View source: R/recommended_estimator.R
| recommended_estimator | R Documentation |
Returns the kernel-density estimator configuration that the simulation
study behind rank_contrasts() used at each dimensionality (Berry,
under review, Table III), so that a contrast measured with phontrast is
scored with the settings the safe-use envelope was calibrated on. Three
settings switch together with the number of features d:
recommended_estimator(d)
d |
Positive integer; the number of acoustic features (dimensions). |
d <= 4Plug-in bandwidth (bw = "Hpi"), the
"ks" engine, densities evaluated at every token, and the partial
leave-one-out correction on. Calibrated at d = 2 and 4.
5 <= d <= 13Diagonal Scott bandwidth
(bw = "scott.diag"), the "fast_diag" engine, densities
evaluated at 200 subsampled tokens per category, leave-one-out on.
Calibrated at d = 8 and 13; dimensionalities between the
calibrated ones take the settings of the next higher calibrated
dimensionality.
d >= 14As above but with no leave-one-out correction,
reproducing the log-space path the study used at d = 32 and
64. phontrast's "fast_diag" engine evaluates kernels in
log space, so this tier runs natively. Above eight dimensions the study
gives ordering evidence only.
A list with elements d, tier (a label for the row of
Table III applied), calibrated_at (the dimensionalities the row was
calibrated on), bw, engine, eval_n, loo, and
note. The bw, engine, eval_n, and loo
elements can be passed straight to jsd_kde_nd(),
estimate_jsd(), or phontrast().
recommended_estimator(2)
recommended_estimator(13)$engine
set.seed(2026)
vowels <- data.frame(
vowel = rep(c("ih", "eh"), each = 40),
f1 = c(rnorm(40, 500, 55), rnorm(40, 565, 60)),
f2 = c(rnorm(40, 1980, 150), rnorm(40, 1870, 155))
)
est <- recommended_estimator(2)
jsd_kde_nd(vowels, c("f1", "f2"), "vowel",
bw = est$bw, engine = est$engine, loo = est$loo)
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