predict_ordinal

Predict ordinal-valued tensor entries given latent parameters and a type of estimations.

Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.

Chanwoo Lee

TensorComplete

Tensor Noise Reduction and Completion Methods

predict_ordinal function

<dl><dt>theta</dt>
<dd>A continuous-valued tensor (latent parameters).</dd>
<dt>omega</dt>
<dd>The cut-off points.</dd>
<dt>type</dt>
<dd>Type of estimations:
<code>"mode"</code> specifies argmax based label estimation.
<code>"mean"</code> specifies mean based label estimation.
<code>"median"</code> specifies median based label estimation.</dd></dl>

Arguments

Predict ordinal-valued tensor entries from the cumulative logistic model. — predict_ordinal

<dl>

<dt>theta</dt>
<dd>A continuous-valued tensor (latent parameters).</dd>


<dt>omega</dt>
<dd>The cut-off points.</dd>


<dt>type</dt>
<dd>Type of estimations:
<code>"mode"</code> specifies argmax based label estimation.
<code>"mean"</code> specifies mean based label estimation.
<code>"median"</code> specifies median based label estimation.</dd>

</dl>

predict_ordinal: Predict ordinal-valued tensor entries from the cumulative logistic model.

Description

Usage

Value

Arguments

References

Examples