survey (version 2.8-1)

svytable: Contingency tables for survey data

Description

Contingency tables and chisquared tests of association for survey data.

Usage

svytable(formula, design, Ntotal = design$fpc, round = FALSE)
svreptable(formula, design, Ntotal = sum(weights(design, "sampling"))), round = FALSE)
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"),...)

Arguments

formula

Model formula specifying margins for the table (using + only)

design

survey object

statistic

See Details below

Ntotal

A population total or set of population stratum totals to normalise to.

round

Should the table entries be rounded to the nearest integer?

na.rm

Remove missing values

object

Output from svytable

...

Other arguments to summary, not used here

Value

• The table commands return an xtabs object, svychisq returns a htest object.

Details

The svytable and svreptable function compute a weighted crosstabulation. 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 is the 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.

References

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

See Also

xtabs, svyby for tables of means, medians, etc.

Examples

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)
  svreptable(~sch.wide+stype, rclus1, round=TRUE)