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

## 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

```
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)
)
```