faoutlier (version 0.2.2)

gCD: Generalized Cook's Distance

Description

Compute generalize Cook's distances (gCD's) for exploratory and confirmatory FA. Can return DFBETA matrix if requested.

Usage

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

  ## S3 method for class 'gCD':
print(x, head = 0.05, DFBETAS = FALSE,
    ...)

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

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 gCD

head

a ratio of how many extreme gCD cases to display

DFBETAS

logical; attach DFBETA matrix attribute to returned result?

...

additional parameters to be passed

y

a NULL value ignored by the plotting function

main

the main title of the plot

type

type of plot to use, default displayes points and lines

ylab

the y label of the plot

Details

Note that gCD 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

LD, obs.resid, robustMD

Examples

data(holzinger)
data(holzinger.outlier)

#Exploratory
nfact <- 3
(gCDresult <- gCD(holzinger, nfact))
(gCDresult.outlier <- gCD(holzinger.outlier, nfact))
plot(gCDresult)
plot(gCDresult.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

(gCDresult2 <- gCD(holzinger, model))
(gCDresult2.outlier <- gCD(holzinger.outlier, model))
plot(gCDresult2)
plot(gCDresult2.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))
	  )

(gCDresult2 <- gCD(holzinger, model))
(gCDresult2.outlier <- gCD(holzinger.outlier, model))
plot(gCDresult2)
plot(gCDresult2.outlier)