# coef.qrjoint: Regression Coefficient Extraction from qrjoint Model Fit In qrjoint: Joint Estimation in Linear Quantile Regression

## Description

Post process MCMC output from `qrjoint` to create summaries of intercept and slope function estimates

## Usage

 ```1 2 3 4``` ``` ## S3 method for class 'qrjoint' coef(object, burn.perc = 0.5, nmc = 200, plot = FALSE, show.intercept = TRUE, reduce = TRUE, ...) ```

## Arguments

 `object` a fitted model of the class `qrjoint`. `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 `plot` logical indicating if plots are to be made `show.intercept` whether to plot the intercept curve when `plot = TRUE` `reduce` logical indicating if the tail-expanded grid of tau values is to be reduced to the regular increment grid `...` limited plotting parameters that are passed onto the call of `getBands`

## 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 two fields.

 `beta.samp` a matrix with `nmc` many columns and `(p+1)*length(tau.grid)` many rows. `beta.est` a list of length (p+1), j-th element giving a 3-column matrix of median, 2.5th and 97.5th percentiles of the posterior distribution of {beta}[j]

## See Also

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ``` ## Plasma data analysis # recoding variables data(plasma) plasma\$Sex <- as.factor(plasma\$Sex) plasma\$SmokStat <- as.factor(plasma\$SmokStat) plasma\$VitUse <- 3 - plasma\$VitUse plasma\$VitUse <- as.factor(plasma\$VitUse) # creating predictors and response (beta carotene concentration in the plasma) X <- model.matrix(BetaPlasma ~ Age + Sex + SmokStat + Quetelet + VitUse + Calories + Fat + Fiber + Alcohol + Cholesterol + BetaDiet, data = plasma)[,-1] Y <- plasma\$BetaPlasma # model fitting with 50 posterior samples from 100 iterations (thin = 2) fit.qrj <- qrjoint(X, Y, 50, 2) ## Not run: betas <- coef(fit.qrj) ## no plots ## End(Not run) betas <- coef(fit.qrj, plot = TRUE, col = "darkgreen") ## estimates are plotted ```

qrjoint documentation built on May 29, 2017, 11:07 p.m.