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
)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.
A mids object, typically created by mice() or
mice.mids().
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$data) plus the
two administrative factors .imp and .id.
Extended formula interface: The primary variable terms (both the LHS
y and RHS x) may consist of multiple terms separated by a
‘+’ sign, e.g., y1 + y2 ~ x | a * b. This formula would be
taken to mean that the user wants to plot both y1 ~ x | a * b and
y2 ~ x | a * b, but with the y1 ~ x and y2 ~ x in
separate panels. This behavior differs from standard lattice.
Only combine terms of the same type, i.e. only factors or only
numerical variables. Mixing numerical and categorical data occasionally
produces odds labeling of vertical axis.
An expression evaluating to a logical vector indicating
which two groups are distinguished (e.g. using different colors) in the
display. The environment in which this expression is evaluated in the
response indicator is.na(x$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.
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.
See xyplot.
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.
See xyplot.
See xyplot.
See xyplot.
Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.
See xyplot.
See xyplot.
Stef van Buuren
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. tools:::Rd_expr_doi("10.18637/jss.v045.i03")
mice, stripplot, densityplot,
bwplot, lattice for an overview of the
package, as well as xyplot,
panel.xyplot,
print.trellis,
trellis.par.set
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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