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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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