misty (version 0.6.7)

na.indicator: Missing Data Indicator Matrix

Description

This function creates a missing data indicator matrix \(R\) that denotes whether values are observed or missing, i.e., \(r = 0\) if a value is observed, and \(r = 1\) if a value is missing.

Usage

na.indicator(..., data = NULL, na = 0, append = TRUE, name = ".i", 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 ....

na

an integer value specifying, i.e., either 1 for 0 = observed and 1 = missing, or 0 (default) for 1 = observed and 0 = missing.

append

logical: if TRUE (default), missing data indicator matrix is appended to the data frame specified in the argument data.

name

a character string indicating the name suffix of indicator variables By default, the indicator variables are named with the ending ".i" resulting in e.g. "x1.i" and "x2.i". Note that when selecting one single variable, the indicator variable is named x.i by default or named after the argument name.

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.

Author

Takuya Yanagida

References

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.

See Also

as.na, na.as, na.auxiliary, na.coverage, na.descript, na.pattern, na.prop, na.test

Examples

# Example 1a: Create missing data indicator matrix \eqn{R}
na.indicator(airquality)

# Example 1b: Alternative specification using the 'data' argument
na.indicator(., data = airquality)

# Example 2: Append missing data indicator matrix to the data frame
na.indicator(., data = airquality)