getcmat

This function returns a matrix where the rows correspond to each possible
point process specification in p-dimensional covariate space; this may be
helpful in specifying the argument <code>settozero</code> in the functions <code>monoreg</code> and <code>monosurv</code>.

An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <DOI:10.1214/22-BA1310>.

Olli Saarela

monoreg

Bayesian Monotonic Regression Using a Marked Point Process
Construction

getcmat function

<dl><dt>p</dt>
<dd>Number of covariate axes.</dd></dl>

Arguments

Olli Saarela &lt;olli.saarela@utoronto.ca&gt;

Author

A matrix of point process configurations in p-dimensional covariate space — getcmat

<dl>

<dt>p</dt>
<dd>Number of covariate axes.</dd>

</dl>

getcmat: A matrix of point process configurations in p-dimensional covariate space

Description

Usage

Value

Arguments

Author