Method fit modifiers — lcFitMethods • latrend

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)

Arguments

method: The lcMethod to use for fitting.
maxRep: The maximum number of fit attempts
rep: The number of fits
metric: The internal metric to assess the fit.
maximize: Whether to maximize the metric. Otherwise, it is minimized.

Details

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.

Examples


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")