Select the lcModel with the highest metric value
# S3 method for lcModels
max(x, name, ...)
The lcModels
object.
The name of the internal metric.
Additional arguments.
The lcModel with the highest metric value
Print an argument summary for each of the models.
Convert to a data.frame
of method arguments.
Subset the list.
Compute an internal metric or external metric.
Obtain the best model according to minimizing or maximizing a metric.
Obtain the summed estimation time.
Plot a metric across a variable.
Other lcModels functions:
as.lcModels()
,
lcModels
,
lcModels-class
,
min.lcModels()
,
plotMetric()
,
print.lcModels()
,
subset.lcModels()
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model1 <- latrend(method, latrendData, nClusters = 1)
model2 <- latrend(method, latrendData, nClusters = 2)
model3 <- latrend(method, latrendData, nClusters = 3)
models <- lcModels(model1, model2, model3)
if (require("clusterCrit")) {
max(models, "Dunn")
}
#> Loading required package: clusterCrit
#> Longitudinal cluster model using lmkm
#> lcMethodLMKM specifying "lm-kmeans"
#> time: "Time"
#> id: "Id"
#> nClusters: 2
#> center: function (x) { mean(x, na.rm = TRUE)}
#> 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", "M
#> formula: Y ~ Time
#>
#> 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
#>