latrend-metrics: Metrics

In latrend: A Framework for Clustering Longitudinal Data

Metrics

Description

The package supports a variety of metrics that help to evaluate and compare estimated models.

• Internal metrics: metrics that assess the adequacy of the model with respect to the data.

• External metrics: metrics that compare two models.

Users can implement new metrics through defineInternalMetric() and defineExternalMetric(). Custom-defined metrics are accessible using the same by-name mechanism as the other metrics.

Supported internal metrics

Metric name	Description	Function / Reference
AIC	Akaike information criterion. A goodness-of-fit estimator that adjusts for model complexity (i.e., the number of parameters). Only available for models that support the computation of the model log-likelihood through logLik.	stats::AIC(), \insertCiteakaike1974newlatrend
APPA.mean	Mean of the average posterior probability of assignment (APPA) across clusters. A measure of the precision of the trajectory classifications. A score of 1 indicates perfect classification.	APPA(), \insertCitenagin2005grouplatrend
APPA.min	Lowest APPA among the clusters	APPA(), \insertCitenagin2005grouplatrend
ASW	Average silhouette width based on the Euclidean distance	\insertCiterousseeuw1987silhouetteslatrend
BIC	Bayesian information criterion. A goodness-of-fit estimator that corrects for the degrees of freedom (i.e., the number of parameters) and sample size. Only available for models that support the computation of the model log-likelihood through logLik.	stats::BIC(), \insertCiteschwarz1978estimatinglatrend
CAIC	Consistent Akaike information criterion	\insertCitebozdogan1987modellatrend
CLC	Classification likelihood criterion	\insertCitemclachlan2000finitelatrend
converged	Whether the model converged during estimation	converged()
deviance	The model deviance	stats::deviance()
Dunn	The Dunn index	\insertCitedunn1974welllatrend
entropy	Entropy of the posterior probabilities
estimationTime	The time needed for fitting the model	estimationTime()
ED	Euclidean distance between the cluster trajectories and the assigned observed trajectories
ED.fit	Euclidean distance between the cluster trajectories and the assigned fitted trajectories
ICL.BIC	Integrated classification likelihood (ICL) approximated using the BIC	\insertCitebiernacki2000assessinglatrend
logLik	Model log-likelihood	stats::logLik()
MAE	Mean absolute error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
Mahalanobis	Mahalanobis distance between the cluster trajectories and the assigned observed trajectories	\insertCitemahalanobis1936generalizedlatrend
MSE	Mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, RE	A measure of the precision of the trajectory classification. A value of 1 indicates perfect classification, whereas a value of 0 indicates a non-informative uniform classification. It is the normalized version of entropy, scaled between [0, 1].	\insertCiteramaswamy1993empiricallatrend, \insertCitemuthen2004latentlatrend
RMSE	Root mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
RSS	Residual sum of squares under most likely cluster allocation
scaledEntropy	See relativeEntropy
sigma	The residual standard deviation	stats::sigma()
ssBIC	Sample-size adjusted BIC	\insertCitesclove1987applicationlatrend
SED	Standardized Euclidean distance between the cluster trajectories and the assigned observed trajectories
SED.fit	The cluster-weighted standardized Euclidean distance between the cluster trajectories and the assigned fitted trajectories
WMAE	MAE weighted by cluster-assignment probability
WMSE	MSE weighted by cluster-assignment probability
WRMSE	RMSE weighted by cluster-assignment probability
WRSS	RSS weighted by cluster-assignment probability

Supported external metrics

Metric name	Description	Function / Reference
adjustedRand	Adjusted Rand index. Based on the Rand index, but adjusted for agreements occurring by chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.	mclustcomp::mclustcomp(), \insertCitehubert1985comparinglatrend
CohensKappa	Cohen's kappa. A partitioning agreement metric correcting for random chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.	psych::cohen.kappa(), \insertCitecohen1960coefficientlatrend
F	F-score	mclustcomp::mclustcomp()
F1	F1-score, also referred to as the Sørensen–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–Simpson coefficient	mclustcomp::mclustcomp() \insertCitevijaymeena2016surveylatrend
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. In other words, it is the edit-distance between the two partitionings.
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

See Also

metric externalMetric

latrend documentation built on March 31, 2023, 5:45 p.m.