faoutlier (version 0.2.2)

LD: Likelihood Distance

Description

Compute likelihood distances between models when removing the $i_{th}$ case.

Usage

LD(data, model, na.rm = TRUE, digits = 5)

  ## S3 method for class 'LD':
print(x, ncases = 10, ...)

  ## S3 method for class 'LD':
plot(x, y = NULL,
    main = "Likelihood Distance", type = c("p", "h"),
    ylab = "LD", ...)

Arguments

data

matrix or data.frame

model

if a single numeric number declares number of factors to extract in exploratory factor ansysis. If class(model) is a sem (or OpenMx model if installed from github) then a confirmatory approach is performed instead

na.rm

logical; remove cases with missing data?

digits

number of digits to round in the final result

x

an object of class LD

ncases

number of extreme cases to display

...

additional parameters to be passed

y

a NULL value ignored by the plotting function

type

type of plot to use, default displayes points and lines

main

the main title of the plot

ylab

the y label of the plot

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.

See Also

gCD, obs.resid, robustMD

Examples

data(holzinger)
data(holzinger.outlier)

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

#Confirmatory with sem
model <- specifyModel()
	  F1 -> V1,    lam11
	  F1 -> V2,    lam21
	  F1 -> V3,    lam31
	  F2 -> V4,    lam41
	  F2 -> V5,    lam52
	  F2 -> V6,    lam62
	  F3 -> V7,    lam73
	  F3 -> V8,    lam83
	  F3 -> V9,    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 using OpenMx (requires github version, see ?faoutlier)
manifests <- colnames(holzinger)
latents <- c("F1","F2","F3")
#specify model, mxData not necessary but useful to check if mxRun works
model <- mxModel("Three Factor",
      type="RAM",
      manifestVars = manifests,
      latentVars = latents,
      mxPath(from="F1", to=manifests[1:3]),
	     mxPath(from="F2", to=manifests[4:6]),
	     mxPath(from="F3", to=manifests[7:9]),
      mxPath(from=manifests, arrows=2),
      mxPath(from=latents, arrows=2,
            free=FALSE, values=1.0),
      mxData(cov(holzinger), type="cov", numObs=nrow(holzinger))
	  )

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