Sweep the lexicon-size prior alpha for an FGT model — fgt2009_sweep

fgt2009()'s alpha trades lexicon size against fit and must scale with corpus size, and the fit-vs-alpha curve is single-peaked – so a coarse sweep is the right way to choose it (and to evaluate the model fairly), rather than a general-purpose optimizer. This runs xsl_run() for each alpha and returns the per-value SSE against the dataset's human accuracy.

Usage

fgt2009_sweep_alpha(
  data,
  alphas,
  rsa = FALSE,
  gold = NULL,
  ...,
  control = xslControl()
)

Arguments

data: An xslData object (or list of them).
alphas: Numeric vector of alpha values to try.
rsa: Logical; if TRUE, sweep fgt2009_rsa() instead of fgt2009().
gold: Optional gold-standard lexicon as list(words, objects). When given, the result gains an f_max column (best-threshold F-score against gold). data must be a single xslData in this case.
...: Further arguments to the model constructor (e.g. gamma, kappa, sampler controls).
control: Control arguments passed to xsl_run().

Value

A data frame with columns alpha and sse (plus unweighted_sse when data is a list, or f_max when gold is given).

Details

For a naturalistic corpus (e.g. rollins_corpus, fm_corpus) there is no human accuracy vector; pass gold (a list(words, objects) lexicon) to also get the best-threshold F-score of the learned matrix against it (via get_roc_max()) at each alpha. Remember such corpora also want the naturalistic-speech gamma = 0.1, kappa = 0.05 rather than the defaults (see fgt2009()).

Examples

fgt2009_sweep_alpha(get_example_ambiguous_condition(),
                    alphas = c(1, 2, 4, 8))
#>   alpha          sse
#> 1     1 0.0002831451
#> 2     2 0.0238093922
#> 3     4 0.0132282220
#> 4     8 0.2031250000