R软件做判别分析

R 软件做判别分析 1 距离判别 1 两样本 discriminiant distance function TrnX1 TrnX2 TstX NULL var equal FALSE if is null TstX TRUE TstX rbind TrnX1 TrnX2 if is vector TstX TRUE TstX t as matrix TstX else if is matrix TstX TRUE TstX as matrix TstX if is matrix TrnX1 TRUE TrnX1 as matrix TrnX1 if is matrix TrnX2 TRUE TrnX2 as matrix TrnX2 nx nrow TstX blong matrix rep 0 nx nrow 1 byrow TRUE dimnames list blong 1 nx mu1 colMeans TrnX1 mu2 colMeans TrnX2 if var equal TRUE var equal T S var rbind TrnX1 TrnX2 w mahalanobis TstX mu2 S mahalanobis TstX mu1 S else S1 var TrnX1 S2 var TrnX2 w 0 blong i 1 else blong i 2 blong 例1 数据 classX1 data frame x1 c 6 60 6 60 6 10 6 10 8 40 7 2 8 40 7 50 7 50 8 30 7 80 7 80 x2 c 39 00 39 00 47 00 47 00 32 00 6 0 113 00 52 00 52 00 113 00 172 00 172 00 x3 c 1 00 1 00 1 00 1 00 2 00 1 0 3 50 1 00 3 50 0 00 1 00 1 50 x4 c 6 00 6 00 6 00 6 00 7 50 7 0 6 00 6 00 7 50 7 50 3 50 3 00 x5 c 6 00 12 00 6 00 12 00 19 00 28 0 18 00 12 00 6 00 35 00 14 00 15 00 x6 c 0 12 0 12 0 08 0 08 0 35 0 3 0 15 0 16 0 16 0 12 0 21 0 21 x7 c 20 00 20 00 12 00 12 00 75 00 30 0 75 00 40 00 40 00 180 00 45 00 45 00 classX2 data frame x1 c 8 40 8 40 8 40 6 3 7 00 7 00 7 00 8 30 8 30 7 2 7 2 7 2 5 50 8 40 8 40 7 50 7 50 8 30 8 30 8 30 8 30 7 80 7 80 x2 c 32 0 32 00 32 00 11 0 8 00 8 00 8 00 161 00 161 0 6 0 6 0 6 0 6 00 113 00 113 00 52 00 52 00 97 00 97 00 89 00 56 00 172 00 283 00 x3 c 1 00 2 00 2 50 4 5 4 50 6 00 1 50 1 50 0 50 3 5 1 0 1 0 2 50 3 50 3 50 1 00 1 00 0 00 2 50 0 00 1 50 1 00 1 00 x4 c 5 00 9 00 4 00 7 5 4 50 7 50 6 00 4 00 2 50 4 0 3 0 6 0 3 00 4 50 4 50 6 00 7 50 6 00 6 00 6 00 6 00 3 50 4 50 x5 c 4 00 10 00 10 00 3 0 9 00 4 00 1 00 4 00 1 00 12 0 3 0 5 0 7 00 6 00 8 00 6 00 8 00 5 00 5 00 10 00 13 00 6 00 6 00 x6 c 0 35 0 35 0 35 0 2 0 25 0 25 0 25 0 08 0 08 0 30 0 3 0 3 0 18 0 15 0 15 0 16 0 16 0 15 0 15 0 16 0 25 0 21 0 18 x7 c 75 00 75 00 75 00 15 0 30 00 30 00 30 00 70 00 70 00 30 0 30 0 30 0 18 00 75 00 75 00 40 00 40 00 180 00 180 00 180 00 180 00 45 00 45 00 source discriminiant distance R discriminiant distance classX1 classX2 var equal TRUE discriminiant distance classX1 classX2 TrnX1 data frame X1 c 13 85 22 31 28 82 15 29 28 79 X2 c 2 79 4 67 4 63 3 54 4 90 X3 c 7 80 12 31 16 18 7 50 16 12 X4 c 49 60 47 80 62 15 43 20 58 10 TrnX2 data frame X1 c 2 18 3 85 11 40 3 66 12 10 X2 c 1 06 0 80 0 00 2 42 0 00 X3 c 1 22 4 06 3 50 2 14 5 68 X4 c 20 60 47 10 0 00 15 10 0 00 TrnX data frame X1 c 8 85 28 60 X2 c 3 38 2 40 X3 c 5 17 1 20 X4 c 26 10 127 0 discriminiant distance TrnX1 TrnX2 2 多样本 distinguish distance function TrnX TrnG TstX NULL var equal FALSE if is factor TrnG FALSE mx nrow TrnX mg nrow TrnG TrnX rbind TrnX TrnG TrnG factor rep 1 2 c mx mg if is null TstX TRUE TstX TrnX if is vector TstX TRUE TstX t as matrix TstX else if is matrix TstX TRUE TstX as matrix TstX if is matrix TrnX TRUE TrnX as matrix TrnX nx nrow TstX blong matrix rep 0 nx nrow 1 dimnames list blong 1 nx g length levels TrnG mu matrix 0 nrow g ncol ncol TrnX for i in 1 g mu i colMeans TrnX TrnG i D matrix 0 nrow g ncol nx if var equal TRUE var equal T for i in 1 g D i mahalanobis TstX mu i var TrnX else for i in 1 g D i mahalanobis TstX mu i var TrnX TrnG i for j in 1 nx dmin Inf for i in 1 g if D i j dmin dmin D i j blong j i blong 多总体距离判别 要求数据矩阵或数据框 X matrix c 34 16 7 44 1 12 7 87 95 19 69 30 33 06 6 34 1 08 6 77 94 08 69 70 36 26 9 24 1 04 8 97 97 30 68 80 40 17 13 45 1 43 13 88 101 2 66 2 50 06 23 03 2 83 23 74 112 52 63 3 33 24 6 24 1 18 22 9 160 01 65 4 32 22 4 22 1 06 20 7 124 7 68 7 41 15 10 08 2 32 32 84 172 06 65 85 53 04 25 74 4 06 34 87 152 03 63 5 38 03 11 2 6 07 27 84 146 32 66 8 34 03 5 41 0 07 5 2 90 10 69 5 32 11 3 02 0 09 3 14 85 15 70 8 44 12 15 12 1 08 15 15 103 12 64 8 54 17 25 03 2 11 25 15 110 14 63 7 28 07 2 01 0 07 3 02 81 22 68 3 nrow 15 ncol 6 byrow TRUE G gl 3 5 Y matrix c 50 22 6 66 1 08 22 54 170 6 65 2 34 64 7 33 1 11 7 78 95 16 69 3 33 42 6 22 1 12 22 95 160 31 68 3 44 02 15 36 1 07 16 45 105 3 64 2 nrow 4 ncol 6 byrow TRUE distinguish distance X G Y var equal TRUE Bayes 判别 1 两样本 discriminiant bayes function TrnX1 TrnX2 rate 1 TstX NULL var equal FALSE if is null TstX TRUE TstX rbind TrnX1 TrnX2 if is vector TstX TRUE TstX t as matrix TstX else if is matrix TstX TRUE TstX as matrix TstX if is matrix TrnX1 TRUE TrnX1 as matrix TrnX1 if is matrix TrnX2 TRUE TrnX2 as matrix TrnX2 nx nrow TstX blong matrix rep 0 nx nrow 1 byrow TRUE dimnames list blong 1 nx mu1 colMeans TrnX1 mu2 colMeans TrnX2 if var equal TRUE var equal T S var rbind TrnX1 TrnX2 beta 2 log rate w mahalanobis TstX mu2 S mahalanobis TstX mu1 S else S1 var TrnX1 S2 var TrnX2 beta 2 log rate log det S1 det S2 w beta blong i 1 else blong i 2 blong 例 2 两样本 TrnX1 matrix c 24 8 24 1 26 6 23 5 25 5 27 4 2 0 2 4 3 0 1 9 2 1 3 1 ncol 2 TrnX2 matrix c 22 1 21 6 22 0 22 8 22 7 21 5 22 1 21 4 0 7 1 4 0 8 1 6 1 5 1 0 1 2 1 3 ncol 2 source discriminiant bayes R discriminiant bayes TrnX1 TrnX2 rate 8 6 var equal TRUE 2 多样本 distinguish bayes function TrnX TrnG p rep 1 length levels TrnG TstX NULL var equal FALSE if is factor TrnG FALSE mx nrow TrnX mg nrow TrnG TrnX rbind TrnX TrnG TrnG factor rep 1 2 c mx mg if is null TstX TRUE TstX TrnX if is vector TstX TRUE TstX t as matrix TstX else if is matrix TstX TRUE TstX as matrix TstX if is matrix TrnX TRUE TrnX as matrix TrnX nx nrow TstX blong matrix rep 0 nx nrow 1 dimnames list blong 1 nx g length levels TrnG mu matrix 0 nrow g ncol ncol TrnX for i in 1 g mu i colMeans TrnX TrnG i D matrix 0 nrow g ncol nx if var equal TRUE var equal T for i in 1 g d2 mahalanobis TstX mu i var TrnX D i d2 2 log p i else for i in 1 g S var TrnX TrnG i d2 mahalanobis TstX mu i S D i d2 2 log p i log det S for j in 1 nx dmin Inf for i in 1 g if D i j dmin dmin D i j blong j i blong X matrix c 34 16 7 44 1 12 7 87 95 19 69 30 33 06 6 34 1 08 6 77 94 08 69 70 36 26 9 24 1 04 8 97 97 30 68 80 40 17 13 45 1 43 13 88 101 2 66 2 50 06 23 03 2 83 23 74 112 52 63 3 33 24 6 24 1 18 22 9 160 01 65 4 32 22 4 22 1 06 20 7 124 7 68 7 41 15 10 08 2 32 32 84 172 06 65 85 53 04 25 74 4 06 34 87 152 03 63 5 38 03 11 2 6 07 27 84 146 32 66 8 34 03 5 41 0 07 5 2 90 10 69 5 32 11 3 02 0 09 3 14 85 15 70 8 44 12 15 12 1 08 15 15 103 12 64 8 54 17 25 03 2 11 25 15 110 14 63 7 28 07 2 01 0 07 3 02 81 22 68 3 nrow 15 ncol 6 byrow TRUE G gl 3 5 Y matrix c 50 22 6 66 1 08 22 54 170 6 65 2 34 64 7 33 1 11 7 78 95 16 69 3 33 42 6 22 1 12 22 95 160 31 68 3 44 02 15 36 1 07 16 45 105 3 64 2 nrow 4 ncol 6 byrow TRUE distinguish bayes X G p rep 1 length levels G Y var equal TRUE 3 Fisher判别 discriminiant fisher function TrnX1 TrnX2 TstX NULL if is null TstX TRUE TstX rbind TrnX1 TrnX2 if is vector TstX TRUE TstX t as matrix TstX else if is matrix TstX TRUE TstX as matrix TstX if is matrix TrnX1 TRUE TrnX1 as matrix TrnX1 if is matrix TrnX2 TRUE TrnX2 as matrix TrnX2 nx nrow TstX blong matrix rep 0 nx nrow 1 byrow TRUE dimnames list blong 1 nx n1 nrow TrnX1 n2 nrow TrnX2 mu1 colMeans TrnX1 mu2 colMeans TrnX2 S n1 1 var TrnX1 n2 1 var TrnX2 mu n1 n1 n2 mu1 n2 n1 n2 mu2 w TstX rep 1 nx o mu solve S mu2 mu1 for i in 1 nx if w i 0 blong i 1 else blong i 2 blong 多总体 Fisher 判别 要求数据框 library MASS X matrix c 34 16 7 44 1 12 7 87 95 19 69 30 33 06 6 34 1 08 6 77 94 08 69 70 36 26 9 24 1 04 8 97 97 30 68 80 40 17 13 45 1 43 13 88 101 2 66 2 50 06 23 03 2 83 23 74 112 52 63 3 33 24 6 24 1 18 22 9 160 01 65 4 32 22 4 22 1 06 20 7 124 7 68 7 41 15 10 08 2 32 32 84 172 06 65 85 53 04 25 74 4 06 34 87 152 03 63 5 38 03 11 2 6 07 27 84 146 32 66 8 34 03 5 41 0 07 5 2 90 10 69 5 32 11 3 02 0 09 3 14 85 15 70 8 44 12 15 12 1 08 15 15 103 12 64 8 54 17 25 03 2 11 25 15 110 14 63 7 28 07 2 01 0 07 3 02 81 22 68 3 nrow 15 ncol 6 byrow TRUE Y data frame X Sp rep c 1 2 3 rep 5 3 Train data frame matrix c 50 22 6 66 1 08 22 54 170 6 65 2 34 64 7 33 1 11 7 78 95 16 69 3 33 42 6 22 1 12 22 95 160 31 68 3 44 02 15 36 1 07 16 45 105 3 64 2 nrow 4 ncol 6 byrow TRUE z lda Sp Y predict z Train 多总体 Bayes 判别 要求数据矩阵或数据框 X matrix c 34 16 7 44 1 12 7 87 95 19 69 30 33 06 6 34 1 08 6 77 94 08 69 70 36 26 9 24 1 04 8 97 97 30 68 80 40 17 13 45 1 43 13 88 101 2 66 2 50 06 23 03 2 83 23 74 112 52 63 3 33 24 6 24 1 18 22 9 160 01 65 4 32 22 4 22 1 06 20 7 124 7 68 7 41 15 10 08 2 32 32 84 172 06 65 85