Visualize individual trajectories with fitted curve and quality of fit
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.
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 = "", ...)
fit.Model |
lme, nlme object produced by nlme package or lmer object produced by lme4 package |
layout |
define the per-page layout when |
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 |
... |
optional plotting specifications |
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.
Ken Kelley (University of Notre Dame; KKelley@ND.Edu) and Po-Ju Wu (Indiana University; pojwu@indiana.edu)
par, nlme, lme4, lme, lmer, vit.fitted
## 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)
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