Description Usage Arguments Details Value Author(s) See Also Examples
(Multiply) complete dataset based on marginal properties of each column
1 2 | rCatsAndCntInDfr(dfr, maxFullNACatCols = 6, howManyIfTooMany = 1000, weightsName = "weights", orgriName = "orgri", reweightPerRow = FALSE, verbosity = 0, ...)
rCatsInDfr(dfr, maxFullNACatCols=6, howManyIfTooMany=1000, onlyCategorical=FALSE, weightsName="weights", orgriName="orgri", reweightPerRow=FALSE, verbosity=0,...)
|
dfr |
|
maxFullNACatCols, howManyIfTooMany |
If a row from |
onlyCategorical |
if |
weightsName |
if not |
orgriName |
if not |
reweightPerRow |
If weights are returned, then for rows having more than |
verbosity |
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) |
... |
Ignored for now |
The 'random subset' is created by drawing the missing categorical values based
on their marginal probability in dfr
.
The continuous missing data is simply filled out with the mean.
Object of the same class as dfr
. Dependent on onlyCategorical
, it
may only contain the categorical columns. For the rest it mainly has the same
structure as dfr
, though it may contain two extra columns based on
weightsName
and orgriName
.
Nick Sabbe (nick.sabbe@ugent.be)
1 2 3 | iris.md<-randomNA(iris, 0.1)
iris.md.nd<-numdfr(iris.md)
iris.nd.rnd<-rCatsAndCntInDfr(iris.md.nd, orgriName=NULL, verbosity=1)
|
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