spineMiss(x, delimiter = NULL, pos = 1, selection = c("any", "all"),
breaks = "Sturges", right = TRUE, col = c("skyblue", "red", "skyblue4",
"red4", "orange", "orange4"), border = NULL, main = NULL, sub = NULL,
xlab = NULL, ylab = NULL, axes = TRUE, labels = axes,
only.miss = TRUE, miss.labels = axes, interactive = TRUE, ...)data.frame.x needs to have
colnames). If given, it is used to determine the corresponding
ix are used for highlighting."any"
(highlighting of missing/imputed values in any of the additional
variables) and "all" (highlighbreaks controls
the breakpoints (see hist for possible values).TRUE and the variable of interest is
numeric, the spinogram cells are right-closed (left-open) intervals.border=NA to omit borders.TRUE, the missing/imputed values in the
variable of interest are also visualized by a cell in the spineplot or
spinogram. Otherwise, a small spineplot is drawn on the right hand side
(see spineplotonly.miss=TRUE, the missing/imputed values in the variable of
interest are also visualized by a cell in the spine plot or spinogram. If
additional variables are supplied, this cell is again split into two parts
according to missingness/number if imputed values in the additional
variables.
Otherwise, a small spineplot that visualizes missing/imputed values in the
variable of interest is drawn on the right hand side. The first cell
corresponds to observed values and the second cell to missing/imputed
values. Each of the two cells is again split into two parts according to
missingness/number of imputed values in the additional variables. Note that
this display does not make sense if only one variable is supplied, therefore
only.miss is ignored in that case.
If interactive=TRUE, clicking in the left margin of the plot results
in switching to the previous variable and clicking in the right margin
results in switching to the next variable. Clicking anywhere else on the
graphics device quits the interactive session.histMiss, barMiss,
mosaicMissdata(tao, package = "VIM")
data(sleep, package = "VIM")
## for missing values
spineMiss(tao[, c("Air.Temp", "Humidity")])
spineMiss(sleep[, c("Exp", "Sleep")])
## for imputed values
spineMiss(kNN(tao[, c("Air.Temp", "Humidity")]), delimiter = "_imp")
spineMiss(kNN(sleep[, c("Exp", "Sleep")]), delimiter = "_imp")Run the code above in your browser using DataLab