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<p class="MsoNormal"><span lang="EN-US">Hello,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">I believe I may have found a bug. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Let us consider a single explanatory variable(NOT ordered) that has four levels: three of them are very similar and one is significantly different. One would expect that these three variables would be merged, however
I have got different results. To make more concrete, let the levels of the explanatory variable be “a”,”b”,”c” and d and the levels of the response “x”,”y” and “z”. Let the cross-classification table be
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> x y z<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> a 33 33 33<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> b 66 34 100<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> c 34 33 33<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> d 33 33 33<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">One would expect that a and d be merged first(for they are identical) and then they would be merged to c, that has a very close distribution. That does not happen. Only the two variables are merged and the result is :<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Model formula:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">y ~ x<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Fitted party:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">[1] root<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">| [2] x in a, d: x (n = 198, err = 66.7%)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">| [3] x in b: z (n = 200, err = 50.0%)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">| [4] x in c: x (n = 100, err = 66.0%)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">I believe the problem lies in the line 184 of chaid.R that reads<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">logpmaxs <- logpmaxs[-min(levindx), -max(levindx)]<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">and should be<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">logpmaxs <- logpmaxs[-max(levindx), -max(levindx)]<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Am I wrong in my understanding? Could be this fixed?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">On a different topic: Is development of this package still being pursued? Could numeric response variables be included or it will stick to the Kass(80) paper?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">The code to run the given example follows:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">padrao <- c(rep("x",33),rep("y",33),rep("z",33))<o:p></o:p></span></p>
<p class="MsoNormal">y<-c(padrao,padrao,padrao,"x" , rep("x",66),rep("y",34),rep("z",100))<o:p></o:p></p>
<p class="MsoNormal"><span lang="EN-US">x<-c(rep("a",99),rep("d",99),rep("c",100),rep("b",200))<o:p></o:p></span></p>
<p class="MsoNormal">x<-as.factor(x)<o:p></o:p></p>
<p class="MsoNormal">y<-as.factor(y)<o:p></o:p></p>
<p class="MsoNormal"><span lang="EN-US">xtabs(~y+x)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">df <- data.frame(y , x )<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">chaid(y ~ x , data = df)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="PT-BR" style="color:#002A3A;mso-fareast-language:ES-AR">Jose Marcos Ferraro
<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height:115%"><span lang="PT-BR" style="mso-fareast-language:ES-AR"><a href="mailto:Jose.ferraro@LOGITeng.com"><span style="color:blue">Jose.ferraro@LOGITeng.com</span></a><u><span style="color:#0057B8"><o:p></o:p></span></u></span></p>
<p class="MsoNormal" style="line-height:115%"><span lang="PT-BR" style="color:#002A3A;mso-fareast-language:ES-AR">tel + 55 11 3474-8585<o:p></o:p></span></p>
<p class="MsoNormal" style="line-height:115%"><span lang="PT-BR" style="color:#002A3A;mso-fareast-language:ES-AR">fax +55 11 3474-8501<o:p></o:p></span></p>
<p class="MsoNormal" style="line-height:115%"><span lang="PT-BR" style="mso-fareast-language:ES-AR"><a href="http://www.LOGITeng.com"><b><span style="color:#002A3A">www.LOGITeng.com</span></b></a><b><span style="color:#002A3A"><o:p></o:p></span></b></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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