Plotting methods for imputed data using lattice.
`xyplot()`

produces a conditional scatterplots. The function
automatically separates the observed (blue) and imputed (red) data. The
function extends the usual features of lattice.

```
# S3 method for mids
xyplot(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"),
..., subscripts = TRUE, subset = TRUE)
```

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.

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`

.

…

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`

.

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.

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()`

.

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. https://www.jstatsoft.org/v45/i03/

`mice`

, `stripplot`

, `densityplot`

,
`bwplot`

, `lattice`

for an overview of the
package, as well as `xyplot`

,
`panel.xyplot`

,
`print.trellis`

,
`trellis.par.set`

```
# NOT RUN {
imp <- mice(boys, maxit=1)
### 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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