GLoMo-package: Naive General Location Model

Description Details Author(s) Examples

Description

General Location Model as e.g. found in "Statistical Analysis with Missing Data". This version can be fit to a data.frame or numdfr without missing data, and supports prediction of new data and 'completion prediction' of data with missing values. It also supports conditional completion.

Details

Package: GLoMo
Type: Package
Version: 1.7.4
Date: 2013-04-17
License: GNU
LazyLoad: yes

Main class: GLoMo-class

  1. Helpers

    1. getGuidData

    2. updateGuidData

    3. randomFillAndRepeatDataRow

    4. rCatsAndCntInDfr

    5. rCatsInDfr

    6. reusableDataForGLoMoSampling

    7. combineGLoMos

  2. Fitting

    1. GLoMo

  3. Prediction

    1. predict.GLoMo

    2. predict.conditional.allrows.GLoMo

    3. predict.conditional.GLoMo

Author(s)

Nick Sabbe

Maintainer: <[email protected]>

Examples

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iris.md<-randomNA(iris, 0.1)
iris.md.nd<-numdfr(iris.md)
iris.nd.rnd<-rCatsAndCntInDfr(iris.md.nd, orgriName=NULL, verbosity=1)
iris.weights<-iris.nd.rnd$weights
iris.nd.rnd<-iris.nd.rnd[,1:5]
iris.glomo<-GLoMo(iris.nd.rnd, weights=iris.weights, verbosity=1)
iris.nsamplesperrow<-sample.int(10, size=nrow(iris.md.nd), replace=TRUE)
iris.pred<-predict(iris.glomo,nobs=iris.nsamplesperrow, newdata=iris.md.nd,
	returnRepeats=TRUE, returnSelectedGlomoRows=TRUE, verbosity=10)

GLoMo documentation built on May 31, 2017, 3:35 a.m.