regcr {mixtools} | R Documentation |
Add a Bayesian Credible Region for Regression Lines to a Scatterplot
Description
Produce a credible region for regression lines based on
a sample of beta parameters (e.g., a sample from the posterior
distribution). The beta parameters are the intercept and slope
from a simple linear regression.
Usage
regcr(beta, x, alpha = .05, nonparametric = FALSE, plot = FALSE,
xyaxes = TRUE, ...)
Arguments
beta |
An nx2 matrix of regression parameters. The first column
gives the intercepts and the second column gives the slopes. |
x |
An n-vector of the predictor variable which is necessary when
nonparametric = TRUE. |
alpha |
The proportion of the beta sample to remove. In other
words, 1-alpha is the level of the credible region. |
nonparametric |
If nonparametric = TRUE, then the region is based on the convex
hull of the remaining beta after trimming, which is accomplished
using a data depth technique.
If nonparametric = FALSE, then the region is based on the
asymptotic normal approximation. |
plot |
If plot = TRUE, lines are added to the existing plot.
The type of plot created depends on the value of xyaxes. |
xyaxes |
If xyaxes = TRUE and plot = TRUE, then a credible region
for the regression lines is plotted on the x-y axes, presumably
overlaid on a scatterplot of the data. If xyaxes = FALSE and
plot = TRUE, the (convex) credible region for the regression line is
plotted on the beta, or intercept-slope, axes, presumably overlaid
on a scatterplot of beta. |
... |
Graphical parameters passed to lines or plot
command. |
Value
regcr
returns a list containing the following items:
boundary |
A matrix of points in beta, or intercept-slope, space
arrayed along the boundary of the credible region. |
upper |
A matrix of points in x-y space arrayed along the upper
crebible limit for the regression line. |
lower |
A matrix of points in x-y space arrayed along the lower
credible limit for the regression line. |
See Also
regmixEM
, regmixMH
Examples
## Nonparametric credible regions for mixtures of regressions fit to NOdata.
data(NOdata)
attach(NOdata)
beta<-matrix(c(1.3, -0.1, 0.6, 0.1), 2, 2)
sigma<-c(.02, .05)
MH.out<-regmixMH(Equivalence, NO, beta = beta, s = sigma,
sampsize = 10000, omega = .0013)
attach(data.frame(MH.out$theta))
beta.c1<-cbind(beta0.1[9950:9999], beta1.1[9950:9999])
beta.c2<-cbind(beta0.2[9950:9999], beta1.2[9950:9999])
plot(NO, Equivalence)
regcr(beta.c1, x = NO, nonparametric = TRUE, plot = TRUE,
col = 2)
regcr(beta.c2, x = NO, nonparametric = TRUE, plot = TRUE,
col = 3)
[Package
mixtools version 0.1.0
Index]