MBESS (version 4.9.3)

vit: Visualize individual trajectories

Description

A function to help visualize individual trajectories in a longitudinal (i.e., analysis of change) context.

Usage

vit(id = "", occasion = "", score = "", Data = NULL, group = NULL, 
subset.ids = NULL, pct.rand = NULL, number.rand = NULL, 
All.in.One = TRUE, ylab = NULL, xlab = NULL, same.scales = TRUE, 
plot.points = TRUE, save.pdf = FALSE, save.eps = FALSE,
 save.jpg = FALSE, file = "", layout = c(3, 3), col = NULL, 
 pch = 16, cex = 0.7, ...)

Value

Returns a plot of individual trajectories with the specifications provided.

Arguments

id

string variable of the column name of id

occasion

string variable of the column name of time variable

score

string variable of the column name where the score (i.e., dependent variable) is located

Data

data set with named column variables (see above)

group

if plotting parameters should be conditional on group membership

subset.ids

id values for a selected subset of individuals

pct.rand

percentage of random trajectories to be plotted

number.rand

number of random trajectories to be plotted

All.in.One

should trajectories be in a single or multiple plots

ylab

label for the ordinate (i.e., y-axis; see par)

xlab

label for the abscissa (i.e., x-axis; see par)

same.scales

should the y-axes have the same scales

plot.points

should the points be plotted

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

layout

define the per-page layout when All.in.One=FALSE

col

color(s) of the line(s) and points

pch

plotting character(s); see par

cex

size of the points (1 is the R default; see par)

...

optional plotting specifications

Author

Ken Kelley (University of Notre Dame; ) and Po-Ju Wu (Indiana University)

Details

This function makes visualizing individual trajectories simple. Data should be in the "univariate format" (i.e., the same format as lmer and nlme data.)

See Also

par, nlme, vit.fitted,

Examples

if (FALSE) {
data(Gardner.LD)

# Although many options are possible, a simple call to
# 'vit' is of the form:
# vit(id="ID", occasion= "Trial", score= "Score", Data=Gardner.LD)

# Now color is conditional on group membership.
# vit(id="ID", occasion= "Trial", score="Score", Data=Gardner.LD, 
# group="Group")

# Now randomly selects 50
# vit(id="ID", occasion= "Trial", score="Score", Data=Gardner.LD, 
# pct.rand=50, group="Group")

# Specified individuals are plotted (by group)
# vit(id="ID", occasion= "Trial", score="Score", Data=Gardner.LD, 
# subset.ids=c(1, 4, 8, 13, 17, 21), group="Group")

# Now colors for groups are changed .
# vit(id="ID", occasion= "Trial", score="Score", Data=Gardner.LD, 
# group="Group",subset.ids=c(1, 4, 8, 13, 17, 21), col=c("Green", "Blue"))

# Now each individual specified is plotted separately.
# vit(id="ID", occasion= "Trial", score="Score", Data=Gardner.LD, 
# group="Group",subset.ids=c(1, 4, 8, 13, 17, 21), col=c("Green", "Blue"),
# All.in.One=FALSE)
}