svydiags (version 0.6)

svystdres: Standardized residuals for models fitted with complex survey data

Description

Compute standardized residuals for fixed effects, linear regression models fitted with data collected from one- and two-stage complex survey designs.

Usage

svystdres(mobj, stvar=NULL, clvar=NULL, doplot=FALSE)

Value

List object with values:

stdresids

Numeric vector whose names are the rows of the data frame in the svydesign object that were used in fitting the model

n

number of sample clusters

mbar

average number of non-missing, sample elements per cluster

rtsighat

estimate of the square root of the model variance of the residuals, \(\sqrt(\sigma^2)\)

rhohat

estimate of the intracluster correlation of the residuals, \(\rho\)

Arguments

mobj

model object produced by svyglm in the survey package

stvar

name of the stratification variable in the svydesign object used to fit the model

clvar

name of the cluster variable in the svydesign object used to fit the model

doplot

if TRUE, plot the standardized residuals vs. their sequence number in data set. Reference lines are drawn at +/-3

Author

Richard Valliant

Details

svystdres computes the standardized residuals, i.e., the residuals divided by an estimate of the model standard deviation of the residuals. Residuals are used from a model object created by svyglm in the R survey package. The output is a vector of the standardized residuals and a scatterplot of them versus the sequence number of the sample element used in fitting the model. By default, svyglm uses only complete cases (i.e., ones for which the dependent variable and all independent variables are non-missing) to fit the model. The rows of the data frame used in fitting the model can be retrieved from the svyglm object via as.numeric(names(mobj$y)). The data for those rows is in mobj$data.

References

Li, J., and Valliant, R. (2011). Linear regression diagnostics for unclustered survey data. Journal of Official Statistics, 27, 99-119.

Li, J., and Valliant, R. (2015). Linear regression diagnostics in cluster samples. Journal of Official Statistics, 31, 61-75.

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

Lumley, T. (2023). survey: analysis of complex survey samples. R package version 4.2.

See Also

svyhat, svyCooksD

Examples

require(survey)
data(api)
    # unstratified design single stage design
d0 <- svydesign(id=~1,strata=NULL, weights=~pw, data=apistrat)
m0 <- svyglm(api00 ~ ell + meals + mobility, design=d0)
svystdres(mobj=m0, stvar=NULL, clvar=NULL)

    # stratified cluster design
require(NHANES)
data(NHANESraw)
dnhanes <- svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTINT2YR, nest=TRUE, data=NHANESraw)
m1 <- svyglm(BPDiaAve ~ as.factor(Race1) + BMI + AlcoholYear, design = dnhanes)
svystdres(mobj=m1, stvar= "SDMVSTRA", clvar="SDMVPSU")