externalMetric,lcModel,lcModel-method: Compute external model metric(s)

Description

Compute one or more external metrics for two or more lcModel objects.

Note that there are many external metrics available, and there exists no external metric that works best in all scenarios. It is recommended to carefully consider which metric is most appropriate for your use case.

Many of the external metrics depend on implementations in other packages:

clusterCrit desgraupes2018clustercritlatrend
mclustcomp you2018mclustcomplatrend
igraph csardi2006igraphlatrend
psych revelle2019psychlatrend

See mclustcomp::mclustcomp() for a grouped overview of similarity metrics.

Call getInternalMetricNames() to retrieve the names of the defined internal metrics. Call getExternalMetricNames() to retrieve the names of the defined internal metrics.

Usage

# S4 method for lcModel,lcModel
externalMetric(
  object,
  object2,
  name = getOption("latrend.externalMetric"),
  ...
)
# S4 method for lcModels,missing
externalMetric(object, object2, name = "adjustedRand")
# S4 method for lcModels,character
externalMetric(object, object2 = "adjustedRand")
# S4 method for lcModels,lcModel
externalMetric(object, object2, name, drop = TRUE)
# S4 method for list,lcModel
externalMetric(object, object2, name, drop = TRUE)

Arguments

object

The lcModel, lcModels, or list of lcModel objects to compute the metrics for.

object2

The other lcModel to compare with.

name

The name(s) of the external metric(s) to compute. If no names are given, the names specified in the latrend.externalMetric option (none by default) are used.

...

Additional arguments.

drop

Whether to return a numeric vector instead of a data.frame in case of a single metric.

Value

For externalMetric(lcModel, lcModel): A numeric vector of the computed metrics.

A named numeric vector containing the computed model metrics.

For externalMetric(lcModels): A distance matrix of class dist representing the pairwise comparisons.

For externalMetric(lcModels, name): A distance matrix of class dist representing the pairwise comparisons.

For externalMetric(lcModels, lcModel): A named numeric vector or data.frame containing the computed model metrics.

For externalMetric(list, lcModel): A named numeric vector or data.frame containing the computed model metrics.

Implementation

See the documentation of the defineExternalMetric() function for details on how to define your own external metrics.

Details

List of currently supported external metrics:

Metric name	Description	Function / Reference
`adjustedRand`	Adjusted Rand index	`mclustcomp::mclustcomp()`, hubert1985comparinglatrend
`CohensKappa`	Cohen's kappa	`psych::cohen.kappa()`, cohen1960coefficientlatrend
`F`	F-score	`mclustcomp::mclustcomp()`
`F1`	F1-score, also referred to as the S<U+00F8>rensen<U+2013>Dice Coefficient, or Dice similarity coefficient	`mclustcomp::mclustcomp()`
`FolkesMallows`	Fowlkes-Mallows index	`mclustcomp::mclustcomp()`
`Hubert`	Hubert index	`clusterCrit::extCriteria()`
`Jaccard`	Jaccard index	`mclustcomp::mclustcomp()`
`jointEntropy`	Joint entropy between model assignments	`mclustcomp::mclustcomp()`
`Kulczynski`	Kulczynski index	`clusterCrit::extCriteria()`
`MaximumMatch`	Maximum match measure	`mclustcomp::mclustcomp()`
`McNemar`	McNemar statistic	`clusterCrit::extCriteria()`
`MeilaHeckerman`	Meila-Heckerman measure	`mclustcomp::mclustcomp()`
`Mirkin`	Mirkin metric	`mclustcomp::mclustcomp()`
`MI`	Mutual information	`mclustcomp::mclustcomp()`
`NMI`	Normalized mutual information	`igraph::compare()`
`NSJ`	Normalized version of `splitJoin`. The proportion of edits relative to the maximum changes (twice the number of ids)
`NVI`	Normalized variation of information	`mclustcomp::mclustcomp()`
`Overlap`	Overlap coefficient, also referred to as the Szymkiewicz<U+2013>Simpson coefficient	`mclustcomp::mclustcomp()` vijaymeena2016surveylatrend
`PD`	Partition difference	`mclustcomp::mclustcomp()`
`Phi`	Phi coefficient.	`clusterCrit::extCriteria()`
`precision`	precision	`clusterCrit::extCriteria()`
`Rand`	Rand index	`mclustcomp::mclustcomp()`
`recall`	recall	`clusterCrit::extCriteria()`
`RogersTanimoto`	Rogers-Tanimoto dissimilarity	`clusterCrit::extCriteria()`
`RusselRao`	Russell-Rao dissimilarity	`clusterCrit::extCriteria()`
`SMC`	Simple matching coefficient	`mclustcomp::mclustcomp()`
`splitJoin`	total split-join index	`igraph::split_join_distance()`
`splitJoin.ref`	Split-join index of the first model to the second model
`SokalSneath1`	Type-1 Sokal-Sneath dissimilarity	`clusterCrit::extCriteria()`
`SokalSneath2`	Type-2 Sokal-Sneath dissimilarity	`clusterCrit::extCriteria()`
`VI`	Variation of information	`mclustcomp::mclustcomp()`
`Wallace1`	Type-1 Wallace criterion	`mclustcomp::mclustcomp()`
`Wallace2`	Type-2 Wallace criterion	`mclustcomp::mclustcomp()`
`WMSSE`	Weighted minimum sum of squared errors between cluster trajectories
`WMMSE`	Weighted minimum mean of squared errors between cluster trajectories
`WMMAE`	Weighted minimum mean of absolute errors between cluster trajectories

References

Examples

Run this code

# NOT RUN {
data(latrendData)
model1 <- latrend(lcMethodKML("Y", id = "Id", time = "Time"), latrendData)
model2 <- latrend(lcMethodLcmmGMM(fixed = Y ~ Time, mixture = ~ Time,
   id = "Id", time = "Time"), latrendData)
ari <- externalMetric(model1, model2, 'adjustedRand')
# }

Run the code above in your browser using DataLab