Description Usage Arguments Details Author(s) References See Also Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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"`

.

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 `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 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 M<c3><bc>ller 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 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 `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. M<c3><bc>ller 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, M<c3><bc>ller 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.

M<c3><bc>ller, 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.

`plot.stslist.statd`

, `plot.stslist.freq`

, `plot.stslist`

, `plot.stslist.modst`

, `plot.stslist.meant`

, `plot.stslist.rep`

, `seqrep`

,
`seqpcplot`

.

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 <- 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)
``` |

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