A collection of special methods that adapt the fitting procedure of the underlying longitudinal cluster method.
NOTE: the underlying implementation is experimental and may change in the future.
Supported fit methods:
lcFitConverged: Fit a method until a converged result is obtained.
lcFitRep: Repeatedly fit a method and return the best result based on a given internal metric.
lcFitRepMin: Repeatedly fit a method and return the best result that minimizes the given internal metric.
lcFitRepMax: Repeatedly fit a method and return the best result that maximizes the given internal metric.
lcFitConverged(method, maxRep = Inf)
lcFitRep(method, rep = 10, metric, maximize)
lcFitRepMin(method, rep = 10, metric)
lcFitRepMax(method, rep = 10, metric)The lcMethod to use for fitting.
The maximum number of fit attempts
The number of fits
The internal metric to assess the fit.
Whether to maximize the metric. Otherwise, it is minimized.
Meta methods are immutable and cannot be updated after instantiation. Calling update() on a meta method is only used to update arguments of the underlying lcMethod object.
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time", nClusters = 2)
metaMethod <- lcFitConverged(method, maxRep = 10)
metaMethod
#> lcFitConverged encapsulating:
#> lcMethodLMKM specifying "lm-kmeans"
#> time: "Time"
#> id: "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
#> with meta-method arguments:
#> lcFitConverged specifying "lm-kmeans"
#> maxRep: 10
model <- latrend(metaMethod, latrendData)
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time", nClusters = 2)
repMethod <- lcFitRep(method, rep = 10, metric = "RSS", maximize = FALSE)
repMethod
#> lcFitRep encapsulating:
#> lcMethodLMKM specifying "lm-kmeans"
#> time: "Time"
#> id: "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
#> with meta-method arguments:
#> lcFitRep specifying "lm-kmeans"
#> rep: 10
#> metric: "RSS"
#> maximize: FALSE
model <- latrend(repMethod, latrendData)
minMethod <- lcFitRepMin(method, rep = 10, metric = "RSS")
maxMethod <- lcFitRepMax(method, rep = 10, metric = "ASW")