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            <h1 id="seo-header">评价临床预测模型</h1>
            
            
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                <p><a target="_blank" rel="noopener" href="https://occdn.limour.top/2110.html">模型来源</a>，<a target="_blank" rel="noopener" href="https://m.iikx.com/e/action/ShowInfo.php?classid=17&id=10597&page=3">参考1</a>、<a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/417683150">参考2</a></p>
<h2 id="安装补充包"><a href="#安装补充包" class="headerlink" title="安装补充包"></a>安装补充包</h2><ul>
<li><a target="_blank" rel="noopener" href="https://occdn.limour.top/1820.html">conda activate ggsurvplot</a></li>
<li>conda install -c conda-forge r-timeroc -y</li>
<li>conda install -c conda-forge r-gert -y</li>
<li>conda install -c conda-forge r-devtools -y</li>
<li>conda install -c r r-proc -y</li>
<li># conda install -c r r-nricens -y</li>
<li>~&#x2F;dev&#x2F;xray&#x2F;xray -c ~&#x2F;etc&#x2F;xui2.json &amp;</li>
<li>wget -e “https_proxy&#x3D;<a target="_blank" rel="noopener" href="http://127.0.0.1:20809/">http://127.0.0.1:20809</a>“ <a target="_blank" rel="noopener" href="https://github.com/yikeshu0611/ggDCA/archive/refs/heads/master.zip">https://github.com/yikeshu0611/ggDCA/archive/refs/heads/master.zip</a> -O ggDCA-master.zip</li>
<li>install.packages(‘nricens’)</li>
<li># install.packages(‘ggDCA’)</li>
<li>devtools::install_local(‘ggDCA-master.zip’)</li>
<li>install.packages(‘rmda’)</li>
</ul>
<figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><code class="hljs R">library<span class="hljs-punctuation">(</span>pROC<span class="hljs-punctuation">)</span> <span class="hljs-comment">#绘制ROC曲线</span><br>library<span class="hljs-punctuation">(</span>timeROC<span class="hljs-punctuation">)</span><br>library<span class="hljs-punctuation">(</span>ggDCA<span class="hljs-punctuation">)</span> <span class="hljs-comment">#绘制DCA曲线</span><br>library<span class="hljs-punctuation">(</span>nricens<span class="hljs-punctuation">)</span> <span class="hljs-comment">#计算NRI值</span><br></code></pre></td></tr></table></figure>

<h2 id="Logistic回归的诺莫图的校准图"><a href="#Logistic回归的诺莫图的校准图" class="headerlink" title="Logistic回归的诺莫图的校准图"></a>Logistic回归的诺莫图的校准图</h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><code class="hljs R">fit <span class="hljs-operator">&lt;-</span> lrm<span class="hljs-punctuation">(</span>formula<span class="hljs-punctuation">(</span>lrmf<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> data<span class="hljs-operator">=</span>df<span class="hljs-punctuation">,</span> x<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> y<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> maxit<span class="hljs-operator">=</span><span class="hljs-number">1000</span><span class="hljs-punctuation">)</span><br>cal <span class="hljs-operator">&lt;-</span> calibrate<span class="hljs-punctuation">(</span>fit<span class="hljs-punctuation">,</span> method<span class="hljs-operator">=</span><span class="hljs-string">&quot;boot&quot;</span><span class="hljs-punctuation">,</span> B<span class="hljs-operator">=</span><span class="hljs-number">1000</span><span class="hljs-punctuation">)</span><br>plot<span class="hljs-punctuation">(</span>cal<span class="hljs-punctuation">,</span><br>　　xlab<span class="hljs-operator">=</span><span class="hljs-string">&quot;Nomogram-predicted probability of nonadherence&quot;</span><span class="hljs-punctuation">,</span><br>　　ylab<span class="hljs-operator">=</span><span class="hljs-string">&quot;Actual diagnosed nonadherence (proportion)&quot;</span><span class="hljs-punctuation">,</span><br>　　sub<span class="hljs-operator">=</span><span class="hljs-built_in">F</span><span class="hljs-punctuation">)</span><br></code></pre></td></tr></table></figure>

<p>其中Bias-corrected为校正曲线，而对角线Ideal为理想的曲线。校正曲线与理想曲线之间越相近，说明模型的预测能力越好。</p>
<h2 id="COX回归的诺莫图的校准图"><a href="#COX回归的诺莫图的校准图" class="headerlink" title="COX回归的诺莫图的校准图"></a>COX回归的诺莫图的校准图</h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><code class="hljs R">f_cph_2 <span class="hljs-operator">&lt;-</span> cph<span class="hljs-punctuation">(</span>formula<span class="hljs-punctuation">(</span>coxmf<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><br>               x<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span> y<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span> surv<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span><br>               data<span class="hljs-operator">=</span>df<span class="hljs-punctuation">)</span><br>cal_2 <span class="hljs-operator">&lt;-</span> calibrate<span class="hljs-punctuation">(</span>f_cph_2<span class="hljs-punctuation">,</span> u<span class="hljs-operator">=</span><span class="hljs-number">5</span><span class="hljs-punctuation">,</span> cmethod<span class="hljs-operator">=</span><span class="hljs-string">&#x27;KM&#x27;</span><span class="hljs-punctuation">,</span> m<span class="hljs-operator">=</span><span class="hljs-number">15</span><span class="hljs-punctuation">,</span> B<span class="hljs-operator">=</span><span class="hljs-number">200</span><span class="hljs-punctuation">)</span><span class="hljs-comment"># usually B=200 or 300, u=5表示五年</span><br>options<span class="hljs-punctuation">(</span>repr.plot.width<span class="hljs-operator">=</span><span class="hljs-number">10</span><span class="hljs-punctuation">,</span> repr.plot.height<span class="hljs-operator">=</span><span class="hljs-number">10</span><span class="hljs-punctuation">)</span><br>plot<span class="hljs-punctuation">(</span>cal_2<span class="hljs-punctuation">,</span>lwd<span class="hljs-operator">=</span><span class="hljs-number">2</span><span class="hljs-punctuation">,</span>lty<span class="hljs-operator">=</span><span class="hljs-number">1</span><span class="hljs-punctuation">,</span>  <span class="hljs-comment">##设置线条宽度和线条类型</span><br>     errbar.col<span class="hljs-operator">=</span><span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span>rgb<span class="hljs-punctuation">(</span><span class="hljs-number">0</span><span class="hljs-punctuation">,</span><span class="hljs-number">118</span><span class="hljs-punctuation">,</span><span class="hljs-number">192</span><span class="hljs-punctuation">,</span>maxColorValue <span class="hljs-operator">=</span> <span class="hljs-number">255</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">##设置一个颜色</span><br>     xlab<span class="hljs-operator">=</span><span class="hljs-string">&#x27;Nomogram-Predicted Probability of 5 years DFS&#x27;</span><span class="hljs-punctuation">,</span><span class="hljs-comment">#便签</span><br>     ylab<span class="hljs-operator">=</span><span class="hljs-string">&#x27;Actual 5 years DFS(proportion)&#x27;</span><span class="hljs-punctuation">,</span><span class="hljs-comment">#标签</span><br>     col<span class="hljs-operator">=</span><span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span>rgb<span class="hljs-punctuation">(</span><span class="hljs-number">192</span><span class="hljs-punctuation">,</span><span class="hljs-number">98</span><span class="hljs-punctuation">,</span><span class="hljs-number">83</span><span class="hljs-punctuation">,</span>maxColorValue <span class="hljs-operator">=</span> <span class="hljs-number">255</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><span class="hljs-comment">#设置一个颜色</span><br>     xlim <span class="hljs-operator">=</span> <span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span><span class="hljs-number">0</span><span class="hljs-punctuation">,</span><span class="hljs-number">1</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span>ylim <span class="hljs-operator">=</span> <span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span><span class="hljs-number">0</span><span class="hljs-punctuation">,</span><span class="hljs-number">1</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><span class="hljs-comment">##x轴和y轴范围</span><br>     mgp <span class="hljs-operator">=</span> <span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span><span class="hljs-number">2</span><span class="hljs-punctuation">,</span> <span class="hljs-number">1</span><span class="hljs-punctuation">,</span> <span class="hljs-number">0</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span> <span class="hljs-comment">#控制坐标轴的位置</span><br></code></pre></td></tr></table></figure>

<h2 id="计算C指数"><a href="#计算C指数" class="headerlink" title="计算C指数"></a><strong>计算C指数</strong></h2><ul>
<li>rcorrcens((dcf_status&#x3D;&#x3D;0)~predict(f2), data &#x3D; df)</li>
<li>&lt;0.5 模型没有任何预测能力</li>
<li>0.51-0.7 较差的准确性</li>
<li>0.71-0.9 中等的准确性</li>
<li>&gt; 0.9 高度的准确性</li>
</ul>
<h2 id="ROC曲线"><a href="#ROC曲线" class="headerlink" title="ROC曲线"></a><strong>ROC曲线</strong></h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><code class="hljs R">gfit <span class="hljs-operator">&lt;-</span> roc<span class="hljs-punctuation">(</span><span class="hljs-punctuation">(</span>dcf_status<span class="hljs-operator">==</span><span class="hljs-number">0</span><span class="hljs-punctuation">)</span><span class="hljs-operator">~</span>predict<span class="hljs-punctuation">(</span>f2<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> data <span class="hljs-operator">=</span> df<span class="hljs-punctuation">)</span><br>options<span class="hljs-punctuation">(</span>repr.plot.width<span class="hljs-operator">=</span><span class="hljs-number">6</span><span class="hljs-punctuation">,</span> repr.plot.height<span class="hljs-operator">=</span><span class="hljs-number">6</span><span class="hljs-punctuation">)</span><br>plot<span class="hljs-punctuation">(</span>gfit<span class="hljs-punctuation">,</span><br>　　print.auc<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#输出AUC值</span><br>　　print.thres<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#输出cut-off值</span><br>　　main <span class="hljs-operator">=</span> <span class="hljs-string">&quot;ROC CURVE&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#设置图形的标题</span><br>　　col<span class="hljs-operator">=</span> <span class="hljs-string">&quot;red&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#曲线颜色</span><br>　　print.thres.col<span class="hljs-operator">=</span><span class="hljs-string">&quot;black&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#cut-off值字体的颜色</span><br>　　identity.col<span class="hljs-operator">=</span><span class="hljs-string">&quot;blue&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#对角线颜色</span><br>　　identity.lty<span class="hljs-operator">=</span><span class="hljs-number">1</span><span class="hljs-punctuation">,</span>identity.lwd<span class="hljs-operator">=</span><span class="hljs-number">1</span><span class="hljs-punctuation">)</span><br></code></pre></td></tr></table></figure>

<ul>
<li>AUC值为 0.656</li>
<li>0.5~0.7 模型的效果较低</li>
<li>0.7~0.85 效果一般</li>
<li>0.85~0.95 效果很好</li>
<li>最佳截断值cut 2.898</li>
<li>当以2.898进行分组时，两组之间具有最佳的区分度</li>
</ul>
<p><img src="https://img-cdn.limour.top/2022/07/17/62d373489eed0.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></p>
<h2 id="Logistic回归的DCA曲线"><a href="#Logistic回归的DCA曲线" class="headerlink" title="Logistic回归的DCA曲线"></a>Logistic回归的<strong>DCA曲线</strong></h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><code class="hljs R">library<span class="hljs-punctuation">(</span>rmda<span class="hljs-punctuation">)</span> <span class="hljs-comment">#绘制DCA曲线</span><br>modul<span class="hljs-operator">&lt;-</span> decision_curve<span class="hljs-punctuation">(</span>data<span class="hljs-operator">=</span> df<span class="hljs-punctuation">,</span><br>　　formula<span class="hljs-punctuation">(</span>lrmf<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><br>　　family <span class="hljs-operator">=</span> binomial<span class="hljs-punctuation">(</span>link <span class="hljs-operator">=</span><span class="hljs-string">&#x27;logit&#x27;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><br>　　thresholds<span class="hljs-operator">=</span> seq<span class="hljs-punctuation">(</span><span class="hljs-number">0</span><span class="hljs-punctuation">,</span><span class="hljs-number">1</span><span class="hljs-punctuation">,</span> by <span class="hljs-operator">=</span> <span class="hljs-number">0.01</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><br>　　confidence.intervals <span class="hljs-operator">=</span> <span class="hljs-number">0.95</span><span class="hljs-punctuation">)</span><br>plot_decision_curve<span class="hljs-punctuation">(</span>modul<span class="hljs-punctuation">,</span><br>　　curve.names<span class="hljs-operator">=</span><span class="hljs-string">&quot;Nonadherence prediction nomogram&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#曲线名称</span><br>　　xlab<span class="hljs-operator">=</span><span class="hljs-string">&quot;Threshold probability&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#x轴名称</span><br>　　cost.benefit.axis <span class="hljs-operator">=</span><span class="hljs-literal">FALSE</span><span class="hljs-punctuation">,</span> col<span class="hljs-operator">=</span> <span class="hljs-string">&quot;blue&quot;</span><span class="hljs-punctuation">,</span><br>　　confidence.intervals<span class="hljs-operator">=</span><span class="hljs-literal">FALSE</span><span class="hljs-punctuation">,</span><br>　　standardize <span class="hljs-operator">=</span> <span class="hljs-literal">FALSE</span><span class="hljs-punctuation">)</span><br></code></pre></td></tr></table></figure>

<h2 id="COX回归的DCA曲线"><a href="#COX回归的DCA曲线" class="headerlink" title="COX回归的DCA曲线"></a>COX回归的<strong>DCA曲线</strong></h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><code class="hljs R">library<span class="hljs-punctuation">(</span>car<span class="hljs-punctuation">)</span><br>library<span class="hljs-punctuation">(</span>rms<span class="hljs-punctuation">)</span><br>library<span class="hljs-punctuation">(</span>pROC<span class="hljs-punctuation">)</span><br>library<span class="hljs-punctuation">(</span>timeROC<span class="hljs-punctuation">)</span><br>library<span class="hljs-punctuation">(</span>ggDCA<span class="hljs-punctuation">)</span><br>df <span class="hljs-operator">&lt;-</span> readRDS<span class="hljs-punctuation">(</span><span class="hljs-string">&#x27;Cox_df.rds&#x27;</span><span class="hljs-punctuation">)</span><br>df<span class="hljs-punctuation">[</span><span class="hljs-punctuation">,</span><span class="hljs-string">&#x27;dcf_status&#x27;</span><span class="hljs-punctuation">]</span> <span class="hljs-operator">=</span> ifelse<span class="hljs-punctuation">(</span>df<span class="hljs-punctuation">[</span><span class="hljs-punctuation">,</span><span class="hljs-string">&#x27;dcf_status&#x27;</span><span class="hljs-punctuation">]</span><span class="hljs-operator">==</span><span class="hljs-number">0</span><span class="hljs-punctuation">,</span><span class="hljs-number">1</span><span class="hljs-punctuation">,</span><span class="hljs-number">2</span><span class="hljs-punctuation">)</span><br>dd<span class="hljs-operator">=</span>datadist<span class="hljs-punctuation">(</span>df<span class="hljs-punctuation">)</span><br>options<span class="hljs-punctuation">(</span>datadist<span class="hljs-operator">=</span><span class="hljs-string">&quot;dd&quot;</span><span class="hljs-punctuation">)</span> <br>coxmf <span class="hljs-operator">&lt;-</span> paste0<span class="hljs-punctuation">(</span><span class="hljs-string">&quot;Surv(dcf_time, dcf_status)~&quot;</span><span class="hljs-punctuation">,</span> paste<span class="hljs-punctuation">(</span>colnames<span class="hljs-punctuation">(</span>df<span class="hljs-punctuation">)</span><span class="hljs-punctuation">[</span><span class="hljs-number">1</span><span class="hljs-operator">:</span><span class="hljs-number">10</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span> collapse <span class="hljs-operator">=</span> <span class="hljs-string">&#x27;+&#x27;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span><br>coxmf<br>f_cph_2 <span class="hljs-operator">&lt;-</span> cph<span class="hljs-punctuation">(</span>formula<span class="hljs-punctuation">(</span>coxmf<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span><br>               x<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span> y<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span> surv<span class="hljs-operator">=</span><span class="hljs-built_in">T</span><span class="hljs-punctuation">,</span><br>               data<span class="hljs-operator">=</span>df<span class="hljs-punctuation">)</span><br>dca_training <span class="hljs-operator">&lt;-</span> dca<span class="hljs-punctuation">(</span>f_cph_2<span class="hljs-punctuation">,</span> times<span class="hljs-operator">=</span><span class="hljs-built_in">c</span><span class="hljs-punctuation">(</span><span class="hljs-number">5</span><span class="hljs-operator">*</span><span class="hljs-number">365</span><span class="hljs-punctuation">,</span> <span class="hljs-number">10</span><span class="hljs-operator">*</span><span class="hljs-number">365</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span> <span class="hljs-comment">#五年、十年</span><br>ggplot<span class="hljs-punctuation">(</span>dca_training<span class="hljs-punctuation">)</span><br></code></pre></td></tr></table></figure>

<h2 id="Logistic回归的NRI指数"><a href="#Logistic回归的NRI指数" class="headerlink" title="Logistic回归的NRI指数"></a>Logistic回归的<strong>NRI指数</strong></h2><figure class="highlight r"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><code class="hljs R">lrmf_a <span class="hljs-operator">&lt;-</span> paste0<span class="hljs-punctuation">(</span><span class="hljs-string">&quot;factor(dcf_status)~&quot;</span><span class="hljs-punctuation">,</span> paste<span class="hljs-punctuation">(</span>colnames<span class="hljs-punctuation">(</span>df<span class="hljs-punctuation">)</span><span class="hljs-punctuation">[</span><span class="hljs-number">1</span><span class="hljs-operator">:</span><span class="hljs-number">9</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span> collapse <span class="hljs-operator">=</span> <span class="hljs-string">&#x27;+&#x27;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span><br>lrmf_b <span class="hljs-operator">&lt;-</span> paste0<span class="hljs-punctuation">(</span><span class="hljs-string">&quot;factor(dcf_status)~&quot;</span><span class="hljs-punctuation">,</span> paste<span class="hljs-punctuation">(</span>colnames<span class="hljs-punctuation">(</span>df<span class="hljs-punctuation">)</span><span class="hljs-punctuation">[</span><span class="hljs-number">1</span><span class="hljs-operator">:</span><span class="hljs-number">10</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span> collapse <span class="hljs-operator">=</span> <span class="hljs-string">&#x27;+&#x27;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">)</span><br>fit_A <span class="hljs-operator">&lt;-</span> glm<span class="hljs-punctuation">(</span>formula<span class="hljs-punctuation">(</span>lrmf_a<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> data <span class="hljs-operator">=</span> df<span class="hljs-punctuation">,</span> family <span class="hljs-operator">=</span> binomial<span class="hljs-punctuation">(</span>link<span class="hljs-operator">=</span><span class="hljs-string">&quot;logit&quot;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> x<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">)</span><br>fit_B <span class="hljs-operator">&lt;-</span> glm<span class="hljs-punctuation">(</span>formula<span class="hljs-punctuation">(</span>lrmf_b<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> data <span class="hljs-operator">=</span> df<span class="hljs-punctuation">,</span> family <span class="hljs-operator">=</span> binomial<span class="hljs-punctuation">(</span>link<span class="hljs-operator">=</span><span class="hljs-string">&quot;logit&quot;</span><span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> x<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">)</span><br>gfit <span class="hljs-operator">&lt;-</span> roc<span class="hljs-punctuation">(</span>factor<span class="hljs-punctuation">(</span>dcf_status<span class="hljs-punctuation">)</span><span class="hljs-operator">~</span>predict<span class="hljs-punctuation">(</span>fit_A<span class="hljs-punctuation">)</span><span class="hljs-punctuation">,</span> data <span class="hljs-operator">=</span> df<span class="hljs-punctuation">)</span><br>options<span class="hljs-punctuation">(</span>repr.plot.width<span class="hljs-operator">=</span><span class="hljs-number">10</span><span class="hljs-punctuation">,</span> repr.plot.height<span class="hljs-operator">=</span><span class="hljs-number">10</span><span class="hljs-punctuation">)</span><br>plot<span class="hljs-punctuation">(</span>gfit<span class="hljs-punctuation">,</span><br>　　print.auc<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#输出AUC值</span><br>　　print.thres<span class="hljs-operator">=</span><span class="hljs-literal">TRUE</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#输出cut-off值</span><br>　　main <span class="hljs-operator">=</span> <span class="hljs-string">&quot;ROC CURVE&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#设置图形的标题</span><br>　　col<span class="hljs-operator">=</span> <span class="hljs-string">&quot;red&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#曲线颜色</span><br>　　print.thres.col<span class="hljs-operator">=</span><span class="hljs-string">&quot;black&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#cut-off值字体的颜色</span><br>　　identity.col<span class="hljs-operator">=</span><span class="hljs-string">&quot;blue&quot;</span><span class="hljs-punctuation">,</span> <span class="hljs-comment">#对角线颜色</span><br>　　identity.lty<span class="hljs-operator">=</span><span class="hljs-number">1</span><span class="hljs-punctuation">,</span>identity.lwd<span class="hljs-operator">=</span><span class="hljs-number">1</span><span class="hljs-punctuation">)</span><br>NRI <span class="hljs-operator">&lt;-</span> nribin<span class="hljs-punctuation">(</span>mdl.std <span class="hljs-operator">=</span> fit_A<span class="hljs-punctuation">,</span> mdl.new <span class="hljs-operator">=</span> fit_B<span class="hljs-punctuation">,</span><br>　　updown <span class="hljs-operator">=</span> <span class="hljs-string">&#x27;diff&#x27;</span><span class="hljs-punctuation">,</span><br>　　cut <span class="hljs-operator">=</span> <span class="hljs-number">0.05</span><span class="hljs-punctuation">,</span> niter <span class="hljs-operator">=</span> <span class="hljs-number">500</span><span class="hljs-punctuation">,</span> alpha <span class="hljs-operator">=</span> <span class="hljs-number">0.05</span><span class="hljs-punctuation">)</span><br>NRI <span class="hljs-operator">&lt;-</span> nribin<span class="hljs-punctuation">(</span>mdl.std <span class="hljs-operator">=</span> fit_A<span class="hljs-punctuation">,</span> mdl.new <span class="hljs-operator">=</span> fit_B<span class="hljs-punctuation">,</span><br>　　updown <span class="hljs-operator">=</span> <span class="hljs-string">&#x27;category&#x27;</span><span class="hljs-punctuation">,</span><br>　　cut <span class="hljs-operator">=</span> <span class="hljs-number">1.791</span><span class="hljs-punctuation">,</span> niter <span class="hljs-operator">=</span> <span class="hljs-number">500</span><span class="hljs-punctuation">,</span> alpha <span class="hljs-operator">=</span> <span class="hljs-number">0.05</span><span class="hljs-punctuation">)</span><br></code></pre></td></tr></table></figure>

<p>根据之前模型的ROC分析确定的切点cut，之后的分析见<a target="_blank" rel="noopener" href="https://m.iikx.com/e/action/ShowInfo.php?classid=17&id=10597&page=3">爱科学</a></p>

                
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