plotCoefConvergence: Plot coefficient evolution of EMLasso.lognet objects

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

Description

Plot coefficient evolution of EMLasso.lognet objects over iterations

Usage

  plotCoefConvergence(cofs, skipForNZ = 5,
    zeroThres = 1e-04, colors = neatColorSet(),
    maxleg = 10, leg.cex = 0.5, verbosity = 0, ...)

Arguments

cofs	matrix (or (sparse) Matrix) of coefficients: rows=iterations, cols=variables
skipForNZ	when checking if a coefficient is ever nonzero, the first skipForNZ (run-in) iterations are skipped
zeroThres	how high does a value have to be (in absolute value) to be nonzero.
colors	colors used for the matplot
maxleg	show legend for (at most) the first maxleg variables
leg.cex	cex passed to legend
...	passed on to matplot
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

nothing

Author(s)

Nick Sabbe [e-mail]

Examples

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,
	lambdas=c(0.03,0.002,0.0003), nrOfSamplesPerMDRow=7, verbosity=2,
	convergenceChecker=convergenceCheckCreator(minIt=5, maxIt=10))
sfStop()
plotCoefConvergence(iris.emlognet$result[[1]]$coefs, lty=1, xlab="iteration", ylab="coefficient")

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