MBESS (version 4.1.0)

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

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.

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)