profiles
and/or
zeta
) and a selected threshold
,
the function returns the identification function, as a S3 class object parsec
. The identification function is computed by uniform sampling of the linear extensions of the poset, through a C implementation of the Bubley - Dyer (1999) algorithm. idn is a simplified and faster version of evaluation, computing just the identification function.idn(
profiles = NULL,
threshold,
error = 10^(-3),
zeta = getzeta(profiles),
weights = {
if (!is.null(profiles))
profiles$freq
else rep(1, nrow(zeta))
},
linext = lingen(zeta),
nit = floor({
n <- nrow(zeta)
n^5 * log(n) + n^4 * log(error^(-1))
}),
maxint = 2^31 - 1
)
wprof
.incidence
.
By default, extracted from profiles
and the order of variable modalities.profiles
is not NULL
,
weights are by default set equal to profile frequencies, otherwise they are
set equal to 1.lingen(zeta)
. Alternatively, it can be provided by
the user through a vector of profile ranks.error
(see Bubley and Dyer, 1999).maxint
iwprof
reporting poset profiles
and their associated frequencies (number of statistical units in each profile).incidence
, incidence matrix of the poset.cover
, cover matrix of the poset.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.NA
use evaluation to obtain this result.profiles <- var2prof(varlen = c(3, 2, 4))
threshold <- c("311", "112")
res <- idn(profiles, threshold, maxint = 10^5)
summary(res)
plot(res)
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