mice (version 2.2)

stripplot.mids: Stripplot of observed and imputed data

Description

Plotting methods for imputed data using lattice. stripplot produces one-dimensional scatterplots. The function automatically separates the observed and imputed data. The functions extend the usual features of lattice.

Usage

## S3 method for class 'mids':
stripplot(x, data, na.groups = NULL, groups = NULL,
  as.table = TRUE, theme = mice.theme(), allow.multiple = TRUE,
  outer = TRUE,
  drop.unused.levels = lattice.getOption("drop.unused.levels"),
  panel = lattice.getOption("panel.stripplot"),
  default.prepanel = lattice.getOption("prepanel.default.stripplot"),
  jitter.data = TRUE, horizontal = FALSE, ..., subscripts = TRUE,
  subset = TRUE)

Arguments

x

A mids object, typically created by mice() or mice.mids().

data

Formula that selects the data to be plotted. This argument follows the lattice rules for formulas, describing the primary variables (used for the per-panel display) and the optional conditioning variables (which define the subs

na.groups

An expression evaluating to a logical vector indicating which two groups are distinguished (e.g. using different colors) in the display. The environment in which this expression is evaluated in the response indicator is.na(x$data).

groups

This is the usual groups arguments in lattice. It differs from na.groups because it evaluates in the completed data data.frame(complete(x, "long", inc=TRUE)) (as usual), whereas na.groups

theme

A named list containing the graphical parameters. The default function mice.theme produces a short list of default colors, line width, and so on. The extensive list may be obtained from trellis.par.get(). Global graphical

jitter.data

See panel.xyplot.

horizontal

See xyplot.

as.table

See xyplot.

panel

See xyplot.

default.prepanel

See xyplot.

outer

See xyplot.

allow.multiple

See xyplot.

drop.unused.levels

See xyplot.

subscripts

See xyplot.

subset

See xyplot.

...

Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.

Value

• The high-level functions documented here, as well as other high-level Lattice functions, return an object of class "trellis". The update method can be used to subsequently update components of the object, and the print method (usually called by default) will plot it on an appropriate plotting device.

Details

The argument na.groups may be used to specify (combinations of) missingness in any of the variables. The argument groups can be used to specify groups based on the variable values themselves. Only one of both may be active at the same time. When both are specified, na.groups takes precedence over groups.

Use the subset and na.groups together to plots parts of the data. For example, select the first imputed data set by by subset=.imp==1.

Graphical paramaters like col, pch and cex can be specified in the arguments list to alter the plotting symbols. If length(col)==2, the color specification to define the observed and missing groups. col[1] is the color of the 'observed' data, col[2] is the color of the missing or imputed data. A convenient color choice is col=mdc(1:2), a transparent blue color for the observed data, and a transparent red color for the imputed data. A good choice is col=mdc(1:2), pch=20, cex=1.5. These choices can be set for the duration of the session by running mice.theme().

References

Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R, Springer. http://lmdvr.r-forge.r-project.org/

van Buuren S and Groothuis-Oudshoorn K (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/

See Also

mice, xyplot, densityplot, bwplot, Lattice for an overview of the package, as well as stripplot, panel.stripplot, print.trellis, trellis.par.set

Examples

require(lattice)

imp <- mice(boys, maxit=1)

### stripplot, all numerical variables
stripplot(imp)

### same, but with improved display
stripplot(imp, col=c("grey",mdc(2)),pch=c(1,20))

### distribution per imputation of height, weight and bmi
### labeled by their own missingness
stripplot(imp, hgt+wgt+bmi~.imp, cex=c(2,4), pch=c(1,20),jitter=FALSE,
layout=c(3,1))

### same, but labeled with the missingness of wgt (just four cases)
stripplot(imp, hgt+wgt+bmi~.imp, na=wgt, cex=c(2,4), pch=c(1,20),jitter=FALSE,
layout=c(3,1))

### distribution of age and height, labeled by missingness in height
### most height values are missing for those around
### the age of two years
### some additional missings occur in region WEST
stripplot(imp, age+hgt~.imp|reg, hgt, col=c(hcl(0,0,40,0.2), mdc(2)),pch=c(1,20))

### heavily jitted relation between two categorical variables
### labeled by missingness of gen
### aggregated over all imputed data sets
stripplot(imp, gen~phb, factor=2, cex=c(8,1), hor=TRUE)

### circle fun
stripplot(imp, gen~.imp, na = wgt, factor = 2, cex = c(8.6),
         hor = FALSE, outer = TRUE, scales = "free", pch = c(1,19))