fs_var {FuzzyPovertyR}

Fuzzy supplementary poverty estimation.

Description

Fuzzy supplementary poverty estimation.

Usage

fs_var(
  data,
  weight = NULL,
  ID = NULL,
  dimensions,
  HCR,
  breakdown = NULL,
  alpha,
  rho = NULL,
  type = "bootstrap",
  R = 500,
  M = NULL,
  stratum,
  psu,
  f = 0.01,
  verbose = TRUE
)

Arguments

data

A matrix or data frame of items.

weight

A numeric vector of sampling weights. if NULL simple random sampling weights will be used

ID

A numeric or character vector of IDs. if NULL (the default) it is set as the row sequence.

dimensions

A numeric vector (of length ncol(data)) of assignments of items in data to dimensions.

HCR

The head count ratio.

breakdown

A factor of sub-domains to calculate estimates for (using the same alpha). If numeric will be coerced to a factor.

alpha

The value of the exponent in equation $E(mu)^(alpha-1) = HCR$. If NULL it is calculated so that it equates the expectation of the membership function to HCR.

rho

The critical value to be used for calculation of weights in the kendall correlation matrix.

type

The variance estimation method chosen. One between bootstrap (default) or jackknife.

R

The number of bootstrap replicates. Default is 500.

M

The size of bootstrap samples. Default is nrow(data).

stratum

The vector identifying the stratum (if 'jackknife' is chosen as variance estimation technique).

psu

The vector identifying the psu (if 'jackknife' is chosen as variance estimation technique).

f

The finite population correction fraction (if 'jackknife' is chosen as variance estimation technique).

verbose

Logical. whether to print the proceeding of the variance estimation procedure.

Value

An object of class FuzzySupplementary containing the estimated variance.

Examples

dimensions = c(1,1,1,1,2,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5)
fs_var(data = eusilc[,4:23], weight = eusilc$DB090, ID = NULL,
dimensions = dimensions, breakdown = NULL, HCR = .16, alpha = 2,
rho = NULL, type = 'bootstrap', M = NULL, R = 2, verbose = TRUE)