predictorModelSamplingReusables: Create reusable part of prediction for repeated sampling

In EMLasso: Fit (logistic) LASSO when there is missing data in the predictors

Description

Create reusable part of prediction for repeated sampling

Usage

  predictorModelSamplingReusables(predictorModel,
    iterCount, previousReusables = NULL, ds, verbosity = 0)

  ## S3 method for class 'GLoMo'
predictorModelSamplingReusables(predictorModel,
    iterCount, previousReusables=NULL, ds, verbosity=0)

Arguments

predictorModel	predictor model (e.g. GLoMo)
iterCount	for the how manieth iteration is this the reusable part
previousReusables	result of this function for the previous iteration or NULL if there was none
ds	dataset for which prediction needs to occur
verbosity	The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output)

Value

for the generic method: depending on the implementation.

(in the GLoMo implementation) an object of class "SamplingReusablesGLoMo", i.e. a list with items:

1. guidDataOfOriginalDfr: return value of getGuidData

2. uidsPerRowOfOriginalDfr: list holding for each row in ds the applicable uids in the GLoMo

Author(s)

Nick Sabbe [e-mail]

See Also

EMLasso.1l

EMLasso documentation built on May 2, 2019, 5:49 p.m.