vit.fitted: Visualize individual trajectories with fitted curve and...

In MBESS: The MBESS R Package

Visualize individual trajectories with fitted curve and quality of fit

Description

A function to help visualize individual trajectories in a longitudinal (i.e., analysis of change) context with fitted curve and quality of fit after analyzing the data with lme, lmer, or nlme function.

Usage

vit.fitted(fit.Model, layout = c(3, 3), ylab = "", xlab = "", 
pct.rand = NULL, number.rand = NULL, subset.ids = NULL, 
same.scales = TRUE, save.pdf = FALSE, save.eps = FALSE, 
save.jpg = FALSE, file = "", ...)

Arguments

fit.Model	lme, nlme object produced by nlme package or lmer object produced by lme4 package
layout	define the per-page layout when All.in.One=FALSE
ylab	label for the ordinate (i.e., y-axis; see par)
xlab	label for the abscissa (i.e., x-axis; see par)
pct.rand	percentage of random trajectories to be plotted
number.rand	number of random trajectories to be plotted
subset.ids	id values for a selected subset of individuals to be plotted
same.scales	should the y-axes have the same scales
save.pdf	save a pdf file
save.eps	save a postscript file
save.jpg	save a jpg file
file	file name and file path for the graph(s) to save, if file="" a file would be saved in the current working directory
...	optional plotting specifications

Details

This function uses the fitted model from nlme and lme functions in nlme package, and lmer function in lme4 package. It returns a set of plots of individual observed data, the fitted curves and the quality of fit.

Author(s)

Ken Kelley (University of Notre Dame; [e-mail]) and Po-Ju Wu (Indiana University; [e-mail])

See Also

par, nlme, lme4, lme, lmer, vit.fitted

Examples

## Not run: 
# Note that the following example works fine in R (<2.7.0), but not in 
# the development version of R-2.7.0 (the cause can be either in this 
# function or in the R program)

# data(Gardner.LD)
# library(nlme)
# Full.grouped.Gardner.LD <- groupedData(Score ~ Trial|ID, data=Gardner.LD, order.groups=FALSE)    

# Examination of the plot reveals that the logistic change model does not adequately describe
# the trajectories of individuals 6 and 19 (a negative exponential change model would be 
# more appropriate). Thus we remove these two subjects.
# grouped.Gardner.LD <- Full.grouped.Gardner.LD[!(Full.grouped.Gardner.LD["ID"]==6 | 
#   Full.grouped.Gardner.LD["ID"]==19),]

# G.L.nlsList<- nlsList(SSlogis,grouped.Gardner.LD)
# G.L.nlme <- nlme(G.L.nlsList)
# to visualize individual trajectories:  vit.fitted(G.L.nlme)
# plot 50 percent random trajectories:  vit.fitted(G.L.nlme, pct.rand = 50)

## End(Not run)

MBESS documentation built on Oct. 26, 2023, 9:07 a.m.