predict.tensor.reg

Prediction from tensor regression (S3 method)

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

predict.tensor.reg function

<dl><dt>object</dt>
<dd>tensor.reg object</dd>
<dt>x.new</dt>
<dd>new tensor predictors (n x p x d)</dd>
<dt>z.new</dt>
<dd>new scalar covariates (n x pgamma)</dd>
<dt>scale</dt>
<dd>Logical; whether to scale predictors</dd>
<dt>...</dt>
<dd>additional arguments</dd></dl>

Arguments

Prediction from tensor regression (S3 method) — predict.tensor.reg

<dl>

<dt>object</dt>
<dd>tensor.reg object</dd>


<dt>x.new</dt>
<dd>new tensor predictors (n x p x d)</dd>


<dt>z.new</dt>
<dd>new scalar covariates (n x pgamma)</dd>


<dt>scale</dt>
<dd>Logical; whether to scale predictors</dd>


<dt>...</dt>
<dd>additional arguments</dd>

</dl>

predict.tensor.reg: Prediction from tensor regression (S3 method)

Description

Usage

Value

Arguments