LD: Likelihood Distance

In faoutlier: Influential Case Detection Methods for Factor Analysis and Structural Equation Models

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 class 'LD'
print(x, ncases = 10, digits = 5, ...)

## S3 method for class '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?

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.

Author(s)

Phil Chalmers [e-mail]

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. doi: 10.1177/0146621615597894

Flora, D. B., LaBrish, C. & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Psychology, 3, 1-21. doi: 10.3389/fpsyg.2012.00055

See Also

gCD, GOF, obs.resid, robustMD, setCluster

Examples

## Not run: 

#run all LD functions using multiple cores
setCluster()

#Exploratory
nfact <- 3
(LDresult <- LD(holzinger, nfact))
(LDresult.outlier <- LD(holzinger.outlier, nfact))
plot(LDresult)
plot(LDresult.outlier)

## add a progress meter
LDresult <- LD(holzinger, nfact, progress = TRUE)

#-------------------------------------------------------------------
#Confirmatory with sem
model <- sem::specifyModel()
  F1 -> Remndrs,    lam11
	  F1 -> SntComp,    lam21
	  F1 -> WrdMean,    lam31
	  F2 -> MissNum,    lam42
	  F2 -> MxdArit,    lam52
	  F2 -> OddWrds,    lam62
	  F3 -> Boots,      lam73
  F3 -> Gloves,     lam83
	  F3 -> Hatchts,    lam93
	  F1 <-> F1,   NA,     1
	  F2 <-> F2,   NA,     1
	  F3 <-> F3,   NA,     1

(LDresult <- LD(holzinger, model))
(LDresult.outlier <- LD(holzinger.outlier, model))
plot(LDresult)
plot(LDresult.outlier)

#-------------------------------------------------------------------
#Confirmatory with lavaan
model <- 'F1 =~  Remndrs + SntComp + WrdMean
F2 =~ MissNum + MxdArit + OddWrds
F3 =~ Boots + Gloves + Hatchts'

(LDresult <- LD(holzinger, model, orthogonal=TRUE))
(LDresult.outlier <- LD(holzinger.outlier, model, orthogonal=TRUE))
plot(LDresult)
plot(LDresult.outlier)

# categorical data with mirt
library(mirt)
data(LSAT7)
dat <- expand.table(LSAT7)
model <- mirt.model('F = 1-5')
LDresult <- LD(dat, model)
plot(LDresult)


## End(Not run)

faoutlier documentation built on Jan. 13, 2021, 7:04 p.m.