Description Usage Arguments Value Note Author(s) See Also Examples
Fit EMLasso to a complete dataset, creating a GLoMo for each lambda
1 2 3 4 5 6 7 | EMLasso(ds, out,
family = c("gaussian", "binomial", "poisson", "multinomial", "cox"),
lambdas = smartLambdaRetriever(),
imputeDs2FitDsProperties = normalImputationConversion(),
..., verbosity = 0, logdir = "./",
saveTempResults = TRUE,
postProcessAll = postProcessEMLasso)
|
ds |
dataset with predictors |
out |
vector (binary factor) of outcomes |
family |
see |
lambdas |
see |
imputeDs2FitDsProperties |
see
|
... |
passed on to |
verbosity |
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) |
logdir |
path to folder where logfiles (and results) of each repeat are stored |
saveTempResults |
if |
postProcessAll |
function, like
|
an object of class "EMLasso", "glmnet" and some other
classes based on the class of the outcome model. The
items added to the glmnet
members are:
result |
list of |
params |
|
logdir |
directory where logging/saving occurred |
combinedGLoMo
|
|
imputeDs2FitDsProperties |
as passed in, but first
fed to |
family |
as passed in |
If lambdas is not passed along or is NULL
, a set
of lambdas is used by utilizing
findReasonableLambdaHelper
Nick Sabbe nick.sabbe@ugent.be
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | y<-rbinom(nrow(iris), 1, 0.5)
require(addendum)
require(NumDfr)
require(GLoMo)
require(snowfall)
require(EMLasso)
sfInit(parallel = FALSE, cpus = 1)
sfLibrary(addendum)
sfLibrary(NumDfr)
sfLibrary(GLoMo)
sfLibrary(EMLasso)
iris.cpy<-randomNA(iris, n=0.1)
iris.emlognet<-EMLasso(ds=numdfr(iris.cpy), out=y, family="binomial",
lambdas=c(0.03,0.002,0.0003), nrOfSamplesPerMDRow=7, verbosity=2,
convergenceChecker=convergenceCheckCreator(minIt=5, maxIt=10))
sfStop()
|
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