deploy: 568b59f

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 214d35bb16c7218d3b7aab3048acdedb
+config: dd3c8afeb18c12fdda28369b78c55497
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/2932d6ac7842b6d781c27c5737ae52fa/plot_marginal_cumulative_incidence_estimation.ipynb

@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The resulting incidence curves are indeed monotonic. However, for smaller\ntraining set sizes of the training set, the resulting models can be\nsignificantly biased, in particular in regions where the CIFs is getting\nflatter. This effect diminishes with larger training set sizes (lower\nepistemic uncertainty).\n\n"
+        "The resulting incidence curves are indeed monotonic. However, for smaller\ntraining set sizes, the resulting models can be significantly biased, in\nparticular large time horizons, where the CIFs are getting flatter. This\neffect diminishes with larger training set sizes (lower epistemic\nuncertainty).\n\n"
       ]
     }
   ],

_downloads/878bb6ffe7fba36a0655277b8151e4cd/plot_marginal_cumulative_incidence_estimation.py

@@ -252,7 +252,7 @@ def plot_cumulative_incidence_functions(distributions, y, gb_incidence=None, aj=
 # %%
 #
 # The resulting incidence curves are indeed monotonic. However, for smaller
-# training set sizes of the training set, the resulting models can be
-# significantly biased, in particular in regions where the CIFs is getting
-# flatter. This effect diminishes with larger training set sizes (lower
-# epistemic uncertainty).
+# training set sizes, the resulting models can be significantly biased, in
+# particular large time horizons, where the CIFs are getting flatter. This
+# effect diminishes with larger training set sizes (lower epistemic
+# uncertainty).

_sources/auto_examples/plot_marginal_cumulative_incidence_estimation.rst.txt

@@ -266,15 +266,15 @@ theoretical CIFs:
 
  .. code-block:: none
 
-    GB Incidence for event 1 fit in 0.572 s
-    GB Incidence for event 1 prediction in 0.124 s
-    Aalen-Johansen for event 1 fit in 6.459 s
-    GB Incidence for event 2 fit in 0.554 s
-    GB Incidence for event 2 prediction in 0.124 s
-    Aalen-Johansen for event 2 fit in 6.501 s
-    GB Incidence for event 3 fit in 0.539 s
-    GB Incidence for event 3 prediction in 0.129 s
-    Aalen-Johansen for event 3 fit in 6.505 s
+    GB Incidence for event 1 fit in 0.715 s
+    GB Incidence for event 1 prediction in 0.150 s
+    Aalen-Johansen for event 1 fit in 7.610 s
+    GB Incidence for event 2 fit in 0.893 s
+    GB Incidence for event 2 prediction in 0.171 s
+    Aalen-Johansen for event 2 fit in 7.699 s
+    GB Incidence for event 3 fit in 0.662 s
+    GB Incidence for event 3 prediction in 0.147 s
+    Aalen-Johansen for event 3 fit in 7.668 s
 
 
 
@@ -345,15 +345,15 @@ increases the amount of censoring.
 
  .. code-block:: none
 
-    GB Incidence for event 1 fit in 0.621 s
-    GB Incidence for event 1 prediction in 0.125 s
-    Aalen-Johansen for event 1 fit in 6.504 s
-    GB Incidence for event 2 fit in 0.582 s
-    GB Incidence for event 2 prediction in 0.125 s
-    Aalen-Johansen for event 2 fit in 6.554 s
-    GB Incidence for event 3 fit in 0.528 s
-    GB Incidence for event 3 prediction in 0.123 s
-    Aalen-Johansen for event 3 fit in 6.490 s
+    GB Incidence for event 1 fit in 0.705 s
+    GB Incidence for event 1 prediction in 0.155 s
+    Aalen-Johansen for event 1 fit in 7.684 s
+    GB Incidence for event 2 fit in 0.691 s
+    GB Incidence for event 2 prediction in 0.157 s
+    Aalen-Johansen for event 2 fit in 7.684 s
+    GB Incidence for event 3 fit in 0.639 s
+    GB Incidence for event 3 prediction in 0.147 s
+    Aalen-Johansen for event 3 fit in 7.573 s
 
 
 
@@ -403,28 +403,28 @@ constraint:
 
  .. code-block:: none
 
-    GB Incidence for event 1 fit in 0.544 s
-    GB Incidence for event 1 prediction in 0.123 s
-    GB Incidence for event 2 fit in 0.549 s
-    GB Incidence for event 2 prediction in 0.128 s
-    GB Incidence for event 3 fit in 0.515 s
-    GB Incidence for event 3 prediction in 0.129 s
+    GB Incidence for event 1 fit in 0.669 s
+    GB Incidence for event 1 prediction in 0.144 s
+    GB Incidence for event 2 fit in 0.675 s
+    GB Incidence for event 2 prediction in 0.156 s
+    GB Incidence for event 3 fit in 0.638 s
+    GB Incidence for event 3 prediction in 0.145 s
 
 
 
 
 .. GENERATED FROM PYTHON SOURCE LINES 253-258
 
 The resulting incidence curves are indeed monotonic. However, for smaller
-training set sizes of the training set, the resulting models can be
-significantly biased, in particular in regions where the CIFs is getting
-flatter. This effect diminishes with larger training set sizes (lower
-epistemic uncertainty).
+training set sizes, the resulting models can be significantly biased, in
+particular large time horizons, where the CIFs are getting flatter. This
+effect diminishes with larger training set sizes (lower epistemic
+uncertainty).
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 49.453 seconds)
+   **Total running time of the script:** (0 minutes 58.568 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_marginal_cumulative_incidence_estimation.py:

_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,8 +6,8 @@
 
 Computation times
 =================
-**00:49.453** total execution time for **auto_examples** files:
+**00:58.568** total execution time for **auto_examples** files:
 
 +---------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_marginal_cumulative_incidence_estimation.py` (``plot_marginal_cumulative_incidence_estimation.py``) | 00:49.453 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_marginal_cumulative_incidence_estimation.py` (``plot_marginal_cumulative_incidence_estimation.py``) | 00:58.568 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+

auto_examples/plot_marginal_cumulative_incidence_estimation.html

@@ -516,15 +516,15 @@ <h2>CIFs estimated on uncensored data<a class="headerlink" href="#cifs-estimated
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png" alt="Cause-specific cumulative incidence functions (0.0% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.572 s
-GB Incidence for event 1 prediction in 0.124 s
-Aalen-Johansen for event 1 fit in 6.459 s
-GB Incidence for event 2 fit in 0.554 s
-GB Incidence for event 2 prediction in 0.124 s
-Aalen-Johansen for event 2 fit in 6.501 s
-GB Incidence for event 3 fit in 0.539 s
-GB Incidence for event 3 prediction in 0.129 s
-Aalen-Johansen for event 3 fit in 6.505 s
+<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png" alt="Cause-specific cumulative incidence functions (0.0% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.715 s
+GB Incidence for event 1 prediction in 0.150 s
+Aalen-Johansen for event 1 fit in 7.610 s
+GB Incidence for event 2 fit in 0.893 s
+GB Incidence for event 2 prediction in 0.171 s
+Aalen-Johansen for event 2 fit in 7.699 s
+GB Incidence for event 3 fit in 0.662 s
+GB Incidence for event 3 prediction in 0.147 s
+Aalen-Johansen for event 3 fit in 7.668 s
 </pre></div>
 </div>
 </section>
@@ -562,15 +562,15 @@ <h2>CIFs estimated on censored data<a class="headerlink" href="#cifs-estimated-o
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_002.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_002.png" alt="Cause-specific cumulative incidence functions (67.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.621 s
-GB Incidence for event 1 prediction in 0.125 s
-Aalen-Johansen for event 1 fit in 6.504 s
-GB Incidence for event 2 fit in 0.582 s
-GB Incidence for event 2 prediction in 0.125 s
-Aalen-Johansen for event 2 fit in 6.554 s
-GB Incidence for event 3 fit in 0.528 s
-GB Incidence for event 3 prediction in 0.123 s
-Aalen-Johansen for event 3 fit in 6.490 s
+<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_002.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_002.png" alt="Cause-specific cumulative incidence functions (67.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.705 s
+GB Incidence for event 1 prediction in 0.155 s
+Aalen-Johansen for event 1 fit in 7.684 s
+GB Incidence for event 2 fit in 0.691 s
+GB Incidence for event 2 prediction in 0.157 s
+Aalen-Johansen for event 2 fit in 7.684 s
+GB Incidence for event 3 fit in 0.639 s
+GB Incidence for event 3 prediction in 0.147 s
+Aalen-Johansen for event 3 fit in 7.573 s
 </pre></div>
 </div>
 <p>Note that the Aalen-Johansen estimator is unbiased and empirically recovers
@@ -595,20 +595,20 @@ <h2>CIFs estimated on censored data<a class="headerlink" href="#cifs-estimated-o
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_003.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_003.png" alt="Cause-specific cumulative incidence functions (67.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.544 s
-GB Incidence for event 1 prediction in 0.123 s
-GB Incidence for event 2 fit in 0.549 s
-GB Incidence for event 2 prediction in 0.128 s
-GB Incidence for event 3 fit in 0.515 s
-GB Incidence for event 3 prediction in 0.129 s
+<img src="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_003.png" srcset="../_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_003.png" alt="Cause-specific cumulative incidence functions (67.4% censoring), Event 1, Event 2, Event 3" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GB Incidence for event 1 fit in 0.669 s
+GB Incidence for event 1 prediction in 0.144 s
+GB Incidence for event 2 fit in 0.675 s
+GB Incidence for event 2 prediction in 0.156 s
+GB Incidence for event 3 fit in 0.638 s
+GB Incidence for event 3 prediction in 0.145 s
 </pre></div>
 </div>
 <p>The resulting incidence curves are indeed monotonic. However, for smaller
-training set sizes of the training set, the resulting models can be
-significantly biased, in particular in regions where the CIFs is getting
-flatter. This effect diminishes with larger training set sizes (lower
-epistemic uncertainty).</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 49.453 seconds)</p>
+training set sizes, the resulting models can be significantly biased, in
+particular large time horizons, where the CIFs are getting flatter. This
+effect diminishes with larger training set sizes (lower epistemic
+uncertainty).</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 58.568 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-marginal-cumulative-incidence-estimation-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/878bb6ffe7fba36a0655277b8151e4cd/plot_marginal_cumulative_incidence_estimation.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_marginal_cumulative_incidence_estimation.py</span></code></a></p>

auto_examples/sg_execution_times.html

@@ -318,11 +318,11 @@
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>00:49.453</strong> total execution time for <strong>auto_examples</strong> files:</p>
+<p><strong>00:58.568</strong> total execution time for <strong>auto_examples</strong> files:</p>
 <table class="table">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_marginal_cumulative_incidence_estimation.html#sphx-glr-auto-examples-plot-marginal-cumulative-incidence-estimation-py"><span class="std std-ref">Estimating marginal cumulative incidence functions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_marginal_cumulative_incidence_estimation.py</span></code>)</p></td>
-<td><p>00:49.453</p></td>
+<td><p>00:58.568</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 </tbody>