Plot state sequence objects

Description

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.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
seqplot(seqdata, group=NULL, type="i", title=NULL,
        cpal=NULL, missing.color=NULL,
        ylab=NULL, yaxis=TRUE, axes="all", xtlab=NULL, cex.plot=1,
        withlegend="auto", ltext=NULL, cex.legend=1,
        use.layout=(!is.null(group) | withlegend!=FALSE),
        legend.prop=NA, rows=NA, cols=NA, ...)

seqdplot(seqdata, group=NULL, title=NULL, ...)
seqfplot(seqdata, group=NULL, title=NULL, ...)
seqiplot(seqdata, group=NULL, title=NULL, ...)
seqIplot(seqdata, group=NULL, title=NULL, ...)
seqHtplot(seqdata, group=NULL, title=NULL, ...)
seqmsplot(seqdata, group=NULL, title=NULL, ...)
seqmtplot(seqdata, group=NULL, title=NULL, ...)

Arguments

seqdata

a state sequence object created with the seqdef function.

group

Plots one plot for each level of the factor given as argument.

type

the type of the plot. Available types are "d" for state distribution plots (chronograms), "f" for sequence frequency plots, "Ht" for transversal entropy plots, "i" for selected sequence index plots, "I" for whole set index plots, "ms" for plotting the sequence of modal states, "mt" for mean times plots, "pc" for parallel coordinate plots and "r" for representative sequence plots.

title

title for the graphic. Default is NULL.

cpal

Color palette used for the states. By default, the cpal attribute of the seqdata sequence object is used (see seqdef). If user specified, a vector of colors with number of elements equal to the number of distinct states.

missing.color

alternative color for representing missing values inside the sequences. By default, this color is taken from the missing.color attribute of the plotted sequence object.

ylab

an optional label for the y-axis. If set to NA, no label is drawn.

yaxis

controls whether a y-axis is plotted. When set to TRUE (default value), sequence indexes are displayed for "i" and "I", mean time values for "mt" and percentages for "d" and "f".

axes

if set to "all" (default value) x axes are drawn for each plot in the graphic. If set to "bottom" and group is used, axes are drawn only under the plots located at the bottom of the graphic area. If FALSE, no x-axis is drawn.

xtlab

optional labels for the x-axis tick labels. If unspecified, the column names of the seqdata sequence object are used (see seqdef).

cex.plot

expansion factor for setting the size of the font for the axis labels and names. The default value is 1. Values lesser than 1 will reduce the size of the font, values greater than 1 will increase the size.

withlegend

defines if and where the legend of the state colors is plotted. The default value "auto" sets the position of the legend automatically. Other possible value is "right". Obsolete value TRUE is equivalent to "auto".

ltext

optional description of the states to appear in the legend. Must be a vector of character strings with number of elements equal to the size of the alphabet. If unspecified, the label attribute of the seqdata sequence object is used (see seqdef).

cex.legend

expansion factor for setting the size of the font for the labels in the legend. The default value is 1. Values lesser than 1 will reduce the size of the font, values greater than 1 will increase the size.

use.layout

if TRUE, layout is used to arrange plots when using the group option or plotting a legend. When layout is activated, the standard 'par(mfrow=....)' for arranging plots does not work. With withlegend=FALSE and group=NULL, layout is automatically deactivated and 'par(mfrow=....)' can be used.

legend.prop

sets the proportion of the graphic area used for plotting the legend when use.layout=TRUE and withlegend=TRUE. Default value is set according to the place (bottom or right of the graphic area) where the legend is plotted. Values from 0 to 1.

rows,cols

optional arguments to arrange plots when use.layout=TRUE.

...

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 weighted argument can be passed to control whether (un)weighted statistics are produced or with.missing argument to take missing values into account when computing transversal or longitudinal state distributions.

Details

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".

State distribution plot (type="d") represent the sequence of the cross-sectional state frequencies by position (time point) computed by the seqstatd function. Such plots are also known as chronograms.

Sequence frequency plots (type="f") display 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 tlim 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 will be returned by seqtab since the default option is weighted=TRUE. 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 tlim for specifying the indexes of the sequences to be plotted (when type="i" defaults to the first ten sequences, i.e tlim=1:10). For plotting nicely a (big) whole set one can use type="I" which is the same as using tlim=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 transversal entropies (Billari, 2001). Transversal 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 dist.matrix argument or by calling the seqdist function if dist.matrix=NULL. The criterion argument sets the representativeness criterion used to sort the sequences. See examples below, the seqrep and plot.stslist.rep manual pages for a complete list of optional arguments and Gabadinho et al. (2009) for more details on the extraction of representative sets.

Author(s)

Alexis Gabadinho (with Gilbert Ritschard for the help page)

References

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., 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.

See Also

plot.stslist.statd, plot.stslist.freq, plot.stslist, plot.stslist.modst, plot.stslist.meant, plot.stslist.rep seqpcplot, seqrplot .

Examples

  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
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
## ======================================================
## 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 <- seqsubm(biofam.seq, method="TRATE")
biofam.om <- seqdist(biofam.seq, method="OM", sm=costs)

## Plot of the representative sets grouped by sex
## using the default density criterion
seqrplot(biofam.seq, group=biofam$sex, dist.matrix=biofam.om)

## Plot of the representative sets grouped by sex
## using the "dist" (centrality) criterion
seqrplot(biofam.seq, group=biofam$sex, criterion="dist", dist.matrix=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$sex, tlim=0, border=NA, space=0,
         sortv=actcal$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)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.