Association measures between domains in multidimensional sequence analysis

Description

Computes various measures of association between dimensions of multidimensional sequence data.

Usage

assoc.domains(dlist, names, djsa)

Arguments

Author(s)

Nicolas Robette

References

Piccarreta R. (2017). Joint Sequence Analysis: Association and Clustering, Sociological Methods and Research, Vol. 46(2), 252-287.

Examples

library(TraMineR)
data(biofam)

## Building one channel per type of event (left, children or married)
bf <- as.matrix(biofam[, 10:25])
children <-  bf==4 | bf==5 | bf==6
married <- bf == 2 | bf== 3 | bf==6
left <- bf==1 | bf==3 | bf==5 | bf==6

## Building sequence objects
child.seq <- seqdef(children)
marr.seq <- seqdef(married)
left.seq <- seqdef(left)

## Using Hamming distance
mcdist <- seqdistmc(channels=list(child.seq, marr.seq, left.seq),
 	method="HAM")
child.dist <- seqdist(child.seq, method="HAM")
marr.dist <- seqdist(marr.seq, method="HAM")
left.dist <- seqdist(left.seq, method="HAM")

## Association between domains
asso <- assoc.domains(list(child.dist,marr.dist,left.dist), c('child','marr','left'), mcdist)
asso

Recoding sequences for qualitative harmonic analysis

Description

Recodes sequence data into the shape used for qualitative harmonic analysis.

Usage

seq2qha(seqdata, periods)

Arguments

Value

A data frame with one column by combination of period and state (i.e. number of columns = number of periods * number of states in the alphabet).

Author(s)

Nicolas Robette

References

Robette N., Thibault N. (2008). Comparing qualitative harmonic analysis and optimal matching. An exploratory study of occupational trajectories, Population-E, Vol. 64(3), 533-556. Deville J-C. (1982). Analyse de données chronologiques qualitatives: comment analyser des calendriers ?, Annales de l’INSEE, 45, 45-104. Deville J-C., Saporta G. (1980). Analyse harmonique qualitative, in Data analysis and informatics, E.Diday (ed.), Amsterdam, North Holland Publishing, 375-389.

Examples

data(trajact)
seqact <- seqdef(trajact)
qha <- seq2qha(seqact, periods=c(1,3,7,12,24))
head(qha)

Index plot of sequences ordered according to a dendrogram

Description

Index plot of state sequences. Sequences are ordered according to the specified dendrogram. The dendrogram is also plotted on the side of the index plot.

Usage

seq_heatmap(seq, tree, with.missing = FALSE, ...)

Arguments

Source

http://joseph.larmarange.net/?Representer-un-tapis-de-sequences

See Also

seqIplot

Examples

if (require(TraMineR)) {
  data(mvad)
  mvad.seq <- seqdef(mvad[,17:86])
  mvad.lcs <- seqdist(mvad.seq, method = "LCS")
  mvad.hc <- hclust(as.dist(mvad.lcs), method = "ward.D2")
  seq_heatmap(mvad.seq, mvad.hc)
}

Sample of mothers and daughters employment histories

Description

A data frame describing mothers employment histories from age 14 to 60 and daughters employment histories from the completion of education to 15 years later. Sequences are sampled (N = 400) from "Biographies et entourage" survey (INED, 2001).

Usage

data("seqgimsa")

Format

A data frame with 400 observations and 62 numeric variables. The first 15 variables (prefixed 'f') describe the daughters employment status a given year : 1 = education, 2 = inactivity, 3 = part-time job, 4 = full-time job. The following 47 variables (prefixed 'm') describe the mothers employment status at a given age : 1 = self-employment, 3 = higher level or intermediate occupation, 5 = lower level occupation, 8 = inactivity, 9 = education.

Examples

data(seqgimsa)
str(seqgimsa)

At least one episode in the states

Description

Returns whether each sequence comprises at least one episode in the states.

Usage

seqi1epi(seqdata)

Arguments

Author(s)

Nicolas Robette

References

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.

See Also

seqistatd, seqinepi, seqifpos

Examples

data(trajact)
seqact <- seqdef(trajact)
stat <- seqi1epi(seqact)
head(stat)

First position in each state

Description

Returns the first position in each state.

Usage

seqifpos(seqdata)

Arguments

Author(s)

Nicolas Robette

References

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.

See Also

seqistatd, seqi1epi, seqinepi

Examples

data(trajact)
seqact <- seqdef(trajact)
stat <- seqifpos(seqact)
head(stat)

Number of episodes in each state

Description

Returns the number of episodes in the states.

Usage

seqinepi(seqdata)

Arguments

Author(s)

Nicolas Robette

References

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.

See Also

seqistatd, seqi1epi, seqifpos

Examples

data(trajact)
seqact <- seqdef(trajact)
stat <- seqinepi(seqact)
head(stat)

Stress measure of multidimensional scaling factors

Description

Computes stress measure of multidimensional scaling data for different number of dimensions of the represented space

Usage

seqmds.stress(seqdist, mds)

Arguments

Value

A numerical vector of stress values.

Author(s)

Nicolas Robette

References

Piccarreta R., Lior O. (2010). Exploring sequences: a graphical tool based on multi-dimensional scaling, Journal of the Royal Statistical Society (Series A), Vol. 173(1), 165-184.

Examples

data(trajact)
seqact <- seqdef(trajact)
dissim <- seqdist(seqact, method="HAM")
mds <- cmdscale(dissim, k=20, eig=TRUE)
stress <- seqmds.stress(dissim, mds)
plot(stress, type='l', xlab='number of dimensions', ylab='stress')

Sample of marital, parental and residential sequences

Description

A data frame describing respectively the matrimonial, parental and residential status from age 14 to age 35. It's sampled (N=500) from "Biographies et entourage" survey (INED, 2001).

Usage

data("seqmsa")

Format

A data frame with 500 observations and 66 variables. The first 22 variables (prefixed 'log') describe the residential status at a given age : 0 = not independent, 1 = independent. The next 22 variables (prefixed 'mat') describe the matrimonial status at a given age : 1 = never been in a relationship, 2 = cohabiting union, 3 = married, 4 = separated. The last 22 variables (prefixed 'nenf') describe the parental status at a given age : 0 = no child, 1 = one child, 2 = two children, 3 = three children or more.

Examples

data(seqmsa)
str(seqmsa)

Smoothing sequence data

Description

Smoothing of sequence data, using for each sequence the medoid of the sequences in its neighborhood. The results can be used to get a smoothed index plot.

Usage

seqsmooth(seqdata, diss, k=20, r=NULL)

Arguments

Value

A list with the following elements:

Author(s)

Nicolas Robette

References

Piccarreta R. (2012). Graphical and Smoothing Techniques for Sequence Analysis, Sociological Methods and Research, Vol. 41(2), 362-380.

Examples

data(trajact)
seqact <- seqdef(trajact)
dissim <- seqdist(seqact, method="LCS")
mds <- cmdscale(dissim, k=1)
smoothed <- seqsmooth(seqact, dissim, k=30)$seqdata
seqIplot(smoothed, sortv=mds, xtlab=14:50, with.legend=FALSE, yaxis=FALSE, ylab=NA)

Sample of sociodemographic variables

Description

A data frame with sociodemographic variables for a sample of 500 interviewees from "Biographies et entourage" survey (INED, 2001).

Usage

data("socdem")

Format

A data frame with 500 observations on the following 9 variables.

annais

year of birth (numeric)

nbenf

number of children (factor)

nbunion

number of relationships (factor)

mereactive

whether mother was active or not (factor)

sexe

gender (factor)

PCS

occupational category (factor)

PCSpere

occupational category of the father (factor)

diplome

degree (factor)

nationalite

nationality (factor)

Examples

data(socdem)
str(socdem)

Symmetric (or canonical) PLS

Description

Computes symmetric (or canonical) PLS for two groups of continuous variables

Usage

symPLS(a,b)

Arguments

Author(s)

Nicolas Robette, Xavier Bry

References

Bry X. (1996). Analyses Factorielles Multiples. Paris, Economica Poche. de Jong S., Wise B.M. and Ricker N.L. (2001). Canonical Partial Least Squares and Continuum Power Regression. Journal of Chemometrics, Vol. 15, 85–100.

Sample of employment histories

Description

A data frame describing the employment status from age 14 to age 50. It's a sample of 500 interviewees from "Biographies et entourage" survey (INED, 2001).

Usage

data("trajact")

Format

A data frame with 500 observations and 37 variables. Each variable is numeric and describes the employment status at a given age : 1 = education, 2 = full-time job, 3 = part-time job, 4 = small jobs, 5 = inactivity, 6 = military service.

Examples

data(trajact)
str(trajact)