robsurvey (version 0.7)

svyreg: Survey Regression Estimator -- Weighted Least Squares

Description

Weighted least squares estimator of regression

Usage

svyreg(formula, design, var = NULL, na.rm = FALSE, ...)

Value

Object of class svyreg_rob.

Arguments

formula

a [formula] object (i.e., symbolic description of the model)

design

an object of class survey.design; see svydesign.

var

a one-sided [formula] object or variable name ([character]) that defines the heteroscedastic variance or [NULL] indicating homoscedastic variance (default: NULL).

na.rm

[logical] indicating whether NA values should be removed before the computation proceeds (default: FALSE).

...

additional arguments (currently not used).

Details

Package survey must be attached to the search path in order to use the functions (see library or require).

svyreg computes the regression coefficients by weighted least squares.

Models for svyreg_rob are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response; see formula and lm.

A formula has an implied intercept term. To remove this use either y ~ x - 1 or y ~ 0 + x; see formula for more details of allowed formulae.

See Also

Overview (of all implemented functions)

summary, coef, residuals, fitted, SE and vcov

plot for regression diagnostic plot methods

Robust estimating methods svyreg_huberM, svyreg_huberGM, svyreg_tukeyM and svyreg_tukeyGM.

Examples

head(workplace)

library(survey)
# Survey design for stratified simple random sampling without replacement
dn <- if (packageVersion("survey") >= "4.2") {
        # survey design with pre-calibrated weights
        svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
                  data = workplace, calibrate.formula = ~-1 + strat)
    } else {
        # legacy mode
        svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
                  data = workplace)
    }

# Compute the regression estimate (weighted least squares)
m <- svyreg(payroll ~ employment, dn)

# Regression inference
summary(m)

# Extract the coefficients
coef(m)

# Extract variance/ covariance matrix
vcov(m)

# Diagnostic plots (e.g., Normal Q-Q-plot)
plot(m, which = 2L)