vit {MBESS} | R Documentation |
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, All.in.One = TRUE, ylim = NULL, xlim = NULL, ylab = "Score", xlab = "Occasion", main = "", plot.points = TRUE, draw.which.lines = "Observed", predicted.id = NULL, predicted.occasion = NULL, predicted.score = NULL, predicted.data = NULL, pch = 16, points.cex = 0.7, lty = 1, lwd = 1, per.page.layout = rbind(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9), c(10, 11, 12)), points.col = "Black", lines.col = "Black", mar = c(2.2, 2, 1, 1), mgp = c(2.2, 1, 0), pty = "s", ...)
id |
string variable of the column name of id |
occasion |
string variable of the column name of id |
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 |
All.in.One |
should trajectories be in a single or multiple plots |
ylim |
optional limit of the ordinate (i.e., y -axis; see par ) |
xlim |
optional limit for the abscissa (i.e., x -axis; see par ) |
ylab |
label for the ordinate (i.e., y -axis; see par ) |
xlab |
label for the abscissa (i.e., x -axis; see par ) |
main |
main title of the plot |
plot.points |
should the points be plotted |
draw.which.lines |
draw the observed or predicted lines |
predicted.id |
like id but for the predicted data |
predicted.occasion |
like occasion but for the predicted data |
predicted.score |
like score but for the predicted scores |
predicted.data |
like Data but for the predicted data |
pch |
plotting character(s); see par |
points.cex |
size of the points (1 is the R default; see par ) |
lty |
type of line to be plotted |
lwd |
width of line to be plotted |
per.page.layout |
define the per-page layout when All.in.One==FALSE |
points.col |
color(s) of the points |
lines.col |
color(s) of the line(s) |
mar |
adjusts the margin spacing (see par ) |
mgp |
adjusts the margin lines (see mgp ) |
pty |
plot region type (see par ; "s" is square) |
... |
optional plotting specifications |
This function makes visualizing individual trajectories simple. If the plot will be saved for a publication,
it is best to call upon a device so that the file can be saved. See Devices
(e.g., use pdf
to
save a PDF file of the figure). Data should be in the "univariate format" (i.e., the same format as
lmer
and nlme
data.
Returns a plot of individual trajectories with the specifications provided.
At present, the use of predicted statements is limited (and not recommended).
Ken Kelley (Indiana University; KKIII@Indiana.Edu)
par
, Devices
, pdf
, jpeg
, bmg
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 line color is conditional on group membership. vit(id="ID", occasion="Trial", score="Score", Data=Gardner.LD, group="Group", draw.which.lines="Observed", pch=16, points.col=c("Black"), lines.col=c("Black", "Blue")) # Now line and plotting character are conditional on group membership vit(id="ID", occasion="Trial", score="Score", Data=Gardner.LD, group="Group", draw.which.lines="Observed", pch=16, points.col=c("Black", "Blue"), lines.col=c("Black", "Blue")) # Now plotting character is different with same color lines. vit(id="ID", occasion="Trial", score="Score", Data=Gardner.LD, group="Group", draw.which.lines="Observed", pch=c(1, 16), points.col=c("Black"), lines.col=c("Black")) # Point color and line color can be mixed (this feature may not # be useful very often, but the function is general nonetheless). vit(id="ID", occasion="Trial", score="Score", Data=Gardner.LD, group="Group", draw.which.lines="Observed", pch=16, points.col=c("Red", "Orange"), lines.col=c("Black", "Blue")) # Now randomly selects 50% of the sample to plot vit(id="ID", occasion="Trial", score="Score", Data=Gardner.LD, pct.rand=50, group="Group", draw.which.lines="Observed", pch=16) # 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", lines.col=c("Black", "Blue"), pch=16)