na.auxiliary | R Documentation |
This function computes (1) Pearson product-moment correlation matrix to identify variables related to the incomplete variable and (2) Cohen's d comparing cases with and without missing values to identify variables related to the probability of missingness.
na.auxiliary(x, tri = c("both", "lower", "upper"), weighted = FALSE,
correct = FALSE, digits = 2, as.na = NULL, check = TRUE,
output = TRUE)
x |
a matrix or data frame with numeric vectors. |
tri |
a character string indicating which triangular of the correlation
matrix to show on the console, i.e., |
weighted |
logical: if |
correct |
logical: if |
digits |
integer value indicating the number of decimal places digits to be used for displaying correlation coefficients and Cohen's d estimates. |
as.na |
a numeric vector indicating user-defined missing values,
i.e. these values are converted to |
check |
logical: if |
output |
logical: if |
Note that non-numeric variables (i.e., factors, character vectors, and logical vectors) are excluded from to the analysis.
Returns an object of class misty.object
, which is a list with following
entries:
call |
function call |
type |
type of analysis |
data |
matrix or data frame specified in |
args |
specification of function arguments |
result |
list with result tables, i.e., |
Takuya Yanagida takuya.yanagida@univie.ac.at
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.
as.na
, na.as
, na.coverage
,
na.descript
, na.indicator
, na.pattern
,
na.prop
, na.test
dat <- data.frame(x1 = c(1, NA, 2, 5, 3, NA, 5, 2),
x2 = c(4, 2, 5, 1, 5, 3, 4, 5),
x3 = c(NA, 3, 2, 4, 5, 6, NA, 2),
x4 = c(5, 6, 3, NA, NA, 4, 6, NA))
# Auxiliary variables
na.auxiliary(dat)
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