Plot state sequence objects
High level plot functions for state sequence objects that can produce state distribution (chronograms), frequency, index, transversal entropy, sequence of modes, meant time, and representative plots.
seqplot(seqdata, group = NULL, type = "i", main = NULL, cpal = NULL, missing.color = NULL, ylab = NULL, yaxis = TRUE, axes = "all", xtlab = NULL, cex.axis = 1, with.legend = "auto", ltext = NULL, cex.legend = 1, use.layout = (!is.null(group) | with.legend != FALSE), legend.prop = NA, rows = NA, cols = NA, title, cex.plot, withlegend, ...) seqdplot(seqdata, group = NULL, main = NULL, ...) seqfplot(seqdata, group = NULL, main = NULL, ...) seqiplot(seqdata, group = NULL, main = NULL, ...) seqIplot(seqdata, group = NULL, main = NULL, ...) seqHtplot(seqdata, group = NULL, main = NULL, ...) seqmsplot(seqdata, group = NULL, main = NULL, ...) seqmtplot(seqdata, group = NULL, main = NULL, ...) seqrplot(seqdata, group = NULL, main = NULL, ...)
seqdata |
State sequence object created with the |
group |
Grouping variable of length equal to the number of sequences. When not |
type |
the type of the plot. Available types are |
main |
Character string. Title for the graphic. Default is |
cpal |
Color palette used for the states. By default, the |
missing.color |
Color for representing missing values inside the sequences. By default, this color is taken from the |
ylab |
Character string. an optional label for the y-axis. If set to |
yaxis |
Logical. Should the y-axis be plotted? When set as |
axes |
Character string or logical. If set as |
xtlab |
Vector of length equal to the number of columns of |
cex.axis |
Real value.
Axis annotation magnification. When |
with.legend |
Character string or logical. Defines if and where the legend of the state colors is plotted. The default value |
ltext |
Vector of character strings of length and order corresponding to |
cex.legend |
Real. Legend magnification. See |
use.layout |
Logical. Should |
legend.prop |
Real in range [0,1]. Proportion of the graphic area devoted to the legend plot when |
rows,cols |
Integers. Number of rows and columns of the plot panel when |
title |
Deprecated. Use |
cex.plot |
Deprecated. Use |
withlegend |
Deprecated. Use |
... |
arguments to be passed to the function called to produce the appropriate statistics and the associated plot method (see details), or other graphical parameters. For example the |
seqplot is the generic function for high level plots of state sequence objects with group splits and automatic display of the color legend. Many different types of plots can be produced by means of the type argument. Except for sequence index plots, seqplot first calls the specific function producing the required statistics and then the plot method for objects produced by this function (see below). For sequence index plots, the state sequence object itself is plotted by calling the plot.stslist method. When splitting by groups and/or displaying the color legend, the layout function is used for arranging the plots.
The seqdplot, seqfplot, seqiplot, seqIplot,
seqHtplot, seqmsplot, seqmtplot, seqpcplot
and seqrplot functions are aliases for calling seqplot
with type argument set respectively to "d", "f",
"i", "I", "Ht", "ms", "mt",
"pc" or "r".
A State distribution plot (type="d") represents the sequence of the cross-sectional state frequencies by position (time point) computed by the seqstatd function and rendered with the plot.stslist.statd method. Such plots are also known as chronograms.
A Sequence frequency plots (type="f") displays the most frequent sequences, each one with an horizontal stack bar of its successive states. Sequences are displayed bottom-up in decreasing order of their frequencies (computed by the seqtab function). The plot.stslist.freq plot method is called for producing the plot.
The idxs optional argument may be specified for selecting the sequences to be plotted (default is 1:10, i.e. the 10 most frequent sequences). The width of the bars representing the sequences is by default proportional to their frequencies, but this can be disabled with the pbarw=FALSE optional argument. If weights have been specified when creating seqdata, weighted frequencies are used unless you set the weighted=TRUE option. See examples below, the seqtab and plot.stslist.freq manual pages for a complete list of optional arguments and Müller et al., (2008) for a description of sequence frequency plots.
In sequence index plots (type="i" or type="I"), the requested individual sequences are rendered with horizontal stacked bars depicting the states over successive positions (time). Optional arguments are idxs for specifying the indexes of the sequences to be plotted (when type="i" defaults to the first ten sequences, i.e idxs=1:10). For plotting nicely a (big) whole set one can use type="I" which is the same as using idxs=0 together with the additional graphical parameters border=NA and space=0 to suppress bar borders and space between bars. The sortv argument can be used to pass a vector of numerical values for sorting the sequences or to specify a sorting method. See plot.stslist for a complete list of optional arguments and their description.
The interest of sequence index plots has, for instance, been stressed by Scherer (2001) and Brzinsky-Fay et al. (2006). Notice that index plots for thousands of sequences result in very heavy PDF or POSTSCRIPT graphic files. Dramatic file size reduction may be achieved by saving the figures in bitmap format with using for instance the png graphic device instead of postscript or pdf.
The transversal entropy plot (type="Ht") displays the evolution over positions of the cross-sectional entropies (Billari, 2001). Cross-sectional entropies are computed by calling seqstatd function and then plotted by calling the plot.stslist.statd plot method.
The modal state sequence plot (type="ms") displays the sequence of the modal states with each mode proportional to its frequency at the given position. The seqmodst function is called which returns the sequence and the result is plotted by calling the plot.stslist.modst plot method.
The mean time plot (type="mt") displays the mean time spent in each state of the alphabet as computed by the seqmeant function. The plot.stslist.meant plot method is used to plot the resulting statistics. Set serr=TRUE to display error bars on the mean time plot.
The representative sequence plot (type="r") displays a reduced, non redundant set of representative sequences extracted from the provided state sequence object and sorted according to a representativeness criterion. The seqrep function is called to extract the representative set which is then plotted by calling the plot.stslist.rep method. A distance matrix is required that is passed with the diss argument or by calling the seqdist function if diss=NULL. The criterion argument sets the representativeness criterion used to sort the sequences. Refer to the seqrep and plot.stslist.rep manual pages for a complete list of optional arguments. See Gabadinho and Ritschard (2013) for more details on the extraction of representative sets. Also look at the examples below.
For decorated parallel coordinate plots (type="pc") see the specific manual page of seqpcplot.
Alexis Gabadinho (with Gilbert Ritschard for the help page)
Billari, F. C. (2001). The analysis of early life courses: Complex description of the transition to adulthood. Journal of Population Research 18(2), 119-142.
Brzinsky-Fay C., U. Kohler, M. Luniak (2006). Sequence Analysis with Stata. The Stata Journal, 6(4), 435-460.
Gabadinho, A., and G. Ritschard (2013), "Searching for typical life trajectories applied to childbirth histories", In Levy, R. & Widmer, E. (eds) Gendered life courses - Between individualization and standardization. A European approach applied to Switzerland, pp. 287-312. Vienna: LIT.
Gabadinho, A., G. Ritschard, N. S. Müller and M. Studer (2011). Analyzing and Visualizing State Sequences in R with TraMineR. Journal of Statistical Software 40(4), 1-37.
Gabadinho A, Ritschard G, Studer M, Müller NS (2011). "Extracting and Rendering Representative Sequences", In A Fred, JLG Dietz, K Liu, J Filipe (eds.), Knowledge Discovery, Knowledge Engineering and Knowledge Management, volume 128 of Communications in Computer and Information Science (CCIS), pp. 94-106. Springer-Verlag.
Müller, N. S., A. Gabadinho, G. Ritschard and M. Studer (2008). Extracting knowledge from life courses: Clustering and visualization. In Data Warehousing and Knowledge Discovery, 10th International Conference DaWaK 2008, Turin, Italy, September 2-5, LNCS 5182, Berlin: Springer, 176-185.
Scherer S (2001). Early Career Patterns: A Comparison of Great Britain and West Germany. European Sociological Review, 17(2), 119-144.
## ======================================================
## Creating state sequence objects from example data sets
## ======================================================
## biofam data set
data(biofam)
## We use only a sample of 300 cases
set.seed(10)
biofam <- biofam[sample(nrow(biofam),300),]
biofam.lab <- c("Parent", "Left", "Married", "Left+Marr",
"Child", "Left+Child", "Left+Marr+Child", "Divorced")
biofam.seq <- seqdef(biofam, 10:25, labels=biofam.lab)
## actcal data set
data(actcal)
## We use only a sample of 300 cases
set.seed(1)
actcal <- actcal[sample(nrow(actcal),300),]
actcal.lab <- c("> 37 hours", "19-36 hours", "1-18 hours", "no work")
actcal.seq <- seqdef(actcal,13:24,labels=actcal.lab)
## ex1 using weights
data(ex1)
ex1.seq <- seqdef(ex1, 1:13, weights=ex1$weights)
## ========================
## Sequence frequency plots
## ========================
## Plot of the 10 most frequent sequences
seqplot(biofam.seq, type="f")
## Grouped by sex
seqfplot(actcal.seq, group=actcal$sex)
## Unweighted vs weighted frequencies
seqfplot(ex1.seq, weighted=FALSE)
seqfplot(ex1.seq, weighted=TRUE)
## =====================
## Modal states sequence
## =====================
seqplot(biofam.seq, type="ms")
## same as
seqmsplot(biofam.seq)
## ====================
## Representative plots
## ====================
## Computing a distance matrix
## with OM metric
costs <- seqcost(biofam.seq, method="INDELSLOG")
biofam.om <- seqdist(biofam.seq, method="OM", sm=costs$sm, indel=costs$indel)
## Plot of the representative sets grouped by sex
## using the default density criterion
seqrplot(biofam.seq, group=biofam$sex, diss=biofam.om)
## Plot of the representative sets grouped by sex
## using the "dist" (centrality) criterion
seqrplot(biofam.seq, group=biofam$sex, criterion="dist", diss=biofam.om)
## ====================
## Sequence index plots
## ====================
## First ten sequences
seqiplot(biofam.seq)
## All sequences sorted by age in 2000
## grouped by sex
## using 'border=NA' and 'space=0' options to have a nicer plot
seqiplot(actcal.seq, group=actcal$sex, idxs=0, border=NA, space=0,
sortv=actcal$age00)
## =======================
## State distribution plot
## =======================
## biofam grouped by sex
seqplot(biofam.seq, type="d", group=actcal$sex)
## actcal grouped by sex
seqplot(actcal.seq, type="d", group=actcal$sex)
## ===================
## Cross-sectional entropy plot
## ===================
seqplot(biofam.seq, type="Ht", group=biofam$sex)
## ===============
## Meant time plot
## ===============
## actcal data set, grouped by sex
seqplot(actcal.seq, type="mt", group=actcal$sex)
## biofam data set, grouped by sex
seqmtplot(biofam.seq, group=biofam$sex)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.