svyplot {survey} | R Documentation |
Because observations in survey samples may represent very different
numbers of units in the population ordinary plots can be misleading.
The svyplot
function produces plots adjusted in various ways
for sampling weights.
svyplot(formula, design, style = c("bubble", "hex", "grayhex","subsample"), sample.size = 500, subset = NULL, legend = 1, inches = 0.05, ...)
formula |
A model formula |
design |
A survey object (svydesign or svrepdesign) |
style |
See Details below |
sample.size |
For style="subsample" |
subset |
expression using variables in the design object |
legend |
For style="hex" or "grayhex" |
inches |
Scale for bubble plots |
... |
Passed to plot methods |
Bubble plots are scatterplots with circles whose area is proportional
to the sampling weight. The two "hex" styles produce hexagonal
binning scatterplots, and require the hexbin
package from
Bioconductor.
The subsample
method uses the sampling weights to create a
sample from approximately the population distribution and passes this to plot
Bubble plots are suited to small surveys, hexagonal binning and subsampling to large surveys where plotting all the points would result in too much overlap.
None
data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svyplot(api00~api99, design=dstrat, style="bubble") ## Not run: ## these two require the hexbin package from Bioconductor svyplot(api00~api99, design=dstrat, style="hex", xlab="1999 API",ylab="2000 API") svyplot(api00~api99, design=dstrat, style="grayhex",legend=0) ## End(Not run) ## Subsampling doesn't really make sense for such a small survey svyplot(api00~api99, design=dstrat, style="subsample") svyplot(api00~stype, design=dstrat, style="subsample")