goodness_of_fit

AIC.spm_fit

BIC.spm_fit

Akaike's (and Bayesian) An Information Criterion for <code>spm_fit</code>
objects.

Provides functions to estimate, predict and interpolate areal
data. For estimation and prediction we assume areal data is an average
of an underlying continuous spatial process as in Moraga et
al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
<doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
<doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
(mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Lucas da Cunha Godoy

smile

Spatial Misalignment: Interpolation, Linkage, and Estimation

goodness_of_fit function

<dl><dt>object</dt>
<dd>a <code>spm_fit</code> object.</dd>
<dt>...</dt>
<dd>optionally more fitted model objects.</dd>
<dt>k</dt>
<dd><code>numeric</code>, the penalty per parameter to be used; the default
'k = 2' is the classical AIC. (for compatibility with <code><a href="/link/stats%3A%3AAIC?package=smile&version=1.1.0" data-mini-rdoc="smile::stats::AIC">stats::AIC</a></code>.</dd></dl>

Arguments

Akaike's (and Bayesian) An Information Criterion for spm_fit
objects. — goodness_of_fit

<dl>

<dt>object</dt>
<dd>a <code>spm_fit</code> object.</dd>


<dt>...</dt>
<dd>optionally more fitted model objects.</dd>


<dt>k</dt>
<dd><code>numeric</code>, the penalty per parameter to be used; the default
'k = 2' is the classical AIC. (for compatibility with <code><a href='https://rdrr.io/r/stats/AIC.html'>stats::AIC</a></code>.</dd>

</dl>

goodness_of_fit: Akaike's (and Bayesian) An Information Criterion for `spm_fit` objects.

Description

Usage

Value

Arguments