This function creates a missing data indicator matrix \(R\) that denotes whether
values are observed or missing, i.e., \(r = 1\) if a value is observed, and
\(r = 0\) if a value is missing.
Usage
na.indicator(..., data = NULL, as.na = NULL, check = TRUE)
Value
Returns a matrix or data frame with \(r = 1\) if a value is observed, and \(r = 0\)
if a value is missing.
Arguments
...
a matrix or data frame with incomplete data, where missing
values are coded as NA. Alternatively, an expression
indicating the variable names in data e.g.,
na.indicator(x1, x2, x3, data = dat). Note that the operators
., +, -, ~, :, ::,
and ! can also be used to select variables, see 'Details'
in the df.subset function.
data
a data frame when specifying one or more variables in the
argument .... Note that the argument is NULL
when specifying a matrix or data frame for the argument ....
as.na
a numeric vector indicating user-defined missing values,
i.e. these values are converted to NA before conducting
the analysis.
check
logical: if TRUE (default), argument specification is checked.
Enders, C. K. (2010). Applied missing data analysis. Guilford Press.
Graham, J. W. (2009). Missing data analysis: Making it work in the real world.
Annual Review of Psychology, 60, 549-576.
https://doi.org/10.1146/annurev.psych.58.110405.085530
van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). Chapman & Hall.
# Example 1a: Create missing data indicator matrix \eqn{R}na.indicator(airquality)
# Example 1b: Alternative specification using the 'data' argumentna.indicator(., data = airquality)