library(ggplot2)
library(MASS)
library(openxlsx)
dbFish <- read.csv("C:\\Users\\alfon\\Downloads\\Fish.csv")
#View(dbFish)dis=lda(Weight~Length1+Height+Width, data=dbFish)
dis## Call:
## lda(Weight ~ Length1 + Height + Width, data = dbFish)
##
## Prior probabilities of groups:
## 0 5.9 6.7 7 7.5 8.7
## 0.006289308 0.006289308 0.006289308 0.006289308 0.006289308 0.006289308
## 9.7 9.8 9.9 10 12.2 13.4
## 0.006289308 0.012578616 0.006289308 0.006289308 0.012578616 0.006289308
## 19.7 19.9 32 40 51.5 55
## 0.006289308 0.006289308 0.006289308 0.012578616 0.006289308 0.006289308
## 60 69 70 78 80 85
## 0.006289308 0.006289308 0.006289308 0.012578616 0.006289308 0.012578616
## 87 90 100 110 115 120
## 0.006289308 0.006289308 0.006289308 0.018867925 0.006289308 0.031446541
## 125 130 135 140 145 150
## 0.006289308 0.018867925 0.006289308 0.012578616 0.025157233 0.025157233
## 160 161 169 170 180 188
## 0.012578616 0.006289308 0.006289308 0.012578616 0.012578616 0.006289308
## 197 200 218 225 242 250
## 0.006289308 0.018867925 0.006289308 0.006289308 0.006289308 0.012578616
## 260 265 270 272 273 290
## 0.006289308 0.006289308 0.012578616 0.006289308 0.006289308 0.012578616
## 300 306 320 340 345 363
## 0.037735849 0.006289308 0.006289308 0.012578616 0.006289308 0.006289308
## 390 430 450 456 475 500
## 0.012578616 0.012578616 0.012578616 0.006289308 0.006289308 0.031446541
## 510 514 540 556 567 575
## 0.006289308 0.006289308 0.012578616 0.006289308 0.006289308 0.006289308
## 600 610 620 650 680 685
## 0.012578616 0.006289308 0.006289308 0.012578616 0.006289308 0.012578616
## 690 700 714 720 725 770
## 0.006289308 0.031446541 0.006289308 0.006289308 0.006289308 0.006289308
## 800 820 840 850 900 920
## 0.006289308 0.012578616 0.006289308 0.012578616 0.012578616 0.006289308
## 925 950 955 975 1000 1015
## 0.006289308 0.012578616 0.006289308 0.006289308 0.031446541 0.006289308
## 1100 1250 1550 1600 1650
## 0.012578616 0.006289308 0.006289308 0.006289308 0.006289308
##
## Group means:
## Length1 Height Width
## 0 19.00000 6.475200 3.351600
## 5.9 7.50000 2.112000 1.408000
## 6.7 9.30000 1.738800 1.047600
## 7 10.10000 1.728400 1.148400
## 7.5 10.00000 1.972000 1.160000
## 8.7 10.80000 1.978200 1.285200
## 9.7 10.40000 2.196000 1.380000
## 9.8 11.05000 2.143800 1.212800
## 9.9 11.30000 2.213900 1.165900
## 10 11.30000 2.213900 1.283800
## 12.2 11.80000 2.183700 1.324700
## 13.4 11.70000 2.430000 1.269000
## 19.7 13.20000 2.872800 2.067200
## 19.9 13.80000 2.932200 1.879200
## 32 12.50000 3.528000 1.999200
## 40 13.35000 3.985600 2.350000
## 51.5 15.00000 4.592400 2.631600
## 55 13.50000 6.847500 2.326500
## 60 14.30000 6.577200 2.314200
## 69 16.50000 5.298300 2.821700
## 70 15.70000 4.588000 2.941500
## 78 17.15000 5.387400 3.013900
## 80 17.20000 5.635800 3.050200
## 85 18.00000 5.109800 2.904400
## 87 18.20000 5.616600 3.174600
## 90 16.30000 7.405200 2.673000
## 100 16.20000 5.222400 3.321600
## 110 19.36667 5.794233 3.648567
## 115 19.00000 5.917500 3.307500
## 120 19.10000 6.494560 3.343820
## 125 19.00000 5.692500 3.667500
## 130 19.93333 6.116667 3.561000
## 135 20.00000 5.875000 3.525000
## 140 20.00000 7.543800 3.309700
## 145 20.75000 7.174625 3.539975
## 150 20.07500 6.676950 3.574300
## 160 20.80000 6.716700 3.810150
## 161 22.00000 6.915300 3.631200
## 169 22.00000 7.534400 3.835200
## 170 20.25000 7.835500 3.567700
## 180 23.30000 6.763050 3.794750
## 188 22.60000 6.733400 4.165800
## 197 23.50000 6.561000 4.239000
## 200 24.43333 7.770200 3.722133
## 218 25.00000 7.168000 4.144000
## 225 22.00000 7.293000 3.723000
## 242 23.20000 11.520000 4.020000
## 250 25.65000 7.551600 4.385200
## 260 25.40000 7.167200 4.335000
## 265 25.40000 7.051600 4.335000
## 270 23.85000 8.262900 4.248050
## 272 25.00000 8.568000 4.773600
## 273 23.00000 11.088000 4.144000
## 290 24.00000 10.678400 4.401200
## 300 29.21667 7.534800 4.428150
## 306 25.60000 8.778000 4.681600
## 320 27.80000 7.615600 4.771600
## 340 26.70000 13.145350 4.884450
## 345 36.00000 6.396000 3.977000
## 363 26.30000 12.730000 4.455500
## 390 28.55000 11.077500 5.022500
## 430 31.00000 9.867000 4.855250
## 450 27.20000 13.803650 4.885600
## 456 40.00000 7.280000 4.322500
## 475 28.40000 14.262800 5.104200
## 500 31.02000 12.699340 4.863300
## 510 40.00000 6.825000 4.459000
## 514 30.50000 10.030000 6.018000
## 540 34.30000 9.265000 5.845800
## 556 32.00000 10.256500 6.387500
## 567 43.20000 7.792000 4.870000
## 575 31.30000 15.128500 5.569500
## 600 29.40000 15.196200 5.375400
## 610 30.90000 15.633000 5.133800
## 620 31.50000 15.522700 5.280100
## 650 33.75000 12.805200 5.865300
## 680 31.80000 15.468600 6.130600
## 685 32.70000 13.437300 6.117200
## 690 34.60000 10.571700 6.366600
## 700 32.24000 13.496600 5.815460
## 714 32.70000 16.517000 5.851500
## 720 32.00000 16.361800 6.090000
## 725 31.80000 16.360000 6.053200
## 770 44.80000 7.680000 5.376000
## 800 33.70000 11.761200 6.573600
## 820 36.85000 11.783150 6.990700
## 840 32.50000 11.488400 7.795700
## 850 34.85000 14.409100 6.652400
## 900 36.75000 11.433300 7.359200
## 920 35.00000 18.036900 6.306300
## 925 36.20000 18.754200 6.749700
## 950 43.15000 13.274850 6.270850
## 955 35.00000 18.084000 6.292000
## 975 37.40000 18.635400 6.747300
## 1000 38.38000 13.667320 7.228600
## 1015 37.00000 12.380800 7.462400
## 1100 39.55000 12.656350 7.142450
## 1250 52.00000 10.686300 6.984900
## 1550 56.00000 9.600000 6.144000
## 1600 56.00000 9.600000 6.144000
## 1650 59.00000 10.812000 7.480000
##
## Coefficients of linear discriminants:
## LD1 LD2 LD3
## Length1 0.5384087 -0.25657455 -0.0186646
## Height 0.8308561 -0.03914415 -0.3911215
## Width 2.1490208 1.55954891 0.9839690
##
## Proportion of trace:
## LD1 LD2 LD3
## 0.9854 0.0096 0.0050Siguientes datos: Weight = 234 Length1 = 25.4 Length2 = 26.7 Length3 = 28.9 Height = 13.6040 Width = 4.043
nueva.observacion=rbind(c(234,25.4,26.7,28.9,13.6040,4.043))
colnames(nueva.observacion)=colnames(dbFish[,2:7])
nueva.observacion=data.frame(nueva.observacion)predict(dis,newdata=nueva.observacion)## $class
## [1] 363
## 101 Levels: 0 5.9 6.7 7 7.5 8.7 9.7 9.8 9.9 10 12.2 13.4 19.7 19.9 32 ... 1650
##
## $posterior
## 0 5.9 6.7 7 7.5
## 1 1.882319e-27 5.503103e-136 7.983469e-137 4.54998e-130 3.618113e-128
## 8.7 9.7 9.8 9.9 10
## 1 5.620822e-121 2.826575e-119 2.209593e-119 1.042604e-118 3.088126e-116
## 12.2 13.4 19.7 19.9 32
## 1 1.086186e-112 1.438769e-112 1.484597e-86 1.493032e-86 4.54455e-86
## 40 51.5 55 60 69 70
## 1 6.244602e-73 2.189574e-58 1.295147e-54 2.185098e-53 2.561622e-46 8.651573e-52
## 78 80 85 87 90 100
## 1 3.782771e-41 1.344806e-39 4.469194e-41 2.625386e-35 1.116529e-37 3.507054e-42
## 110 115 120 125 130 135
## 1 1.695649e-26 2.611905e-30 1.795279e-26 3.010842e-28 2.311675e-24 5.770374e-26
## 140 145 150 160 161 169
## 1 1.009644e-20 2.149599e-18 1.52115e-21 1.948081e-18 1.131787e-16 1.733211e-13
## 170 180 188 197 200 218
## 1 2.939319e-17 2.015087e-13 3.294444e-13 7.368304e-12 5.582573e-09 2.156744e-08
## 225 242 250 260 265 270
## 1 7.216262e-15 0.008913682 8.837925e-06 2.725704e-07 1.512355e-07 9.297327e-07
## 272 273 290 300 306 320 340
## 1 0.0002112139 0.002977722 0.02461228 0.01952502 0.001128 0.001049191 0.0964333
## 345 363 390 430 450 456
## 1 0.001705241 0.694525 0.06915003 0.06127045 0.01743509 3.09031e-06
## 475 500 510 514 540 556
## 1 3.374597e-05 0.00101287 3.50529e-06 3.158402e-07 2.253797e-08 1.43945e-11
## 567 575 600 610 620 650
## 1 1.441549e-13 7.214832e-14 2.344302e-09 2.893202e-11 5.137265e-13 2.598034e-14
## 680 685 690 700 714 720
## 1 5.493094e-21 2.321498e-16 2.146273e-15 1.177359e-12 1.343954e-23 5.508574e-24
## 725 770 800 820 840 850
## 1 3.850365e-23 8.966812e-20 1.132511e-18 1.053505e-29 9.027014e-31 7.01217e-30
## 900 920 925 950 955 975
## 1 3.210172e-33 1.1139e-41 5.480154e-56 1.959502e-44 1.023816e-41 1.174618e-59
## 1000 1015 1100 1250 1550 1600
## 1 3.524249e-44 4.250051e-39 2.330139e-42 2.037379e-76 1.164192e-74 1.164192e-74
## 1650
## 1 1.89356e-122
##
## $x
## LD1 LD2 LD3
## 1 2.588461 -0.5480214 -2.164739