Deploying to gh-pages from @ 756be30 🚀

analyze-areal-data.html

@@ -860,7 +860,7 @@ <h1 class="title"><span id="sec-analyze-areal-data" class="quarto-section-identi
 weights: nb2listw(k4.48)  
 number of simulations + 1: 500 
 
-statistic = 0.29439, observed rank = 500, p-value = 0.002
+statistic = 0.29439, observed rank = 497, p-value = 0.006
 alternative hypothesis: greater</code></pre>
 </div>
 </div>

analyze-text-data.html

@@ -778,8 +778,8 @@ <h1 class="title"><span id="sec-analysis-text-data" class="quarto-section-identi
 <span id="cb19-10"><a href="#cb19-10"></a>    <span class="at">n_check_convergence =</span> <span class="dv">25</span>, <span class="at">progressbar =</span> <span class="cn">FALSE</span></span>
 <span id="cb19-11"><a href="#cb19-11"></a>  )</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>#&gt; INFO  [00:07:25.800] early stopping at 175 iteration
-#&gt; INFO  [00:07:26.277] early stopping at 50 iteration</code></pre>
+<pre><code>#&gt; INFO  [02:23:45.809] early stopping at 175 iteration
+#&gt; INFO  [02:23:46.294] early stopping at 50 iteration</code></pre>
 </div>
 </div>
 <p>下图展示主题的分布，各个主题及其所占比例。</p>

classification-problems.html

@@ -1010,12 +1010,12 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
  parameter : cost C = 1 
 
 Gaussian Radial Basis kernel function. 
- Hyperparameter : sigma =  0.895137052930221 
+ Hyperparameter : sigma =  1.71885532355183 
 
-Number of Support Vectors : 60 
+Number of Support Vectors : 71 
 
-Objective Function Value : -4.8002 -5.388 -20.049 
-Training error : 0.013333 </code></pre>
+Objective Function Value : -7.0844 -8.0239 -19.4162 
+Training error : 0.02 </code></pre>
 </div>
 </div>
 <p><strong>kernlab</strong> 包 <span class="citation" data-cites="kernlab2004">(<a href="references.html#ref-kernlab2004" role="doc-biblioref">Karatzoglou 等 2004</a>)</span> 的绘图函数 <code><a href="https://rdrr.io/r/graphics/plot.default.html">plot()</a></code> 仅支持二分类模型。</p>
@@ -1026,8 +1026,8 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
 <pre><code>             
 iris_pred_svm setosa versicolor virginica
    setosa         50          0         0
-   versicolor      0         49         1
-   virginica       0          1        49</code></pre>
+   versicolor      0         49         2
+   virginica       0          1        48</code></pre>
 </div>
 </div>
 </section><section id="sec-k-nearest-neighbour" class="level2" data-number="40.6"><h2 data-number="40.6" class="anchored" data-anchor-id="sec-k-nearest-neighbour">
@@ -1064,20 +1064,20 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
 <div class="cell-output cell-output-stdout">
 <pre><code>a 4-4-3 network with 35 weights
 options were - softmax modelling 
- b-&gt;h1 i1-&gt;h1 i2-&gt;h1 i3-&gt;h1 i4-&gt;h1 
- -1.51  -7.80  -3.64  -8.08  -3.22 
- b-&gt;h2 i1-&gt;h2 i2-&gt;h2 i3-&gt;h2 i4-&gt;h2 
- -4.53  -4.09 -18.72  33.13  17.26 
- b-&gt;h3 i1-&gt;h3 i2-&gt;h3 i3-&gt;h3 i4-&gt;h3 
- -0.78  -1.37  -0.48  -1.07  -1.56 
- b-&gt;h4 i1-&gt;h4 i2-&gt;h4 i3-&gt;h4 i4-&gt;h4 
-  1.26   7.02   6.04   0.53  -0.37 
- b-&gt;o1 h1-&gt;o1 h2-&gt;o1 h3-&gt;o1 h4-&gt;o1 
- 15.05   1.07 -18.13  -0.54  -5.78 
- b-&gt;o2 h1-&gt;o2 h2-&gt;o2 h3-&gt;o2 h4-&gt;o2 
- -8.05  38.34   8.77  -1.76   3.50 
- b-&gt;o3 h1-&gt;o3 h2-&gt;o3 h3-&gt;o3 h4-&gt;o3 
- -7.30 -38.74   9.47   2.49   2.05 </code></pre>
+  b-&gt;h1  i1-&gt;h1  i2-&gt;h1  i3-&gt;h1  i4-&gt;h1 
+ -22.96 -144.54  -70.04 -108.98  -38.18 
+  b-&gt;h2  i1-&gt;h2  i2-&gt;h2  i3-&gt;h2  i4-&gt;h2 
+1510.87  122.74  215.56 -433.43 -468.63 
+  b-&gt;h3  i1-&gt;h3  i2-&gt;h3  i3-&gt;h3  i4-&gt;h3 
+  46.92  193.06  202.11  -74.71  -42.17 
+  b-&gt;h4  i1-&gt;h4  i2-&gt;h4  i3-&gt;h4  i4-&gt;h4 
+  -9.98   -8.54  -34.81   69.41   45.94 
+  b-&gt;o1  h1-&gt;o1  h2-&gt;o1  h3-&gt;o1  h4-&gt;o1 
+ -53.04  -58.28  615.48 -283.46 -337.81 
+  b-&gt;o2  h1-&gt;o2  h2-&gt;o2  h3-&gt;o2  h4-&gt;o2 
+   6.78   24.71  128.58   93.46  235.31 
+  b-&gt;o3  h1-&gt;o3  h2-&gt;o3  h3-&gt;o3  h4-&gt;o3 
+  45.55   34.17 -743.57  189.94  103.97 </code></pre>
 </div>
 </div>
 <p>size 隐藏层中的神经元数量</p>
@@ -1088,8 +1088,8 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
 <pre><code>              
 iris_pred_nnet setosa versicolor virginica
     setosa         50          0         0
-    versicolor      0         24        25
-    virginica       0         26        25</code></pre>
+    versicolor      0         49         0
+    virginica       0          1        50</code></pre>
 </div>
 </div>
 </section><section id="sec-recursive-partitioning" class="level2" data-number="40.8"><h2 data-number="40.8" class="anchored" data-anchor-id="sec-recursive-partitioning">
@@ -1209,12 +1209,12 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
                      Number of trees: 500
 No. of variables tried at each split: 2
 
-        OOB estimate of  error rate: 4.67%
+        OOB estimate of  error rate: 5.33%
 Confusion matrix:
            setosa versicolor virginica class.error
 setosa         50          0         0        0.00
 versicolor      0         47         3        0.06
-virginica       0          4        46        0.08</code></pre>
+virginica       0          5        45        0.10</code></pre>
 </div>
 </div>
 <div class="cell">

common-statistical-tests_files/figure-html/fig-two-samples-means-1.tex

@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-28 00:06:03
+% Created by tikzDevice version 0.12.6 on 2024-02-28 02:22:23
 % !TEX encoding = UTF-8 Unicode
 \documentclass[tikz]{standalone}
 

generalized-additive-models.html

@@ -1354,15 +1354,15 @@ <h1 class="title"><span id="sec-generalized-additive-models" class="quarto-secti
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1"></a><span class="co"># 截距</span></span>
 <span id="cb58-2"><a href="#cb58-2"></a>res<span class="sc">$</span>summary.fixed</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>#&gt;        mean         sd 0.025quant 0.5quant 0.975quant     mode         kld
-#&gt; b0 1.828027 0.06147356   1.706422 1.828283   1.948169 1.828279 1.78255e-08</code></pre>
+<pre><code>#&gt;        mean         sd 0.025quant 0.5quant 0.975quant     mode          kld
+#&gt; b0 1.828027 0.06147356   1.706422 1.828284   1.948169 1.828279 1.782562e-08</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1"></a><span class="co"># 超参数</span></span>
 <span id="cb60-2"><a href="#cb60-2"></a>res<span class="sc">$</span>summary.hyperpar</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>#&gt;                   mean         sd 0.025quant  0.5quant 0.975quant      mode
-#&gt; Theta1 for s  2.000684 0.06235047   1.876512  2.001169   2.122006  2.003209
-#&gt; Theta2 for s -4.851258 0.12973326  -5.105059 -4.851807  -4.594253 -4.854094</code></pre>
+#&gt; Theta1 for s  2.000684 0.06235041   1.876512  2.001169   2.122006  2.003209
+#&gt; Theta2 for s -4.851258 0.12973264  -5.105058 -4.851807  -4.594255 -4.854094</code></pre>
 </div>
 </div>
 <p>提取预测数据，并整理数据。</p>

hierarchical-normal-models.html

@@ -790,7 +790,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; theta[8]   8.59    0.18 7.84  -7.59   4.16   8.67  12.92  25.19  1936    1
 #&gt; lp__     -50.40    0.12 2.60 -55.92 -51.98 -50.14 -48.53 -46.08   499    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 00:16:09 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 02:32:33 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -807,7 +807,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt;     mean se_mean   sd 2.5% 97.5% n_eff Rhat
 #&gt; tau 6.69    0.21 5.52 0.25 20.81   688    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 00:16:09 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 02:32:33 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -825,21 +825,21 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">str</span>(eight_schools_sim)</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>#&gt; List of 5
-#&gt;  $ mu   : num [1:2000(1d)] 8.21 2.244 11.077 -0.202 8.479 ...
+#&gt;  $ mu   : num [1:2000(1d)] 4.68 7.71 2.23 9.05 7.25 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL
-#&gt;  $ tau  : num [1:2000(1d)] 0.738 4.866 1.637 4.56 0.555 ...
+#&gt;  $ tau  : num [1:2000(1d)] 12.07 2.96 12.63 6.57 2.76 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL
-#&gt;  $ eta  : num [1:2000, 1:8] 1.79169 1.715398 -0.461639 0.000265 -1.506403 ...
+#&gt;  $ eta  : num [1:2000, 1:8] 0.451 -0.451 0.377 0.659 -0.203 ...
 #&gt;   ..- attr(*, "dimnames")=List of 2
 #&gt;   .. ..$ iterations: NULL
 #&gt;   .. ..$           : NULL
-#&gt;  $ theta: num [1:2000, 1:8] 9.531 10.591 10.322 -0.201 7.643 ...
+#&gt;  $ theta: num [1:2000, 1:8] 10.13 6.37 6.99 13.38 6.69 ...
 #&gt;   ..- attr(*, "dimnames")=List of 2
 #&gt;   .. ..$ iterations: NULL
 #&gt;   .. ..$           : NULL
-#&gt;  $ lp__ : num [1:2000(1d)] -54 -52.5 -49.8 -50 -51.5 ...
+#&gt;  $ lp__ : num [1:2000(1d)] -46.6 -48.9 -47.7 -47.8 -54.1 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL</code></pre>
 </div>
@@ -2270,7 +2270,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; alpha0       106.4     0.1  3.5   99.4  104.0  106.4  108.7  113.4  2300    1
 #&gt; lp__        -438.6     0.3  6.6 -452.7 -442.9 -438.1 -434.1 -426.3   703    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 00:17:48 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Wed Feb 28 02:34:13 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -2712,14 +2712,14 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; 
 #&gt; Model hyperparameters:
 #&gt;                                             mean      sd 0.025quant 0.5quant
-#&gt; Precision for the Gaussian observations 4522.629 666.545   3334.871 4480.319
+#&gt; Precision for the Gaussian observations 4522.629 666.545   3334.872 4480.319
 #&gt; Precision for rats (component 1)          32.097   7.956     18.939   31.260
 #&gt; Precision for rats (component 2)          33.237   8.227     19.605   32.379
 #&gt; Rho1:2 for rats                           -0.001   0.172     -0.335   -0.001
 #&gt;                                         0.975quant     mode
-#&gt; Precision for the Gaussian observations   5952.949 4407.854
-#&gt; Precision for rats (component 1)            50.049   29.769
-#&gt; Precision for rats (component 2)            51.777   30.861
+#&gt; Precision for the Gaussian observations   5952.951 4407.854
+#&gt; Precision for rats (component 1)            50.049   29.770
+#&gt; Precision for rats (component 2)            51.776   30.861
 #&gt; Rho1:2 for rats                              0.334   -0.001
 #&gt; 
 #&gt;  is computed</code></pre>

hierarchical-normal-models_files/figure-html/fig-8schools-funnels-1.tex

@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-28 00:16:44
+% Created by tikzDevice version 0.12.6 on 2024-02-28 02:33:09
 % !TEX encoding = UTF-8 Unicode
 \documentclass[tikz]{standalone}
 

index.html

@@ -628,7 +628,7 @@ <h1 class="title">R 语言数据分析实战</h1>
 <p>本书初稿是在 RStudio IDE 内使用 <a href="https://quarto.org/">Quarto</a> 编辑的，Quarto 是继<a href="https://rmarkdown.rstudio.com/">R Markdown</a>之后，一个新的开源的科学和技术发布系统，它基于 <a href="https://pandoc.org/">Pandoc</a>支持输出多种格式的书稿，比如 HTML 网页、EPUB 电子书、DOCX 文档和 PDF 便携式文档等。Quarto 吸收了过去 10 年 R Markdown 取得的经验和教训，在书籍写作、创建博客、制作简历和幻灯片等系列场景中支持更加统一的使用语法，一份源文档输出多种格式，使文档内容在不同场景中的迁移成本更低。了解更多 Quarto 特性，请访问 <a href="https://quarto.org/" class="uri">https://quarto.org/</a>。</p>
 <p>书中的代码字体采用美观的 <a href="https://ctan.org/pkg/sourcecodepro">Source Code Pro</a> 字体， 为方便跨操作系统编译书籍电子版，正文的中文字体采用开源的 <a href="https://ctan.org/pkg/fandol">fandol</a> 字体。此外，考虑到美观性，本书图形使用了 Noto 系列中英文字体，它们来自 <a href="https://fonts.google.com/">Google Fonts 字体库</a>，分别是 Noto Sans 无衬线英文字体和 Noto Serif SC 宋体中文字体。</p>
 <p>书中 R 包名以粗体表示，如 <strong>knitr</strong> 包，函数名以等宽体表示，如 <code><a href="https://rdrr.io/r/graphics/plot.default.html">plot()</a></code>，函数的参数名同理。代码块内注释用 <code>#</code> 表示，运行结果每一行开头以 <code>#&gt;</code> 标记。本书写作过程中，依赖 <strong>knitr</strong> <span class="citation" data-cites="xie2015">(<a href="references.html#ref-xie2015" role="doc-biblioref">Xie 2015</a>)</span>、<strong>ggplot2</strong> <span class="citation" data-cites="Hadley2016">(<a href="references.html#ref-Hadley2016" role="doc-biblioref">Wickham 2016</a>)</span> 和 <strong>lattice</strong> <span class="citation" data-cites="Deepayan2008">(<a href="references.html#ref-Deepayan2008" role="doc-biblioref">Sarkar 2008</a>)</span> 等众多 R 包。考虑到要同时支持 DOCX、EPUB、PDF 和 HTML 四种书籍格式，书中使用 <strong>knitr</strong> 包和 <a href="https://github.com/rstudio/gt"><strong>gt</strong></a> 包制作静态的表格。</p>
-<p>为方便测试贡献者提供的 PR，本书托管在 Github 上，同时启用 Github Action 服务，为书籍自定义了一个可复现全书内容的运行环境，包括 R 软件、扩展包和系统软件依赖，详见仓库中的 DESCRIPTION 文件。你现在看到的是在线编译版本，使用 Quarto 1.4.550，最新一次编译时间是 2024-02-28 08:00:52。</p>
+<p>为方便测试贡献者提供的 PR，本书托管在 Github 上，同时启用 Github Action 服务，为书籍自定义了一个可复现全书内容的运行环境，包括 R 软件、扩展包和系统软件依赖，详见仓库中的 DESCRIPTION 文件。你现在看到的是在线编译版本，使用 Quarto 1.4.550，最新一次编译时间是 2024-02-28 10:17:13。</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>xfun<span class="sc">::</span><span class="fu">session_info</span>(<span class="at">packages =</span> <span class="fu">c</span>(</span>
 <span id="cb1-2"><a href="#cb1-2"></a>  <span class="st">"ggplot2"</span>, <span class="st">"gganimate"</span>, <span class="st">"ggrepel"</span>, <span class="st">"ggdensity"</span>,</span>

interactive-applications.html

@@ -1141,8 +1141,8 @@ <h1 class="title"><span id="sec-interactive-applications" class="quarto-section-
 <div class="cell">
 <div id="fig-shiny-ecosystem" class="cell-output-display quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure"><div aria-describedby="fig-shiny-ecosystem-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<div class="nomnoml html-widget html-fill-item" id="htmlwidget-a651e6f22b78dce2458e" style="width:100%;height:464px;"></div>
-<script type="application/json" data-for="htmlwidget-a651e6f22b78dce2458e">{"x":{"code":"\n#fill: #FEFEFF\n#lineWidth: 1\n#zoom: 4\n#direction: right\n\n#stroke: #26A63A\n#.box: dashed visual=ellipse\n#direction: down\n\n[<box>HTML]       -> [网页三剑客]\n[<box>JavaScript] -> [网页三剑客]\n[<box>CSS]        -> [<table>网页三剑客|htmlwidgets|htmltools||sass|bslib||thematic|jquerylib]\n\n[设计布局|bs4Dash|flexdashboard|shinydashboard] -> [<actor>开发应用|R Shiny]\n[设计交互|waiter|shinyFeedback|shinyToastify] -> [<actor>开发应用|R Shiny]\n[权限代理|shinyproxy|shinyauthr|shinymanager] -> [<actor>开发应用|R Shiny]\n\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n\n[开发应用] <- [<table>处理数据|Base R|SQL||data.table|dplyr||tidyr|purrr]\n[开发应用] <- [<table>制作表格|DT|gt||reactable|formattable||kableExtra|sparkline]\n[开发应用] <- [<table>制作图形|ggplot2|plotly||echarts4r|leaflet||dygraphs|visNetwork]","svg":false,"png":null},"evals":[],"jsHooks":[]}</script>
+<div class="nomnoml html-widget html-fill-item" id="htmlwidget-c4f8ac4b9c8a0d418db5" style="width:100%;height:464px;"></div>
+<script type="application/json" data-for="htmlwidget-c4f8ac4b9c8a0d418db5">{"x":{"code":"\n#fill: #FEFEFF\n#lineWidth: 1\n#zoom: 4\n#direction: right\n\n#stroke: #26A63A\n#.box: dashed visual=ellipse\n#direction: down\n\n[<box>HTML]       -> [网页三剑客]\n[<box>JavaScript] -> [网页三剑客]\n[<box>CSS]        -> [<table>网页三剑客|htmlwidgets|htmltools||sass|bslib||thematic|jquerylib]\n\n[设计布局|bs4Dash|flexdashboard|shinydashboard] -> [<actor>开发应用|R Shiny]\n[设计交互|waiter|shinyFeedback|shinyToastify] -> [<actor>开发应用|R Shiny]\n[权限代理|shinyproxy|shinyauthr|shinymanager] -> [<actor>开发应用|R Shiny]\n\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n[网页三剑客]  -> [<actor>开发应用|R Shiny]\n\n[开发应用] <- [<table>处理数据|Base R|SQL||data.table|dplyr||tidyr|purrr]\n[开发应用] <- [<table>制作表格|DT|gt||reactable|formattable||kableExtra|sparkline]\n[开发应用] <- [<table>制作图形|ggplot2|plotly||echarts4r|leaflet||dygraphs|visNetwork]","svg":false,"png":null},"evals":[],"jsHooks":[]}</script>
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-shiny-ecosystem-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 图&nbsp;15.1: Shiny 生态系统