mice: stripplot.mids – R documentation

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stripplot.mids

Stripplot of observed and imputed data

Description

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 class '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`.

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().

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.

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.

Author(s)

Stef van Buuren

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

Examples

imp <- mice(boys, maxit = 1)

### stripplot, all numerical variables
## Not run: 
stripplot(imp)

## End(Not run)

### same, but with improved display
## Not run: 
stripplot(imp, col = c("grey", mdc(2)), pch = c(1, 20))

## End(Not run)

### distribution per imputation of height, weight and bmi
### labeled by their own missingness
## Not run: 
stripplot(imp, hgt + wgt + bmi ~ .imp,
  cex = c(2, 4), pch = c(1, 20), jitter = FALSE,
  layout = c(3, 1)
)

## End(Not run)

### same, but labeled with the missingness of wgt (just four cases)
## Not run: 
stripplot(imp, hgt + wgt + bmi ~ .imp,
  na = wgt, cex = c(2, 4), pch = c(1, 20), jitter = FALSE,
  layout = c(3, 1)
)

## End(Not run)

### 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
## Not run: 
stripplot(imp, age + hgt ~ .imp | reg, hgt,
  col = c(grDevices::hcl(0, 0, 40, 0.2), mdc(2)), pch = c(1, 20)
)

## End(Not run)

### heavily jitted relation between two categorical variables
### labeled by missingness of gen
### aggregated over all imputed data sets
## Not run: 
stripplot(imp, gen ~ phb, factor = 2, cex = c(8, 1), hor = TRUE)

## End(Not run)

### circle fun
stripplot(imp, gen ~ .imp,
  na = wgt, factor = 2, cex = c(8.6),
  hor = FALSE, outer = TRUE, scales = "free", pch = c(1, 19)
)

mice

Multivariate Imputation by Chained Equations

v3.13.0

GPL-2 | GPL-3

Authors

Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]

Initial release

2021-01-26