DPpackage (version 1.1-6)

PTglmm: Bayesian analysis for a semiparametric generalized linear mixed model using a MMPT

Description

This function generates a posterior density sample for a semiparametric generalized linear mixed model, using a Mixture of Multivariate Polya Trees prior for the distribution of the random effects.

Usage

PTglmm(fixed,random,family,offset,n,prior,mcmc,state,status, data=sys.frame(sys.parent()),na.action=na.fail)

Arguments

fixed

a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right.

random

a one-sided formula of the form ~z1+...+zn | g, with z1+...+zn specifying the model for the random effects and g the grouping variable. The random effects formula will be repeated for all levels of grouping.

family

a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. The families(links) considered by PTglmm so far are binomial(logit), binomial(probit), Gamma(log), and poisson(log). The gaussian(identity) case is implemented separately in the function PTlmm.

offset

this can be used to specify an a priori known component to be included in the linear predictor during the fitting (only for poisson and gamma models).

n

this can be used to indicate the total number of cases in a binomial model (only implemented for the logistic link). If it is not specified the response variable must be binary.

prior

a list giving the prior information. The list include the following parameter: a0 and b0 giving the hyperparameters for prior distribution of the precision parameter of the Polya Tree (PT) prior, alpha giving the value of the precision parameter (it must be specified if a0 and b0 are missing, see details below), nu0 and tinv giving the hyperparameters of the inverted Wishart prior distribution for the scale matrix of the normal baseline distribution, sigma giving the value of the covariance matrix of the centering distribution (it must be specified if nu0 and tinv are missing), mub and Sb giving the hyperparameters of the normal prior distribution for the mean of the normal baseline distribution, mu giving the value of the mean of the centering distribution (it must be specified if mub and Sb are missing), beta0 and Sbeta0 giving the hyperparameters of the normal prior distribution for the fixed effects (must be specified only if fixed effects are considered in the model), M giving the finite level of the PT prior to be considered, and frstlprob a logical variable indicating whether the first level probabilities of the PT are fixed or not (the default is FALSE), tau1 and tau2 giving the hyperparameters for the prior distribution for the inverse of the precision parameter of the Gamma model (they must be specified only if the Gamma model is considered), and typep indicating whether the type of decomposition of the centering covariance matrix is random (1) or not (0).

mcmc

a list giving the MCMC parameters. The list must include the following integers: nburn giving the number of burn-in scans, nskip giving the thinning interval, nsave giving the total number of scans to be saved, ndisplay giving the number of saved scans to be displayed on screen (the function reports on the screen when every ndisplay iterations have been carried out), nbase giving the number scans to be performed before the parameters of the centering distribution and the precision parameter are updated (i.e., the update of this parameters is invoked only once in every nbase scans) (the default value is 1), tune1, tune2, tune3, tune4 and tune5 giving the Metropolis tuning parameter for the baseline mean, variance, precision parameter, partition and dispersion parameter (only for the Gamma mode), respectively. If tune1, tune2, tune3 or tune4 are not specified or negative, an adpative Metropolis algorithm is performed. If tune5 is not specified, a default value of 1.1 is assumed. Finally, the integer samplef indicates whether the functional parameters must be sample (1) or not (0).

state

a list giving the current value of the parameters. This list is used if the current analysis is the continuation of a previous analysis.

status

a logical variable indicating whether this run is new (TRUE) or the continuation of a previous analysis (FALSE). In the latter case the current value of the parameters must be specified in the object state.

data

data frame.

na.action

a function that indicates what should happen when the data contain NAs. The default action (na.fail) causes PTglmm to print an error message and terminate if there are any incomplete observations.

Value

An object of class PTglmm representing the generalized linear mixed-effects model fit. Generic functions such as print, plot, and summary have methods to show the results of the fit. The results include betaR, betaF, mu, the elements of Sigma, the precision parameter alpha, the dispersion parameter of the Gamma model, and ortho.The function PTrandom can be used to extract the posterior mean of the random effects.The list state in the output object contains the current value of the parameters necessary to restart the analysis. If you want to specify different starting values to run multiple chains set status=TRUE and create the list state based on this starting values. In this case the list state must include the following objects:

alpha

giving the value of the precision parameter.

b

a matrix of dimension (nsubjects)*(nrandom effects) giving the value of the random effects for each subject.

beta

giving the value of the fixed effects.

mu

giving the mean of the normal baseline distributions.

sigma

giving the variance matrix of the normal baseline distributions.

phi

giving the precision parameter for the Gamma model (if needed).

ortho

giving the orthogonal matrix H, used in the decomposition of the covariance matrix.

Details

This generic function fits a generalized linear mixed-effects model using a Mixture of Multivariate Polya Trees prior (see, Lavine 1992; 1994, for details about univariate PT) for the distribution of the random effects as described in Jara, Hanson and Lesaffre (2009). The linear predictor is modeled as follows: $$\eta_i = X_i \beta_F + Z_i \beta_R + Z_i b_i, i=1,\ldots,n$$ $$\theta_i | G \sim G$$ $$G | \alpha,\mu,\Sigma,O \sim PT^M(\Pi^{\mu,\Sigma,O},\mathcal{A})$$

where, $thetai = betaR + bi$, $beta = betaF$, and $O$ is an orthogonal matrix defining the decomposition of the centering covariance matrix. As in Hanson (2006), the PT prior is centered around the $N_d(mu,Sigma)$ distribution. However, we consider the class of partitions $Pi^{mu,Sigma, O}$. The partitions starts with base sets that are Cartesian products of intervals obtained as quantiles from the standard normal distribution. A multivariate location-scale transformation $theta=mu+Sigma^{1/2} z$ is applied to each base set yielding the final sets. Here $Sigma^{1/2}=T'O'$, where $T$ is the unique upper triangular Cholesky matrix of $Sigma$. The family $A={alphae: e \in E*}$, where $E*=U_{m=0}^{M} E_d^m$, with $E_d$ and $E_m$ the $d$-fold product of $E=\{0,1\}$ and the the $m$-fold product of $E_d$, respectively. The family $A$ was specified as $alpha{e1 \ldots em}=\alpha m^2$.

To complete the model specification, independent hyperpriors are assumed, $$\alpha | a_0, b_0 \sim Gamma(a_0,b_0)$$ $$\beta | \beta_0, S_{\beta_0} \sim N(\beta_0,S_{\beta_0})$$ $$\mu | \mu_b, S_b \sim N(\mu_b,S_b)$$ $$\Sigma | \nu_0, T \sim IW(\nu_0,T)$$ $$O \sim Haar(q)$$

Note that the inverted-Wishart prior is parametrized such that $E(Sigma)= Tinv/(nu0-q-1)$.

The precision parameter, $alpha$, of the PT prior can be considered as random, having a gamma distribution, $Gamma(a0,b0)$, or fixed at some particular value.

The inverse of the dispersion parameter of the Gamma model is modeled using gamma distribution, $Gamma(tau1/2,tau2/2)$.

The computational implementation of the model is based on the marginalization of the PT as discussed in Jara, Hanson and Lesaffre (2009).

References

Hanson, T. (2006) Inference for Mixtures of Finite Polya Trees. Journal of the American Statistical Association, 101: 1548-1565.

Jara, A., Hanson, T., Lesaffre, E. (2009) Robustifying Generalized Linear Mixed Models using a New Class of Mixtures of Multivariate Polya Trees. Journal of Computational and Graphical Statistics, 18(4): 838-860.

Lavine, M. (1992) Some aspects of Polya tree distributions for statistical modelling. The Annals of Statistics, 20: 1222-11235.

Lavine, M. (1994) More aspects of Polya tree distributions for statistical modelling. The Annals of Statistics, 22: 1161-1176.

See Also

PTrandom, PTglmm , PTolmm, DPMglmm, DPMlmm, DPMolmm, DPlmm , DPglmm, DPolmm

Examples

## Not run: 
#     # Respiratory Data Example
#       data(indon)
#       attach(indon)
# 
#       baseage2 <- baseage**2
#       follow <- age-baseage
#       follow2 <- follow**2 
# 
#     # Prior information
# 
#       prior <- list(alpha=1,
#                     M=4, 
#                     frstlprob=FALSE,
#                     nu0=4,
#                     tinv=diag(1,1),
#                     mub=rep(0,1),
#                     Sb=diag(1000,1),
#                     beta0=rep(0,9),
#                     Sbeta0=diag(10000,9))
# 
#     # Initial state
#       state <- NULL
# 
#     # MCMC parameters
# 
#       nburn <- 5000
#       nsave <- 5000
#       nskip <- 20
#       ndisplay <- 100
#       mcmc <- list(nburn=nburn,
#                    nsave=nsave,
#                    nskip=nskip,
#                    ndisplay=ndisplay,
#                    tune1=0.5,tune2=0.5,
#                    samplef=1)
# 
#     # Fitting the Logit model
#       fit1 <- PTglmm(fixed=infect~gender+height+cosv+sinv+xero+baseage+baseage2+
#                      follow+follow2,random=~1|id,family=binomial(logit),
#                      prior=prior,mcmc=mcmc,state=state,status=TRUE)
# 
#       fit1 
# 
#       plot(PTrandom(fit1,predictive=TRUE))
# 
#     # Plot model parameters (to see the plots gradually set ask=TRUE)
#       plot(fit1,ask=FALSE)
#       plot(fit1,ask=FALSE,nfigr=2,nfigc=2)	
# 
#     # Extract random effects
#       PTrandom(fit1)
#       PTrandom(fit1,centered=TRUE)
# 
#     # Extract predictive information of random effects
#       PTrandom(fit1,predictive=TRUE)
# 
#     # Predictive marginal and joint distributions      
#       plot(PTrandom(fit1,predictive=TRUE))
# 
#     # Fitting the Probit model
#       fit2 <- PTglmm(fixed=infect~gender+height+cosv+sinv+xero+baseage+baseage2+
#                      follow+follow2,random=~1|id,family=binomial(probit),
#                      prior=prior,mcmc=mcmc,state=state,status=TRUE)
#       fit2 
# 
#     # Plot model parameters (to see the plots gradually set ask=TRUE)
#       plot(fit2,ask=FALSE)
#       plot(fit2,ask=FALSE,nfigr=2,nfigc=2)	
# 
#     # Extract random effects
#       PTrandom(fit2)
# 
#     # Extract predictive information of random effects
#       PTrandom(fit2,predictive=TRUE)
# 
#     # Predictive marginal and joint distributions      
#       plot(PTrandom(fit2,predictive=TRUE))
# ## End(Not run)