qMSE

Computing the q-quantile relative squared error as a generalised error measure on relative mean squared error

To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base 'array' class to some data classes, this package serves as an alternative resort to handle tensor only as 'array' class.
Some functionalities related to missingness are also supported.

Zetai Cen

tensorMiss

Handle Missing Tensor Data with C++ Integration

qMSE function

<dl><dt>x_true</dt>
<dd>True values, written in a vector of length n.</dd>
<dt>x_est</dt>
<dd>Imputed or estimated values, written in a vector of length n.</dd>
<dt>q</dt>
<dd>Number of partition intervals. If q equals n, then output is essentially relative mean squared error. Default is 100.</dd></dl>

Arguments

Quantile relative squared error — qMSE

<dl>

<dt>x_true</dt>
<dd>True values, written in a vector of length n.</dd>


<dt>x_est</dt>
<dd>Imputed or estimated values, written in a vector of length n.</dd>


<dt>q</dt>
<dd>Number of partition intervals. If q equals n, then output is essentially relative mean squared error. Default is 100.</dd>

</dl>

qMSE: Quantile relative squared error

Description

Usage

Value

Arguments

Examples