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Plotting methods for imputed data using lattice. densityplot produces plots of the densities. The function automatically separates the observed and imputed data. The functions extend the usual features of lattice.


# S3 method for mids
  na.groups = NULL,
  groups = NULL,
  as.table = TRUE,
  plot.points = FALSE,
  theme = mice.theme(),
  mayreplicate = TRUE,
  thicker = 2.5,
  allow.multiple = TRUE,
  outer = TRUE,
  drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"),
  panel = lattice::lattice.getOption("panel.densityplot"),
  default.prepanel = lattice::lattice.getOption("prepanel.default.densityplot"),
  subscripts = TRUE,
  subset = TRUE



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.

The function densityplot does not use the y terms in the formula. Density plots for x1 and x2 are requested as ~ x1 + x2.


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$data).

The default = NULL contrasts the observed and missing data in the LHS y variable of the display, i.e. groups created by The expression y creates the groups according to The expression y1 & y2 creates groups by &, and y1 | y2 creates groups as |, 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 logical used in densityplot that signals whether the points should be plotted.


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.


A logical indicating whether color, line widths, and so on, may be replicated. The graphical functions attempt to choose "intelligent" graphical parameters. For example, the same color can be replicated for different element, e.g. use all reds for the imputed data. Replication may be switched off by setting the flag to FALSE, in order to allow the user to gain full control.


Used in densityplot. Multiplication factor of the line width of the observed density. thicker=1 uses the same thickness for the observed and imputed data.


See xyplot.


See xyplot.


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.


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().


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.

densityplot errs on empty groups, which occurs if all observations in the subgroup contain NA. The relevant error message is: Error in density.default: ... need at least 2 points to select a bandwidth automatically. There is yet no workaround for this problem. Use the more robust bwplot or stripplot as a replacement.


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


Stef van Buuren


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

### density plot of head circumference per imputation
### blue is observed, red is imputed
densityplot(imp, ~ hc | .imp)

### All combined in one panel.
densityplot(imp, ~hc)