MCMCquantreg: Bayesian quantile regression using Gibbs sampling

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

This function fits quantile regression models under Bayesian inference. The function samples from the posterior distribution using Gibbs sampling with data augmentation. A multivariate normal prior is assumed for β. The user supplies the prior parameters. A sample of the posterior distribution is returned as an mcmc object, which can then be analysed by functions in the coda package.

Usage

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MCMCquantreg(
  formula,
  data = NULL,
  tau = 0.5,
  burnin = 1000,
  mcmc = 10000,
  thin = 1,
  verbose = 0,
  seed = sample(1:1e+06, 1),
  beta.start = NA,
  b0 = 0,
  B0 = 0,
  ...
)

Arguments

formula

Model formula.

data

Data frame.

tau

The quantile of interest. Must be between 0 and 1. The default value of 0.5 corresponds to median regression.

burnin

The number of burn-in iterations for the sampler.

mcmc

The number of MCMC iterations after burnin.

thin

The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.

verbose

A switch which determines whether or not the progress of the sampler is printed to the screen. If verbose is greater than 0 the iteration number and the most recently sampled values of β and σ are printed to the screen every verboseth iteration.

seed

The seed for the random number generator. If NA, the Mersenne Twister generator is used with default seed 12345; if an integer is passed it is used to seed the Mersenne twister. The default value for this argument is a random integer between 1 and 1,000,000. This default value ensures that if the function is used again with a different value of τ, it is extremely unlikely that the seed will be identical. The user can also pass a list of length two to use the L'Ecuyer random number generator, which is suitable for parallel computation. The first element of the list is the L'Ecuyer seed, which is a vector of length six or NA (if NA a default seed of rep(12345,6) is used). The second element of list is a positive substream number. See the MCMCpack specification for more details.

beta.start

The starting values for β. This can either be a scalar or a column vector with dimension equal to the dimension of β. The default value of NA will use the OLS estimate \hat{β} with \hat{σ}Φ^{-1}(τ) added on to the first element of \hat{β} as the starting value. (\hat{σ}^2 denotes the usual unbiased estimator of σ^2 under ordinary mean regression and Φ^{-1}(τ) denotes the inverse of the cumulative density function of the standard normal distribution.) Note that the default value assume that an intercept is included in the model. If a scalar is given, that value will serve as the starting value for all β.

b0

The prior mean of β. This can either be a scalar or a column vector with dimension equal to the dimension of

β. If this takes a scalar value, then that value will serve as the prior mean for all β.

B0

The prior precision of β. This can either be a scalar or a square matrix with dimensions equal to the number of betas. If this takes a scalar value, then that value times an identity matrix serves as the prior precision of β. Default value of 0 is equivalent to an improper uniform prior for β.

...

further arguments to be passed

Details

MCMCquantreg simulates from the posterior distribution using Gibbs sampling with data augmentation (see http://people.brunel.ac.uk/~mastkky/). β are drawn from a multivariate normal distribution. The augmented data are drawn conditionally from the inverse Gaussian distribution. The simulation is carried out in compiled C++ code to maximise efficiency. Please consult the coda documentation for a comprehensive list of functions that can be used to analyse the posterior sample.

We assume the model

Q_{τ}(y_i|x_i) = x_i'β

where Q_{τ}(y_i|x_i) denotes the conditional τth quantile of y_i given x_i, and β=β(τ) are the regression parameters possibly dependent on τ. The likelihood is formed based on assuming independent Asymmetric Laplace distributions on the y_i with skewness parameter τ and location parameters x_i'β. This assumption ensures that the likelihood function is maximised by the τth conditional quantile of the response variable. We assume standard, semi-conjugate priors on β:

β \sim \mathcal{N}(b_0,B_0^{-1})

Only starting values for β are allowed for this sampler.

Value

An mcmc object that contains the posterior sample. This object can be summarised by functions provided by the coda package.

Author(s)

Craig Reed

References

Daniel Pemstein, Kevin M. Quinn, and Andrew D. Martin. 2007. Scythe Statistical Library 1.2. http://scythe.lsa.umich.edu.

Craig Reed and Keming Yu. 2009. “An Efficient Gibbs Sampler for Bayesian Quantile Regression.” Technical Report.

Keming Yu and Jin Zhang. 2005. “A Three Parameter Asymmetric Laplace Distribution and it's extensions.” Communications in Statistics - Theory and Methods, 34, 1867-1879.

Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2006. “Output Analysis and Diagnostics for MCMC (CODA)”, R News. 6(1): 7-11. https://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf.

See Also

MCMCregress, plot.mcmc, summary.mcmc, lm, rq

Examples

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## Not run: 

x<-rep(1:10,5)
y<-rnorm(50,mean=x)
posterior_50 <- MCMCquantreg(y~x)
posterior_95 <- MCMCquantreg(y~x, tau=0.95, verbose=10000,
    mcmc=50000, thin=10, seed=2)
plot(posterior_50)
plot(posterior_95)
raftery.diag(posterior_50)
autocorr.plot(posterior_95)
summary(posterior_50)
summary(posterior_95)

## End(Not run)

MCMCpack documentation built on Jan. 20, 2021, 5:07 p.m.