sample_bpr {bpr}  R Documentation 
The function generates draws from the posterior distribution of the coefficients of Poisson regression models. The method allows for Gaussian and horseshoe (Carvalho et al, 2010) prior distributions, and relies on a MetropolisHastings or importance sampler algorithm.
sample_bpr(
formula = NULL,
data = NULL,
iter,
burnin = NULL,
prior = list(type = "gaussian", b = NULL, B = NULL, tau = NULL),
pars = list(method = "MH", max_dist = 50, max_r = NULL, max_dist_burnin = 1e+06),
state = NULL,
thin = 1,
verbose = TRUE,
seed = NULL,
nchains = 1,
perc_burnin = 0.25
)
formula 
an object of class "formula": a symbolic description of the model to be fitted. 
data 
data frame or matrix containing the variables in the model. 
iter 
number of algorithm iterations. 
burnin 
(optional) a positive integer specifying the length of the burnin.
If a value > 1 is provided, the first 
prior 
a named list of parameters to select prior type and parameters, with arguments:

pars 
a named list of parameters to select algorithm type and tuning parameters, with arguments:

state 
optional vector providing the starting points of the chains. 
thin 
a positive integer specifying the period for saving samples. The default is 1. 
verbose 
logical (default = TRUE) indicating whether to print messages on the progress of the algorithm and possible convergence issues. 
seed 
(optional) positive integer: the seed of random number generator. 
nchains 
(optional) positive integer specifying the number of Markov chains. The default is 1. 
perc_burnin 
(default = 0.25) percentage of the chain to be discarded to perform inference. If both burnin and perc_burnin are specified, the most conservative burnin is considered. 
This function fits a Bayesian Poisson regression model with Gaussian prior distributions on the regression coefficients:
Y ~ Poisson(\lambda) , \lambda = exp{X \beta}
where Y
is a size n
vector of counts and X
is a n x p
matrix of coefficients; and (\beta   )
has a Gaussian distribution (possibly conditionally on some parameters).
Specifically, the function allows for informative Gaussian prior distribution on the parameters,
i.e. (\beta_1,...,\beta_p) ~ N_p(b, B)
, and for a horseshoe prior distribution (Carvalho et al, 2010).
The horseshoe prior is a scale mixture of normals, which is typically used in highdimension settings to induce sparsity and
regularization of the coefficients.
The implemented MetropolisHastings and importance sampler exploit as proposal density a multivariate Gaussian approximation of the posterior distribution. Such proposal is based on the convergence of the negative binomial distribution to the Poisson distribution and on the Polyagamma data augmentation of Polson et al. (2013).
The output of the sampling is an object of class poisreg
and admits classspecific methods to perform inference.
The function summary.poisreg
can be used to obtain or print a summary of the results and of the algorithm diagnostics.
The function mcmc_diagnostics
can be used to obtain or print convergence diagnostics for the sampled chains.
The function plot.poisreg
prints the trace of the sampled values and a density estimate of the regression coefficients.
See plot.mcmc
.
The function posterior_predictive
can be used to compute the posterior predictive distributions to check the model.
See also the related function plot.ppc
.
An object of S3 class poisreg
containing the results of the sampling.
poisreg
is a list containing at least the following elements:
sim
: list of the results of the sampling. It contains the following elements:
beta
: mcmc
object of posterior draws of the regression coefficients.
r
: the sequence of adaptive tuning parameters used in each iteration.
time
: the total amount of time to perform the simulation.
formula
: the formula
object used.
data
: list with elements the matrix of covariates X
and response variable y
.
state
: the starting points of the chain.
burnin
: length of the used burnin.
prior
: whether a Gaussian or horseshoe prior was used.
prior_pars
: prior parameters.
thin
: thinning frequency passed to the thin
parameter.
nchains
: number of chains. If nchains
was chosen >1, the output list will also include additional
numbered sim
elements, one for each sampled chain.
perc_burnin
: percentage of the chain used as burnin.
Carvalho, C., Polson, N., & Scott, J. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465480.
van der Pas, S., Szabo, B. and van der Vaart, A. (2017), Adaptive posterior contractionrates for the horseshoe, Electronic Journal of Statistics, 11(2), 31963225.
summary.poisreg
, mcmc_diagnostics
, plot.poisreg
,
merge_sim
, posterior_predictive
require(MASS) # load the data set
head(epil)
fit = sample_bpr( y ~ lbase*trt + lage + V4, data = epil,
iter = 1000)
summary(fit) # summary of posterior inference
mcmc_diagnostics(fit) # summary of MCMC convergence diagnostics
plot(fit)
## Examples with different options
# Select prior parameters and set tuning parameter
fit2 = sample_bpr( y ~ lbase*trt + lage + V4, data = epil,
iter = 1000,
prior = list( type = "gaussian", b = rep(0, 6),
B = diag(6) * 3 ),
pars = list( max_dist = 10 ))
# Simulate multiple chains and merge outputs after checking convergence
fit3 = sample_bpr( y ~ lbase*trt + lage + V4, data = epil,
iter = 1000,
nchains = 4, thin = 2)
# fit3 now contains additional elements ($sim2, $sim3, $sim4)
mcmc_diagnostics(fit3)
# the GelmanRubin diagnostics confirms convergence of the 4
# independent chains to the same stationary distribution
fit3b = merge_sim(fit3)
str(fit3b$sim)
# fit 3b contains only one MCMC chain of length 1500
# (after thinning and burnin)
## introduce more variables and use regularization
epil2 < epil[epil$period == 1, ]
epil2["period"] < rep(0, 59); epil2["y"] < epil2["base"]
epil["time"] < 1; epil2["time"] < 4
epil2 < rbind(epil, epil2)
epil2$pred < unclass(epil2$trt) * (epil2$period > 0)
epil2$subject < factor(epil2$subject)
epil3 < aggregate(epil2, list(epil2$subject, epil2$period > 0),
function(x) if(is.numeric(x)) sum(x) else x[1])
epil3$pred < factor(epil3$pred,
labels = c("base", "placebo", "drug"))
contrasts(epil3$pred) < structure(contr.sdif(3),
dimnames = list(NULL, c("placebobase", "drugplacebo")))
fit4 = sample_bpr(y ~ pred + factor(subject), data = epil3,
pars = list(max_dist = 0.3),
prior = list(type = "horseshoe", tau = 2),
iter = 3000, burnin = 1000)
summary(fit4)
mcmc_diagnostics(fit4)
plot(posterior_predictive(fit4), stats = c("mean", "sd", "max"))