rmono

Randomly impose a monotone missingness pattern by replacing the ends
 of each column of the input matrix by a random number of <code>NA</code>s

 datagen 

Estimation of multivariate normal (MVN) and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
See Pantaleo and Gramacy (2010) <doi:10.48550/arXiv.0907.2135>.
Through the use of parsimonious/shrinkage regressions
(plsr, pcr, lasso, ridge, etc.), where standard regressions fail,
the package can handle a nearly arbitrary amount of missing data.
The current version supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for Gibbs sampling.
Monotone data augmentation extends this Bayesian approach to arbitrary
missingness patterns. A fully functional standalone interface to the
Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
Horseshoe (from Carvalho, Polson, & Scott), and ridge regression
with model selection via Reversible Jump, and student-t errors
(from Geweke) is also provided.

Robert Gramacy

monomvn

Estimation for MVN and Student-t Data with Monotone Missingness

rmono function

<dl> <dt>x</dt>
<dd>data <code>matrix</code></dd> <dt>m</dt>
<dd>minimum number of non-<code>NA</code> entries in each column</dd> <dt>ab</dt>
<dd>a two-vector of \(\alpha\) (<code>ab[1]</code>) and
 \(\beta\) (<code>ab[2]</code>) parameters to a
 Beta\((\alpha, \beta)\) distribution
 describing the proportion of <code>NA</code> entries in each column.
 The default setting <code>ab = NULL</code> yields a uniform distribution</dd></dl>

Arguments

Robert B. Gramacy <a href="/link/rbg%40vt.edu?package=monomvn&version=1.9-21" data-mini-rdoc="monomvn::rbg@vt.edu">rbg@vt.edu</a>

Author

Randomly Impose a Monotone Missingness Pattern — rmono

<dl>

 <dt>x</dt>
<dd>data <code>matrix</code></dd>

 <dt>m</dt>
<dd>minimum number of non-<code>NA</code> entries in each column</dd>

 <dt>ab</dt>
<dd>a two-vector of \(\alpha\) (<code>ab[1]</code>) and
 \(\beta\) (<code>ab[2]</code>) parameters to a
 Beta\((\alpha, \beta)\) distribution
 describing the proportion of <code>NA</code> entries in each column.
 The default setting <code>ab = NULL</code> yields a uniform distribution</dd>

</dl>

Robert B. Gramacy <a href='mailto:rbg@vt.edu'>rbg@vt.edu</a>

rmono: Randomly Impose a Monotone Missingness Pattern

Description

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