survey (version 3.23-2)

svyfactanal: Factor analysis in complex surveys (experimental).

Description

This function fits a factor analysis model or SEM, by maximum weighted likelihood.

Usage

svyfactanal(formula, design, factors, n = c("none", "sample", "degf","effective", "min.effective"), ...)

Arguments

formula

Model formula specifying the variables to use

design

Survey design object

factors

Number of factors to estimate

n

Sample size to be used for testing: see below

...

Other arguments to pass to factanal.

Value

• An object of class factanal

Details

The population covariance matrix is estimated by svyvar and passed to factanal

Although fitting these models requires only the estimated covariance matrix, inference requires a sample size. With n="sample", the sample size is taken to be the number of observations; with n="degf", the survey degrees of freedom as returned by degf. Using "sample" corresponds to standardizing weights to have mean 1, and is known to result in anti-conservative tests.

The other two methods estimate an effective sample size for each variable as the sample size where the standard error of a variance of a Normal distribution would match the design-based standard error estimated by svyvar. With n="min.effective" the minimum sample size across the variables is used; with n="effective" the harmonic mean is used. For svyfactanal the test of model adequacy is optional, and the default choice, n="none", does not do the test.

References

.

See Also

factanal

Examples

data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

svyfactanal(~api99+api00+hsg+meals+ell+emer, design=dclus1, factors=2)

svyfactanal(~api99+api00+hsg+meals+ell+emer, design=dclus1, factors=2, n="effective")

##Population dat for comparison
factanal(~api99+api00+hsg+meals+ell+emer, data=apipop, factors=2)