svydiags: Vmat – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Vmat

Compute covariance matrix of residuals for linear models fitted with complex survey data

Description

Compute a covariance matrix using residuals from a fixed effects, linear regression model fitted with data collected from one- and two-stage complex survey designs.

Usage

Vmat(mobj, stvar = NULL, clvar = NULL)

Arguments

`mobj`	model object produced by `svyglm`
`stvar`	field in `mobj` that contains the stratum variable in the complex sample design; use `stvar = NULL` if there are no strata
`clvar`	field in `mobj` that contains the cluster variable in the complex sample design; use `clvar = NULL` if there are no clusters

Details

Vmat computes a covariance matrix among the residuals returned from svyglm in the survey package. The matrix that is computed is appropriate under these model assumptions: (1) in single-stage, unclustered sampling, units are assumed to be uncorrelated but can have different model variances, (2) in single-stage, stratified sampling, units are assumed to be uncorrelated within strata and between strata but can have different model variances; (3) in unstratified, clustered samples, units in different clusters are assumed to be uncorrelated but units within clusters are correlated; (3) in stratified, clustered samples, units in different strata or clusters are assumed to be uncorrelated but units within clusters are correlated.

Value

n \times n matrix where n is the number of cases used in the linear regression model

Author(s)

Richard Valliant

References

Liao, D, and Valliant, R. (2012). Variance inflation factors in the analysis of complex survey data. Survey Methodology, 38, 53-62.

Lumley, T. (2010). Complex Surveys. New York: John Wiley & Sons.

Lumley, T. (2014). survey: analysis of complex survey samples. R package version 3.30.

Examples

require(Matrix)
require(survey)
data(nhanes2007)
black <- nhanes2007$RIDRETH1 == 4
X <- nhanes2007
X <-  cbind(X, black)
X1 <- X[order(X$SDMVSTRA, X$SDMVPSU),]

    # unstratified, unclustered design
nhanes.dsgn <- svydesign(ids = 1:nrow(X1),
                         strata = NULL,
                         weights = ~WTDRD1, data=X1)
m1 <- svyglm(BMXWT ~ RIDAGEYR + as.factor(black) + DR1TKCAL, design=nhanes.dsgn)
summary(m1)

V <- Vmat(mobj = m1,
          stvar = NULL,
          clvar = NULL)

    # stratified, clustered design
nhanes.dsgn <- svydesign(ids = ~SDMVPSU,
                         strata = ~SDMVSTRA,
                         weights = ~WTDRD1, nest=TRUE, data=X1)
m1 <- svyglm(BMXWT ~ RIDAGEYR + as.factor(black) + DR1TKCAL, design=nhanes.dsgn)
summary(m1)
V <- Vmat(mobj = m1,
          stvar = "SDMVSTRA",
          clvar = "SDMVPSU")

svydiags

Linear Regression Model Diagnostics for Survey Data

v0.3

GPL (>= 2)

Authors