Description Usage Arguments Value Author(s) References See Also Examples
Function to create objects of class longCat
.
1 2 |
y |
a data matrix or data frame of numeric states in wide (as opposed to long) format with cases in rows and repeated observations in columns. It is reccomended that |
times |
The If If When The |
Labels |
a vector of numeric or character labels for the response options in |
tLabels |
numeric or character labels for the time points in |
id |
An optional variable identifying or naming the rows of |
events |
An event |
event.times |
A The |
eventLabels |
If |
longCat
returns an object of class longCat
which is a list containing at least the following components:
y |
|
y.sorted |
|
dim |
the dimension of |
times |
the |
endt |
the |
times.sorted |
if |
endt.sorted |
|
labels |
the |
tLabels |
the |
factors |
a vector containing the unique values in |
IndTime |
a logical indicator of whether |
nfactors |
the number of unique values in |
sorted |
a logical indicator of whether |
ascending |
logical indicator. If |
group |
a vector of the same length as the number of rows in |
groupLabels |
a optional vector of character or numeric labels for the |
order.y |
A matrix with identification (see input |
events |
A matrix of events. |
event.times |
A matrix of event times. |
eventLabels |
A vector of event labels. |
Stephen Tueller
Tueller, S. J., Van Dorn, R. A., & Bobashev, G. V. (2016). Visualization of categorical longitudinal and times series data (Report No. MR-0033-1602). Research Triangle Park, NC: RTI Press. http://www.rti.org/publication/visualization-categorical-longitudinal-and-times-series-data
longCatPlot
to plot longCat
objects created by the longCat
function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # create the longcat object similar to Figure 2 in Tueller (2016)
times <- c(1,100,200,300,400,500,600)
f2lc <- longCat(example2cat, times)
# object summary
summary(f2lc)
# compare growth curves to longCat
par(mfrow=c(1,2), bg='cornsilk3')
longContPlot(example2cat, times, ylim=c(1,5),
main='Growth Curves', ylab='', xlab='Days')
longCatPlot(f2lc, lwd=4, main='Horizontal Line Plot', colScheme='heat', legendBuffer=.2)
par(mfrow=c(1,1), bg='transparent')
# illustrate individually varying times of observation
set.seed(642531)
y <- matrix(sample(1:5, 500, replace=TRUE), 100, 5)
set.seed(963854)
times <- matrix(runif(600, 1, 3), 100, 6)
# times must be cumulative
times <- t(apply(times, 1, cumsum))
lc <- longCat(y, times=times)
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
cols <- longCatPlot(lc, legendBuffer=0, groupBuffer=0,
main='Individually Varying Times of Observation')
legend(15.5, 100, legend=lc$Labels, lty=1, col=cols, lwd=2)
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)
# illustrate the adding event indicators
set.seed(45962)
events <- matrix(sample(1:3, 200, replace=TRUE), 100, 2)
set.seed(23498)
event.times <- matrix(sample(c(times), 200, replace=FALSE), 100, 2)
labels <- c('Street', 'Drug Tx', 'Jail', 'Prison', 'Unknown')
eventLabels=c('Arrest', 'Drug Test', 'Hearing')
lc <- longCat(y, times=times, Labels=labels,
events=events, event.times=event.times,
eventLabels=eventLabels)
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 12.1), xpd=TRUE)
cols <- longCatPlot(lc, legendBuffer=0, groupBuffer=0,
main='Superimpose Events Over States')
legend(15.5, 100, legend=lc$Labels, lty=1, col=cols, lwd=2)
legend(15.5, 40, legend=lc$eventLabels, pch=1:length(lc$eventLabels))
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)
## Not run:
# illustrate handling non time-ordered input (e.g., factor analysis data)
y <- matrix(sample(c('1', '2', '3', '4', '5'), 500, replace=TRUE), 100, 5)
lc <- longCat(y)
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
cols <- longCatPlot(lc, legendBuffer=0)
legend(6, 100, legend=lc$factors, lty=1, col=cols, lwd=2)
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)
# illustrate plotting with more than 9 categories
# (a warning is issued)
y <- matrix(sample(1:18, 500, replace=TRUE), 100, 5)
lc <- longCat(y)
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
cols <- longCatPlot(lc, legendBuffer=0)
legend(6, 100, legend=lc$factors, lty=1, col=cols, lwd=2)
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)
## End(Not run)
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