lin.ratio: Linearization of the ratio estimator

In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Description

Computes linearized variable for the ratio estimator.

Usage

lin.ratio(Y, Z, weight, Dom = NULL, dataset = NULL, percentratio = 1, checking = TRUE)

Arguments

Y	Matrix of numerator variables. Any object convertible to data.table with numeric values, NA values are not allowed.
Z	Matrix of denominator variables. Any object convertible to data.table with numeric values, NA values are not allowed.
weight	Weight variable. One dimensional object convertible to one-column data.table.
Dom	Optional variables used to define population domains. If supplied, the linearized variables are computed for each domain. An object convertible to data.table.
dataset	Optional survey data object convertible to data.table.
percentratio	Positive integer value. All linearized variables are multiplied with percentratio value, by default - 1.
checking	Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE.

Value

The function returns the data.table of the linearized variables for the ratio estimator.

References

Carl-Erik Sarndal, Bengt Swensson, Jan Wretman. Model Assisted Survey Sampling. Springer-Verlag, 1992, p.178.

See Also

domain, vardom, vardomh, vardcros, vardchanges, vardannual

Examples

Y <- data.table(Y = rchisq(10, 3))
Z <- data.table(Z = rchisq(10, 3))
weights <- rep(2, 10)
data.table(Y, Z, weights, 
           V1 = lin.ratio(Y, Z, weights, percentratio = 1),
           V10 = lin.ratio(Y, Z, weights, percentratio = 10),
           V100 = lin.ratio(Y, Z, weights, percentratio = 100))

vardpoor documentation built on Sept. 25, 2017, 9:03 a.m.