Description Usage Arguments Details Value Note Author(s) References See Also Examples
fitdmm
fits mixtures of hidden/latent Markov
models on arbitrary length time series of mixed categorical and
continuous data. This includes latent class models and finite mixture
models (for time series of length 1), which are in effect independent
mixture models.
posterior
computes the most likely
latent state sequence for a given dataset and model.
1 2 3 4 5 6 7 8 9 10 11 12  fitdmm(dat, dmm, printlevel = 1, poster = TRUE, tdcov = 0,
ses = TRUE, method = "optim", vfactor=15, der = 1, iterlim = 100,
kmst = !dmm$st, kmrep = 5, postst = FALSE)
loglike(dat, dmm, tdcov = 0, grad = FALSE, hess = FALSE, set
= TRUE, grInd = 0, sca = 1, printlevel = 1)
posterior(dat,dmm,tdcov=0,printlevel=1)
computeSes(dat,dmm)
bootstrap(object,dat,samples=100, pvalonly=0,...)
## S3 method for class 'fit'
summary(object, precision=3, fd=1, ...)
oneliner(object,precision=3)

dat 
An object (or list of objects) of class 
dmm 
An object (or a list of objects) of class 
printlevel 

poster 
By default posteriors are computed, the result of which can be found in fit\$post. 
method 
This is the optimization algorithm that is used. donlp2 from the Rdonlp2 package is the default method. There is optional support for NPSOL. 
der 
Specifies whether derivatives are to be used in optimization. 
vfactor 
vfactor controls optimization in optim and nlm. Since in those routines there is no possibility for enforcing constraints, constraints are enforced by adding a penalty term to the loglikelihood. The penalty term is printed at the end of optimization if it is not close enough to zero. This may have several reasons. When parameters are estimated at bounds for example. This can be solved by fixing those parameters on their boundary values. When this is not acceptable vfactor may be increased such that the penalty is larger and the probability that they actually hold in the fitted model is correspondingly higher. 
tdcov 
Logical, when set to TRUE, given that the model and data have covariates, the corresponding parameters will be estimated. 
ses 
Logical, determines whether standard errors are computed after optimization. 
iterlim 
The iteration limit for npsol, defaults to 100, which may be too low for large models. 
grad 
logical; if TRUE the gradients are returned. 
hess 
logical; if TRUE the hessian is returned; it is not implemented currently and hence setting it to true will produce a warning. 
set 
Whith the default value TRUE, the data and models parameters are sent to the C/C++ routines before computing the loglikelihood. When set is FALSE, this is not done. If an incorrect model was set earlier in the Croutines this may cause serious errors and/or crashes. 
sca 
If set to 1.0 the negative loglikelihood, gradients and hessian are returned. 
object 
An object of class 
kmst,postst 
These arguments control the generation of starting values by kmeans and posterior estimates respectively. 
kmrep 
If no starting values are provided, 
grInd 
Logical argument; if TRUE, individual contributions of each independent realization to the gradient vector will be returned. 
fd 
Print the finite difference based standard errors in the summary if both those and bootstrapped standard errors are available. 
samples 
The number of samples to be used in bootstrapping. 
pvalonly 
Logical, if 1 only a bootstrapped pvalue is returned and not fitted paramaters to compute standard errors, optimization is truncated when the loglikelihood is better than the original loglikelihood. 
precision 
Precision sets the number of digits to be printed in the summary functions. 
... 
Used in summary. 
The function fitdmm
optimizes the parameters of a mixture of
dmm
s using a general purpose optimization routine subject to linear
constraints on the parameters.
fitdmm
returns an object of class fit
which has a summary
method that prints the summary of the fitted model, and the following fields:
date,timeUsed,totMem 
The date that the model was fitted, the time it took to so and the memory usage. 
loglike 
The loglikelihood of the fitted model. 
aic 
The AIC of the fitted model. 
bic 
The BIC of the fitted model. 
mod 
The fitted model. 
post 
See function posterior for details. 
loglike
returns a list of the following:
logl 
The loglikelihood. 
gr,grset 

hs,hsset 

posterior
returns lists of the following:
states 
A matrix of dimension 2+sum(nstates) by sum(length(ntimes)) containing in the first column the a posteriori component, in the second column the a posteriori state and in the remaining column the posterior probabilities of all states. 
comp 
Contains the posterior component number for each independent realization; all ones for a single component model. 
computeSes
returns a vector of length npars
with the standard
errors and a matrix hs
with the hessian used to compute them. The
routine is not fail safe and can produce errors, ie when the (corrected)
hessian is singular; a warning is issued when the hessian is close to being
singular.
bootstrap
returns an object of class fit
with three extra
fields, the bootstrapped standard errors, bse, a matrix with
goodnessoffit measures of the bootstrap samples, ie logl, AIC and BIC and
pbetter, which is the proportion of bootstrap samples that resulted in
better fits than the original model.
summary.fit
prettyprints the outputs.
oneliner
returns a vector of loglike, aic, bic, mod$npars,
mod$freepars, date.
fitdmm
fits time series of arbitrary length and mixtures of
dmm
s, where, to the best of my knowledge, other packages are limited
due to the different optimization routines that are commonly used for these
types of models.
Ingmar Visser i.visser@uva.nl, Development of this pacakge was supported by European Commission grant 51652 (NEST) and by a VENI grant from the Dutch Organization for Scientific Research (NWO).
Lawrence R. Rabiner (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE, 772, p. 267295.
Theodore C. Lystig and James P. Hughes (2002). Exact computation of the observed information matrix for hidden Markov models. Journal of Computational and Graphical Statistics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117  # COMBINED RT AND CORRECT/INCORRECT SCORES from a 'switching' experiment
data(speed)
mod < dmm(nsta=2,itemt=c(1,2)) # gaussian and binary items
ll < loglike(speed,mod)
fit1 < fitdmm(dat=speed,dmm=mod)
summary(fit1)
ll < loglike(speed,fit1)
# bootstrap
## Not run:
pst < posterior(dat=speed,dmm=fit1)
bs < bootstrap(fit1,speed,samples=50)
## End(Not run) # end not run
# add some constraints using conpat
conpat=rep(1,15)
conpat[1]=0
conpat[14:15]=0
conpat[8:9]=0
# use starting values from the previous model fit, except for the guessing
# parameters which should really be 0.5
stv=c(1,.896,.104,.084,.916,5.52,.20,.5,.5,6.39,.24,.098,.90,0,1)
mod=dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat)
fit2 < fitdmm(dat=speed,dmm=mod)
summary(fit2)
# add covariates to the model to incorporate the fact the accuracy pay off changes per trial
# 2state model with covariates + other constraints
## Not run:
conpat=rep(1,15)
conpat[1]=0
conpat[8:9]=0
conpat[14:15]=0
conpat[2]=2
conpat[5]=2
stv=c(1,0.9,0.1,0.1,0.9,5.5,0.2,0.5,0.5,6.4,0.25,0.9,0.1,0,1)
tdfix=rep(0,15)
tdfix[2:5]=1
stcov=rep(0,15)
stcov[2:5]=c(0.4,0.4,0.15,0.15)
mod<dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat,tdfix=tdfix,tdst=stcov,
modname="twoboth+cov")
fit3 < fitdmm(dat=speed,dmm=mod,tdcov=1,der=0,ses=0,vfa=80)
summary(fit3)
# split the data into three time series
data(speed)
r1=markovdata(dat=speed[1:168,],item=itemtypes(speed))
r2=markovdata(dat=speed[169:302,],item=itemtypes(speed))
r3=markovdata(dat=speed[303:439,],item=itemtypes(speed))
# define 2state model with constraints
conpat=rep(1,15)
conpat[1]=0
conpat[8:9]=0
conpat[14:15]=0
stv=c(1,0.9,0.1,0.1,0.9,5.5,0.2,0.5,0.5,6.4,0.25,0.9,0.1,0,1)
mod<dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat)
# define 3group model with equal transition parameters, and no
# equalities between the obser parameters
mgr <mgdmm(dmm=mod,ng=3,trans=TRUE,obser=FALSE)
fitmg < fitdmm(dat=list(r1,r2,r3),dmm=mgr)
summary(fitmg)
## End(Not run) # end not run
# LEARNING DATA AND MODELS (with absorbing states)
## Not run:
data(discrimination)
# all or none model with error prob in the learned state
fixed = c(0,0,0,1,1,1,1,0,0,0,0)
stv = c(1,1,0,0.03,0.97,0.1,0.9,0.5,0.5,0,1)
allor < dmm(nstates=2,itemtypes=2,fixed=fixed,stval=stv,modname="Allornone")
# Concept identification model: learning only after an error
st=c(1,1,0,0,0,0.5,0.5,0.5,0.25,0.25,0.05,0.95,0,1,1,0,0.25,0.375,0.375)
# fix some parameters
fx=rep(0,19)
fx[8:12]=1
fx[17:19]=1
# add a couple of constraints
conr1 < rep(0,19)
conr1[9]=1
conr1[10]=1
conr2 < rep(0,19)
conr2[18]=1
conr2[19]=1
conr3 < rep(0,19)
conr3[8]=1
conr3[17]=2
conr=c(conr1,conr2,conr3)
cim < dmm(nstates=3,itemtypes=2,fixed=fx,conrows=conr,stval=st,modname="CIM")
# define a mixture of the above models ...
mix < mixdmm(dmm=list(allor,cim),modname="MixAllCim")
# ... and fit it on the combined data discrimination
fitmix < fitdmm(discrimination,mix)
summary(fitmix)
## End(Not run) # end not run

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