A general overview of the lcModels class can be found here.
The lcModels() function creates a flat (named) list of lcModel objects. Duplicates are preserved.
lcModels(...)A lcModels object containing all specified lcModel objects.
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-class,
max.lcModels(),
min.lcModels(),
plotMetric(),
print.lcModels(),
subset.lcModels()
lmkmMethod <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
lmkmModel <- latrend(lmkmMethod, latrendData)
rngMethod <- lcMethodRandom("Y", id = "Id", time = "Time")
rngModel <- latrend(rngMethod, latrendData)
lcModels(lmkmModel, rngModel)
#> List of 2 lcModels with
#> .name .method
#> 1 1 lmkm
#> 2 2 part
#> standardize
#> 1 new("standardGeneric", .Data = function (x, center = TRUE, scale = TRUE) standardGeneric("scale"), generic = structure("scale", package = "base"), package = "base", group = list(), valueClass = character(0), signature = c("x", "center", "scale"), default = new("derivedDefaultMethod", .Data = function (x, center = TRUE, scale = TRUE) UseMethod("scale"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = structure("scale", package = "base")), skeleton = (new("derivedDefaultMethod", .Data = function (x, center = TRUE, scale = TRUE) UseMethod("scale"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = structure("scale", package = "base")))(x, center, scale))
#> 2 <NA>
#> method model y qr singular.ok iter.max nstart
#> 1 qr TRUE FALSE TRUE TRUE 10 1
#> 2 <NA> NA NA NA NA NA NA
#> algorithm formula alpha name
#> 1 c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen") Y ~ Time NA <NA>
#> 2 <NA> <NA> 10 random
#> response
#> 1 <NA>
#> 2 Y
lcModels(defaults = c(lmkmModel, rngModel))
#> List of 2 lcModels with
#> .name .method
#> 1 defaults1 lmkm
#> 2 defaults2 part
#> standardize
#> 1 new("standardGeneric", .Data = function (x, center = TRUE, scale = TRUE) standardGeneric("scale"), generic = structure("scale", package = "base"), package = "base", group = list(), valueClass = character(0), signature = c("x", "center", "scale"), default = new("derivedDefaultMethod", .Data = function (x, center = TRUE, scale = TRUE) UseMethod("scale"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = structure("scale", package = "base")), skeleton = (new("derivedDefaultMethod", .Data = function (x, center = TRUE, scale = TRUE) UseMethod("scale"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = structure("scale", package = "base")))(x, center, scale))
#> 2 <NA>
#> method model y qr singular.ok iter.max nstart
#> 1 qr TRUE FALSE TRUE TRUE 10 1
#> 2 <NA> NA NA NA NA NA NA
#> algorithm formula alpha name
#> 1 c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen") Y ~ Time NA <NA>
#> 2 <NA> <NA> 10 random
#> response
#> 1 <NA>
#> 2 Y