This vignette describes the core functionality of the package by
identifying common trends in the longitudinal dataset that is included
with the package. We begin by loading the required package and the
`latrendData`

dataset.

```
library(latrend)
library(ggplot2)
```

The `latrendData`

is a synthetic dataset for which the
reference group of each trajectory is available, as indicated by the
`Class`

column. We will use this column at the end of this
vignette to validate the identified model.

```
data(latrendData)
head(latrendData)
#> Id Time Y Class
#> 1 1 0.0000000 -1.08049205 Class 1
#> 2 1 0.2222222 -0.68024151 Class 1
#> 3 1 0.4444444 -0.65148373 Class 1
#> 4 1 0.6666667 -0.39115398 Class 1
#> 5 1 0.8888889 -0.19407876 Class 1
#> 6 1 1.1111111 -0.02991783 Class 1
```

Many of the functions of the package require the specification of the
trajectory identifier variable (named `Id`

) and the time
variable (named `Time`

). For convenience, we specify these
variables as package options.

`options(latrend.id = "Id", latrend.time = "Time")`

Prior to attempting to model the data, it is worthwhile to visually inspect it.

`plotTrajectories(latrendData, response = "Y")`

The presence of clusters is not apparent from the plot. With any longitudinal analysis, one should first consider whether clustering brings any benefit to the representation of heterogeneity of the data over a single common trend representation, or a multilevel model. In this demonstration, we omit this step under the prior knowledge that the data was generated via distinct mechanisms.

Assuming the appropriate (cluster) trajectory model is not known in advance, non-parametric longitudinal cluster models can provide a suitable starting point.

As an example, we apply longitudinal \(k\)-means (KML). First, we need to define
the method. At the very least we need to indicate the response variable
the method should operate on. Secondly, we should indicate how many
clusters we expect. We do not need to define the `id`

and
`time`

arguments as we have set these as package options. We
use the `nbRedrawing`

argument provided by the KML package
for reducing the number of repeated random starts to only a single model
estimation, in order to reduce the run-time of this example.

```
<- lcMethodKML(response = "Y", nClusters = 2, nbRedrawing = 1)
kmlMethod
kmlMethod#> lcMethodKML specifying "longitudinal k-means (KML)"
#> time: getOption("latrend.time")
#> id: getOption("latrend.id")
#> nClusters: 2
#> nbRedrawing: 1
#> maxIt: 200
#> imputationMethod:"copyMean"
#> distanceName: "euclidean"
#> power: 2
#> distance: function() {}
#> centerMethod: meanNA
#> startingCond: "nearlyAll"
#> nbCriterion: 1000
#> scale: TRUE
#> response: "Y"
```

As seen in the output from the `lcMethodKML`

object, the
KML method is defined by additional arguments. These are specific to the
`kml`

package.

The KML model is estimated on the dataset via the
`latrend`

function.

```
<- latrend(kmlMethod, data = latrendData)
kmlModel #> ~ Fast KmL ~
#> *
```

Now that we have fitted the KML model with 2 clusters, we can print a summary by calling:

```
kmlModel#> Longitudinal cluster model using longitudinal k-means (KML)
#> lcMethodKML specifying "longitudinal k-means (KML)"
#> time: "Time"
#> id: "Id"
#> nClusters: 2
#> nbRedrawing: 1
#> maxIt: 200
#> imputationMethod:"copyMean"
#> distanceName: "euclidean"
#> power: 2
#> distance: function () {}
#> centerMethod: `meanNA`
#> startingCond: "nearlyAll"
#> nbCriterion: 1000
#> scale: TRUE
#> response: "Y"
#>
#> Cluster sizes (K=2):
#> A B
#> 120 (60%) 80 (40%)
#>
#> Number of obs: 2000, strata (Id): 200
#>
#> Scaled residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.57615 -0.62971 0.05638 0.00000 0.65400 3.20251
```

As we do not know the best number of clusters needed to represent the data, we should consider fitting the KML model for a range of clusters. We can then select the best representation by comparing the solutions by one or more cluster metrics.

We can specify a range of `lcMethodKML`

methods based on a
prototype method using the `lcMethods`

function. This method
outputs a list of `lcMethod`

objects. A structured summary is
obtained by calling `as.data.frame`

.

```
<- lcMethods(kmlMethod, nClusters = 1:7)
kmlMethods
as.data.frame(kmlMethods)
#> .class time id nClusters nbRedrawing maxIt imputationMethod distanceName
#> 1 lcMethodKML Time Id 1 1 200 copyMean euclidean
#> 2 lcMethodKML Time Id 2 1 200 copyMean euclidean
#> 3 lcMethodKML Time Id 3 1 200 copyMean euclidean
#> 4 lcMethodKML Time Id 4 1 200 copyMean euclidean
#> 5 lcMethodKML Time Id 5 1 200 copyMean euclidean
#> 6 lcMethodKML Time Id 6 1 200 copyMean euclidean
#> 7 lcMethodKML Time Id 7 1 200 copyMean euclidean
#> power distance centerMethod startingCond nbCriterion scale response
#> 1 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 2 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 3 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 4 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 5 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 6 2 function() {} meanNA nearlyAll 1000 TRUE Y
#> 7 2 function() {} meanNA nearlyAll 1000 TRUE Y
```

The list of `lcMethod`

objects can be fitted using the
`latrendBatch`

function, returning a list of
`lcModel`

objects.

```
<- latrendBatch(kmlMethods, data = latrendData, verbose = FALSE)
kmlModels
kmlModels#> List of 7 lcModels with
#> .name .method seed nClusters
#> 1 1 kml 1753232290 1
#> 2 2 kml 1711075799 2
#> 3 3 kml 690659598 3
#> 4 4 kml 265368763 4
#> 5 5 kml 758296545 5
#> 6 6 kml 1649722645 6
#> 7 7 kml 2137634742 7
```

We can compare each of the solutions via one or more cluster metrics.
Considering the consistent improvements achieved by KML for an
increasing number of clusters, identifying the best solution by
minimizing a metric would lead to an overestimation. Instead, we perform
the selection via a manual elbow method, using the
`plotMetric`

function.

`plotMetric(kmlModels, c("logLik", "BIC", "WMAE"))`

We have selected the 4-cluster model as the preferred representation.
We will now inspect this solution in more detail. Before we can start,
we first obtain the fitted `lcModel`

object from the list of
fitted models.

```
<- subset(kmlModels, nClusters == 4, drop = TRUE)
kmlModel4
kmlModel4#> Longitudinal cluster model using longitudinal k-means (KML)
#> lcMethodKML specifying "longitudinal k-means (KML)"
#> seed: 265368763
#> time: "Time"
#> id: "Id"
#> nClusters: 4
#> nbRedrawing: 1
#> maxIt: 200
#> imputationMethod:"copyMean"
#> distanceName: "euclidean"
#> power: 2
#> distance: function () {}
#> centerMethod: `meanNA`
#> startingCond: "nearlyAll"
#> nbCriterion: 1000
#> scale: TRUE
#> response: "Y"
#>
#> Cluster sizes (K=4):
#> A B C D
#> 70 (35%) 50 (25%) 45 (22.5%) 35 (17.5%)
#>
#> Number of obs: 2000, strata (Id): 200
#>
#> Scaled residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.22269 -0.61623 -0.05837 0.00000 0.62240 3.63398
```

The `plotClusterTrajectories`

function shows the estimated
cluster trajectories of the model.

`plotClusterTrajectories(kmlModel4)`

We can get a better sense of the representation of the cluster trajectories when plotted against the trajectories that have been assigned to the respective cluster.

`plot(kmlModel4)`

The list of currently supported internal model metrics can be
obtained by calling the `getInternalMetricNames`

function.

```
getInternalMetricNames()
#> [1] "AIC" "APPA.mean" "APPA.min" "ASW"
#> [5] "BIC" "CAIC" "CLC" "CalinskiHarabasz"
#> [9] "DaviesBouldin" "Dunn" "ED" "ED.fit"
#> [13] "ICL.BIC" "MAE" "MSE" "Mahalanobis"
#> [17] "RE" "RMSE" "RSS" "SED"
#> [21] "SED.fit" "WMAE" "WMSE" "WRMSE"
#> [25] "WRSS" "converged" "deviance" "entropy"
#> [29] "estimationTime" "logLik" "relativeEntropy" "scaledEntropy"
#> [33] "sigma" "ssBIC"
```

As an example, we will compute the APPA (a measure of cluster separation), and the WRSS and WMAE metrics (measures of model error).

```
metric(kmlModel, c("APPA.mean", "WRSS", "WMAE"))
#> APPA.mean WRSS WMAE
#> 0.9999979 152.7557738 0.2158131
```

The quantile-quantile (QQ) plot can be used to assess the model for structural deviations.

```
qqPlot(kmlModel4)
#> Loading required namespace: qqplotr
#> Warning: The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
```

Overall, the unexplained errors closely follow a normal distribution. In situations where structural deviations from the expected distribution are apparent, it may be fruitful to investigate the QQ plot on a per-cluster basis.

```
qqPlot(kmlModel4, byCluster = TRUE, detrend = TRUE)
#> Warning: The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
#> The following aesthetics were dropped during statistical transformation: sample
#> â„¹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> â„¹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
```

The KML analysis has provided us with clues on an appropriate model for the cluster trajectories. We can use these insights to define a parametric representation of each of the trajectories and cluster them using k-means.

```
<- lcMethodLMKM(formula = Y ~ Time)
lmkmMethod
lmkmMethod#> lcMethodLMKM specifying "lm-kmeans"
#> time: getOption("latrend.time")
#> id: getOption("latrend.id")
#> nClusters: 2
#> center: meanNA
#> standardize: scale
#> method: "qr"
#> model: TRUE
#> y: FALSE
#> qr: TRUE
#> singular.ok: TRUE
#> contrasts: NULL
#> iter.max: 10
#> nstart: 1
#> algorithm: c("Hartigan-Wong", "Lloyd", "Forgy", "Ma
#> formula: Y ~ Time
```

We fit LMKM for 1 to 4 clusters.

```
<- lcMethods(lmkmMethod, nClusters = 1:5)
lmkmMethods
<- latrendBatch(lmkmMethods, data = latrendData, verbose = FALSE) lmkmModels
```

`plotMetric(lmkmModels, c("logLik", "BIC", "WMAE"))`

All metrics clearly point to the three-cluster solution.

```
<- subset(lmkmModels, nClusters == 3, drop=TRUE)
bestLmkmModel plot(bestLmkmModel)
```

Since we have established a preferred clustered representation of the
data heterogeneity, we can now compare the resulting cluster assignments
to the ground truth from which the `latrendData`

data was
generated.

Using the reference assignments, we can also plot a non-parametric estimate of the cluster trajectories. Note how it looks similar to the cluster trajectories found by our model.

`plotClusterTrajectories(latrendData, response = "Y", cluster = "Class")`

In order to compare the reference assignments to the trajectory
assignments generated by our model, we can create a `lcModel`

object based on the reference assignments using the
`lcModelPartition`

function.

```
<- aggregate(Class ~ Id, data = latrendData, FUN = data.table::first)
refTrajAssigns <- lcModelPartition(data = latrendData, response = "Y", trajectoryAssignments = refTrajAssigns$Class)
refModel
refModel#> Longitudinal cluster model using part
#> lcMethod specifying "undefined"
#> no arguments
#>
#> Cluster sizes (K=3):
#> Class 1 Class 2 Class 3
#> 80 (40%) 70 (35%) 50 (25%)
#>
#> Number of obs: 2000, strata (Id): 200
#>
#> Scaled residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.67125 -0.55452 -0.04079 0.00000 0.58663 3.76931
```

`plot(refModel)`

By constructing a reference model, we can make use of the
standardized way in which `lcModel`

objects can be compared.
A list of supported comparison metrics can be obtained via the
`getExternalMetricNames`

function.

```
getExternalMetricNames()
#> [1] "CohensKappa" "F" "F1" "FolkesMallows"
#> [5] "Hubert" "Jaccard" "Kulczynski" "MI"
#> [9] "MaximumMatch" "McNemar" "MeilaHeckerman" "Mirkin"
#> [13] "NMI" "NSJ" "NVI" "Overlap"
#> [17] "PD" "Phi" "Rand" "RogersTanimoto"
#> [21] "RusselRao" "SMC" "SokalSneath1" "SokalSneath2"
#> [25] "VI" "WMMAE" "WMMAE.ref" "WMMSE"
#> [29] "WMMSE.ref" "WMRSS" "WMRSS.ref" "Wallace1"
#> [33] "Wallace2" "adjustedRand" "jointEntropy" "precision"
#> [37] "recall" "splitJoin" "splitJoin.ref"
```

Lastly, we compare the agreement in trajectory assignments via the adjusted Rand index.

```
externalMetric(bestLmkmModel, refModel, "adjustedRand")
#> adjustedRand
#> 0.9669189
```

With a score of
`externalMetric(bestLmkmModel, refModel, "adjustedRand")`

, we
have a near-perfect match. This result is expected, as the dataset was
generated using a growth mixture model.