# maritalData: Marital status sequences In lifecourse: Quantification of Lifecourse Fluidity

## Description

Each sequence contains the marital status for a given indivudal across 12 years. The sequence dataset is derived from data from the British Household Panel Survey.

## Usage

 `1` ```data("maritalData") ```

## Format

A data frame with 4728 observations across 12 BHPS census waves.

## Details

Each sequence represents a unique individual. The sequence alphabet is of length seven and the possible states are: "divorced", "have a dissoved civil partnership","in a civil partnership", "married", "never married", "separated", and "widowed".

## 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``` ```library(TraMineR) data(maritalData) #------------------------------------------- # Converting the data into a sequence object #------------------------------------------- #balphabet = c( "divorced" , "have a dissolved civil partnership" , #"in a civil partnership", "married" , #"never married" ,"separated" ,"widowed" ) #blabels = c( "divorced" , "have a dissolved civil partnership" , #"in a civil partnership", "married" , #"never married" ,"separated" ,"widowed" ) #bcodes = c( "divorced" , "have a dissolved civil partnership", #"in a civil partnership", "married" , #"never married" ,"separated" ,"widowed" ) #bseq = seqdef(maritalData, alphabet=balphabet, #states = bcodes, labels = blabels) # forming a sequence object #----------------------------- # Calculating the mobility index # per sequence and storing # the indices in an array #----------------------------- #lseq = length(bseq[,1]) #wseq = length(bseq[1,]) #sequence_summary = array(0,c(lseq,1)) #for(i in 1:lseq){ #sequence_summary[i] = mobility_index(bseq[i,],balphabet,7) #} #plot(hist(sequence_summary),xlim=c(1,7),col="red") #--------------------------------------- # Generating subsets of the sequence data #-------------------------------------- #seqIplot(bseq, sortv = "from.start",cex.legend=1) #mseq = bseq[which(bseq[,1]=="married"),] # sequences which start with the married state #seqIplot(mseq, sortv = "from.start",cex.legend=1) #sseq = bseq[which(bseq[,1]=="never married"),] # sequences which start with the never married state #seqIplot(sseq, sortv = "from.start",cex.legend=1) #------------------------------------ # Lifecourse destandardization #------------------------------------ #object = summary(as.factor(sequence_summary)) #summarize = chisq.test(object,p = rep(1/length(object),9)) # assuming the "standard" probabilities is p = rep(1/length(object),9) # alternative standard probabilities can be used. #summary(maritalData) ```

lifecourse documentation built on May 29, 2017, 5:40 p.m.