Description Usage Arguments Value Note Author(s) References See Also Examples
Calculate GLoMo data that is usable for repeated 'completing' of the same data row.
1  reusableDataForGLoMoSampling(glomo, dfr, forrows = seq(nrow(dfr)), guiddata = NULL, verbosity = 0)

glomo 

dfr 

forrows 
which of the rows in 
guiddata 
"GuidData" class object for the rows in question, or character vector of uids
for those rows, or 
verbosity 
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) 
A list of class "ReusableDataForGLoMoSampling", holding items:
guiddata 
like to the return value of 
forrows 
copy of the 
perrow 
list that holds one item of class "ReusableDataForGLoMoSamplingForOneRow" per element of 
The structure of lists of class "ReusableDataForGLoMoSamplingForOneRow" is like this:
a 
See "Analysis of Incomplete Multivariate Data" around p349 
useSigma 
covariance for missing continuous values of that row 
sigLeft 
See "Analysis of Incomplete Multivariate Data" around p349 
probs 
conditional probabilities of each 'cell' (see the members of 
whichCntColNotNA 
within the continuous columns, the how manieth of them was not NA for this row 
whichCntColNA 
within the continuous columns, the how manieth of them was NA for this row 
presentCntColsInDfr 
column indices within 
missingCntColsInDfr 
column indices within 
This method is mainly present because of predict.conditional.GLoMo
,
where there is repeated sampling from the same row.
Nick Sabbe ([email protected])
"Analysis of Incomplete Multivariate Data" around p349, and also "Statistical Analysis with Missing Values"
GLoMopackage
, NumDfr
, predict.conditional.GLoMo
1 2 3 4 5 6 7 8 9  iris.md<randomNA(iris, 0.1)
iris.md.nd<numdfr(iris.md)
mdrow<min(which(apply(iris.md, 1, function(currow){any(is.na(currow))})))
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)
reusableDataForGLoMoSampling(glomo=iris.glomo, dfr=iris.md.nd, forrows = mdrow,
verbosity = 1)

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