predict.semiparMF

Generates predictions for new data.
Reference: "Predicted values are calculated by simply adding up the scores... and the linear combination".

Fits a semiparametric spatiotemporal model for data with mixed frequencies,
specifically where the response variable is observed at a lower frequency than some covariates.
The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline
for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects,
and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022)
<https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.

Jezzu Morrisen Quimosing

SemiparMF

Semiparametric Spatiotemporal Model with Mixed Frequencies

predict.semiparMF function

<dl><dt>object</dt>
<dd>An object of class <code>semiparMF</code>.</dd>
<dt>new_high_freq</dt>
<dd>Numeric array (N x T_new x K) for the high-frequency covariate.</dd>
<dt>new_z</dt>
<dd>Numeric matrix (N x T_new) for the parametric covariate.</dd>
<dt>new_w</dt>
<dd>Numeric matrix (N x T_new) for the neighborhood covariate.</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Predict Method for SemiparMF — predict.semiparMF

<dl>

<dt>object</dt>
<dd>An object of class <code>semiparMF</code>.</dd>


<dt>new_high_freq</dt>
<dd>Numeric array (N x T_new x K) for the high-frequency covariate.</dd>


<dt>new_z</dt>
<dd>Numeric matrix (N x T_new) for the parametric covariate.</dd>


<dt>new_w</dt>
<dd>Numeric matrix (N x T_new) for the neighborhood covariate.</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

predict.semiparMF: Predict Method for SemiparMF

Description

Usage

Value

Arguments