Pols.text.Discriminant 14 KB

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  1. File type = "ooTextFile"
  2. Object class = "Discriminant 1"
  3. eigen? <exists>
  4. numberOfEigenvalues = 3
  5. dimension = 3
  6. eigenvalues []:
  7. eigenvalues [1] = 21.139001553325855
  8. eigenvalues [2] = 4.355295817120757
  9. eigenvalues [3] = 0.6828853876561901
  10. eigenvectors [] []:
  11. eigenvectors [1]:
  12. eigenvectors [1] [1] = -0.5095945937147198
  13. eigenvectors [1] [2] = 0.8585858858717426
  14. eigenvectors [1] [3] = -0.056068053636316804
  15. eigenvectors [2]:
  16. eigenvectors [2] [1] = 0.8327148188826579
  17. eigenvectors [2] [2] = 0.5085421803078848
  18. eigenvectors [2] [3] = 0.21902255879458066
  19. eigenvectors [3]:
  20. eigenvectors [3] [1] = -0.21656264791037275
  21. eigenvectors [3] [2] = -0.06492401273441407
  22. eigenvectors [3] [3] = 0.9741075362096886
  23. numberOfGroups = 12
  24. groups? <exists>
  25. size = 12
  26. item []:
  27. item [1]:
  28. class = "SSCP"
  29. name = "\as"
  30. numberOfColumns = 3
  31. columnLabels []:
  32. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  33. numberOfRows = 3
  34. row [1]: "standardized log (%F__1_)" 7.18330857960738 3.097330033949894 -1.7789564005106775
  35. row [2]: "standardized log (%F__2_)" 3.097330033949894 3.303987612651738 -0.29125922448881675
  36. row [3]: "standardized log (%F__3_)" -1.7789564005106775 -0.29125922448881675 20.17242895674037
  37. numberOfObservations = 50
  38. centroid []:
  39. centroid [1] = 1.2736938484743978
  40. centroid [2] = -0.7980324268009589
  41. centroid [3] = 0.52208953580356
  42. item [2]:
  43. class = "SSCP"
  44. name = "\ct"
  45. numberOfColumns = 3
  46. columnLabels []:
  47. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  48. numberOfRows = 3
  49. row [1]: "standardized log (%F__1_)" 4.749666736033554 1.7690171500295402 1.7501030793290935
  50. row [2]: "standardized log (%F__2_)" 1.7690171500295402 2.8643729450602207 -1.1734401148458047
  51. row [3]: "standardized log (%F__3_)" 1.7501030793290935 -1.1734401148458047 23.40462437949652
  52. numberOfObservations = 50
  53. centroid []:
  54. centroid [1] = 0.4589868093363824
  55. centroid [2] = -1.351739427763397
  56. centroid [3] = 0.7838758634620263
  57. item [3]:
  58. class = "SSCP"
  59. name = "\ep"
  60. numberOfColumns = 3
  61. columnLabels []:
  62. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  63. numberOfRows = 3
  64. row [1]: "standardized log (%F__1_)" 6.456192196293692 0.37968370644642313 2.3634733235211782
  65. row [2]: "standardized log (%F__2_)" 0.37968370644642313 3.5702296082868155 7.363574885015937
  66. row [3]: "standardized log (%F__3_)" 2.3634733235211782 7.363574885015937 36.64260597216446
  67. numberOfObservations = 50
  68. centroid []:
  69. centroid [1] = 0.7966958782135842
  70. centroid [2] = 0.6155709107830968
  71. centroid [3] = -0.05494363352048529
  72. item [4]:
  73. class = "SSCP"
  74. name = "\ic"
  75. numberOfColumns = 3
  76. columnLabels []:
  77. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  78. numberOfRows = 3
  79. row [1]: "standardized log (%F__1_)" 9.24423571578407 -0.9820105168212969 -0.9152576701472434
  80. row [2]: "standardized log (%F__2_)" -0.9820105168212969 3.226249064932281 7.241190360568189
  81. row [3]: "standardized log (%F__3_)" -0.9152576701472434 7.241190360568189 25.166342943866987
  82. numberOfObservations = 50
  83. centroid []:
  84. centroid [1] = -0.4924408746702678
  85. centroid [2] = 1.0434578136051709
  86. centroid [3] = 0.3408123049008417
  87. item [5]:
  88. class = "SSCP"
  89. name = "\o/"
  90. numberOfColumns = 3
  91. columnLabels []:
  92. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  93. numberOfRows = 3
  94. row [1]: "standardized log (%F__1_)" 5.190475238923332 0.7396990707845167 2.2127653907239013
  95. row [2]: "standardized log (%F__2_)" 0.7396990707845167 2.451257558642389 3.724682064961762
  96. row [3]: "standardized log (%F__3_)" 2.2127653907239013 3.724682064961762 17.377966252491305
  97. numberOfObservations = 50
  98. centroid []:
  99. centroid [1] = -0.06611599614664286
  100. centroid [2] = 0.21489702324229196
  101. centroid [3] = -0.8937572629989501
  102. item [6]:
  103. class = "SSCP"
  104. name = "\yc"
  105. numberOfColumns = 3
  106. columnLabels []:
  107. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  108. numberOfRows = 3
  109. row [1]: "standardized log (%F__1_)" 6.078893644563719 0.22074613549497368 2.2865518846979147
  110. row [2]: "standardized log (%F__2_)" 0.22074613549497368 3.853359342917027 8.425393204522178
  111. row [3]: "standardized log (%F__3_)" 2.2865518846979147 8.425393204522178 29.326003094517056
  112. numberOfObservations = 50
  113. centroid []:
  114. centroid [1] = -0.10308759884806179
  115. centroid [2] = 0.20958314569196762
  116. centroid [3] = -0.5180249531801528
  117. item [7]:
  118. class = "SSCP"
  119. name = "a"
  120. numberOfColumns = 3
  121. columnLabels []:
  122. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  123. numberOfRows = 3
  124. row [1]: "standardized log (%F__1_)" 7.005522628945976 2.090280751104145 1.5234216789425978
  125. row [2]: "standardized log (%F__2_)" 2.090280751104145 3.107213048517132 2.6192383675582107
  126. row [3]: "standardized log (%F__3_)" 1.5234216789425978 2.6192383675582107 28.064397805934156
  127. numberOfObservations = 50
  128. centroid []:
  129. centroid [1] = 1.766873453008129
  130. centroid [2] = -0.1885036859990809
  131. centroid [3] = 0.3137871302347796
  132. item [8]:
  133. class = "SSCP"
  134. name = "e"
  135. numberOfColumns = 3
  136. columnLabels []:
  137. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  138. numberOfRows = 3
  139. row [1]: "standardized log (%F__1_)" 8.484623546640897 0.10938909606862843 2.0246126475151693
  140. row [2]: "standardized log (%F__2_)" 0.10938909606862843 2.662191149790692 6.007252897802119
  141. row [3]: "standardized log (%F__3_)" 2.0246126475151693 6.007252897802119 21.29122318311569
  142. numberOfObservations = 50
  143. centroid []:
  144. centroid [1] = -0.33939072236689405
  145. centroid [2] = 1.065193874702519
  146. centroid [3] = 0.278016691702723
  147. item [9]:
  148. class = "SSCP"
  149. name = "i"
  150. numberOfColumns = 3
  151. columnLabels []:
  152. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  153. numberOfRows = 3
  154. row [1]: "standardized log (%F__1_)" 8.081690218264765 0.6318778113546369 1.9790354205211989
  155. row [2]: "standardized log (%F__2_)" 0.6318778113546369 2.4004531823129542 5.524087378886767
  156. row [3]: "standardized log (%F__3_)" 1.9790354205211989 5.524087378886767 24.65880692527169
  157. numberOfObservations = 50
  158. centroid []:
  159. centroid [1] = -1.3625596113923355
  160. centroid [2] = 1.3241682172552764
  161. centroid [3] = 1.0457830819884582
  162. item [10]:
  163. class = "SSCP"
  164. name = "o"
  165. numberOfColumns = 3
  166. columnLabels []:
  167. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  168. numberOfRows = 3
  169. row [1]: "standardized log (%F__1_)" 3.5939776313049254 1.6702307126016365 -0.37920099349106506
  170. row [2]: "standardized log (%F__2_)" 1.6702307126016365 3.901282219123324 0.9504479893814092
  171. row [3]: "standardized log (%F__3_)" -0.37920099349106506 0.9504479893814092 37.931161876785254
  172. numberOfObservations = 50
  173. centroid []:
  174. centroid [1] = 0.23540526426271752
  175. centroid [2] = -1.2107919088306194
  176. centroid [3] = -0.018039991648837916
  177. item [11]:
  178. class = "SSCP"
  179. name = "u"
  180. numberOfColumns = 3
  181. columnLabels []:
  182. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  183. numberOfRows = 3
  184. row [1]: "standardized log (%F__1_)" 8.904999958603216 1.041629226100712 2.5884622002583324
  185. row [2]: "standardized log (%F__2_)" 1.041629226100712 4.495561429410953 -0.29212116261459065
  186. row [3]: "standardized log (%F__3_)" 2.5884622002583324 -0.29212116261459065 37.14543437527783
  187. numberOfObservations = 50
  188. centroid []:
  189. centroid [1] = -0.9137597633205335
  190. centroid [2] = -1.5485260139424413
  191. centroid [3] = -0.6503310662477612
  192. item [12]:
  193. class = "SSCP"
  194. name = "y"
  195. numberOfColumns = 3
  196. columnLabels []:
  197. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  198. numberOfRows = 3
  199. row [1]: "standardized log (%F__1_)" 9.905167008234246 0.6668046403405659 3.2565058777165925
  200. row [2]: "standardized log (%F__2_)" 0.6668046403405659 3.178145328787026 8.007415551311658
  201. row [3]: "standardized log (%F__3_)" 3.2565058777165925 8.007415551311658 43.47637108433997
  202. numberOfObservations = 50
  203. centroid []:
  204. centroid [1] = -1.254300686550484
  205. centroid [2] = 0.6247224780561897
  206. centroid [3] = -1.1492677004962346
  207. total? <exists>
  208. numberOfColumns = 3
  209. columnLabels []:
  210. "standardized log (%F__1_)" "standardized log (%F__2_)" "standardized log (%F__3_)"
  211. numberOfRows = 3
  212. row [1]: "standardized log (%F__1_)" 598.9999999999995 -180.9945547854731 116.52712787173051
  213. row [2]: "standardized log (%F__2_)" -180.9945547854731 598.9999999999997 71.98818443063034
  214. row [3]: "standardized log (%F__3_)" 116.52712787173051 71.98818443063034 598.9999999999995
  215. numberOfObservations = 600
  216. centroid []:
  217. centroid [1] = -7.206272615671121e-16
  218. centroid [2] = 1.2472661792273243e-15
  219. centroid [3] = -2.7715734799797314e-15
  220. aprioriProbabilities []:
  221. aprioriProbabilities [1] = 0.08333333333333333
  222. aprioriProbabilities [2] = 0.08333333333333333
  223. aprioriProbabilities [3] = 0.08333333333333333
  224. aprioriProbabilities [4] = 0.08333333333333333
  225. aprioriProbabilities [5] = 0.08333333333333333
  226. aprioriProbabilities [6] = 0.08333333333333333
  227. aprioriProbabilities [7] = 0.08333333333333333
  228. aprioriProbabilities [8] = 0.08333333333333333
  229. aprioriProbabilities [9] = 0.08333333333333333
  230. aprioriProbabilities [10] = 0.08333333333333333
  231. aprioriProbabilities [11] = 0.08333333333333333
  232. aprioriProbabilities [12] = 0.08333333333333333
  233. costs [] []:
  234. costs [1]:
  235. costs [1] [1] = 0
  236. costs [1] [2] = 1
  237. costs [1] [3] = 1
  238. costs [1] [4] = 1
  239. costs [1] [5] = 1
  240. costs [1] [6] = 1
  241. costs [1] [7] = 1
  242. costs [1] [8] = 1
  243. costs [1] [9] = 1
  244. costs [1] [10] = 1
  245. costs [1] [11] = 1
  246. costs [1] [12] = 1
  247. costs [2]:
  248. costs [2] [1] = 1
  249. costs [2] [2] = 0
  250. costs [2] [3] = 1
  251. costs [2] [4] = 1
  252. costs [2] [5] = 1
  253. costs [2] [6] = 1
  254. costs [2] [7] = 1
  255. costs [2] [8] = 1
  256. costs [2] [9] = 1
  257. costs [2] [10] = 1
  258. costs [2] [11] = 1
  259. costs [2] [12] = 1
  260. costs [3]:
  261. costs [3] [1] = 1
  262. costs [3] [2] = 1
  263. costs [3] [3] = 0
  264. costs [3] [4] = 1
  265. costs [3] [5] = 1
  266. costs [3] [6] = 1
  267. costs [3] [7] = 1
  268. costs [3] [8] = 1
  269. costs [3] [9] = 1
  270. costs [3] [10] = 1
  271. costs [3] [11] = 1
  272. costs [3] [12] = 1
  273. costs [4]:
  274. costs [4] [1] = 1
  275. costs [4] [2] = 1
  276. costs [4] [3] = 1
  277. costs [4] [4] = 0
  278. costs [4] [5] = 1
  279. costs [4] [6] = 1
  280. costs [4] [7] = 1
  281. costs [4] [8] = 1
  282. costs [4] [9] = 1
  283. costs [4] [10] = 1
  284. costs [4] [11] = 1
  285. costs [4] [12] = 1
  286. costs [5]:
  287. costs [5] [1] = 1
  288. costs [5] [2] = 1
  289. costs [5] [3] = 1
  290. costs [5] [4] = 1
  291. costs [5] [5] = 0
  292. costs [5] [6] = 1
  293. costs [5] [7] = 1
  294. costs [5] [8] = 1
  295. costs [5] [9] = 1
  296. costs [5] [10] = 1
  297. costs [5] [11] = 1
  298. costs [5] [12] = 1
  299. costs [6]:
  300. costs [6] [1] = 1
  301. costs [6] [2] = 1
  302. costs [6] [3] = 1
  303. costs [6] [4] = 1
  304. costs [6] [5] = 1
  305. costs [6] [6] = 0
  306. costs [6] [7] = 1
  307. costs [6] [8] = 1
  308. costs [6] [9] = 1
  309. costs [6] [10] = 1
  310. costs [6] [11] = 1
  311. costs [6] [12] = 1
  312. costs [7]:
  313. costs [7] [1] = 1
  314. costs [7] [2] = 1
  315. costs [7] [3] = 1
  316. costs [7] [4] = 1
  317. costs [7] [5] = 1
  318. costs [7] [6] = 1
  319. costs [7] [7] = 0
  320. costs [7] [8] = 1
  321. costs [7] [9] = 1
  322. costs [7] [10] = 1
  323. costs [7] [11] = 1
  324. costs [7] [12] = 1
  325. costs [8]:
  326. costs [8] [1] = 1
  327. costs [8] [2] = 1
  328. costs [8] [3] = 1
  329. costs [8] [4] = 1
  330. costs [8] [5] = 1
  331. costs [8] [6] = 1
  332. costs [8] [7] = 1
  333. costs [8] [8] = 0
  334. costs [8] [9] = 1
  335. costs [8] [10] = 1
  336. costs [8] [11] = 1
  337. costs [8] [12] = 1
  338. costs [9]:
  339. costs [9] [1] = 1
  340. costs [9] [2] = 1
  341. costs [9] [3] = 1
  342. costs [9] [4] = 1
  343. costs [9] [5] = 1
  344. costs [9] [6] = 1
  345. costs [9] [7] = 1
  346. costs [9] [8] = 1
  347. costs [9] [9] = 0
  348. costs [9] [10] = 1
  349. costs [9] [11] = 1
  350. costs [9] [12] = 1
  351. costs [10]:
  352. costs [10] [1] = 1
  353. costs [10] [2] = 1
  354. costs [10] [3] = 1
  355. costs [10] [4] = 1
  356. costs [10] [5] = 1
  357. costs [10] [6] = 1
  358. costs [10] [7] = 1
  359. costs [10] [8] = 1
  360. costs [10] [9] = 1
  361. costs [10] [10] = 0
  362. costs [10] [11] = 1
  363. costs [10] [12] = 1
  364. costs [11]:
  365. costs [11] [1] = 1
  366. costs [11] [2] = 1
  367. costs [11] [3] = 1
  368. costs [11] [4] = 1
  369. costs [11] [5] = 1
  370. costs [11] [6] = 1
  371. costs [11] [7] = 1
  372. costs [11] [8] = 1
  373. costs [11] [9] = 1
  374. costs [11] [10] = 1
  375. costs [11] [11] = 0
  376. costs [11] [12] = 1
  377. costs [12]:
  378. costs [12] [1] = 1
  379. costs [12] [2] = 1
  380. costs [12] [3] = 1
  381. costs [12] [4] = 1
  382. costs [12] [5] = 1
  383. costs [12] [6] = 1
  384. costs [12] [7] = 1
  385. costs [12] [8] = 1
  386. costs [12] [9] = 1
  387. costs [12] [10] = 1
  388. costs [12] [11] = 1
  389. costs [12] [12] = 0