vardpoor (version 0.6.2)

residual_est: Residual estimation of calibration

Description

Computes the estimation residuals of calibration.

Usage

residual_est(Y, X, weight, q)

Arguments

Y

Matrix of the variable of interest.

X

Matrix of the auxiliary variables for the calibration estimator. This is the matrix of the sample calibration variables.

weight

Weight variable. One dimensional object convertible to one-column data.frame.

q

Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to one-column data.frame.

Value

• A numeric data.frame containing the estimated residuals of calibration.

Details

The function implements the following estimator: $$e_k=Y_k-X_k^{'}B$$ where $$\hat{B} = \left(\sum_{s} weight_k q_k X_k X^{'}_{k} \right)^{-1} \left(\sum_{s} weight_k q_k X_k Y_k \right)$$.

References

Sixten Lundstrom and Carl-Erik Sarndal. Estimation in the presence of Nonresponse and Frame Imperfections. Statistics Sweden, 2001, p. 43-44.

See Also

domain, lin.ratio, linarpr, linarpt, lingini, lingini2, lingpg, linpoormed, linqsr, linrmpg, vardom, vardomh, varpoord, variance_est, variance_othstr

Examples

Y <- matrix(rchisq(10, 3), 10, 1)
X <- matrix(rchisq(20, 3), 10, 2)
w <- rep(2, 10)
q <- rep(1, 10)
residual_est(Y, X, w, q)

### Test2
Y <- matrix(rchisq(10, 3), 10, 1)
X <- matrix(c(rchisq(10, 2), rchisq(10, 2) + 10), 10, 2)
w <- rep(2, 10)
q <- rep(1, 10)
residual_est(Y, X, w, q)
as.matrix(lm(Y ~ X - 1, weights = w * q)$residuals)