
Author(s)
Ingmar Visser i.visser@uva.nl
See Also
dmm
on defining single component models, and
mixdmm
for defining mixtures of dmm
's.
Examples
# create a 2 state model with one continuous and one binary response
# with start values provided in st
st <- c(1,0.9,0.1,0.2,0.8,2,1,0.7,0.3,5,2,0.2,0.8,0.5,0.5)
mod <- dmm(nsta=2,itemt=c(1,2), stval=st)
# define 3-group model with equal transition parameters, and no
# equalities between the obser parameters
mgr <- mgdmm(dmm=mod,ng=3,trans=TRUE,obser=FALSE)
summary(mgr)