Author: Matthias Studer

# Short R Tutorial: Validating Sequence Analysis Typologies Using Parametric Bootstrap

#### Matthias Studer

# Introduction

This document provides a very quick introduction to the `R` code needed to use parametric bootstraps for typology validation in sequence analysis. Readers interested in the methods and the exact interpretation of the results are referred to:

- Studer, M. (2021). Validating Sequence Analysis Typologies Using Parametric Bootstraps. Sociological Methodology 51(2), 290–318. https://doi.org/10.1177/00811750211014232

You are kindly asked to cite the above reference if you use the methods presented in this document.

Warning!! To avoid lengthy computations (and overloading the CRAN server), we restricted the number of bootstraps to 50. We recommend using a higher value (i.e., 1000).

Let’s start by setting the seed for reproducible results.

    set.seed(1)

Time for this code chunk to run: 0.01 seconds

# Creating the State Sequence Object

For this example, we use the `mvad` dataset. Let’s start with the creation of the state sequence object.

    ## Loading the TraMineR library
    library(TraMineR)
    ## Loading the data
    data(mvad)

    ## State properties
    mvad.alphabet <- c("employment", "FE", "HE", "joblessness", "school", "training")
    mvad.lab <- c("employment", "further education", "higher education", "joblessness", "school", "training")
    mvad.shortlab <- c("EM","FE","HE","JL","SC","TR")

    ## Creating the state sequence object
    mvad.seq <- seqdef(mvad, 17:86, alphabet = mvad.alphabet, states = mvad.shortlab, labels = mvad.lab, xtstep = 6)

Time for this code chunk to run: 0.18 seconds

# Creating the typology

We will now create a typology using cluster analysis. Readers interested in more detail are referred to the `WeightedCluster` library manual (also available as a vignette), which goes into the details of the creation and computation of cluster quality measures.

We start by computing dissimilarities with the `seqdist` function using the Hamming distance. We then use Ward clustering to create a typology of the trajectories. For this step, we recommend the use of the `fastcluster` library, which considerably speed up the computations.

    ## Using fastcluster for hierarchical clustering
    library(fastcluster)
    ## Distance computation
    diss <- seqdist(mvad.seq, method="HAM")
    ## Hierarchical clustering
    hc <- hclust(as.dist(diss), method="ward.D")

Time for this code chunk to run: 0.86 seconds

We can now compute several cluster quality indices using `as.clustrange` function from two to ten groups.

    # Loading the WeightedCluster library
    library(WeightedCluster)
    # Computing cluster quality measures.
    clustqual <- as.clustrange(hc, diss=diss, ncluster=10)
    clustqual

    ##            PBC   HG HGSD  ASW ASWw     CH   R2   CHsq R2sq   HC
    ## cluster2  0.55 0.71 0.69 0.39 0.39 184.57 0.21 347.47 0.33 0.14
    ## cluster3  0.49 0.57 0.57 0.27 0.27 139.53 0.28 261.70 0.42 0.22
    ## cluster4  0.46 0.58 0.57 0.24 0.24 124.40 0.35 228.94 0.49 0.23
    ## cluster5  0.51 0.68 0.67 0.27 0.28 118.20 0.40 241.78 0.58 0.18
    ## cluster6  0.52 0.70 0.70 0.28 0.29 117.08 0.45 241.96 0.63 0.17
    ## cluster7  0.55 0.78 0.78 0.32 0.32 114.14 0.49 245.83 0.68 0.13
    ## cluster8  0.56 0.82 0.82 0.32 0.33 109.22 0.52 244.85 0.71 0.11
    ## cluster9  0.57 0.85 0.85 0.34 0.35 105.74 0.55 255.12 0.74 0.09
    ## cluster10 0.55 0.85 0.84 0.32 0.33 101.81 0.57 244.05 0.76 0.10

Time for this code chunk to run: 0.27 seconds

# Parametric Bootstrap

Parametric bootstrap aims to provide baseline values obtained by clustering similar but non-clustered data (Studer 2021). This can be computed using the `seqnullcqi` function with the following parameters:

- `R`: number of bootstraps.
- `model`: The null model (see table below
- `seqdist.args`: list of arguments passed to `seqdist` (should be identical to first call to seqdist).
- `hclust.method`: hierarchical clustering method (should be identical to orginal clustering).
- `kmedoid`: If `TRUE`, use PAM (and the `wcKMedRange` function) instead of hierarchical clustering.
- `parallel`: If `TRUE`, use parallel computing to speed up the computations.

## Combined Randomization

The following `R` code estimate expected values of the cluster quality indices when clustering similar sequences that are not clustered according to the `"combined"` model, using the Hamming distance and Ward hierarchical clustering. We set `parallel=TRUE` to use parallel computing. You can use `progressbar=TRUE` to show a progress bar and an estimation of the computation remaining time (not meaningful here within a document):

    bcq.combined <- seqnullcqi(mvad.seq, clustqual, R=50, model="combined", seqdist.args=list(method="HAM"), hclust.method="ward.D", parallel = TRUE)

Time for this code chunk to run: 78.61 seconds

Once the parametric bootstrap is computed (may take a while…), the results are stored in the `bcq.combined` object. Printing the object (just by writing its name), already provides several information, the standardized cluster quality indices and the associated inconclusive intervals. Here, 2, 9 and 10 groups stand out.

    bcq.combined

    ## Parametric bootstrap cluster analysis validation
    ## Sequence analysis null model: list(model = "combined")
    ## Number of bootstraps: 50
    ## Clustering method: hclust with ward.D
    ## Seqdist arguments: list(method = "HAM")
    ##
    ##
    ##                                    PBC            HG          HGSD          ASW
    ## cluster2                          5.88          6.03          6.12        16.03
    ## cluster3                          3.93          4.04          4.12         8.04
    ## cluster4                          1.92          2.26          2.32         4.95
    ## cluster5                          1.81          2.11          2.16         5.08
    ## cluster6                          1.09          1.42          1.45         4.46
    ## cluster7                          2.23          2.84          2.87         8.31
    ## cluster8                          3.18          3.97             4         9.59
    ## cluster9                          3.87          4.67           4.7        10.99
    ## cluster10                         3.71          4.45          4.48         10.8
    ##
    ## Null Max-T 0.95 interval [-0.14; 2.26] [-0.06; 2.14] [-0.05; 2.15] [0.18; 2.16]
    ##                                  ASWw            CH            R2          CHsq
    ## cluster2                        15.94         23.07         19.58         25.86
    ## cluster3                         7.99         20.81         16.89          21.1
    ## cluster4                          4.9          20.7         16.14         18.14
    ## cluster5                         5.05         21.57         16.17          19.5
    ## cluster6                         4.42         23.17         16.64         18.74
    ## cluster7                         8.24         26.57         18.37         22.16
    ## cluster8                         9.48         30.66         20.68         26.37
    ## cluster9                        10.88         35.08         23.05         33.16
    ## cluster10                       10.65         37.34         24.11         33.98
    ##
    ## Null Max-T 0.95 interval [0.19; 2.16] [-1.06; 2.48] [-1.07; 2.41] [-0.98; 2.49]
    ##                                   R2sq           HC
    ## cluster2                         19.32        -5.39
    ## cluster3                         14.92        -4.48
    ## cluster4                         12.42        -2.76
    ## cluster5                         12.14        -2.74
    ## cluster6                          11.2        -2.29
    ## cluster7                         12.44        -4.55
    ## cluster8                         14.11         -6.2
    ## cluster9                         16.48        -7.11
    ## cluster10                        16.87        -7.46
    ##
    ## Null Max-T 0.95 interval [-0.99; 2.36] [0.09; 2.52]

Time for this code chunk to run: 0.05 seconds

To get non-standardized values, use `norm=FALSE`. Notice that the ASW inconclusive intervals are well below the values recommended by Kaufman and Rousseeuw (over 0.5).

    print(bcq.combined, norm=FALSE)

    ## Parametric bootstrap cluster analysis validation
    ## Sequence analysis null model: list(model = "combined")
    ## Number of bootstraps: 50
    ## Clustering method: hclust with ward.D
    ## Seqdist arguments: list(method = "HAM")
    ##
    ##
    ##                                   PBC           HG         HGSD          ASW
    ## cluster2                         0.55         0.71         0.69         0.39
    ## cluster3                         0.49         0.57         0.57         0.27
    ## cluster4                         0.46         0.58         0.57         0.24
    ## cluster5                         0.51         0.68         0.67         0.27
    ## cluster6                         0.52          0.7          0.7         0.28
    ## cluster7                         0.55         0.78         0.78         0.32
    ## cluster8                         0.56         0.82         0.82         0.32
    ## cluster9                         0.57         0.85         0.85         0.34
    ## cluster10                        0.55         0.85         0.84         0.32
    ##
    ## Null Max-T 0.95 interval [0.46; 0.56] [0.69; 0.78] [0.69; 0.78] [0.14; 0.21]
    ##                                  ASWw            CH           R2
    ## cluster2                         0.39        184.57         0.21
    ## cluster3                         0.27        139.53         0.28
    ## cluster4                         0.24         124.4         0.35
    ## cluster5                         0.28         118.2          0.4
    ## cluster6                         0.29        117.08         0.45
    ## cluster7                         0.32        114.14         0.49
    ## cluster8                         0.33        109.22         0.52
    ## cluster9                         0.35        105.74         0.55
    ## cluster10                        0.33        101.81         0.57
    ##
    ## Null Max-T 0.95 interval [0.15; 0.22] [38.84; 59.2] [0.31; 0.35]
    ##                                     CHsq         R2sq           HC
    ## cluster2                          347.47         0.33         0.14
    ## cluster3                           261.7         0.42         0.22
    ## cluster4                          228.94         0.49         0.23
    ## cluster5                          241.78         0.58         0.18
    ## cluster6                          241.96         0.63         0.17
    ## cluster7                          245.83         0.68         0.13
    ## cluster8                          244.85         0.71         0.11
    ## cluster9                          255.12         0.74         0.09
    ## cluster10                         244.05         0.76          0.1
    ##
    ## Null Max-T 0.95 interval [75.26; 103.53] [0.48; 0.54] [0.34; 0.45]

Time for this code chunk to run: 0.04 seconds

Several plots can then be used to inspect the results using the `plot` command and the `type` argument. First, one can look at the sequences generated by the null model by using `type="seqdplot"`.

    plot(bcq.combined, type="seqdplot")

Time for this code chunk to run: 0.53 seconds

The overall distribution of the CQI values can be plotted using `type="density"`. In this case, one also needs to specify the CQI to be used. All tested number of groups are found to be significant. Any CQI computed by `as.clustrange()` can be used here. To show the density of the average silhouette width (`"ASW"`), one can use:

    plot(bcq.combined, stat="ASW", type="density")

Time for this code chunk to run: 0.09 seconds

By using `type="line"`, we plot the obtained and bootstrapped CQI values depending on the number of groups. Here again

    plot(bcq.combined, stat="ASW", type="line")

Time for this code chunk to run: 0.16 seconds

## Randomized Sequencing

To use another null model, one needs to change the `model` argument of the `seqnullcqi` function. The randomized sequencing keep the duration attached to each state, but randomizes the ordering of the spells. It can be used to uncover sequencing structure of the data.

    bcq.seq <- seqnullcqi(mvad.seq, clustqual, R=50, model="sequencing", seqdist.args=list(method="HAM"), hclust.method="ward.D", parallel = TRUE)

Time for this code chunk to run: 70.13 seconds

We can then plot the results as before. Notice that solutions between 3 and 6 are below the critical line.

    plot(bcq.seq, stat="ASW", type="line")

Time for this code chunk to run: 0.19 seconds

## Randomized Duration

The randomized duration keeps the same ordering of the states, but randomizes the time spent in each spell. It can be used to uncover the duration-related structure of the data.

    bcq.dur <- seqnullcqi(mvad.seq, clustqual, R=50, model="duration", seqdist.args=list(method="HAM"), hclust.method="ward.D", parallel = TRUE)

Time for this code chunk to run: 69.73 seconds

We can then plot the results as before. The solutions 3 and 4 groups solutions are below the “significance line”. Otherwise, the ranking of the solutions is the same.

    plot(bcq.dur, stat="ASW", type="line")

Time for this code chunk to run: 0.18 seconds

## State Independence

The state independence null model generates sequence, position by position, independently of the previous state. This is a quite unrealistic assumption for longitudinal data, but a common one in statistical modeling.

    bcq.stateindep <- seqnullcqi(mvad.seq, clustqual, R=50, model="stateindep", seqdist.args=list(method="HAM"), hclust.method="ward.D", parallel = TRUE)

Time for this code chunk to run: 69.4 seconds

Bootstrapped CQI values are extremely low compared to our clustering, meaning that we have a strong longitudinal structure (not surprising!).

    plot(bcq.stateindep, stat="ASW", type="line")

Time for this code chunk to run: 0.13 seconds

## First-order Markov Null Model

The first-order Markov null model generates sequences using time-invariant transition rates. As a result, the generated sequences are often quite similar to the observed ones. This model can uncover structure stemming from time-dependent transition rates.

    bcq.Markov <- seqnullcqi(mvad.seq, clustqual, R=50, model="Markov", seqdist.args=list(method="HAM"), hclust.method="ward.D", parallel = TRUE)

Time for this code chunk to run: 69.18 seconds

    plot(bcq.Markov, stat="ASW", type="line")

Time for this code chunk to run: 0.2 seconds

# Choosing a Solution

The various null models lead to the same conclusions and ranking of the solutions. Solutions between 3 and 6 groups were not always above the critical lines (in the sequencing null model for instance), and can be avoided. We generally saw good clustering quality for a clustering in 9 groups. The solution is shown below.

    seqdplot(mvad.seq, clustqual$clustering$cluster9, border=NA)

Time for this code chunk to run: 0.66 seconds

Studer, Matthias. 2021. “Validating Sequence Analysis Typologies Using Parametric Bootstrap.” Sociological Methodology 51 (2): 290–318. https://doi.org/10.1177/00811750211014232.