## 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"),na.rm=TRUE,...)
## S3 method for class 'svytable':
summary(object, statistic = c("F", "Chisq","Wald","adjWald"),...)+ only)svytablextabs object, svychisq
returns a htest object.svytable and svreptable function compute a weighted
crosstabulation. In many cases it is easier to use
svytotal or svymean, which also produce
standard errors, design effects, etc. If the sampling probabilities supplied to svydesign were actual
probabilities (rather than relative probabilities) this estimates a
full population crosstabulation. Otherwise it estimates only relative
proportions and should 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.
At the moment, svychisq works only for 2-dimensional tables.
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
svyby and ftable.svystat for tables of means, medians, etc.data(api)
xtabs(~sch.wide+stype, data=apipop)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
summary(dclus1)
svytable(~sch.wide+stype, dclus1)
svychisq(~sch.wide+stype, dclus1)
svychisq(~sch.wide+stype, dclus1, statistic="Chisq")
svychisq(~sch.wide+stype, dclus1, statistic="adjWald")
rclus1 <- as.svrepdesign(dclus1)
svytable(~sch.wide+stype, rclus1, round=TRUE)Run the code above in your browser using DataLab