Multivariate and Cluster
FRANCISCO GORDILLO
26 de agosto de 2015
Tomate uva organico
oscar <- read.delim("F:/oscar.txt")
data <- oscar#correlaccion de los datos de las variables fresco:gbrix
cor(oscar[,2:24])## NFRAM1 PRAM1 NFRAM2 PRAM2 NFRAM3
## NFRAM1 1.00000000 -0.34244878 0.07326329 -0.83227815 -0.777982961
## PRAM1 -0.34244878 1.00000000 0.63013120 0.60948869 -0.050072092
## NFRAM2 0.07326329 0.63013120 1.00000000 0.48902992 0.086187736
## PRAM2 -0.83227815 0.60948869 0.48902992 1.00000000 0.761139782
## NFRAM3 -0.77798296 -0.05007209 0.08618774 0.76113978 1.000000000
## PRAM3 -0.65026765 -0.49108721 -0.57783604 0.27859815 0.761522625
## NFRAM4 0.36735282 -0.82459445 -0.88991928 -0.80476883 -0.331421764
## PRAM4 -0.41631089 -0.18494638 -0.87228946 -0.13804578 -0.004048602
## V1 -0.87228209 0.73295888 0.14456917 0.81635431 0.438234053
## V2 -0.80321673 0.44522132 -0.33712631 0.48496442 0.262914589
## V3 -0.27967812 0.53560567 -0.29162711 0.03095640 -0.384663985
## V4 -0.14671307 0.97064820 0.57472920 0.40100147 -0.288544553
## V5 0.15716707 -0.97996104 -0.71878596 -0.49730771 0.177900732
## V6 -0.13392228 0.95200478 0.50039096 0.34576726 -0.341502394
## V7 0.72057106 -0.87177161 -0.27172030 -0.74466252 -0.232792751
## V8 -0.10504066 -0.86061684 -0.47370434 -0.12080490 0.551584641
## V9 0.11975001 -0.91616275 -0.88574318 -0.57032577 0.038420512
## V10 0.19044081 -0.98060857 -0.75126687 -0.54714113 0.116251473
## V11 -0.20746510 -0.80466491 -0.45346714 -0.02043745 0.632892896
## V12 -0.22954180 -0.77690275 -0.40674088 0.02564615 0.667632439
## V13 0.33328796 0.77104333 0.65588474 0.03243480 -0.595683329
## V14 -0.86784884 0.18051162 0.19277373 0.88800506 0.972133422
## V15 -0.61322138 -0.52023881 -0.48645069 0.30307978 0.813657461
## PRAM3 NFRAM4 PRAM4 V1 V2
## NFRAM1 -0.65026765 0.36735282 -0.4163108932 -0.8722821 -0.80321673
## PRAM1 -0.49108721 -0.82459445 -0.1849463835 0.7329589 0.44522132
## NFRAM2 -0.57783604 -0.88991928 -0.8722894633 0.1445692 -0.33712631
## PRAM2 0.27859815 -0.80476883 -0.1380457823 0.8163543 0.48496442
## NFRAM3 0.76152263 -0.33142176 -0.0040486019 0.4382341 0.26291459
## PRAM3 1.00000000 0.32646810 0.5359939857 0.2161734 0.38502289
## NFRAM4 0.32646810 1.00000000 0.5752892624 -0.5750181 -0.11285608
## PRAM4 0.53599399 0.57528926 1.0000000000 0.3361268 0.74779886
## V1 0.21617344 -0.57501809 0.3361267892 1.0000000 0.87496358
## V2 0.38502289 -0.11285608 0.7477988645 0.8749636 1.00000000
## V3 -0.17332383 0.01718469 0.6571457442 0.5941863 0.78511142
## V4 -0.64875935 -0.70598134 -0.1653248065 0.6026432 0.37304069
## V5 0.64670413 0.82446674 0.3286684390 -0.5830963 -0.27065374
## V6 -0.64549432 -0.63932095 -0.0891640858 0.5989590 0.41150800
## V7 0.03671238 0.65027924 -0.2322990891 -0.9672359 -0.81022767
## V8 0.79312949 0.51491144 0.1414016698 -0.3941257 -0.24725885
## V9 0.62996304 0.92863470 0.5633279395 -0.4801169 -0.06993546
## V10 0.61640998 0.86178269 0.3613541321 -0.5972252 -0.26116386
## V11 0.84284258 0.45116728 0.1618734268 -0.2972554 -0.17071226
## V12 0.84083767 0.40094294 0.1242890670 -0.2755299 -0.17563027
## V13 -0.93245736 -0.55826945 -0.4370527694 0.1504580 -0.07671600
## V14 0.65842141 -0.49508259 0.0006222033 0.6227875 0.40158519
## V15 0.98914679 0.27408190 0.4078425513 0.1531597 0.27178409
## V3 V4 V5 V6 V7
## NFRAM1 -0.27967812 -0.14671307 0.15716707 -0.13392228 0.72057106
## PRAM1 0.53560567 0.97064820 -0.97996104 0.95200478 -0.87177161
## NFRAM2 -0.29162711 0.57472920 -0.71878596 0.50039096 -0.27172030
## PRAM2 0.03095640 0.40100147 -0.49730771 0.34576726 -0.74466252
## NFRAM3 -0.38466398 -0.28854455 0.17790073 -0.34150239 -0.23279275
## PRAM3 -0.17332383 -0.64875935 0.64670413 -0.64549432 0.03671238
## NFRAM4 0.01718469 -0.70598134 0.82446674 -0.63932095 0.65027924
## PRAM4 0.65714574 -0.16532481 0.32866844 -0.08916409 -0.23229909
## V1 0.59418635 0.60264320 -0.58309632 0.59895905 -0.96723591
## V2 0.78511142 0.37304069 -0.27065374 0.41150800 -0.81022767
## V3 1.00000000 0.61451612 -0.45502631 0.67988824 -0.67686279
## V4 0.61451612 1.00000000 -0.98048962 0.99568660 -0.78420773
## V5 -0.45502631 -0.98048962 1.00000000 -0.95806019 0.75724430
## V6 0.67988824 0.99568660 -0.95806019 1.00000000 -0.78263500
## V7 -0.67686279 -0.78420773 0.75724430 -0.78263500 1.00000000
## V8 -0.65191566 -0.95779647 0.90734946 -0.97004258 0.61385585
## V9 -0.18158823 -0.88299631 0.95766468 -0.83615354 0.63876101
## V10 -0.40854327 -0.96591958 0.99757095 -0.93777895 0.76184535
## V11 -0.62726456 -0.92335548 0.86831575 -0.93880739 0.52862864
## V12 -0.65023317 -0.90546289 0.84083859 -0.92576778 0.50756378
## V13 0.37790123 0.87897738 -0.87417035 0.87219191 -0.39360259
## V14 -0.21865555 -0.06026688 -0.04289549 -0.11313029 -0.44866058
## V15 -0.31051204 -0.69075739 0.66129425 -0.69959285 0.10273825
## V8 V9 V10 V11 V12
## NFRAM1 -0.1050407 0.11975001 0.1904408 -0.20746510 -0.22954180
## PRAM1 -0.8606168 -0.91616275 -0.9806086 -0.80466491 -0.77690275
## NFRAM2 -0.4737043 -0.88574318 -0.7512669 -0.45346714 -0.40674088
## PRAM2 -0.1208049 -0.57032577 -0.5471411 -0.02043745 0.02564615
## NFRAM3 0.5515846 0.03842051 0.1162515 0.63289290 0.66763244
## PRAM3 0.7931295 0.62996304 0.6164100 0.84284258 0.84083767
## NFRAM4 0.5149114 0.92863470 0.8617827 0.45116728 0.40094294
## PRAM4 0.1414017 0.56332794 0.3613541 0.16187343 0.12428907
## V1 -0.3941257 -0.48011689 -0.5972252 -0.29725538 -0.27552992
## V2 -0.2472588 -0.06993546 -0.2611639 -0.17071226 -0.17563027
## V3 -0.6519157 -0.18158823 -0.4085433 -0.62726456 -0.65023317
## V4 -0.9577965 -0.88299631 -0.9659196 -0.92335548 -0.90546289
## V5 0.9073495 0.95766468 0.9975709 0.86831575 0.84083859
## V6 -0.9700426 -0.83615354 -0.9377790 -0.93880739 -0.92576778
## V7 0.6138559 0.63876101 0.7618453 0.52862864 0.50756378
## V8 1.0000000 0.78018701 0.8761023 0.99436025 0.98924903
## V9 0.7801870 1.00000000 0.9716639 0.74132373 0.70258234
## V10 0.8761023 0.97166389 1.0000000 0.83288129 0.80210113
## V11 0.9943602 0.74132373 0.8328813 1.00000000 0.99843115
## V12 0.9892490 0.70258234 0.8021011 0.99843115 1.00000000
## V13 -0.9441200 -0.82534952 -0.8501458 -0.95793813 -0.94671565
## V14 0.3442849 -0.15350248 -0.1021365 0.43867661 0.47766726
## V15 0.8455616 0.60210771 0.6235176 0.89352140 0.89694135
## V13 V14 V15
## NFRAM1 0.3332880 -0.8678488421 -0.6132214
## PRAM1 0.7710433 0.1805116215 -0.5202388
## NFRAM2 0.6558847 0.1927737277 -0.4864507
## PRAM2 0.0324348 0.8880050631 0.3030798
## NFRAM3 -0.5956833 0.9721334217 0.8136575
## PRAM3 -0.9324574 0.6584214095 0.9891468
## NFRAM4 -0.5582694 -0.4950825901 0.2740819
## PRAM4 -0.4370528 0.0006222033 0.4078426
## V1 0.1504580 0.6227875013 0.1531597
## V2 -0.0767160 0.4015851901 0.2717841
## V3 0.3779012 -0.2186555483 -0.3105120
## V4 0.8789774 -0.0602668807 -0.6907574
## V5 -0.8741703 -0.0428954876 0.6612942
## V6 0.8721919 -0.1131302924 -0.6995929
## V7 -0.3936026 -0.4486605828 0.1027383
## V8 -0.9441200 0.3442849410 0.8455616
## V9 -0.8253495 -0.1535024762 0.6021077
## V10 -0.8501458 -0.1021365495 0.6235176
## V11 -0.9579381 0.4386766093 0.8935214
## V12 -0.9467156 0.4776672573 0.8969414
## V13 1.0000000 -0.4235031450 -0.9416968
## V14 -0.4235031 1.0000000000 0.6965246
## V15 -0.9416968 0.6965245526 1.0000000# Componentes principales y estandarizar los datos, excepto cuando todo tenga las mismas unidades.
PCs <- prcomp(data[-1:-9],scale=T)
summary(PCs)## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 3.1722 1.8540 1.22455 4.432e-15
## Proportion of Variance 0.6709 0.2292 0.09997 0.000e+00
## Cumulative Proportion 0.6709 0.9000 1.00000 1.000e+00PCs## Standard deviations:
## [1] 3.172236e+00 1.854021e+00 1.224550e+00 4.432089e-15
##
## Rotation:
## PC1 PC2 PC3 PC4
## V1 0.17403654 0.449713516 0.003256083 0.3699108
## V2 0.11069071 0.441775332 -0.370502010 -0.1836989
## V3 0.20267484 0.156587113 -0.578799952 -0.1583304
## V4 0.31399625 0.033948683 0.050883136 0.1742388
## V5 -0.30404621 -0.032073763 -0.210098857 0.1649377
## V6 0.31456240 0.031163152 -0.024833991 -0.4241801
## V7 -0.23502276 -0.359449947 0.004667989 -0.1321286
## V8 -0.30864872 0.094454832 0.084404460 -0.1312450
## V9 -0.26900496 -0.004475368 -0.425688361 -0.1624599
## V10 -0.29766222 -0.048292829 -0.258712099 0.0773744
## V11 -0.30029457 0.147578794 0.108578820 -0.1860189
## V12 -0.29598468 0.156616799 0.150775474 -0.2640804
## V13 0.28424117 -0.224990696 0.092999584 -0.5762487
## V14 -0.04609421 0.453758487 0.425015246 -0.1463781
## V15 -0.23286655 0.362789605 0.035538773 -0.2048562#Vectores de carga
PCs$rotation## PC1 PC2 PC3 PC4
## V1 0.17403654 0.449713516 0.003256083 0.3699108
## V2 0.11069071 0.441775332 -0.370502010 -0.1836989
## V3 0.20267484 0.156587113 -0.578799952 -0.1583304
## V4 0.31399625 0.033948683 0.050883136 0.1742388
## V5 -0.30404621 -0.032073763 -0.210098857 0.1649377
## V6 0.31456240 0.031163152 -0.024833991 -0.4241801
## V7 -0.23502276 -0.359449947 0.004667989 -0.1321286
## V8 -0.30864872 0.094454832 0.084404460 -0.1312450
## V9 -0.26900496 -0.004475368 -0.425688361 -0.1624599
## V10 -0.29766222 -0.048292829 -0.258712099 0.0773744
## V11 -0.30029457 0.147578794 0.108578820 -0.1860189
## V12 -0.29598468 0.156616799 0.150775474 -0.2640804
## V13 0.28424117 -0.224990696 0.092999584 -0.5762487
## V14 -0.04609421 0.453758487 0.425015246 -0.1463781
## V15 -0.23286655 0.362789605 0.035538773 -0.2048562#Despliegue los primeros renglones de los vectores de récords
head(PCs$x)## PC1 PC2 PC3 PC4
## [1,] -4.712709 0.02592122 -0.2532319 4.329870e-15
## [2,] 1.321244 2.31528318 0.8805266 3.622103e-15
## [3,] 1.204347 -2.22187629 1.0020975 3.469447e-15
## [4,] 2.187118 -0.11932811 -1.6293922 3.774758e-15#Grafica con los porcentajes de varianzas explicadas para los componentes
PCs_var <- PCs$sdev^2
porc_var <- PCs_var/(sum(PCs_var))
plot(porc_var,main="%de varianzas",col="salmon4", xlab="PC's",ylab="% varianzas PC's",type="b",lwd=2)
porc_var_acum <- cumsum(porc_var)#Grafica con los porcentajes acumulados de varianzas explicadas
porc_var_acum <- cumsum(porc_var)
plot(porc_var_acum,main="% acumulados de Varianzas",col="tomato4",xlab="PC's",ylab="% varianzas PC's",type="b",lwd=2)
# Construccion del biplot
biplot (PCs, col=c("black","blue"), xlab="PC1 (67.09 %)",ylab="PC2 (22.92 %)", cex= c(1,.8))
abline(h=0, v=0 , col="black",cex= c(1,.7))
#Score
scores <- PCs$x
scores## PC1 PC2 PC3 PC4
## [1,] -4.712709 0.02592122 -0.2532319 4.329870e-15
## [2,] 1.321244 2.31528318 0.8805266 3.622103e-15
## [3,] 1.204347 -2.22187629 1.0020975 3.469447e-15
## [4,] 2.187118 -0.11932811 -1.6293922 3.774758e-15#Cluster Dendograma
#Prepare hierarchical cluster
score.cp <- read.delim("F:/ARTICULO Y CONGRESO/score.cp.txt")
data <- score.cp
attach(data)
di <- dist(data[-1], method="euclidean")
tree <- hclust(di, method="ward.D")
data$hcluster <- as.factor((cutree(tree, k=3)-2) %% 3 +1)# Agrupaccion de los hermanos mas proximos
plot(tree, xlab="")
datos <- read.delim("F:/ARTICULO Y CONGRESO/datos.txt")
attach(datos)
datos## X V1 V2 V3 V4 V5 V6 V7 V8
## 1 TRAT RENDPLT ALT DIATLL FIRZ SST SPAD PARMAX PARPROM
## 2 ORG1 1214.568 186.3 0.709 355 12.71 50.42 578.7707407 118.697037
## 3 ORG2 1340.622 205.02 0.755 388 12.02 51.92 432.6577778 83.27074074
## 4 ORG3 1215.96 174.36 0.715 385 12.07 51.78 541.0740741 74.53740741
## 5 CONTROL 1283 204.28 0.817 389 12.13 52.12 475.7848148 67.06925926
## V9 V10 V11 V12 V13 V14 V15
## 1 PARMIN TEMPMAX TEMPPROM TEMPMIN HRMAX HRPROM HRMIN
## 2 0.218518519 39.9084 21.1428 11.891 95.889 77.3402 31.553
## 3 0.072222222 32.4874 17.7762 9.0054 97.3718 77.8214 28.4994
## 4 0.072222222 33.2378 16.228 7.495 98.5064 77.0462 19.2716
## 5 0.130740741 34.205 15.5538 6.672 98.0084 76.965 21.7888