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

In gamlss: Generalised Additive Models for Location Scale and Shape

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.

Note

Note that similar plot can be fount in the library nlme by Pinheiro and Bates

Author(s)

Mikis Stasinopoulos

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/).

See Also

gamlss

Examples

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)

gamlss documentation built on Oct. 4, 2023, 5:08 p.m.