predict_tensor_reg

Predict tensor regression (wrapper)

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>fit</dt>
<dd>tensor.reg object</dd>
<dt>x.new</dt>
<dd>new tensor predictors</dd>
<dt>z.new</dt>
<dd>new scalar covariates</dd>
<dt>scale</dt>
<dd>Logical; whether to scale predictors</dd></dl>

Arguments

Predict tensor regression (wrapper) — predict_tensor_reg

<dl>

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


<dt>x.new</dt>
<dd>new tensor predictors</dd>


<dt>z.new</dt>
<dd>new scalar covariates</dd>


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

</dl>

predict_tensor_reg: Predict tensor regression (wrapper)

Description

Usage

Value

Arguments