latrend (version 1.2.1)

metric: Compute internal model metric(s)

Description

Compute one or more internal metrics for the given lcModel object.

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

Recommended overview papers:

• vandernest2020overview;textuallatrend provide an overview of metrics for mixture models (GBTM, GMM); primarily likelihood-based or posterior probability-based metrics.

• henson2007detecting;textuallatrend provide an overview of likelihood-based metrics for mixture models.

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

See the Details section below for a list of supported metrics.

Usage

# S4 method for lcModel
metric(object, name = getOption("latrend.metric", c("WRSS", "APPA.mean")), ...)
# S4 method for list
metric(object, name, drop = TRUE)
# S4 method for lcModels
metric(object, name, drop = TRUE)

Arguments

object

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

name

The name(s) of the metric(s) to compute. If no names are given, the names specified in the latrend.metric option (WRSS, APPA, AIC, BIC) are used.

...

Additional arguments.

drop

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

Value

For metric(lcModel): A named numeric vector with the computed model metrics.

For metric(list): A data.frame with a metric per column.

For metric(lcModels): A data.frame with a metric per column.

Implementation

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

Details

List of currently supported metrics:

Metric name	Description	Function / Reference
AIC	Akaike information criterion	stats::AIC(), akaike1974newlatrend
APPA.mean	Mean of the average posterior probability of assignment (APPA) across clusters	APPA(), nagin2005grouplatrend
APPA.min	Lowest APPA among the clusters	APPA(), nagin2005grouplatrend
BIC	Bayesian information criterion	stats::BIC(), schwarz1978estimatinglatrend
CAIC	Consistent Akaike information criterion	bozdogan1987modellatrend
CLC	Classification likelihood criterion	mclachlan2000finitelatrend
converged	Whether the model converged during estimation	converged()
deviance	The model deviance	stats::deviance()
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	biernacki2000assessinglatrend
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	mahalanobis1936generalizedlatrend
MSE	Mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, RE	The normalized version of entropy, scaled between [0, 1].	ramaswamy1993empiricallatrend, muthen2004latentlatrend
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	sclove1987applicationlatrend
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
WRSS	RSS weighted by cluster-assignment probability

References

See Also

externalMetric min.lcModels max.lcModels

Other metric functions: defineExternalMetric(), defineInternalMetric(), externalMetric,lcModel,lcModel-method, getExternalMetricDefinition(), getExternalMetricNames(), getInternalMetricDefinition(), getInternalMetricNames()

Examples

# NOT RUN {
data(latrendData)
model <- latrend(lcMethodLcmmGMM(fixed = Y ~ Time, mixture = ~ Time,
   id = "Id", time = "Time"), latrendData)
bic <- metric(model, "BIC")

ic <- metric(model, c("AIC", "BIC"))


# }