LazyData
.msaenet.sim.gaussian()
.penalty.factor.init
to support customized
penalty factor applied to each coefficient in the initial estimation step.
This is useful for incorporating prior information about variable weights,
for example, emphasizing specific clinical variables.
We thank Xin Wang from University of Michigan for this feedback (#4).type = "dotplot"
in plot.msaenet()
.
This plot offers a direct visualization of the model coefficients
at the optimal step.init = "ridge"
.lower.limits
and upper.limits
to support coefficient
constraints in aenet()
and msaenet()
(#1).README.md
.plot.msaenet()
for extra flexibility:
it is now possible to set important properties of the label appearance
such as position, offset, font size, and axis titles via the new arguments
label.pos
, label.offset
, label.cex
, xlab
, and ylab
.init = "ridge"
,
by using the ridge estimation implementation from glmnet
.
As a benefit, we now have a more aligned baseline for the comparison
between elastic-net based models and MCP-net/SCAD-net based models when
init = "ridge"
.tune
and tune.nsteps
to controls this for
selecting the optimal model for each step, and the optimal model
among all steps (i.e. the optimal step).ebic.gamma
and ebic.gamma.nsteps
to control the
EBIC tuning parameter, if ebic
is specified by tune
or tune.nsteps
.?plot.msaenet
for details.gamma
(scaling factor for adaptive weights)
to scale
to avoid possible confusion.gammas
to be
3.7 for SCAD-net and 3 for MCP-net.family
in all model types to be
"gaussian"
, "binomial"
, "poisson"
, and "cox"
.msaenet.sim.binomial()
, msaenet.sim.poisson()
,
msaenet.sim.cox()
to generate simulation data for logistic, Poisson,
and Cox regression models.msaenet.fn()
for computing the number of false negative
selections in msaenet models.msaenet.mse()
for computing mean squared error (MSE).msaenet.sim.gaussian()
by more vectorization
when generating correlation matrices.max.iter
and epsilon
for MCP-net and SCAD-net
related functions to have finer control over convergence criterion.
By default, max.iter = 10000
and epsilon = 1e-4
.amnet()
to support adaptive MCP-net.asnet()
to support adaptive SCAD-net.msamnet()
to support multi-step adaptive MCP-net.msasnet()
to support for multi-step adaptive SCAD-net.msaenet.nzv.all()
for displaying the indices of non-zero variables
in all adaptive estimation steps.predict.msaenet
method allowing users to specify prediction type.coef
for extracting model coefficients.
See ?coef.msaenet
for details.Add the following code to your website.
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