Description
Usage
Arguments
Value
See Also
View source: R/zzz.R
Additional options that control the estimation algorithm in tmlenet
package
 (useglm = , parfit = ,
bin.method = ("equal.len", "equal.mass", "dhist"), nbins = ,
maxncats = 20, poolContinVar = , maxNperBin = 1000)

useglm 
Set to FALSE to estimate with speedglm.wfit and TRUE for
glm.fit .

parfit 
Default is FALSE . Set to TRUE to use foreach package and its functions
foreach and dopar to perform
parallel logistic regression fits and predictions for discretized continuous outcomes. This functionality
requires registering a parallel backend prior to running tmlenet function, e.g.,
using doParallel R package and running registerDoParallel(cores = ncores) for integer
ncores parallel jobs. For an example, see a test in "./tests/RUnit/RUnit_tests_04_netcont_sA_tests.R".

bin.method 
The method for choosing bins when discretizing and fitting the conditional continuous summary
exposure variable sA . The default method is "equal.len" , which partitions the range of sA
into equal length nbins intervals. Method "equal.mass" results in a dataadaptive selection of the bins
based on equal mass (equal number of observations), i.e., each bin is defined so that it contains an approximately
the same number of observations across all bins. The maximum number of observations in each bin is controlled
by parameter maxNperBin . Method "dhist" uses a mix of the above two approaches,
see Denby and Mallows "Variations on the Histogram" (2009) for more detail.

nbins 
Set the default number of bins when discretizing a continous outcome variable under setting
bin.method = "equal.len" .
If left as NA the total number of equal intervals (bins) is determined by the nearest integer of
nobs /maxNperBin , where nobs is the total number of observations in the input data.

maxncats 
Max number of unique categories a categorical variable sA[j] can have.
If sA[j] has more it is automatically considered continuous.

poolContinVar 
Set to TRUE for fitting a pooled regression which pools bin indicators across all bins.
When fitting a model for binirized continuous outcome, set to TRUE
for pooling bin indicators across several bins into one outcome regression?

maxNperBin 
Max number of observations per 1 bin for a continuous outcome (applies directly when
bin.method="equal.mass" and indirectly when bin.method="equal.len" , but nbins = NA ).

Invisibly returns a list with old option settings.
print_tmlenet_opts
tmlenet documentation built on May 29, 2017, 2:22 p.m.