plot.hnp: Plot Method for hnp Objects

Description

The plot method for objects of class hnp.

Usage

# S3 method for hnp
plot(x, cex, pch, colour, lty, lwd, type,
     xlab, ylab, main, legpos, legcex, ...)

Arguments

object of class "hnp".

cex

character expansion size.

pch

character string or vector of one character or integer for plotting characters, see points.

colour

vector of colours.

lty

vector of line types.

lwd

vector of line widths.

type

type of plot for each envelope band and points. Default is c("l","l","l","p").

xlab

title for x axis, as in plot

ylab

title for y axis, as in plot

main

plot title.

legpos

if print.on=TRUE, represents the position where the information should be printed ("topright", "topleft", "bottomright", "bottomleft"), as in legend.

legcex

if print.on=TRUE, character expansion size of legend.

…

extra graphical arguments passed to matplot.

Value

None.

References

Moral, R. A., Hinde, J. and Dem<U+00E9>trio, C. G. B. (2017) Half-normal plots and overdispersed models in R: the hnp package. Journal of Statistical Software 81(10):1-23.

Dem<U+00E9>trio, C. G. B. and Hinde, J. (1997) Half-normal plots and overdispersion. GLIM Newsletter 27:19-26.

Hinde, J. and Dem<U+00E9>trio, C. G. B. (1998) Overdispersion: models and estimation. Computational Statistics and Data Analysis 27:151-170.

Dem<U+00E9>trio, C. G. B., Hinde, J. and Moral, R. A. (2014) Models for overdispersed data in entomology. In Godoy, W. A. C. and Ferreira, C. P. (Eds.) Ecological modelling applied to entomology. Springer.

Examples

Run this code

# NOT RUN {
## Simple Poisson regression
set.seed(100)
counts <- c(rpois(5, 2), rpois(5, 4), rpois(5, 6), rpois(5, 8))
treatment <- gl(4, 5)
fit <- glm(counts ~ treatment, family=poisson)
anova(fit, test="Chisq")

## half-normal plot
hnp(fit)

## or save it in an object and then use the plot method
my.hnp <- hnp(fit, print.on=TRUE, plot=FALSE)
plot(my.hnp)

## changing graphical parameters
plot(my.hnp, lty=2, pch=4, cex=1.2)
plot(my.hnp, lty=c(2,3,2), pch=4, cex=1.2, col=c(2,2,2,1))
plot(my.hnp, main="Half-normal plot", xlab="Half-normal scores",
     ylab="Deviance residuals", legpos="bottomright")
# }