Compare MMM fits across different k

Usage

compare_mmm(data, k = 2:5, return_fits = FALSE, ...)

# S3 method for class 'mmm_compare'
print(x, ...)

# S3 method for class 'mmm_compare'
summary(object, ...)

# S3 method for class 'mmm_compare'
plot(x, ...)

Arguments

data: Data frame, netobject, or tna model.
k: Integer vector of component counts. Values must be whole finite numbers >= 2. Default: 2:5.
return_fits: Logical. When TRUE the fitted models are retained on the result via attr(result, "fits") (a list of net_mmm objects, named by k), so the user can pick the chosen model without re-running the EM. Default FALSE keeps the historical lightweight return shape – only the comparison table is allocated.
...: Arguments passed to build_mmm. In plot.mmm_compare(), print.mmm_compare() and summary.mmm_compare(): Unsupported. Supplying unused arguments raises an error.
x: For the print() and plot() methods: an object of class mmm_compare.
object: For the summary() method: an object of class mmm_compare.

Value

A mmm_compare data frame, one row per requested k, with columns k, log_likelihood, AIC, BIC, ICL, AvePP, Entropy and converged. When return_fits = TRUE, the fitted net_mmm models are attached as attr(result, "fits").

In print.mmm_compare(): The comparison table, invisibly, with the printed best marker column ("<-- BIC" / "<-- ICL") added.

In summary.mmm_compare(): A tidy data frame with one row per k, plus a best character column flagging the minimum-BIC and minimum-ICL solutions.

In plot.mmm_compare(): A ggplot object, invisibly.

Examples

seqs <- data.frame(V1 = sample(c("A","B","C"), 30, TRUE),
                   V2 = sample(c("A","B","C"), 30, TRUE))
comp <- compare_mmm(seqs, k = 2:3, n_starts = 1, max_iter = 10, seed = 1)
comp
#> MMM Model Comparison
#> 
#>  k log_likelihood AIC      BIC      ICL      AvePP     Entropy   converged
#>  2 -62.32972      158.6594 182.4798 184.6518 0.9647836 0.2121121  TRUE    
#>  3 -62.32987      176.6597 213.0909 217.2174 0.9343265 0.2487206 FALSE    
#>  best   
#>  <-- BIC
#>         
# \donttest{
seqs <- data.frame(
  V1 = sample(LETTERS[1:3], 30, TRUE), V2 = sample(LETTERS[1:3], 30, TRUE),
  V3 = sample(LETTERS[1:3], 30, TRUE), V4 = sample(LETTERS[1:3], 30, TRUE)
)
comp <- compare_mmm(seqs, k = 2:3, seed = 42)
print(comp)
#> MMM Model Comparison
#> 
#>  k log_likelihood AIC      BIC      ICL      AvePP     Entropy   converged
#>  2 -122.4385      278.8771 302.6974 310.2070 0.8974786 0.3160632 TRUE     
#>  3 -118.1764      288.3527 324.7838 332.3711 0.9006835 0.1882551 TRUE     
#>  best   
#>  <-- BIC
#>         

# Retain the fits so the chosen model needs no re-run; summary() marks
# the minimum-BIC and minimum-ICL rows in its `best` column.
comp_with_fits <- compare_mmm(seqs, k = 2:3, seed = 42, return_fits = TRUE)
summary(comp_with_fits)
#>   k log_likelihood      AIC      BIC      ICL     AvePP   Entropy converged
#> 1 2      -122.4385 278.8771 302.6974 310.2070 0.8974786 0.3160632      TRUE
#> 2 3      -118.1764 288.3527 324.7838 332.3711 0.9006835 0.1882551      TRUE
#>   best
#> 1  BIC
#> 2     
# }