LD: Likelihood Distance

Description

Compute likelihood distances between models when removing the \(i_{th}\) case. If there are no missing data then the GOF will often provide equivalent results. If mirt is used, then the values will be associated with the unique response patterns instead.

Usage

LD(data, model, progress = TRUE, ...)
# S3 method for LD
print(x, ncases = 10, digits = 5, ...)
# S3 method for LD
plot(
  x,
  y = NULL,
  main = "Likelihood Distance",
  type = c("p", "h"),
  ylab = "LD",
  absolute = FALSE,
  ...
)

Arguments

data: matrix or data.frame
model: if a single numeric number declares number of factors to extract in exploratory factor analysis (requires complete dataset, i.e., no missing). If class(model) is a sem (semmod), or lavaan (character), then a confirmatory approach is performed instead. Finally, if the model is defined with mirt::mirt.model() then distances will be computed for categorical data with the mirt package
progress: logical; display the progress of the computations in the console?
...: additional parameters to be passed
x: an object of class LD
ncases: number of extreme cases to display
digits: number of digits to round in the printed result
y: a NULL value ignored by the plotting function
main: the main title of the plot
type: type of plot to use, default displays points and lines
ylab: the y label of the plot
absolute: logical; use absolute values instead of deviations?

Author

Phil Chalmers rphilip.chalmers@gmail.com

Details

Note that LD is not limited to confirmatory factor analysis and can apply to nearly any model being studied where detection of influential observations is important.

References

Chalmers, R. P. & Flora, D. B. (2015). faoutlier: An R Package for Detecting Influential Cases in Exploratory and Confirmatory Factor Analysis. Applied Psychological Measurement, 39, 573-574. tools:::Rd_expr_doi("10.1177/0146621615597894")