getmean

posterior mean for tensor regression

Provides methods for low-rank tensor regression with tensor-valued predictors
and scalar covariates. Model estimation is performed using stochastic optimization
with random-walk updates for low-rank factor matrices. Computationally intensive
components for coefficient estimation and prediction are implemented in C++ via
'Rcpp'. The package also includes tools for cross-validation and prediction error
assessment.

Ritwick Mondal

TensorMCMC

Tensor Regression with Stochastic Low-Rank Updates

getmean function

<dl><dt>X</dt>
<dd>Input tensor.</dd>
<dt>beta</dt>
<dd>Estimated coefficient tensor.</dd>
<dt>rank</dt>
<dd>Rank used in tensor decomposition.</dd>
<dt>rank.exclude</dt>
<dd>Optional rank values to exclude.</dd></dl>

Arguments

posterior mean for tensor regression — getmean

<dl>

<dt>X</dt>
<dd>Input tensor.</dd>


<dt>beta</dt>
<dd>Estimated coefficient tensor.</dd>


<dt>rank</dt>
<dd>Rank used in tensor decomposition.</dd>


<dt>rank.exclude</dt>
<dd>Optional rank values to exclude.</dd>

</dl>

getmean: posterior mean for tensor regression

Description

Usage

Value

Arguments