## S3 method for class 'survey.design':
svytable(formula, design, Ntotal = NULL, round = FALSE,...)
## S3 method for class 'svyrep.design':
svytable(formula, design, Ntotal = sum(weights(design, "sampling")), round = FALSE,...)
## S3 method for class 'survey.design':
svychisq(formula, design, statistic = c("F", "Chisq","Wald","adjWald","lincom","saddlepoint"),na.rm=TRUE,...)
## S3 method for class 'svyrep.design':
svychisq(formula, design, statistic = c("F", "Chisq","Wald","adjWald","lincom","saddlepoint"),na.rm=TRUE,...)
## S3 method for class 'svytable':
summary(object, statistic = c("F",
"Chisq","Wald","adjWald","lincom","saddlepoint"),...)
degf(design, ...)
## S3 method for class 'survey.design2':
degf(design, ...)
## S3 method for class 'svyrep.design':
degf(design, tol=1e-5,...)+ only)svytableqr in computing the matrix rankxtabs object, svychisq
returns a htest object.svytable function computes a weighted
crosstabulation. In many cases it is easier to use
svytotal or svymean, which also produce
standard errors, design effects, etc.The frequencies in the table can be normalised to some convenient total
such as 100 or 1.0 by specifying the Ntotal argument. If the
formula has a left-hand side the mean or sum of this variable rather
than the frequency is tabulated.
The Ntotal argument can be either a single number or a data
frame whose first column gives the (first-stage) sampling strata and
second column the population size in each stratum. In this second case
the svytable command performs `post-stratification': tabulating
and scaling to the population within strata and then adding up the
strata.
As with other xtabs objects, the output of svytable can be
processed by ftable for more attractive display. The
summary method for svytable objects calls svychisq
for a test of independence.
svychisq computes first and second-order Rao-Scott corrections to
the Pearson chisquared test, and two Wald-type tests.
The default (statistic="F") is the Rao-Scott second-order
correction. The p-values are computed with a Satterthwaite
approximation to the distribution. The alternative
statistic="Chisq" adjusts the Pearson chisquared statistic by a
design effect estimate and then compares it to the chisquared
distribution it would have under simple random sampling.
The statistic="Wald" test is that proposed by Koch et al (1975)
and used by the SUDAAN software package. It is a Wald test based on the
differences between the observed cells counts and those expected under
independence. The adjustment given by statistic="adjWald" reduces
the statistic when the number of PSUs is small compared to the number of
degrees of freedom of the test. Rao and Thomas (1990) compare these
tests and find the adjustment benefical.
statistic="lincom" uses the exact asymptotic distribution, which
is a linear combination of chi-squared variables (see
pchisqsum, and statistic="saddlepoint" uses a
saddlepoint approximation to this distribution.
For designs using replicate weights the code is essentially the same as
for designs with sampling structure, since the necessary variance
computations are done by the appropriate methods of
svytotal and svymean. The exception is that
the degrees of freedom is computed as one less than the rank of the
matrix of replicate weights (by degf).
At the moment, svychisq works only for 2-dimensional tables.
Koch, GG, Freeman, DH, Freeman, JL (1975) "Strategies in the multivariate analysis of data from complex surveys" International Statistical Review 43: 59-78 Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contigency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60.
Sribney WM (1998) "Two-way contingency tables for survey or clustered data" Stata Technical Bulletin 45:33-49.
Thomas, DR, Rao, JNK (1990) "Small-sample comparison of level and power for simple goodness-of-fit statistics under cluster sampling" JASA 82:630-636
svytotal and svymean report totals
and proportions by category for factor variables.
See svyby and ftable.svystat to construct
more complex tables of summary statistics. See svyloglin for loglinear models.
data(api)
xtabs(~sch.wide+stype, data=apipop)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
summary(dclus1)
(tbl <- svytable(~sch.wide+stype, dclus1))
svychisq(~sch.wide+stype, dclus1)
summary(tbl, statistic="Chisq")
svychisq(~sch.wide+stype, dclus1, statistic="adjWald")
rclus1 <- as.svrepdesign(dclus1)
summary(svytable(~sch.wide+stype, rclus1))
svychisq(~sch.wide+stype, rclus1, statistic="adjWald")Run the code above in your browser using DataLab