summary_spm_fit

Provides a <code>data.frame</code> with point estimates and
confidence intervals for the parameters of the model fitted using the
<code>spm_fit</code> function.

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

summary_spm_fit function

<dl><dt>x</dt>
<dd>a <code>spm_fit</code> object.</dd>
<dt>sig</dt>
<dd>a real number between 0 and 1 indicating significance level to be
used to compute the confidence intervals for the parameter estimates.</dd></dl>

Arguments

Summarizing spm_fit — summary_spm_fit

<dl>

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


<dt>sig</dt>
<dd>a real number between 0 and 1 indicating significance level to be
used to compute the confidence intervals for the parameter estimates.</dd>

</dl>

summary_spm_fit: Summarizing `spm_fit`

Description

Usage

Value

Arguments