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mice (version 2.1)

stripplot: Box-and-whisker plot, stripplot, density plot and scatterplot for imputed data

Description

Plotting methods for imputed data using lattice. bwplot produces box-and-whisker plots, stripplot produces one-dimensional scatterplots, densityplot produces plots of the densities, and xyplot produces a conditional scatterplots. Each function automatically separates the observed and imputed data in a natural way. The functions extend the usual features of lattice.

Usage

## S3 method for class 'mids':
bwplot(
    x,
    data,
    na.groups = NULL,
    groups = NULL,
    as.table = TRUE,
    theme = mice.theme(),
    mayreplicate = TRUE,
    allow.multiple = TRUE,
    outer = TRUE,
    drop.unused.levels = lattice.getOption("drop.unused.levels"),
    ...,
    subscripts = TRUE,
    subset = TRUE)
## 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)
## S3 method for class 'mids':
densityplot(
    x,
    data,
    na.groups = NULL,
    groups = NULL,
    as.table = TRUE,
    plot.points = FALSE,
    theme = mice.theme(),
    mayreplicate = TRUE,
    thicker = 2.5,
    allow.multiple = TRUE,
    outer = TRUE,
    drop.unused.levels = lattice.getOption("drop.unused.levels"),
    panel = lattice.getOption("panel.densityplot"),
    default.prepanel =
    lattice.getOption("prepanel.default.densityplot"),
   ...,
    subscripts = TRUE,
    subset = TRUE)
## S3 method for class 'mids':
xyplot(
    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"),
    ...,
    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
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.grou
plot.points
A logical used in densityplot that signals whether the points should be plotted.
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 graph
mayreplicate
A logical indicating whether color, line widths, and so on, may be replicated. The graphical functions attempt to choose "intelligent" graphical parameters. For example, the same color can be replicated for different element, e.g. use all reds
thicker
Used in densityplot. Multiplication factor of the line width of the observed density. thicker=1 uses the same thickness for the observed and imputed data.
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/

See Also

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

Examples

Run this code
imp <- mice(boys, maxit=2)

### box-and-whisker plot per imputation of all numerical variables
bwplot(imp)

### tv (testicular volume), conditional on region
bwplot(imp, tv~.imp|reg)

### same data, organized in a different way 
bwplot(imp, tv~reg|.imp, theme=list())

### 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, factor=2, cex=c(8,6), hor=FALSE, na=wgt,outer=TRUE,scales="free",pch=c(1,19)) 

### density plot of head circumference per imputation
### blue is observed, red is imputed
densityplot(imp, ~hc|.imp)

### All combined in one panel.
densityplot(imp, ~hc)

### The more powerful density plot of all 
### numerical variables with at least
### two missing values. 
densityplot(imp)


### xyplot: scatterplot by imputation number
### observe the erroneous outlying imputed values
### (caused by imputing hgt from bmi)
xyplot(imp, hgt~age|.imp, pch=c(1,20),cex=c(1,1.5))

### same, but label with missingness of wgt (four cases)
xyplot(imp, hgt~age|.imp, na.group=wgt, pch=c(1,20),cex=c(1,1.5))

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