GLoMo-package: Naive General Location Model

In GLoMo: Naive General Location Model

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: <[e-mail]>

Examples

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 2, 2019, 5:26 p.m.