glib {BMA}

Model uncertainty in generalized linear models using Bayes factors

Description

Function to evaluate Bayes factors and account for model uncertainty in generalized linear models.

Usage

glib(x, ...)

## S3 method for class 'matrix'
glib(x, y, n = rep(1, nrow(x)), 
     error = "poisson", link = "log", scale = 1, 
     models = NULL, phi = c(1, 1.65, 5), psi = 1, 
     nu = 0, pmw = rep(1, nrow(models)), glimest = TRUE, 
     glimvar = FALSE, output.priorvar = FALSE, 
     post.bymodel = TRUE, output.postvar = FALSE, 
     priormean = NULL, priorvar = NULL, 
     nbest = 150, call = NULL, ...)

## S3 method for class 'data.frame'
glib(x, y, n = rep(1, nrow(x)), 
     error = "poisson", link = "log", scale = 1, 
     models = NULL,  phi = c(1, 1.65, 5), 
     psi = 1, nu = 0, pmw = rep(1, nrow(models)), 
     glimest = TRUE, glimvar = FALSE, output.priorvar = FALSE, 
     post.bymodel = TRUE, output.postvar = FALSE, 
     priormean = NULL, priorvar = NULL, 
     nbest = 150, call = NULL, ...)

## S3 method for class 'bic.glm'
glib(x, scale = 1, phi = 1, psi = 1, nu = 0, 
     glimest = TRUE, glimvar = FALSE, output.priorvar = FALSE, 
     post.bymodel = TRUE, output.postvar = FALSE, 
     priormean = NULL, priorvar = NULL, call = NULL, ...)

as.bic.glm(g, ...)

## S3 method for class 'glib'
as.bic.glm( g, index.phi=1, ...)

Arguments

x

an n x p matrix of independent variables

g

an object of type bic.glm

y

a vector of values for the dependent variable

n

an optional vector of weights to be used.

error

a string indicating the error family to use. Currently "gaussian", "gamma", "inverse gaussian", "binomial" and "poisson" are implemented.

link

a string indicating the link to use. Currently "identity", "log", "logit", "probit", "sqrt", "inverse" and "loglog" are implemented.

scale

the scale factor for the model. May be either a numeric constant or a string specifying the estimation, either "deviance" or "pearson". The default value is 1 for "binomial" and "poisson" error structures, and "pearson" for the others.

models

an optional matrix representing the models to be averaged over. models is a n x p matrix in which each row represents a model. The corresponding entry in the row is 1 if that variable is included in the model; 0 if not. The default value is NULL which will cause glib to call bic.glm with the parameter occam.window set to FALSE to obtain the models to average over.

phi

a vector of phi values. Default: 1.

psi

a scalar prior parameter. Default: 1.

nu

a scalar prior parameter. Default: 0

pmw

a vector of prior model weights. These must be positive, but do not have to sum to one. The prior model probabilities are given by pmw/sum(pmw). The default is a vector of 1's of length nrow(models)

glimest

a logical value specifying whether to output estimates and standard errors for each model.

glimvar

a logical value specifying whether glim variance matrices are output for each model.

output.priorvar

a logical value specifying whether the prior variance is output for each model and value of phi combination.

post.bymodel

a logical value specifying whether to output the posterior mean and sd for each model and value of phi combination.

output.postvar

a logical value specifying whether to output the posterior variance matrix for each model and value of phi combination.

priormean

an optional vector of length p+1 containing a user specified prior mean on the variables (including the intercept), where p=number of independent variables.

priorvar

an optional matrix containing a user specified prior variance matrix, a (p+1) x (p+1) matrix. Default has the prior variance estimated as in Raftery(1996).

nbest

an integer giving the number of best models of each size to be returned by bic.glm if models == NULL

call

the call to the function

index.phi

an index to the value of phi to use when converting a glib object to a bic.glm object

...

unused

Details

Function to evaluate Bayes factors and account for model uncertainty in generalized linear models. This also calculates posterior distributions from a set of reference proper priors. as.bic.glm creates a 'bic.glm' object from a 'glib' object.

Value

glib returns an object of type glib, which is a list containing the following items:

inputs

a list echoing the inputs (x,y,n,error,link,models,phi,psi,nu)

bf

a list containing the model comparison results:

twologB10

an nmodel x nphi matrix whose [i,j] element is 2logB10 for model i against the null model with phi=phi[j]. A Laplace approximation (one-step Newton) is used.

postprob

a matrix containing the posterior probabilities of the models for each value of phi.

deviance

a vector containing the deviances for the models.

chi2

a vector containing the (DV0-DV1)/scale for the models

npar

a vector containing the number of parameters estimated for each model.

scale

the estimated or assigned scale used

posterior

a list containing the Bayesian model mixing results:

prob0

an ncol(x) x nphi matrix whose [k,j] element is the posterior probability that the parameter corresponding to the k-th column of x is zero, for the j-th value of phi.

mean

a ncol(x) x nphi matrix whose [k,j] element is the posterior mean of the parameter corresponding to the k-th column of x, for the j-th value of phi.

sd

as for mean, but for the posterior standard deviation. NOTE: Both mean and sd are CONDITIONAL on the parameter being non-zero. They do not include the intercept.

glim.est

a list containing the GLIM estimates for the different models:

coef

An nmodel-list, each of whose elements is the coef value from "glim" for one of the models.

se

as coef, but contains standard errors.

var

as coef, but contains variance matrices of the estimates.

posterior.bymodel

a list containing model-specific posterior means and sds:

mean

a list with nmodel elements, whose ith element is a npar[i]xnphi matrix, containing the posterior means of the npar[i] parameters of model i, for each value of phi.

sd

as for mean, but for posterior standard deviations.

var

a list with nmodel elements, whose ith element is a npar[i] by npar[i] by nphi array, containing the posterior variance matrix of the parameters of model i for each value of phi.

prior

a list containing the prior distributions:

mean

prior mean for the biggest model (this doesn't depend on phi)

var

similar to corresponding member of posterior.bymodel.

models

an array containing the models used.

glm.out

an object of type 'bic.glm' containing the results of any call to bic.glm

call

the call to the function

Note

The outputs controlled by glimvar, output.priorvar and output.postvar can take up a lot of space, which is why these control parameters are F by default.

Author(s)

Original Splus code developed by Adrian Raftery [e-mail] and revised by Chris T. Volinsky. Translation to R by Ian S. Painter.

References

Raftery, A.E. (1988). Approximate Bayes factors for generalized linear models. Technical Report no. 121, Department of Statistics, University of Washington.

Raftery, Adrian E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.

Raftery, A.E. (1996). Approximate Bayes factors and accounting for model uncertainty in generalized linear models. Biometrika (83: 251-266).

See Also

bic.glm, summary.glib

Examples

## Not run: 
### Finney data
data(vaso)
x<- vaso[,1:2]
y<- vaso[,3]
n<- rep(1,times=length(y))

finney.models<- rbind(
    c(1, 0),
    c(0, 1),
    c(1, 1))

finney.glib <- glib (x,y,n, error="binomial", link="logit", 
                     models=finney.models, glimvar=TRUE, 
                     output.priorvar=TRUE, output.postvar=TRUE)
summary(finney.glib)

finney.bic.glm<- as.bic.glm(finney.glib)
plot(finney.bic.glm,mfrow=c(2,1))

## End(Not run)

### Yates (teeth) data. 

x<- rbind(
    c(0, 0, 0),
    c(0, 1, 0),
    c(1, 0, 0),
    c(1, 1, 1))

y<-c(4, 16, 1, 21)
n<-c(1,1,1,1)

models<- rbind(
    c(1, 1, 0),
    c(1, 1, 1))

glib.yates <- glib ( x, y, n, models=models, glimvar=TRUE,
                     output.priorvar=TRUE, output.postvar=TRUE) 
summary(glib.yates)

## Not run: 
### logistic regression with no models specified
library("MASS")
data(birthwt)
y<- birthwt$lo
x<- data.frame(birthwt[,-1])
x$race<- as.factor(x$race)
x$ht<- (x$ht>=1)+0
x<- x[,-9]
x$smoke <- as.factor(x$smoke)
x$ptl<- as.factor(x$ptl)
x$ht <- as.factor(x$ht)
x$ui <- as.factor(x$ui)

glib.birthwt<- glib(x,y, error="binomial", link = "logit")
summary(glib.birthwt)

glm.birthwt<- as.bic.glm(glib.birthwt)

imageplot.bma(glm.birthwt)
plot(glm.birthwt)

## End(Not run)