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 mids
stripplot(
x,
data,
na.groups = NULL,
groups = NULL,
as.table = TRUE,
theme = mice.theme(),
allow.multiple = TRUE,
outer = TRUE,
drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"),
panel = lattice::lattice.getOption("panel.stripplot"),
default.prepanel = lattice::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 subsets plotted in different panels) to be used in the plot.

The formula is evaluated on the complete data set in the long form. Legal variable names for the formula include names(x$data) plus the two administrative factors .imp and .id. Extended formula interface: The primary variable terms (both the LHS y and RHS x) may consist of multiple terms separated by a ‘+’ sign, e.g., y1 + y2 ~ x | a * b. This formula would be taken to mean that the user wants to plot both y1 ~ x | a * b and y2 ~ x | a * b, but with the y1 ~ x and y2 ~ x in separate panels. This behavior differs from standard lattice. Only combine terms of the same type, i.e. only factors or only numerical variables. Mixing numerical and categorical data occasionally produces odds labeling of vertical axis. For convenience, in stripplot() and bwplot the formula y~.imp may be abbreviated as y. This applies only to a single y, and does not (yet) work for y1+y2~.imp. na.groups An expression evaluating to a logical vector indicating which two groups are distinguished (e.g. using different colors) in the display. The environment in which this expression is evaluated in the response indicator is.na(x$data).

The default na.group = NULL contrasts the observed and missing data in the LHS y variable of the display, i.e. groups created by is.na(y). The expression y creates the groups according to is.na(y). The expression y1 & y2 creates groups by is.na(y1) & is.na(y2), and y1 | y2 creates groups as is.na(y1) | is.na(y2), and so on.

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 evaluates in the response indicator. See xyplot for more details. When both na.groups and groups are specified, na.groups takes precedence, and groups is ignored.

as.table

See xyplot.

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 parameters like col or cex in high-level calls are still honored, so first experiment with the global parameters. Many setting consists of a pair. For example, mice.theme defines two symbol colors. The first is for the observed data, the second for the imputed data. The theme settings only exist during the call, and do not affect the trellis graphical parameters.

allow.multiple

See xyplot.

outer

See xyplot.

drop.unused.levels

See xyplot.

panel

See xyplot.

default.prepanel

See xyplot.

jitter.data

See panel.xyplot.

horizontal

See xyplot.

...

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

subscripts

See xyplot.

subset

See xyplot.

## 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 parameters 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().

## Note

The first two arguments (x and data) are reversed compared to the standard Trellis syntax implemented in lattice. This reversal was necessary in order to benefit from automatic method dispatch.

In mice the argument x is always a mids object, whereas in lattice the argument x is always a formula.

In mice the argument data is always a formula object, whereas in lattice the argument data is usually a data frame.

All other arguments have identical interpretation.

## References

Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R, Springer.

van Buuren S and Groothuis-Oudshoorn K (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. doi:10.18637/jss.v045.i03

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

Stef van Buuren

## Examples

imp <- mice(boys, maxit = 1)
#>
#>  iter imp variable
#>   1   1  hgt  wgt  bmi  hc  gen  phb  tv  reg
#>   1   2  hgt  wgt  bmi  hc  gen  phb  tv  reg
#>   1   3  hgt  wgt  bmi  hc  gen  phb  tv  reg
#>   1   4  hgt  wgt  bmi  hc  gen  phb  tv  reg
#>   1   5  hgt  wgt  bmi  hc  gen  phb  tv  reg

### stripplot, all numerical variables
if (FALSE) {
stripplot(imp)
}

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

### distribution per imputation of height, weight and bmi
### labeled by their own missingness
if (FALSE) {
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)
if (FALSE) {
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
if (FALSE) {
stripplot(imp, age + hgt ~ .imp | reg, hgt,
col = c(grDevices::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
if (FALSE) {
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)
)