R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Forward search algorithm for outlier detection

forward.search

R Documentation

Forward search algorithm for outlier detection

Description

The forward search algorithm begins by selecting a homogeneous subset of cases based on a maximum likelihood criteria and continues to add individual cases at each iteration given an acceptance criteria. By default the function will add cases that contribute most to the likelihood function and that have the closest robust Mahalanobis distance, however model implied residuals may be included as well.

Usage

forward.search(data, model, criteria = c("GOF", "mah"), n.subsets = 1000,
  p.base = 0.4, print.messages = TRUE, ...)

## S3 method for class 'forward.search'
print(x, ncases = 10, stat = "GOF", ...)

## S3 method for class 'forward.search'
plot(x, y = NULL, stat = "GOF",
  main = "Forward Search", type = c("p", "h"), ylab = "obs.resid", ...)

Arguments

`data`	matrix or data.frame
`model`	if a single numeric number declares number of factors to extract in exploratory factor analysis. If `class(model)` is a sem (semmod), or lavaan (character), then a confirmatory approach is performed instead
`criteria`	character strings indicating the forward search method Can contain `'GOF'` for goodness of fit distance, `'mah'` for Mahalanobis distance, or `'res'` for model implied residuals
`n.subsets`	a scalar indicating how many samples to draw to find a homogeneous starting base group
`p.base`	proportion of sample size to use as the base group
`print.messages`	logical; print how many iterations are remaining?
`...`	additional parameters to be passed
`x`	an object of class `forward.search`
`ncases`	number of final cases to print in the sequence
`stat`	type of statistic to use. Could be 'GOF', 'RMR', or 'gCD' for the model chi squared value, root mean square residual, or generalized Cook's distance, respectively
`y`	a `null` value ignored by `plot`
`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

Details

Note that forward.search 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 rphilip.chalmers@gmail.com

Examples


## Not run: 

#run all internal gCD and GOF functions using multiple cores
setCluster()

#Exploratory
nfact <- 3
(FS <- forward.search(holzinger, nfact))
(FS.outlier <- forward.search(holzinger.outlier, nfact))
plot(FS)
plot(FS.outlier)

#Confirmatory with sem
model <- sem::specifyModel()
  F1 -> Remndrs,    lam11
	  F1 -> SntComp,    lam21
	  F1 -> WrdMean,    lam31
	  F2 -> MissNum,    lam41
	  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


(FS <- forward.search(holzinger, model))
(FS.outlier <- forward.search(holzinger.outlier, model))
plot(FS)
plot(FS.outlier)

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

(FS <- forward.search(holzinger, model))
(FS.outlier <- forward.search(holzinger.outlier, model))
plot(FS)
plot(FS.outlier)



## End(Not run)