【正文】 COV (XX ) + ln |COV | ed6e74e0641c5cc279a1942ed79030e9 商務(wù)數(shù)據(jù)分析 電子商務(wù)系列 上海財經(jīng)大學(xué)經(jīng)濟(jì)信息管理系 IS/SHUFE Page 20 of 70 j (X)j (X)j (X)j (X)j Posterior Probability of Membership in each SPECIES: 2 2 Pr(j|X) = exp( D (X)) / SUM exp( D (X)) j k k Posterior Probability of Membership in SPECIES: Obs From Classified SPECIES into SPECIES Setosa Versicolor Virginica 12 Versicolor Virginica * 25 Virginica Versicolor * 63 Virginica Versicolor * 83 Virginica Versicolor * 118 Versicolor Virginica * 131 Versicolor Virginica * 148 Versicolor Virginica * * Misclassified observation Discriminant Analysis Classification Summary for Calibration Data: Crossvalidation Summary using Quadratic Discriminant Function Generalized Squared Distance Function: 2 _ 1 _ D (X) = (XX )39。th Group 1 __ N(i)/2 || |Within SS Matrix(i)| V = N/2 |Pooled SS Matrix| _ _ 2 | 1 1 | 2P + 3P 1 RHO = | SUM | |_ N(i) N _| 6(P+1)(K1) DF = .5(K1)P(P+1) _ _ | PN/2 | | N V | Under null hypothesis:2 RHO ln | | | __ PN(i)/2 | |_ || N(i) _| is distributed approximately as chisquare(DF) Test ChiSquare Value = with 2 DF Prob ChiSq = Since the chisquare value is significant at the level, the within covariance matrices will be used in the discriminant function. Reference: Morrison, . (1976) Multivariate Statistical Methods p252. ed6e74e0641c5cc279a1942ed79030e9 商務(wù)數(shù)據(jù)分析 電子商務(wù)系列 上海財經(jīng)大學(xué)經(jīng)濟(jì)信息管理系 IS/SHUFE Page 19 of 70 Discriminant Analysis Univariate Test Statistics F Statistics, Num DF= 2 Den DF= 147 Total Pooled Between RSQ/ Variable STD STD STD RSquared (1RSQ) PETALLEN Univariate Test Statistics Variable F Pr F Label PETALLEN Petal Length in mm. Average RSquared: Unweighted = Weighted by Variance = Discriminant Analysis Classification Summary for Calibration Data: Resubstitution Summary using Quadratic Discriminant Function Generalized Squared Distance Function: 2 _ 1 _ D (X) = (XX )39。 表 對四變量進(jìn)行逐步判別分析 Stepwise Discriminant Analysis 150 Observations 4 Variable(s) in the Analysis 3 Class Levels 0 Variable(s) will be included The Method for Selecting Variables will be: STEPWISE Significance Level to Enter = Significance Level to Stay = Class Level Information SPECIES Frequency Weight Proportion Setosa 50 Versicolor 50 Virginica 50 Stepwise Selection: Summary Variable Number Partial F Prob Wilks39。第一個判別分析過程 discrim，是對變量 petalwid進(jìn)行判別分析，并建立關(guān)于 petalwid 的判別函數(shù)式，為什么選擇變量 petalwid進(jìn)行判別分析，是由 stepdisc 過程得出的結(jié)論：變量 petalwid比其他變量能更有效地區(qū)分類別 ；第二個判別分析過程 discrim，是對所有四個變量進(jìn)行判別分析，并建立關(guān) 于它們的判別函數(shù)式，同時輸出數(shù)據(jù)集 plotiris，當(dāng)對數(shù)據(jù)計算后的協(xié)方差矩陣不滿足齊性時，此 plotiris 數(shù)據(jù)集能獲得二次型判別函數(shù)的系數(shù)。 程序說明：由于在實際的指標(biāo)數(shù)據(jù)之間可能彼此相關(guān)，選擇其中相互獨(dú)立的幾個指標(biāo) 用于建立判別函數(shù)式，不僅函數(shù)形式會更簡單，而且效果也會更好。 proc print data=plotiris。 var petallen petalwid sepalwid sepallen 。 proc discrim data=iris outstat=plotiris ed6e74e0641c5cc279a1942ed79030e9 商務(wù)數(shù)據(jù)分析 電子商務(wù)系列 上海財經(jīng)大學(xué)經(jīng)濟(jì)信息管理系 IS/SHUFE Page 17 of 70 method=normal pool=test manova listerr crosslisterr。 var petallen。 proc discrim data=iris method=normal pool=test anova short crosslisterr。 var sepallen sepalwid petallen petalwid 。編程方法如下： proc stepdisc data=iris short sle= sls=。 2. 調(diào)用 判別分析 discrim 過程?，F(xiàn)在 format 過程自定義了 2 和 3 轉(zhuǎn)換成指定的字符串顯示。 程序說明： format 過程 自定義了一種輸出格式。 proc print data=iris。 50 33 14 02 1 64 28 56 22 3 65 28 46 15 2 67 31 56 24 3 63 28 51 15 3 46 34 14 03 1 69 31 51 23 3 62 22 45 15 2 ? ? ? 63 33 60 25 3 53 37 15 02 1 。Petal Width in mm.39。Petal Length in mm.39。Sepal Width in mm.39。Sepal Length in mm.39。 format specie