monoreg: Bayesian monotonic regression

Description Usage Arguments Value Author(s) References Examples

View source: R/monoreg.R

Description

This function implements an extended version of the Bayesian monotonic regression procedure for continuous outcomes described in Saarela & Arjas (2011), allowing for multiple additive monotonic components. The simulated example below replicates the one in the reference.

Usage

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monoreg(niter=15000, burnin=5000, adapt=5000, refresh=10, thin=5, 
        birthdeath=10, seed=1, rhoa=0.1, rhob=0.1, deltai=0.1, 
        drange=2.0, predict, include, response, offset=NULL, 
        axes, covariates, settozero, package)

Arguments

niter

Total number of MCMC iterations.

burnin

Number of iterations used for burn-in period.

adapt

Number of iterations used for adapting Metropolis-Hastings proposals.

refresh

Interval for producing summary output of the state of the MCMC sampler.

thin

Interval for saving the state of the MCMC sampler.

birthdeath

Number of birth-death proposals attempted at each iteration.

seed

Seed for the random generator.

rhoa

Shape parameter of a Gamma hyperprior for the Poisson process rate parameters.

rhob

Scale parameter of a Gamma hyperprior for the Poisson process rate parameters.

deltai

Range for a uniform proposal for the function level parameters.

drange

Allowed range for the monotonic function components.

predict

Indicator vector for the observations for which absolute risks are calculated (not included in the likelihood expression).

include

Indicator vector for the observations to be included in the likelihood expression.

response

Vector of response variable values.

offset

Vector of offset terms to be included in the linear predictor (optional).

axes

A matrix where the columns specify the covariate axes in the non-parametrically specified regression functions. Each variable here must be scaled to zero-one interval.

covariates

A matrix of additional covariates to be included in the linear predictor of the events of interest. Must include at least a vector of ones to specify an intercept term.

settozero

A zero-one matrix specifying the point process construction. Each row represents a point process, while columns correspond to the columns of the argument axes, indicating whether the column is one of the dimensions specifying the domain of the point process. (See function getcmat.)

package

An integer vector specifying the additive component into which each point process (row) specified in argument settozero is placed.

Value

A list with elements

steptotal

A sample of total number of points in the marked point process construction.

steps

A sample of the number of points used per each additive component.

rho

A sample of the Poisson process rate parameters (one per each point process specified).

loglik

A sample of log-likelihood values.

beta

A sample of regression coefficients for the variables specified in the argument covariates.

phi

A sample of non-parametric regression function levels for each observation specified in the argument predict.

pred

A sample of predicted means for each observation specified in the argument predict.

sigma

A sample of residual standard error parameters.

Author(s)

Olli Saarela <[email protected]>

References

Saarela O., Arjas E. (2011). A method for Bayesian monotonic multiple regression. Scandinavian Journal of Statistics, 38:499–513.

Examples

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library(monoreg)
set.seed(1)
# nobs <- 1000
nobs <- 50
sigma <- 0.01
x1 <- runif(nobs)
x2 <- runif(nobs)

# 6 different monotonic regression surfaces:
# mu <- sqrt(x1)
mu <- 0.5 * x1 + 0.5 * x2
# mu <- pmin(x1, x2)
# mu <- 0.25 * x1 + 0.25 * x2 + 0.5 * (x1 + x2 > 1.0)
# mu <- 0.25 * x1 + 0.25 * x2 + 0.5 * (pmax(x1, x2) > 0.5)
# mu <- ifelse((x1 - 1.0)^2 + (x2 - 1.0)^2 < 1.0, sqrt(1.0 - (x1 - 1.0)^2 - (x2 - 1.0)^2), 0.0)

y <- rnorm(nobs, mu, sigma)

# results <- monoreg(niter=15000, burnin=5000, adapt=5000, refresh=10, 
results <- monoreg(niter=5000, burnin=2500, adapt=2500, refresh=10, 
                   thin=5, birthdeath=10, seed=1, rhoa=0.1, rhob=0.1, 
                   deltai=0.1, drange=2.0, predict=rep(1.0, nobs), 
                   include=rep(1.0, nobs), response=y, offset=NULL, 
                   axes=cbind(x1,x2), covariates=rep(1.0, nobs), 
                   settozero=getcmat(2), package=rep(1,3)) 

# pdf(file.path(getwd(), 'pred3d.pdf'), width=6.0, height=6.0, paper='special')
op <- par(mar=c(2,2,0,0), oma=c(0,0,0,0), mgp=c(2.5,1,0), cex=0.75)
pred <- colMeans(results$pred)
idx <- order(pred, decreasing=TRUE)

tr <- persp(z=matrix(c(NA,NA,NA,NA), 2, 2), zlim=c(0,1), 
            xlim=c(0,1), ylim=c(0,1),
            ticktype='detailed', theta=-45, phi=25, ltheta=25, 
            xlab='X1', ylab='X2', zlab='mu')
for (i in 1:nobs) {
    lines(c(trans3d(x1[idx[i]], x2[idx[i]], 0.0, tr)$x, 
            trans3d(x1[idx[i]], x2[idx[i]], pred[idx[i]], tr)$x),
          c(trans3d(x1[idx[i]], x2[idx[i]], 0.0, tr)$y, 
            trans3d(x1[idx[i]], x2[idx[i]], pred[idx[i]], tr)$y),
          col='gray70')
}
points(trans3d(x1[idx], x2[idx], pred[idx], tr), pch=21, bg='white')
par(op)
# dev.off()

monoreg documentation built on Nov. 17, 2017, 7:34 a.m.

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