Description Usage Arguments Details Author(s) References See Also Examples

View source: R/forward.search.R

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.

1 2 3 4 5 6 7 8 9 | ```
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", ...)
``` |

`data` |
matrix or data.frame |

`model` |
if a single numeric number declares number of factors to extract in
exploratory factor analysis. If |

`criteria` |
character strings indicating the forward search method
Can contain |

`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 |

`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 |

`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 |

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.

Phil Chalmers [email protected]

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

`gCD`

, `GOF`

, `LD`

,
`robustMD`

, `setCluster`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ```
## 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)
``` |

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