fit.pdf.mcmc {CoMiRe}

Posterior mean density plot for dose intervals

Description

Pointwise posterior mean (continuous blue lines), and credible bands (shaded blue areas) for f (y | x, z) calculated in x.val under the the model fitted in fit.

Usage

fit.pdf.mcmc(y, x, fit, mcmc, J=10, H = 10, alpha = 0.05, 
max.x = max(x), x.val, y.grid = NULL, xlim = c(0, max(x)), 
ylim = c(0, 1), xlab = NULL)

Arguments

y

optional numeric vector for the response used in comire.gibbs. If y is missing, y.grid must be provided.

x

numeric vector for the covariate relative to the dose of exposure used in comire.gibbs.

fit

the output of comire.gibbs opportunely trasformed in classCoMiRe class.

mcmc

a list giving the MCMC parameters.

J

parameter controlling the number of elements of the I-spline basis

H

total number of components in the mixture at x_0.

alpha

level of the credible bands.

max.x

maximum value allowed for x.

x.val

central points of each dose interval to be used in the posterior estimation of the probability density function.

y.grid

optional numerical vector giving the actual values of the grid for y for plotting the posterior mean density. If y.grid is not provided, standard grids are automatically used.

xlim, ylim

numeric vectors of length 2, giving the x and y coordinates ranges for the plot.

xlab

the title of the x axis.

Author(s)

Antonio Canale, Arianna Falcioni

Examples

{
data(CPP)
attach(CPP)

n <- NROW(CPP)
J <- H <- 10

premature <- as.numeric(gestage<=37)

mcmc <- list(nrep=5000, nb=2000, thin=5, ndisplay=4)

## too few iterations to be meaningful. see below for safer and more comprehensive results

mcmc <- list(nrep=10, nb=2, thin=1, ndisplay=4) 

prior <- list(mu.theta=mean(gestage), k.theta=10, eta=rep(1, J)/J, 
              alpha=rep(1,H)/H, a=2, b=2, J=J, H=H)
              
fit.dummy <- comire.gibbs(gestage, dde, family="continuous", 
                     mcmc=mcmc, prior=prior, seed=1, max.x=180)
                     
fit.pdf.mcmc(y = gestage, x = dde, fit = fit.dummy, mcmc = mcmc, J = 10, H = 10, 
                         alpha = 0.05, max.x = max(dde), x.val = 125, 
                         xlim = c(25,48), ylim = c(0,0.25),
                         xlab = "Gest. age. for DDE = 125")
                         

## safer procedure with more iterations (it may take some time)

mcmc <- list(nrep=5000, nb=2000, thin=5, ndisplay=4)

## Fit the model for continuous y 

prior <- list(mu.theta=mean(gestage), k.theta=10, eta=rep(1, J)/J, 
              alpha=rep(1,H)/H, a=2, b=2, J=J, H=H)
              
fit1 <- comire.gibbs(gestage, dde, family="continuous", 
                     mcmc=mcmc, prior=prior, seed=5, max.x=180)
          
fit.pdf.mcmc(y = gestage, x = dde, fit = fit1, mcmc = mcmc, J = 10, H = 10, 
                         alpha = 0.05, max.x = max(dde), x.val = 125, 
                         xlim = c(25,48), ylim = c(0,0.25),
                         xlab = "Gest. age. for DDE = 125")
                         

}