LD
(Package: faoutlier) :
Likelihood Distance
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.
● Data Source:
CranContrib
● Keywords: cooks
● Alias: LD, plot.LD, print.LD
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Obtain Mahalanobis distances using the robust computing methods found in the MASS package. This function is generally only applicable to models with continuous variables.
● Data Source:
CranContrib
● Keywords: covariance
● Alias: plot.robmah, print.robmah, robustMD
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obs.resid
(Package: faoutlier) :
Model predicted residual outliers
Compute model predicted residuals for each variable using regression estimated factor scores.
● Data Source:
CranContrib
● Keywords: covariance
● Alias: obs.resid, plot.obs.resid, print.obs.resid
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faoutlier
(Package: faoutlier) :
Influential case detection methods for FA and SEM
Influential case detection methods for factor analysis and SEM
● Data Source:
CranContrib
● Keywords: package
● Alias: faoutlier, faoutlier-package
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gCD
(Package: faoutlier) :
Generalized Cook's Distance
Compute generalize Cook's distances (gCD's) for exploratory and confirmatory FA. Can return DFBETA matrix if requested. If mirt is used, then the values will be associated with the unique response patterns instead.
● Data Source:
CranContrib
● Keywords: cooks
● Alias: gCD, plot.gCD, print.gCD
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GOF
(Package: faoutlier) :
Goodness of Fit Distance
Compute Goodness of Fit distances between models when removing the i_{th} case. If mirt is used, then the values will be associated with the unique response patterns instead.
● Data Source:
CranContrib
● Keywords: cooks
● Alias: GOF, plot.GOF, print.GOF
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forward.search
(Package: faoutlier) :
Forward search algorithm for outlier detection
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.
● Data Source:
CranContrib
● Keywords: forward.search
● Alias: forward.search, plot.forward.search, print.forward.search
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setCluster
(Package: faoutlier) :
Define a parallel cluster object to be used in internal functions
This function defines a object that is placed in a relevant internal environment defined in faoutlier. Internal functions will utilize this object automatically to capitalize on parallel processing architecture. The object defined is a call from parallel::makeCluster(). Note that if you are defining other parallel objects (for simulation desings, for example) it is not recommended to define a cluster.
● Data Source:
CranContrib
● Keywords: parallel
● Alias: setCluster
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holzinger
(Package: faoutlier) :
Description of holzinger data
A sample of 100 simulated cases from the infamous Holzinger dataset using 9 variables.
● Data Source:
CranContrib
● Keywords: data
● Alias: holzinger
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holzinger.outlier
(Package: faoutlier) :
Description of holzinger data with 1 outlier
A sample of 100 simulated cases from the infamous Holzinger dataset using 9 variables, but with 1 outlier added to the dataset. The first row was replaced by adding 2 to five of the observed variables (odd-numbered items) and subtracting 2 from the other four observed variables (even-numbered items).
● Data Source:
CranContrib
● Keywords: data
● Alias: holzinger.outlier
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