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.
Usage
# 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::lattice.getOption("drop.unused.levels"),
...,
subscripts = TRUE,
subset = TRUE
)
Arguments
- x
A
mids
object, typically created bymice()
ormice.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 includenames(x$data)
plus the two administrative factors.imp
and.id
.Extended formula interface: The primary variable terms (both the LHS
y
and RHSx
) may consist of multiple terms separated by a ‘+’ sign, e.g.,y1 + y2 ~ x | a * b
. This formula would be taken to mean that the user wants to plot bothy1 ~ x | a * b
andy2 ~ x | a * b
, but with they1 ~ x
andy2 ~ x
in separate panels. This behavior differs from standard lattice. Only combine terms of the same type, i.e. only factors or only numerical variables. Mixing numerical and categorical data occasionally produces odds labeling of vertical axis.- na.groups
An expression evaluating to a logical vector indicating which two groups are distinguished (e.g. using different colors) in the display. The environment in which this expression is evaluated in the response indicator
is.na(x$data)
.The default
na.group = NULL
contrasts the observed and missing data in the LHSy
variable of the display, i.e. groups created byis.na(y)
. The expressiony
creates the groups according tois.na(y)
. The expressiony1 & y2
creates groups byis.na(y1) & is.na(y2)
, andy1 | y2
creates groups asis.na(y1) | is.na(y2)
, and so on.- groups
This is the usual
groups
arguments in lattice. It differs fromna.groups
because it evaluates in the completed datadata.frame(complete(x, "long", inc=TRUE))
(as usual), whereasna.groups
evaluates in the response indicator. Seexyplot
for more details. When bothna.groups
andgroups
are specified,na.groups
takes precedence, andgroups
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 fromtrellis.par.get()
. Global graphical parameters likecol
orcex
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
.
Value
The high-level functions documented here, as well as other high-level
Lattice functions, return an object of class "trellis"
. The
update.trellis
method can be used to
subsequently update components of the object, and the
print.trellis
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
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
# 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))