vit function

Visualize individual trajectories

Visualize individual trajectories

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

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, ...)

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

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.)

Returns

Returns a plot of individual trajectories with the specifications provided.

Author(s)

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

See Also

par, nlme, vit.fitted,

Examples

## Not run:

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)
## End(Not run)
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