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@edzer
edzer committed Nov 13, 2024
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8 changes: 0 additions & 8 deletions docs/day2.html
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<p>Correction for <code>edge</code> effect?</p>
<div class="cell">
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/density.html">density</a></span><span class="op">(</span><span class="va">CSR</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co"># Warning in seq.default(xrange[1L], xrange[2L], length = n + 1L):</span></span>
<span><span class="co"># partial argument match of 'length' to 'length.out'</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">CSR</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, col <span class="op">=</span> <span class="st">'green'</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
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<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/density.html">density</a></span><span class="op">(</span><span class="va">ppi</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co"># Warning in seq.default(xrange[1L], xrange[2L], length = n + 1L):</span></span>
<span><span class="co"># partial argument match of 'length' to 'length.out'</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">ppi</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, col <span class="op">=</span> <span class="st">'green'</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
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<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/density.html">density</a></span><span class="op">(</span><span class="va">ppi</span>, sigma <span class="op">=</span> <span class="fl">.05</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co"># Warning in seq.default(xrange[1L], xrange[2L], length = n + 1L):</span></span>
<span><span class="co"># partial argument match of 'length' to 'length.out'</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">ppi</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, col <span class="op">=</span> <span class="st">'green'</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># assuming Inhomogeneous Poisson:</span></span>
<span><span class="fu"><a href="https://rdrr.io/pkg/spatstat.model/man/ppm.html">ppm</a></span><span class="op">(</span><span class="va">ppi</span>, <span class="op">~</span><span class="va">x</span><span class="op">)</span></span>
<span><span class="co"># Warning in FIT$coef: partial match of 'coef' to 'coefficients'</span></span>
<span><span class="co"># Nonstationary Poisson process</span></span>
<span><span class="co"># Fitted to point pattern dataset 'ppi'</span></span>
<span><span class="co"># </span></span>
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<span><span class="co"># x 1.96 0.247 1.48 2.45 *** 7.96</span></span>
<span><span class="co"># assuming Inhomogeneous clustered:</span></span>
<span><span class="fu"><a href="https://rdrr.io/pkg/spatstat.model/man/kppm.html">kppm</a></span><span class="op">(</span><span class="va">Y</span>, <span class="op">~</span><span class="va">x</span><span class="op">)</span></span>
<span><span class="co"># Warning in FIT$coef: partial match of 'coef' to 'coefficients'</span></span>
<span><span class="co"># Inhomogeneous cluster point process model</span></span>
<span><span class="co"># Fitted to point pattern dataset 'Y'</span></span>
<span><span class="co"># Fitted by minimum contrast</span></span>
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4 changes: 2 additions & 2 deletions docs/day3.html
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<li>Which model do you like the best? Can the SSErr attribute of the fitted model be used to compare the models? How else can variogram model fits be compared?</li>
</ol></section><section id="interpolation" class="level2" data-number="3.5"><h2 data-number="3.5" class="anchored" data-anchor-id="interpolation">
<span class="header-section-number">3.5</span> Interpolation</h2>
<p>For interpolation, we first need a target grid (point patterns have an observation window, geostatistical data not!)</p>
<p>For interpolation, we first need a target grid (point patterns have an observation window, geostatistical data do not!)</p>
<p>A simple interpolator (that is hard to beat) is the inverse distance interpolator, <span class="math display">\[\hat{Z}(s_0) = \sum_{j=1}^n \lambda_j Z(s_i)\]</span> with <span class="math inline">\(\lambda_j\)</span> proportional to <span class="math inline">\(||s_i - s_0||^{-p}\)</span> and normalized to sum to one (weighted mean), and <span class="math inline">\(p\)</span> tunable but defaulting to 2.</p>
<p>Using the data range:</p>
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<span id="cb22-132"><a href="#cb22-132" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-133"><a href="#cb22-133" aria-hidden="true" tabindex="-1"></a><span class="fu">## Interpolation</span></span>
<span id="cb22-134"><a href="#cb22-134" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-135"><a href="#cb22-135" aria-hidden="true" tabindex="-1"></a>For interpolation, we first need a target grid (point patterns have an observation window, geostatistical data not!)</span>
<span id="cb22-135"><a href="#cb22-135" aria-hidden="true" tabindex="-1"></a>For interpolation, we first need a target grid (point patterns have an observation window, geostatistical data do not!)</span>
<span id="cb22-136"><a href="#cb22-136" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-137"><a href="#cb22-137" aria-hidden="true" tabindex="-1"></a>A simple interpolator (that is hard to beat) is the inverse distance interpolator, $$\hat{Z}(s_0) = \sum_{j=1}^n \lambda_j Z(s_i)$$ with $\lambda_j$ proportional to $||s_i - s_0||^{-p}$ and normalized to sum to one (weighted mean), and $p$ tunable but defaulting to 2.</span>
<span id="cb22-138"><a href="#cb22-138" aria-hidden="true" tabindex="-1"></a></span>
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