0102 dgplot image fix

_site/materials/glm-practical-logistic-binary.html

@@ -598,15 +598,7 @@ <h1 class="title">
 <div class="cell">
 <div class="cell-output-display">
 <div>
-<figure class="figure"><p><img src="glm-practical-logistic-binary_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid figure-img" width="96"></p>
-</figure>
-</div>
-</div>
-</div>
-<div class="cell">
-<div class="cell-output-display">
-<div>
-<figure class="figure"><p><img src="images/dgplots/2024_01_31-02-26-30_PM_dgplots.png" class="img-fluid figure-img" width="804"></p>
+<figure class="figure"><p><img src="images/dgplots/2024_02_01-07-29-38_AM_dgplots.png" class="img-fluid figure-img" width="805"></p>
 </figure>
 </div>
 </div>
@@ -751,8 +743,8 @@ <h1 class="title">
 Model Family:                Binomial   Df Model:                            1
 Link Function:                  Logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -4.5939
-Date:                Wed, 31 Jan 2024   Deviance:                       9.1879
-Time:                        14:26:31   Pearson chi2:                     15.1
+Date:                Thu, 01 Feb 2024   Deviance:                       9.1879
+Time:                        07:29:39   Pearson chi2:                     15.1
 No. Iterations:                     8   Pseudo R-squ. (CS):             0.7093
 Covariance Type:            nonrobust                                         
 ==============================================================================
@@ -1002,7 +994,7 @@ <h1 class="title">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>(ggplot(glm_bks_py_resid,</span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>         aes(x <span class="op">=</span> <span class="st">"obs"</span>,</span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>             y <span class="op">=</span> <span class="st">"cooks_d"</span>)) <span class="op">+</span></span>
-<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>     geom_segment(aes(x <span class="op">=</span> <span class="st">"obs"</span>,y <span class="op">=</span> <span class="st">"cooks_d"</span>, xend <span class="op">=</span> <span class="st">"obs"</span>, yend <span class="op">=</span> <span class="dv">0</span>)) <span class="op">+</span></span>
+<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>     geom_segment(aes(x <span class="op">=</span> <span class="st">"obs"</span>, y <span class="op">=</span> <span class="st">"cooks_d"</span>, xend <span class="op">=</span> <span class="st">"obs"</span>, yend <span class="op">=</span> <span class="dv">0</span>)) <span class="op">+</span></span>
 <span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a>     geom_point())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
@@ -1085,8 +1077,8 @@ <h1 class="title">
 Model Family:                Binomial   Df Model:                            0
 Link Function:                  Logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -42.274
-Date:                Wed, 31 Jan 2024   Deviance:                       84.548
-Time:                        14:26:35   Pearson chi2:                     61.0
+Date:                Thu, 01 Feb 2024   Deviance:                       84.548
+Time:                        07:29:43   Pearson chi2:                     61.0
 No. Iterations:                     3   Pseudo R-squ. (CS):              0.000
 Covariance Type:            nonrobust                                         
 ==============================================================================
@@ -1140,7 +1132,7 @@ <h1 class="title">
 <p>This shouldn’t be surprising since we already saw that our overall model was better than the null model, which in this case is exactly the same as asking whether the beak length term is significant. However, in more complicated models with multiple predictors these two comparisons (and p-values) won’t be the same.</p>
 </div>
 <div id="tabset-15-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-15-2-tab">
-<p>Alas, for some inexplicable reason this is not (yet?) possible to do in Python. At least, not beknownst to me…</p>
+<p>Alas, for some inexplicable reason this is not (yet?) possible to do in Python. At least, unbeknownst to me…</p>
 </div>
 </div>
 </div>
@@ -1356,8 +1348,8 @@ <h1 class="title">
 Model Family:                Binomial   Df Model:                            1
 Link Function:                  Logit   Scale:                          1.0000
 Method:                          IRLS   Log-Likelihood:                -376.10
-Date:                Wed, 31 Jan 2024   Deviance:                       752.20
-Time:                        14:26:37   Pearson chi2:                     713.
+Date:                Thu, 01 Feb 2024   Deviance:                       752.20
+Time:                        07:29:45   Pearson chi2:                     713.
 No. Iterations:                     4   Pseudo R-squ. (CS):             0.2238
 Covariance Type:            nonrobust                                         
 ==============================================================================

materials/glm-practical-logistic-binary.qmd

@@ -217,11 +217,14 @@ lm_bks_py = model.fit()
 Next, we can create the diagnostic plots:
 ```{python}
 #| eval: false
+#| results: hide
 dgplots(lm_bks_py)
 ```
 
 ```{python}
 #| echo: false
+#| fig-keep: none
+#| fig-show: hide
 # load dgplots function for knitr
 exec(open('setup_files/dgplots_knitr.py').read())
 # create rendered diagnostic plots image
@@ -368,7 +371,6 @@ There’s a lot to unpack here, but let's start with what we're familiar with: c
 ## Parameter interpretation
 
 ::: {.panel-tabset group="language"}
-
 ## R
 
 The coefficients or parameters can be found in the `Coefficients` block. The main numbers to extract from the output are the two numbers underneath `Estimate.Std`:
@@ -574,7 +576,7 @@ We can use these to create the plot:
 (ggplot(glm_bks_py_resid,
          aes(x = "obs",
              y = "cooks_d")) +
-     geom_segment(aes(x = "obs",y = "cooks_d", xend = "obs", yend = 0)) +
+     geom_segment(aes(x = "obs", y = "cooks_d", xend = "obs", yend = 0)) +
      geom_point())
 ```
 
@@ -673,13 +675,11 @@ This shouldn’t be surprising since we already saw that our overall model was b
 
 ## Python
 
-Alas, for some inexplicable reason this is not (yet?) possible to do in Python. At least, not beknownst to me...
+Alas, for some inexplicable reason this is not (yet?) possible to do in Python. At least, unbeknownst to me...
 
 :::
 
 
-
-
 ## Exercises
 
 ### Diabetes {#sec-exr_diabetes}