# Fitting Dependent Mixture Models `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. ```r 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) ``` ## Arguments - `dat`: An object (or list of objects) of class `md`, see markovdata. If dat is a list of objects of class `md` a multigroup model is fitted on these data sets. - `dmm`: An object (or a list of objects) of class `dmm`, see dmm. If dmm is a list of objects of class `dmm`, these are taken to components of a mixture of dmm's model and will be coerced to class `mixdmm`. In any case, the model that is fitted a multigroup mixture of dmm's with default ngroups=1 and number of components=1. - `printlevel`: `printlevel` controls the output provided by the C-routines that are called to optimize the parameters. The default of 1 provides minmal output: just the initial and final loglikelihood of the model. Setting higher values will provide more output on the progress the iterations. - `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 C-routines 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 `fit`, ie the return value of fitdmm. - `kmst,postst`: These arguments control the generation of starting values by kmeans and posterior estimates respectively. - `kmrep`: If no starting values are provided, `kmrep` sets of starting values are generated using kmeans in appropriate cases. The best resulting set of starting values is optimized further. - `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. ## Details 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. ## Returns `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**: `gr` contains the gradients. `grset` is a logical vector giving information as to which gradients are set, currently all gradients are set except the gradients for the mixing proportions. - **hs,hsset**: `hs` contains the hessian. `hsset` is a logical giving information as to which elements are computed. `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 goodness-of-fit 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` pretty-prints the outputs. `oneliner` returns a vector of loglike, aic, bic, mod$npars, mod$freepars, date. ## Author(s) 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). ## References Lawrence R. Rabiner (1989). A tutorial on hidden Markov models and selected applications in speech recognition. **Proceedings of IEEE**, 77-2, p. 267-295. 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**. ## Note `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. ## See Also `dmm`,`markovdata` ## Examples ```r # 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 # 2-state 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 2-state 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 3-group 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="All-or-none") # 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 ```

fitdmm function