PlotLmSpikeFit

Plot actual values vs. predictions in an lm.spike model.

models

regression

plot.lm.spike.fit

Spike and slab regression with a variety of residual error
distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
few others. Spike and slab regression is Bayesian regression with prior
distributions containing a point mass at zero. The posterior updates the
amount of mass on this point, leading to a posterior distribution that is
actually sparse, in the sense that if you sample from it many coefficients are
actually zeros. Sampling from this posterior distribution is an elegant way
to handle Bayesian variable selection and model averaging. See
<DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.

Steven Scott

BoomSpikeSlab

MCMC for Spike and Slab Regression

plot.lm.spike.fit function

<dl>
 <dt>object</dt>
<dd>A model object inheriting from <code>lm.spike</code>.</dd>
 <dt>burn</dt>
<dd>The number of MCMC iterations to be discarded as burn-in
 before computing posterior means.</dd>
 <dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/plot?package=BoomSpikeSlab&version=1.2.6" data-mini-rdoc="BoomSpikeSlab::plot">plot</a>.</code></dd>
</dl>

Arguments

Author

Predicted vs actual plot for lm.spike. — plot.lm.spike.fit

<dl>


 <dt>object</dt>
<dd>A model object inheriting from <code>lm.spike</code>.</dd>


 <dt>burn</dt>
<dd>The number of MCMC iterations to be discarded as burn-in
 before computing posterior means.</dd>


 <dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a>.</code></dd>


</dl>

plot.lm.spike.fit: Predicted vs actual plot for lm.spike.

Description

Usage

Arguments

Author

Details

See Also