svystandardize: Direct standardization within domains

Description Usage Arguments Value Note References See Also Examples

View source: R/stdize.R

Description

In health surveys it is often of interest to standardize domains to have the same distribution of, eg, age as in a target population. The operation is similar to post-stratification, except that the totals for the domains are fixed at the current estimates, not at known population values. This function matches the estimates produced by the (US) National Center for Health Statistics.

Usage

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svystandardize(design, by, over, population, excluding.missing = NULL)

Arguments

design

survey design object

by

A one-sided formula specifying the variables whose distribution will be standardised

over

A one-sided formula specifying the domains within which the standardisation will occur

population

Desired population totals or proportions for the levels of combinations of variables in by

excluding.missing

Optionally, a one-sided formula specifying variables whose missing values should be dropped before calculating the domain totals.

Value

A new survey design object of the same type as the input.

Note

The standard error estimates do not exactly match the NCHS estimates

References

National Center for Health Statistics http://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/agestandardization/age_standardization_intro.htm

See Also

postStratify, svyby

Examples

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## matches http://www.cdc.gov/nchs/data/databriefs/db92_fig1.png
data(nhanes)
popage <- c( 55901 , 77670 , 72816 , 45364 )
design<-svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, data=nhanes, nest=TRUE)
stdes<-svystandardize(design, by=~agecat, over=~race+RIAGENDR, 
   population=popage, excluding.missing=~HI_CHOL)
svyby(~HI_CHOL, ~race+RIAGENDR, svymean, design=subset(stdes, agecat!="(0,19]"))

Example output

Loading required package: grid
Loading required package: Matrix
Loading required package: survival

Attaching package: 'survey'

The following object is masked from 'package:graphics':

    dotchart

    race RIAGENDR   HI_CHOL          se
1.1    1        1 0.1543786 0.008318204
2.1    2        1 0.1142946 0.010182838
3.1    3        1 0.1020776 0.013547678
4.1    4        1 0.1358312 0.042274271
1.2    1        2 0.1316436 0.013418637
2.2    2        2 0.1543247 0.008932134
3.2    3        2 0.1025411 0.018953586
4.2    4        2 0.1197434 0.040091106

survey documentation built on Oct. 12, 2018, 9:05 a.m.