mase (version 0.1.3)

ratioEstimator: Compute a ratio estimator

Description

Calculates a ratio estimator for a finite population mean/proportion or total based on sample data collected from a complex sampling design and auxiliary population data.

Usage

ratioEstimator(
  y,
  xsample,
  xpop,
  datatype = "raw",
  pi = NULL,
  N = NULL,
  pi2 = NULL,
  var_est = FALSE,
  var_method = "LinHB",
  B = 1000
)

Value

List of output containing:

• pop_total:Estimate of population total

• pop_mean:Estimate of population mean

• pop_total_var: Estimated variance of population total estimate

• pop_mean_var: Estimated variance of population mean estimate

Arguments

y

A numeric vector of the sampled response variable.

xsample

A numeric vector of the sampled auxiliary variable.

xpop

A numeric vector of population level auxiliary information. Must come in the form of raw data, population total or population mean.

datatype

A string that specifies the form of population auxiliary data. The possible values are "raw", "total" or "mean". If datatype = "raw", then xpop must contain a numeric vector of the auxiliary variable for each unit in the population. If datatype = "total" or "mean", then contains either the population total or population mean for the auxiliary variable.

pi

A numeric vector of inclusion probabilities for each sampled unit in y. If NULL, then simple random sampling without replacement is assumed.

N

A numeric value of the population size. If NULL, it is estimated with the sum of the inverse of the pis.

pi2

A square matrix of the joint inclusion probabilities. Needed for the "LinHT" variance estimator.

var_est

A logical indicating whether or not to compute a variance estimator. Default is FALSE.

var_method

The method to use when computing the variance estimator. Options are a Taylor linearized technique: "LinHB"= Hajek-Berger estimator, "LinHH" = Hansen-Hurwitz estimator, "LinHTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "LinHT" = Horvitz-Thompson estimator or a resampling technique: "bootstrapSRS" = bootstrap variance estimator under simple random sampling without replacement. The default is "LinHB".

B

The number of bootstrap samples if computing the bootstrap variance estimator. Default is 1000.

References

coc77mase sar92mase

Examples

library(survey)
data(api)
ratioEstimator(y = apisrs$api00, xsample = apisrs$meals, 
xpop = sum(apipop$meals), datatype = "total", pi = apisrs$pw^(-1), 
N = dim(apipop)[1])