vit.fitted: 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

Author

Ken Kelley (University of Notre Dame; KKelley@ND.Edu) and Po-Ju Wu (Indiana University; pojwu@indiana.edu)

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.

Examples

Run this code

if (FALSE) {
# 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)
}