na.auxiliary: Auxiliary Variables Analysis

In misty: Miscellaneous Functions 'T. Yanagida'

Auxiliary Variables Analysis

Description

This function computes (1) Pearson product-moment correlation matrix to identify variables related to the incomplete variable (i.e., correlates of incomplete variables), (2) Cohen's d matrix comparing cases with and without missing values to identify variables related to the probability of missingness(i.e., correlates of missingness), and (3) semi-partial correlations of an outcome variable conditional on the predictor variables of a substantive model with a set of candidate auxiliary variables to identify correlates of an incomplete outcome variable as suggested by Raykov and West (2016).

Usage

na.auxiliary(data, ..., model = NULL, estimator = c("ML", "MLR"),
             missing = c("fiml", "two.stage", "robust.two.stage", "doubly.robust"),
             tri = c("both", "lower", "upper"), weighted = FALSE, correct = FALSE,
             digits = 2, p.digits = 3, as.na = NULL, write = NULL, append = TRUE,
             check = TRUE, output = TRUE)

Arguments

data	a data frame with incomplete data, where missing values are coded as NA.
...	an expression indicating the variable names in data, e.g., na.auxiliary(dat, x1, x2, x3). Note that the operators ., +, -, ~, :, ::, and ! can also be used to select variables, see 'Details' in the df.subset function.
model	a character string specifying the substantive model predicting an continuous outcome variable using a set of predictor variables to estimate semi-partial correlations between the outcome variable and a set of candidate auxiliary variables. The default setting is model = NULL, i.e., the function computes Pearson product-moment correlation matrix and Cohen's d matrix.
estimator	a character string indicating the estimator to be used when estimating semi-partial correlation coefficients, i.e., "ML" for maximum likelihood parameter estimates with conventional standard errors or "MLR" (default) maximum likelihood parameter estimates with Huber-White robust standard errors.
missing	a character string indicating how to deal with missing data when estimating semi-partial correlation coefficients, i.e., "fiml" for full information maximum likelihood method, two.stage for two-stage maximum likelihood method, robust.two.stage for robust two-stage maximum likelihood method, and doubly-robust for doubly-robust method (see 'Details' in the item.cfa function). The default setting is missing = "fiml".
tri	a character string indicating which triangular of the correlation matrix to show on the console, i.e., both for upper and lower triangular, lower (default) for the lower triangular, and upper for the upper triangular.
weighted	logical: if TRUE (default), the weighted pooled standard deviation is used.
correct	logical: if TRUE, correction factor for Cohen's d to remove positive bias in small samples is used.
digits	integer value indicating the number of decimal places digits to be used for displaying correlation coefficients and Cohen's d estimates.
p.digits	an integer value indicating the number of decimal places to be used for displaying the p-value.
as.na	a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis.
write	a character string naming a file for writing the output into either a text file with file extension ".txt" (e.g., "Output.txt") or Excel file with file extension ".xlsx" (e.g., "Output.xlsx"). If the file name does not contain any file extension, an Excel file will be written.
append	logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.
check	logical: if TRUE (default), argument specification is checked.
output	logical: if TRUE (default), output is shown on the console.

Details

Note that non-numeric variables (i.e., factors, character vectors, and logical vectors) are excluded from to the analysis.

Value

Returns an object of class misty.object, which is a list with following entries:

call	function call
type	type of analysis
data	data frame used for the current analysis
model	lavaan model syntax for estimating the semi-partial correlations
model.fit	fitted lavaan model for estimating the semi-partial correlations
args	pecification of function arguments
result	list with result tables

Author(s)

Takuya Yanagida [e-mail]

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

Raykov, T., & West, B. T. (2016). On enhancing plausibility of the missing at random assumption in incomplete data analyses via evaluation of response-auxiliary variable correlations. Structural Equation Modeling, 23(1), 45–53. https://doi.org/10.1080/10705511.2014.937848

van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). Chapman & Hall.

See Also

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

Examples

# Example 1: Auxiliary variables
na.auxiliary(airquality)

# Example 2: Semi-partial correlation coefficients
na.auxiliary(airquality, model = "Ozone ~ Solar.R + Wind")

## Not run: 
# Example 3a: Write Results into a text file
na.auxiliary(airquality, write = "NA_Auxiliary.txt")

# Example 3a: Write Results into an Excel file
na.auxiliary(airquality, write = "NA_Auxiliary.xlsx")
## End(Not run)

misty documentation built on April 4, 2025, 3:49 a.m.