coef.qde: Coefficient Extraction from qde Model Fit

In qrjoint: Joint Estimation in Linear Quantile Regression

Coefficient Extraction from qde Model Fit

Description

Post process MCMC output from qde to create summaries of the quantile estimates.

Usage

 
## S3 method for class 'qde'
coef(object, burn.perc = 0.5, nmc = 200, reduce = TRUE, ...)

Arguments

object	a fitted model of the class qde.
burn.perc	a positive fraction indicating what fraction of the saved draws are to be discarded as burn-in
nmc	integer giving the number of samples, post burn-in, to be used in Monte Carlo averaging
reduce	logical indicating if the tail-expanded grid of tau values is to be reduced to the regular increment grid
...	not currently implemented

Value

Extracts posterior draws of intercept and slope functions from saved draws of model parameters. A plot may be obtained if plot = TRUE. A list is returned invisibly with three fields.

beta.samp	a matrix with nmc many columns and length(tau.grid) many rows.
beta.est	a 3-column matrix of median, 2.5th and 97.5th percentiles of the posterior distribution of \beta_0
parametric	a matrix with 3 columns giving the estimate (posterior median) and the lower and upper end points of the 95% posterior credible interval for \gamma_0, \sigma, and, \nu.

See Also

qde and summary.qde for model fitting under qrjoint. Also see getBands for plotting credible bands for coefficients.

Examples

## Plasma data analysis
data(plasma)
Y <- plasma$BetaPlasma
Y <- Y + 0.1 * rnorm(length(Y)) ## remove atomicity

# model fitting with 50 posterior samples from 100 iterations (thin = 2)
fit.qde <- qde(Y, 50, 2)
betas <- coef(fit.qde)
signif(betas$parametric, 3)

qrjoint documentation built on April 6, 2023, 1:07 a.m.