idn {parsec}

Multidimensional evaluation on posets (Identification Function only)

Description

Given a partial order (arguments profiles and/or zeta) and a selected threshold, the function computes the identification function, as a S3 class object parsec. The identification function is computed by uniform sampling of the linear extensions of the input 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.

Usage

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
)

Arguments

profiles

an object of S3 class wprof.

threshold

a vector identifying the threshold. It can be a vector of indexes (numeric), a vector of poset element names (character) or a boolean vector of length equal to the number of elements.

error

the "distance" from uniformity in the sampling distribution of linear extensions.

zeta

the incidence matrix of the poset. An object of S3 class incidence. By default, extracted from profiles.

weights

weights assigned to profiles. If the argument profiles is not NULL, weights are by default set equal to profile frequencies, otherwise they are set equal to 1.

linext

the linear extension initializing the sampling algorithm. By default, it is generated by lingen(zeta). Alternatively, it can be provided by the user through a vector of elements positions.

nit

Number of iterations in the Bubley-Dyer algorithm, by default evaluated using a formula of Karzanov and Khachiyan based on the number of poset elements and the argument error (see Bubley and Dyer, 1999).

maxint

Maximum integer. By default the maximum integer obtainable in a 32bit system. This argument is used to group iterations and run the compiled C code more times, so as to avoid memory indexing problems. User can set a lower value to maxint in case of lower RAM availability.

Value

profiles

an object of S3 class wprof reporting poset profiles and their associated frequencies (number of statistical units in each profile).

number_of_profiles

number of profiles.

number_of_variables

number of variables.

incidence

S3 class incidence, incidence matrix of the poset.

cover

S3 class cover, cover matrix of the poset.

threshold

boolean vector specifying whether a profile belongs to the threshold.

number_of_iterations

number of iterations performed by the Bubley Dyer algorithm.

rank_dist

matrix reporting by rows the relative frequency distribution of the poverty ranks of each profile, over the set of sampled linear extensions.

thr_dist

vector reporting the relative frequency a profile is used as threshold in the sampled linear extensions. This result is useful for a posteriori valuation of the poset threshold.

prof_w

vector of weights assigned to each profile.

edges_weights

matrix of distances between profiles, used to evaluate the measures of gap.

idn_f

vector reporting the identification function, computed as the fraction of sampled linear extensions where a profile is in the downset of the threshold.

svr_abs

NA use evaluation to obtain this result.

svr_rel

NA use evaluation to obtain this result.

wea_abs

NA use evaluation to obtain this result.

wea_rel

NA use evaluation to obtain this result.

poverty_gap

NA use evaluation to obtain this result.

wealth_gap

NA use evaluation to obtain this result.

inequality

NA use evaluation to obtain this result.

References

Bubley R., Dyer M. (1999), Faster random generation of linear extensions, Discrete Math., 201, 81-88.

Fattore M., Arcagni A. (2013), Measuring multidimensional polarization with ordinal data, SIS 2013 Statistical Conference, BES-M3.1 - The BES and the challenges of constructing composite indicators dealing with equity and sustainability

Examples

profiles <- var2prof(varlen = c(3, 2, 4))
threshold <- c("311", "112")

res <- idn(profiles, threshold, maxint = 10^5)

summary(res)
plot(res)