par.plot: A function to plot parallel plot for repeated measurement data

Description

This function can be used to plot parallel plots for each individual in a repeated measurement study. It is based on the coplot() function of R.

Usage

par.plot(formula = NULL, data = NULL, subjects = NULL, 
          color = TRUE, show.given = TRUE, ...)

Arguments

formula

a formula describing the form of conditioning plot. A formula of the form y ~ x | a indicates that plots of y versus x should be produced conditional on the variable a. A formula of the form y ~ x| a * b indicates that plots of y versus x should be produced conditional on the two variables a and b.

data

a data frame containing values for any variables in the formula. This argument is compulsory.

subjects

a factor which distinguish between the individual participants

color

whether the parallel plot are shown in colour, color=TRUE (the default) or not color=FALSE

show.given

logical (possibly of length 2 for 2 conditioning variables): should conditioning plots be shown for the corresponding conditioning variables (default 'TRUE')

…

for extra arguments

Value

It returns a plot.

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), App. Statist., 54, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

Examples

Run this code

# NOT RUN {
library(nlme)
data(Orthodont)
par.plot(distance~age,data=Orthodont,subject=Subject)
par.plot(distance~age|Sex,data=Orthodont,subject=Subject)
par.plot(distance~age|Subject,data=Orthodont,subject=Subject,show.given=FALSE)

# }