R: Plots for survey data (original) (raw)
| svyplot {survey} | R Documentation |
|---|
Description
Because observations in survey samples may represent very different numbers of units in the population ordinary plots can be misleading. The svyplot function produces scatterplots adjusted in various ways for sampling weights.
Usage
svyplot(formula, design,...)
## Default S3 method:
svyplot(formula, design, style = c("bubble", "hex", "grayhex","subsample","transparent"),
sample.size = 500, subset = NULL, legend = 1, inches = 0.05,
amount=NULL, basecol="black",
alpha=c(0, 0.8),xbins=30,...)
Arguments
| 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 |
| amount | list with x and y components for amount of jittering to use in subsample plots, or NULL for the default amount |
| basecol | base color for transparent plots, or a function to compute the color (see below), or color for bubble plots |
| alpha | minimum and maximum opacity for transparent plots |
| xbins | Number of (x-axis) bins for hexagonal binning |
| ... | Passed to plot methods |
Details
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 "transparent" style plots points with opacity proportional to sampling weight.
The subsample method uses the sampling weights to create a sample from approximately the population distribution and passes this to [plot](../../base/html/plot.html)
Bubble plots are suited to small surveys, hexagonal binning and transparency to large surveys where plotting all the points would result in too much overlap.
basecol can be a function taking one data frame argument, which will be passed the data frame of variables from the survey object. This could be memory-intensive for large data sets.
Value
None
References
Korn EL, Graubard BI (1998) "Scatterplots with Survey Data" The American Statistician 52: 58-69
Lumley T, Scott A (2017) "Fitting Regression Models to Survey Data" Statistical Science 32: 265-278
See Also
[symbols](../../graphics/html/symbols.html) for other options (such as colour) for bubble plots.
[svytable](../../survey/help/svytable.html) for plots of discrete data.
Examples
data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
svyplot(api00~api99, design=dstrat, style="bubble")
svyplot(api00~api99, design=dstrat, style="transparent",pch=19)
## these two require the hexbin package
svyplot(api00~api99, design=dstrat, style="hex", xlab="1999 API",ylab="2000 API")
svyplot(api00~api99, design=dstrat, style="grayhex",legend=0)
dclus2<-svydesign(id=~dnum+snum, weights=~pw,
data=apiclus2, fpc=~fpc1+fpc2)
svyplot(api00~api99, design=dclus2, style="subsample")
svyplot(api00~api99, design=dclus2, style="subsample",
amount=list(x=25,y=25))
svyplot(api00~api99, design=dstrat,
basecol=function(df){c("goldenrod","tomato","sienna")[as.numeric(df$stype)]},
style="transparent",pch=19,alpha=c(0,1))
legend("topleft",col=c("goldenrod","tomato","sienna"), pch=19, legend=c("E","H","M"))
## For discrete data, estimate a population table and plot the table.
plot(svytable(~sch.wide+comp.imp+stype,design=dstrat))
fourfoldplot(svytable(~sch.wide+comp.imp+stype,design=dstrat,round=TRUE))
## To draw on a hexbin plot you need grid graphics, eg,
library(grid)
h<-svyplot(api00~api99, design=dstrat, style="hex", xlab="1999 API",ylab="2000 API")
s<-svysmooth(api00~api99,design=dstrat)
grid.polyline(s$api99$x,s$api99$y,vp=h$plot.vp@hexVp.on,default.units="native",
gp=gpar(col="red",lwd=2))
[Package _survey_ version 4.4-2 Index]