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<a href="LinearRegression_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">// --------------------------------------------------------------------------</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// OpenMS -- Open-Source Mass Spectrometry</span></div>
<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// --------------------------------------------------------------------------</span></div>
<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,</span></div>
<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment">// ETH Zurich, and Freie Universitaet Berlin 2002-2013.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment">//</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">// This software is released under a three-clause BSD license:</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment">// * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// notice, this list of conditions and the following disclaimer.</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">// * Redistributions in binary form must reproduce the above copyright</span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// notice, this list of conditions and the following disclaimer in the</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// documentation and/or other materials provided with the distribution.</span></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment">// * Neither the name of any author or any participating institution</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// may be used to endorse or promote products derived from this software</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment">// without specific prior written permission.</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment">// For a full list of authors, refer to the file AUTHORS.</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment">// --------------------------------------------------------------------------</span></div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment">// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"</span></div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment">// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE</span></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE</span></div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING</span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,</span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment">// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;</span></div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,</span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment">// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR</span></div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF</span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span></div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment">//</span></div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">// --------------------------------------------------------------------------</span></div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment">// $Maintainer: Clemens Groepl $</span></div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment">// $Authors: $</span></div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment">// --------------------------------------------------------------------------</span></div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#ifndef OPENMS_MATH_STATISTICS_LINEARREGRESSION_H</span></div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor"></span><span class="preprocessor">#define OPENMS_MATH_STATISTICS_LINEARREGRESSION_H</span></div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor"></span></div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <<a class="code" href="Types_8h.html">OpenMS/CONCEPT/Types.h</a>></span></div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <<a class="code" href="Exception_8h.html">OpenMS/CONCEPT/Exception.h</a>></span></div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <iostream></span></div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#include <vector></span></div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include <gsl/gsl_fit.h></span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor">#include <gsl/gsl_statistics.h></span></div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor">#include <gsl/gsl_cdf.h></span></div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace </span>OpenMS</div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> {</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">namespace </span>Math</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div>
<div class="line"><a name="l00069"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html"> 69</a></span>  <span class="keyword">class </span>OPENMS_DLLAPI <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html">LinearRegression</a></div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  {</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="keyword">public</span>:</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div>
<div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a33f04840f1f440c8ab14254ce127cf9f"> 74</a></span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a33f04840f1f440c8ab14254ce127cf9f">LinearRegression</a>() :</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  intercept_(0),</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  slope_(0),</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  x_intercept_(0),</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  lower_(0),</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  upper_(0),</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  t_star_(0),</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  r_squared_(0),</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  stand_dev_residuals_(0),</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  mean_residuals_(0),</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  stand_error_slope_(0),</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  chi_squared_(0),</div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  rsd_(0)</div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  {</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div>
<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a86f37b18fdbf5502d6450ce7542c9fc1"> 91</a></span>  <span class="keyword">virtual</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a86f37b18fdbf5502d6450ce7542c9fc1">~LinearRegression</a>()</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordtype">void</span> computeRegression(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin);</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordtype">void</span> computeRegressionNoIntercept(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin);</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordtype">void</span> computeRegressionWeighted(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin);</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classdouble.html">DoubleReal</a> getIntercept() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classdouble.html">DoubleReal</a> getSlope() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="classdouble.html">DoubleReal</a> getXIntercept() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classdouble.html">DoubleReal</a> getLower() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="classdouble.html">DoubleReal</a> getUpper() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classdouble.html">DoubleReal</a> getTValue() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classdouble.html">DoubleReal</a> getRSquared() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="classdouble.html">DoubleReal</a> getStandDevRes() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="classdouble.html">DoubleReal</a> getMeanRes() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classdouble.html">DoubleReal</a> getStandErrSlope() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="classdouble.html">DoubleReal</a> getChiSquared() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classdouble.html">DoubleReal</a> getRSD() <span class="keyword">const</span>;</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div>
<div class="line"><a name="l00180"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe"> 180</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe">intercept_</a>;</div>
<div class="line"><a name="l00182"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce"> 182</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce">slope_</a>;</div>
<div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a321d51de8675c2849b088f88dbb2a8ce"> 184</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a321d51de8675c2849b088f88dbb2a8ce">x_intercept_</a>;</div>
<div class="line"><a name="l00186"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a3785493880563067522dafc8b933b4be"> 186</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a3785493880563067522dafc8b933b4be">lower_</a>;</div>
<div class="line"><a name="l00188"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a829ff0e25c50e0fe9c3d41a14ed2f3f0"> 188</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a829ff0e25c50e0fe9c3d41a14ed2f3f0">upper_</a>;</div>
<div class="line"><a name="l00190"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a89f82fed143d8e6d0faf791983e6b775"> 190</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a89f82fed143d8e6d0faf791983e6b775">t_star_</a>;</div>
<div class="line"><a name="l00192"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a1243d6935368ecf2a9c9b69db96047fd"> 192</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a1243d6935368ecf2a9c9b69db96047fd">r_squared_</a>;</div>
<div class="line"><a name="l00194"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#acd59989a454f367b849167936370153c"> 194</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#acd59989a454f367b849167936370153c">stand_dev_residuals_</a>;</div>
<div class="line"><a name="l00196"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#acd3cd62e6c70a9e26bb18823e1a45128"> 196</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#acd3cd62e6c70a9e26bb18823e1a45128">mean_residuals_</a>;</div>
<div class="line"><a name="l00198"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#aeebe85655b4c6494a78b8a12f04b88db"> 198</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#aeebe85655b4c6494a78b8a12f04b88db">stand_error_slope_</a>;</div>
<div class="line"><a name="l00200"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622"> 200</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622">chi_squared_</a>;</div>
<div class="line"><a name="l00202"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#ae420fad2f5cf2eba659b3c789d7ae444"> 202</a></span>  <span class="keywordtype">double</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ae420fad2f5cf2eba659b3c789d7ae444">rsd_</a>;</div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordtype">void</span> computeGoodness_(<span class="keywordtype">double</span> * X, <span class="keywordtype">double</span> * Y, <span class="keywordtype">int</span> N, <span class="keywordtype">double</span> confidence_interval_P);</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordtype">void</span> iteratorRange2Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, <span class="keywordtype">double</span> * x_array, <span class="keywordtype">double</span> * y_array);</div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordtype">void</span> iteratorRange3Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, <span class="keywordtype">double</span> * x_array, <span class="keywordtype">double</span> * y_array, <span class="keywordtype">double</span> * w_array);</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="keyword">private</span>:</div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html">LinearRegression</a>(<span class="keyword">const</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html">LinearRegression</a> & arg);</div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html">LinearRegression</a> & operator=(<span class="keyword">const</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html">LinearRegression</a> & arg);</div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  };</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a115e2ca09974d554cc194957cf254213"> 225</a></span>  <span class="keywordtype">void</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a115e2ca09974d554cc194957cf254213">LinearRegression::computeRegression</a>(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  {</div>
<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">int</span> N = int(distance(x_begin, x_end));</div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordtype">double</span> * X = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">double</span> * Y = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a66e8bbf38afe7934556ea36e418ec010">iteratorRange2Arrays_</a>(x_begin, x_end, y_begin, X, Y);</div>
<div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordtype">double</span> cov00, cov01, cov11;</div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="comment">// Compute the unweighted linear fit.</span></div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X</span></div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// and the value of Chi squared, the covariances of the intercept and the slope</span></div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordtype">int</span> error = gsl_fit_linear(X, 1, Y, 1, N, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe">intercept_</a>, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce">slope_</a>, &cov00, &cov01, &cov11, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622">chi_squared_</a>);</div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span> (!error)</div>
<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  {</div>
<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a825c92fe4a74acc9b984ba05d236bd6d">computeGoodness_</a>(X, Y, N, confidence_interval_P);</div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">delete</span>[] X;</div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">delete</span>[] Y;</div>
<div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div>
<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">if</span> (error)</div>
<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div>
<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">throw</span> <a class="code" href="classOpenMS_1_1Exception_1_1UnableToFit.html">Exception::UnableToFit</a>(__FILE__, __LINE__, __PRETTY_FUNCTION__, <span class="stringliteral">"UnableToFit-LinearRegression"</span>, <span class="stringliteral">"Could not fit a linear model to the data"</span>);</div>
<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div>
<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div>
<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div>
<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00255"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#aa2ee8e6e79a594c3dfd5aefaf2846e72"> 255</a></span>  <span class="keywordtype">void</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#aa2ee8e6e79a594c3dfd5aefaf2846e72">LinearRegression::computeRegressionNoIntercept</a>(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)</div>
<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  {</div>
<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordtype">int</span> N = int(distance(x_begin, x_end));</div>
<div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div>
<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordtype">double</span> * X = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordtype">double</span> * Y = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a66e8bbf38afe7934556ea36e418ec010">iteratorRange2Arrays_</a>(x_begin, x_end, y_begin, X, Y);</div>
<div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div>
<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordtype">double</span> cov;</div>
<div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div>
<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="comment">// Compute the linear fit.</span></div>
<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X</span></div>
<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="comment">// and the value of Chi squared, the covariances of the intercept and the slope</span></div>
<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordtype">int</span> error = gsl_fit_mul(X, 1, Y, 1, N, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce">slope_</a>, &cov, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622">chi_squared_</a>);</div>
<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe">intercept_</a> = 0.0;</div>
<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div>
<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">if</span> (!error)</div>
<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  {</div>
<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a825c92fe4a74acc9b984ba05d236bd6d">computeGoodness_</a>(X, Y, N, confidence_interval_P);</div>
<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div>
<div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div>
<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">delete</span>[] X;</div>
<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">delete</span>[] Y;</div>
<div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div>
<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">if</span> (error)</div>
<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  {</div>
<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordflow">throw</span> <a class="code" href="classOpenMS_1_1Exception_1_1UnableToFit.html">Exception::UnableToFit</a>(__FILE__, __LINE__, __PRETTY_FUNCTION__, <span class="stringliteral">"UnableToFit-LinearRegression"</span>, <span class="stringliteral">"Could not fit a linear model to the data"</span>);</div>
<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div>
<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  }</div>
<div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div>
<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a5cea2666293a4c3987a9c64d96170758"> 286</a></span>  <span class="keywordtype">void</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a5cea2666293a4c3987a9c64d96170758">LinearRegression::computeRegressionWeighted</a>(<span class="keywordtype">double</span> confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin)</div>
<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  {</div>
<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">int</span> N = int(distance(x_begin, x_end));</div>
<div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div>
<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordtype">double</span> * X = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordtype">double</span> * Y = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordtype">double</span> * W = <span class="keyword">new</span> <span class="keywordtype">double</span>[N];</div>
<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ae88bf8b1583b4d94a3d254cb676c1fc8">iteratorRange3Arrays_</a>(x_begin, x_end, y_begin, w_begin, X, Y, W);</div>
<div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div>
<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">double</span> cov00, cov01, cov11;</div>
<div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div>
<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// Compute the weighted linear fit.</span></div>
<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">// Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X</span></div>
<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="comment">// and the value of Chi squared, the covariances of the intercept and the slope</span></div>
<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordtype">int</span> error = gsl_fit_wlinear(X, 1, W, 1, Y, 1, N, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe">intercept_</a>, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce">slope_</a>, &cov00, &cov01, &cov11, &<a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622">chi_squared_</a>);</div>
<div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div>
<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">if</span> (!error)</div>
<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  {</div>
<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a825c92fe4a74acc9b984ba05d236bd6d">computeGoodness_</a>(X, Y, N, confidence_interval_P);</div>
<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  }</div>
<div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div>
<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">delete</span>[] X;</div>
<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">delete</span>[] Y;</div>
<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">delete</span>[] W;</div>
<div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div>
<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">if</span> (error)</div>
<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div>
<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">throw</span> <a class="code" href="classOpenMS_1_1Exception_1_1UnableToFit.html">Exception::UnableToFit</a>(__FILE__, __LINE__, __PRETTY_FUNCTION__, <span class="stringliteral">"UnableToFit-LinearRegression"</span>, <span class="stringliteral">"Could not fit a linear model to the data"</span>);</div>
<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div>
<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div>
<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div>
<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00318"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#a66e8bbf38afe7934556ea36e418ec010"> 318</a></span>  <span class="keywordtype">void</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#a66e8bbf38afe7934556ea36e418ec010">LinearRegression::iteratorRange2Arrays_</a>(Iterator x_begin, Iterator x_end, Iterator y_begin, <span class="keywordtype">double</span> * x_array, <span class="keywordtype">double</span> * y_array)</div>
<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  {</div>
<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordtype">int</span> i = 0;</div>
<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordflow">while</span> (x_begin < x_end)</div>
<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div>
<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  x_array[i] = *x_begin;</div>
<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  y_array[i] = *y_begin;</div>
<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  ++x_begin;</div>
<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  ++y_begin;</div>
<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  ++i;</div>
<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div>
<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div>
<div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div>
<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> Iterator></div>
<div class="line"><a name="l00332"></a><span class="lineno"><a class="line" href="classOpenMS_1_1Math_1_1LinearRegression.html#ae88bf8b1583b4d94a3d254cb676c1fc8"> 332</a></span>  <span class="keywordtype">void</span> <a class="code" href="classOpenMS_1_1Math_1_1LinearRegression.html#ae88bf8b1583b4d94a3d254cb676c1fc8">LinearRegression::iteratorRange3Arrays_</a>(Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, <span class="keywordtype">double</span> * x_array, <span class="keywordtype">double</span> * y_array, <span class="keywordtype">double</span> * w_array)</div>
<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div>
<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordtype">int</span> i = 0;</div>
<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">while</span> (x_begin < x_end)</div>
<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  {</div>
<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  x_array[i] = *x_begin;</div>
<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  y_array[i] = *y_begin;</div>
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  w_array[i] = *w_begin;</div>
<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  ++x_begin;</div>
<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  ++y_begin;</div>
<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  ++w_begin;</div>
<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  ++i;</div>
<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div>
<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div>
<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div>
<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  } <span class="comment">// namespace Math</span></div>
<div class="line"><a name="l00348"></a><span class="lineno"> 348</span> } <span class="comment">// namespace OpenMS</span></div>
<div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div>
<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div>
<div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="preprocessor">#endif</span></div>
<div class="ttc" id="Types_8h_html"><div class="ttname"><a href="Types_8h.html">Types.h</a></div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a86f37b18fdbf5502d6450ce7542c9fc1"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a86f37b18fdbf5502d6450ce7542c9fc1">OpenMS::Math::LinearRegression::~LinearRegression</a></div><div class="ttdeci">virtual ~LinearRegression()</div><div class="ttdoc">Destructor. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:91</div></div>
<div class="ttc" id="classdouble_html"><div class="ttname"><a href="classdouble.html">double</a></div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a33f04840f1f440c8ab14254ce127cf9f"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a33f04840f1f440c8ab14254ce127cf9f">OpenMS::Math::LinearRegression::LinearRegression</a></div><div class="ttdeci">LinearRegression()</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:74</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_ae420fad2f5cf2eba659b3c789d7ae444"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#ae420fad2f5cf2eba659b3c789d7ae444">OpenMS::Math::LinearRegression::rsd_</a></div><div class="ttdeci">double rsd_</div><div class="ttdoc">the relative standard deviation </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:202</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a829ff0e25c50e0fe9c3d41a14ed2f3f0"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a829ff0e25c50e0fe9c3d41a14ed2f3f0">OpenMS::Math::LinearRegression::upper_</a></div><div class="ttdeci">double upper_</div><div class="ttdoc">The upper bound of the confidence intervall. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:188</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_abc77697d8737fc270dc9b23923743622"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#abc77697d8737fc270dc9b23923743622">OpenMS::Math::LinearRegression::chi_squared_</a></div><div class="ttdeci">double chi_squared_</div><div class="ttdoc">The value of the Chi Squared statistic. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:200</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a825c92fe4a74acc9b984ba05d236bd6d"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a825c92fe4a74acc9b984ba05d236bd6d">OpenMS::Math::LinearRegression::computeGoodness_</a></div><div class="ttdeci">void computeGoodness_(double *X, double *Y, int N, double confidence_interval_P)</div><div class="ttdoc">Computes the goodness of the fitted regression line. </div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a5cea2666293a4c3987a9c64d96170758"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a5cea2666293a4c3987a9c64d96170758">OpenMS::Math::LinearRegression::computeRegressionWeighted</a></div><div class="ttdeci">void computeRegressionWeighted(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin)</div><div class="ttdoc">This function computes the best-fit linear regression coefficients of the model for the weighted da...</div><div class="ttdef"><b>Definition:</b> LinearRegression.h:286</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html">OpenMS::Math::LinearRegression</a></div><div class="ttdoc">This class offers functions to perform least-squares fits to a straight line model, . </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:69</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a321d51de8675c2849b088f88dbb2a8ce"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a321d51de8675c2849b088f88dbb2a8ce">OpenMS::Math::LinearRegression::x_intercept_</a></div><div class="ttdeci">double x_intercept_</div><div class="ttdoc">The intercept of the fitted line with the x-axis. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:184</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a89f82fed143d8e6d0faf791983e6b775"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a89f82fed143d8e6d0faf791983e6b775">OpenMS::Math::LinearRegression::t_star_</a></div><div class="ttdeci">double t_star_</div><div class="ttdoc">The value of the t-statistic. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:190</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_aeebe85655b4c6494a78b8a12f04b88db"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#aeebe85655b4c6494a78b8a12f04b88db">OpenMS::Math::LinearRegression::stand_error_slope_</a></div><div class="ttdeci">double stand_error_slope_</div><div class="ttdoc">The standard error of the slope. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:198</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_acd3cd62e6c70a9e26bb18823e1a45128"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#acd3cd62e6c70a9e26bb18823e1a45128">OpenMS::Math::LinearRegression::mean_residuals_</a></div><div class="ttdeci">double mean_residuals_</div><div class="ttdoc">Mean of residuals. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:196</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_ac2ec09e825c213e90a799cc765d599ce"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#ac2ec09e825c213e90a799cc765d599ce">OpenMS::Math::LinearRegression::slope_</a></div><div class="ttdeci">double slope_</div><div class="ttdoc">The slope of the fitted line. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:182</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_ae88bf8b1583b4d94a3d254cb676c1fc8"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#ae88bf8b1583b4d94a3d254cb676c1fc8">OpenMS::Math::LinearRegression::iteratorRange3Arrays_</a></div><div class="ttdeci">void iteratorRange3Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, double *x_array, double *y_array, double *w_array)</div><div class="ttdoc">Copy the distance(x_begin,x_end) elements starting at x_begin, y_begin and w_begin into the arrays x_...</div><div class="ttdef"><b>Definition:</b> LinearRegression.h:332</div></div>
<div class="ttc" id="classOpenMS_1_1Exception_1_1UnableToFit_html"><div class="ttname"><a href="classOpenMS_1_1Exception_1_1UnableToFit.html">OpenMS::Exception::UnableToFit</a></div><div class="ttdoc">Exception used if an error occurred while fitting a model to a given dataset. </div><div class="ttdef"><b>Definition:</b> Exception.h:662</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_aa2ee8e6e79a594c3dfd5aefaf2846e72"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#aa2ee8e6e79a594c3dfd5aefaf2846e72">OpenMS::Math::LinearRegression::computeRegressionNoIntercept</a></div><div class="ttdeci">void computeRegressionNoIntercept(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)</div><div class="ttdoc">This function computes the best-fit linear regression coefficient of the model for the dataset ...</div><div class="ttdef"><b>Definition:</b> LinearRegression.h:255</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_acd59989a454f367b849167936370153c"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#acd59989a454f367b849167936370153c">OpenMS::Math::LinearRegression::stand_dev_residuals_</a></div><div class="ttdeci">double stand_dev_residuals_</div><div class="ttdoc">The standard deviation of the residuals. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:194</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a66e8bbf38afe7934556ea36e418ec010"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a66e8bbf38afe7934556ea36e418ec010">OpenMS::Math::LinearRegression::iteratorRange2Arrays_</a></div><div class="ttdeci">void iteratorRange2Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, double *x_array, double *y_array)</div><div class="ttdoc">Copies the distance(x_begin,x_end) elements starting at x_begin and y_begin into the arrays x_array a...</div><div class="ttdef"><b>Definition:</b> LinearRegression.h:318</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a3785493880563067522dafc8b933b4be"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a3785493880563067522dafc8b933b4be">OpenMS::Math::LinearRegression::lower_</a></div><div class="ttdeci">double lower_</div><div class="ttdoc">The lower bound of the confidence intervall. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:186</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a1243d6935368ecf2a9c9b69db96047fd"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a1243d6935368ecf2a9c9b69db96047fd">OpenMS::Math::LinearRegression::r_squared_</a></div><div class="ttdeci">double r_squared_</div><div class="ttdoc">The squared correlation coefficient (Pearson) </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:192</div></div>
<div class="ttc" id="Exception_8h_html"><div class="ttname"><a href="Exception_8h.html">Exception.h</a></div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_ac18fa814926f1654c334223771c29fbe"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#ac18fa814926f1654c334223771c29fbe">OpenMS::Math::LinearRegression::intercept_</a></div><div class="ttdeci">double intercept_</div><div class="ttdoc">The intercept of the fitted line with the y-axis. </div><div class="ttdef"><b>Definition:</b> LinearRegression.h:180</div></div>
<div class="ttc" id="classOpenMS_1_1Math_1_1LinearRegression_html_a115e2ca09974d554cc194957cf254213"><div class="ttname"><a href="classOpenMS_1_1Math_1_1LinearRegression.html#a115e2ca09974d554cc194957cf254213">OpenMS::Math::LinearRegression::computeRegression</a></div><div class="ttdeci">void computeRegression(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)</div><div class="ttdoc">This function computes the best-fit linear regression coefficients of the model for the dataset ...</div><div class="ttdef"><b>Definition:</b> LinearRegression.h:225</div></div>
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<TD align="right"><font color="#c0c0c0">Documentation generated on Thu Nov 14 2013 11:19:16 using doxygen 1.8.5</font></TD>
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