plotCurveEstimate: Generic function for plotting a fractional polynomial curve...

In bfp: Bayesian Fractional Polynomials

Generic function for plotting a fractional polynomial curve estimate

Description

Plot a fractional polynomial curve estimate for either a single model or a Bayesian model average over BayesMfp objects. Optionally, credible intervals and / or bands can be added to the plot.

Usage

plotCurveEstimate(model, termName, plevel = 0.95, slevel = plevel,
plot = TRUE, legendPos = "topleft", rug = FALSE, partialResids=TRUE, 
hpd=TRUE,..., main = NULL) 

Arguments

model	an object of class BayesMfp or BmaSamples
termName	string denoting an FP term, as written by the summary method
plevel	credible level for pointwise intervals, and NULL means no pointwise intervals (default: 0.95)
slevel	credible level for simultaneous credible band (SCB), NULL means no SCB (defaults to plevel)
plot	if FALSE, only return values needed to produce the plot, but do not plot (default is TRUE, so a plot is made)
legendPos	position of coefficient estimates (for BayesMfp) or sample size (for BmaSamples) in the plot, NULL suppresses the printing (default is “topleft”)
rug	add a rug to the plot? (default: FALSE)
partialResids	add partial residuals to the plot? (default: TRUE)
hpd	use HPD intervals (TRUE, default) or quantile-based (FALSE) intervals?
...	further arguments in case of a BayesMfp object (see details) and arguments for plotting with matplot
main	optional main argument for the plot

Details

Further arguments for application on a BayesMfp object:

grid

vector of unscaled abscissae, default is a length gridSize grid over the observed range specified by providing the argument NULL.

post

list with posterior parameters of the model, which may be provided manually to accelerate plotting in a loop

gridSize

default number of grid points used when no grid is supplied (default is 201)

numSim

number of simulations for estimation of the SCB (default is 500)

Value

a list of various plotting information:

original	grid on the original covariate scale
grid	grid on the transformed scale
mode	mode curve values, only for BayesMfp object
mean	pointwise mean curve values, only for BmaSamples object
median	pointwise median curve values, only for BmaSamples object
plower	lower boundaries for pointwise intervals
pupper	upper boundaries for pointwise intervals
slower	lower boundaries for SCB
supper	upper boundaries for SCB
obsVals	observed values of the covariate on the original scale
sampleSize	sample size underlying the curve estimate, only for BmaSamples object
partialResids	partial residuals
transform	vector of shift and scale parameter

See Also

BayesMfp, BmaSamples

Examples

## construct a BayesMfp object
set.seed(19)
x1 <- rnorm (n=15)
x2 <- rbinom (n=15, size=20, prob=0.5) 
x3 <- rexp (n=15)
y <- rt (n=15, df=2)

test <- BayesMfp (y ~ bfp (x2, max = 4) + uc (x1 + x3), nModels = 100,
method="exhaustive")

## plot the x2 curve estimate for the 20-th best model
p1 <- plotCurveEstimate (test[20], "x2")

## look at the returned list
str(p1)

## plot the BMA curve estimate for the same covariate
testBma <- BmaSamples (test)
p2 <- plotCurveEstimate (testBma, "x2")

## look at the returned list
str(p2)

## try the new options:
plotCurveEstimate (testBma, "x2", partialResids=FALSE, hpd=FALSE)

bfp documentation built on March 19, 2024, 3:07 a.m.