Construct a list of lcModel objects

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(...)

Arguments

...

lcModel, lcModels, or a recursive list of lcModel objects. Arguments may be named.

Value

A lcModels object containing all specified lcModel objects.

Functionality

• 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.

• Plot the cluster trajectories.

See also

Other lcModels functions: as.lcModels(), lcModels-class, max.lcModels(), min.lcModels(), plotMetric(), print.lcModels(), subset.lcModels()

Examples

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                                                  standardize
#> 1     1    lmkm function (x, center = TRUE, scale = TRUE) UseMethod("scale")
#> 2     2    part                                                         <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 method model
#> 1 function (x, center = TRUE, scale = TRUE) UseMethod("scale")     qr  TRUE
#> 2                                                         <NA>   <NA>    NA
#>       y   qr singular.ok iter.max nstart
#> 1 FALSE TRUE        TRUE       10      1
#> 2    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