Description Usage Arguments Details Value Note Author(s) See Also Examples
The method predict.GLoMo
can sample to fill out missing values in
a dataset. There, this happens with only the GLoMo in mind. This function allows
to provide an extra function that might reject sampled data based on other
criteria. The non-allrows version does so for 1 row at a time.
1 2 3 4 5 6 7 | ## S3 method for class 'allrows.GLoMo'
predict.conditional(object, nobs = 1, dfr, forrows = seq(nrow(dfr)), validateFunction = validateFunction.default, guiddata = NULL, otherData = NULL, initialSuccessRateGuess = 0.5, verbosity = 0, minimumSuccessRate=0.001,...)
## S3 method for class 'GLoMo'
predict.conditional(object, nobs=1, dfr, forrows, validateFunction=validateFunction.default, guiddata=NULL, otherData=NULL, initialSuccessRateGuess=0.5, verbosity=0, minimumSuccessRate=0.001,...)
validateFunction.acceptall(attempts, otherData, forrow, verbosity = 0)
validateFunction.useprob(attempts, otherData, forrow, verbosity = 0)
validateFunction.default(attempts, otherData, forrow, verbosity = 0)
|
object |
|
nobs |
number of observations to sample. Can be a single integer or (for
|
dfr |
|
forrows, forrow |
Which of the row(s) from |
validateFunction |
After the standard sampling of |
guiddata |
see |
otherData |
Passed on to |
initialSuccessRateGuess |
Used to sample too many rows with |
verbosity |
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) |
minimumSuccessRate |
To prevent conditional prediction to run 'forever' because all observations are simply unlikely, you can pass along a minimum success rate (between 0 and 1): if the attained success rate goes below this, one more attempt is done, and, if need be, predictions are accepted randomly to get enough of them. |
... |
Ignored for now |
attempts |
|
This function is mostly provided with the MCMC of EMLasso in mind (i.e. reject
based on a glmnet fit and matching true outcomes for each row in
dfr
.
Typically, other validateFunction
s will have to be created for this to
make sense. It is then up to the creator/user to make sure otherData
is
consistent with what this specific validateFunction
expects.
The signature of a validateFunction
can be easily spied from
validateFunction.default
(and is not repeated here to avoid maintenance
issues).
Specifically, validateFunction.acceptall
accepts all rows,
validateFunction.useprob
expects a passed along probability per row in
otherData
and rejects with this probability, while
validateFunction.default
does the same, but always with probability 0.5.
List with two items
predicted |
|
glomorowsused |
vector that holds 1 item per row in |
The non-allrows version works only for 1 row at a time.
Nick Sabbe (nick.sabbe@ugent.be)
GLoMo-package
, NumDfr
, predict
1 2 3 4 5 6 7 | 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.pred.cond<-predict.conditional.allrows.GLoMo(iris.glomo, nobs=5, dfr=iris.md.nd, verbosity=10)
|
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