ALVARO <- read.delim("~/R/ALVARO.txt")
attach(ALVARO)
#PARCELAS SUBDIVIDAS TOMATES HEIRLOM
library (ExpDes.pt)
#ANALISIS DE LAS VARIABLES AGRONOMICAS
psub2.dbc(Genotipo, Acolchado, REP, DT, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 50.171 16.7238 47.176 0.000145 ***
## Bloco 2 1.742 0.8709 2.457 0.166184
## Erro a 6 2.127 0.3545
## Acolchado 3 0.376 0.1253 0.508 0.680337
## Genotipo*Acolchado 9 2.854 0.3171 1.286 0.294440
## Erro b 24 5.917 0.2465
## Total 47 63.186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 5.417681 %
## CV 2 = 4.517913 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## Teste de Tukey
## ------------------------------------------------------------------------
## Grupos Tratamentos Medias
## a V 12.69453
## b BR 10.84195
## b E 10.356
## b P 10.06716
## ------------------------------------------------------------------------
##
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 10.98664
## 2 G 10.86950
## 3 N 11.11945
## 4 SA 10.98406
## ------------------------------------------------------------------------
psub2.dbc(Genotipo, Acolchado, REP, AP, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 5.0553 1.68509 16.3372 0.002719 **
## Bloco 2 0.2075 0.10377 1.0060 0.419967
## Erro a 6 0.6189 0.10314
## Acolchado 3 0.0891 0.02969 0.3818 0.767024
## Genotipo*Acolchado 9 0.2131 0.02368 0.3044 0.965988
## Erro b 24 1.8666 0.07778
## Total 47 8.0504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 11.42907 %
## CV 2 = 9.924465 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## Teste de Tukey
## ------------------------------------------------------------------------
## Grupos Tratamentos Medias
## a E 3.186806
## a P 3.061806
## b BR 2.591667
## b V 2.399861
## ------------------------------------------------------------------------
##
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 2.851250
## 2 G 2.853194
## 3 N 2.780139
## 4 SA 2.755556
## ------------------------------------------------------------------------
psub2.dbc(Genotipo, Acolchado, REP, Nfto, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 24473 8157.8 85.472 2.6e-05 ***
## Bloco 2 1166 583.1 6.109 0.03572 *
## Erro a 6 573 95.4
## Acolchado 3 1359 453.1 0.464 0.71006
## Genotipo*Acolchado 9 2104 233.7 0.239 0.98461
## Erro b 24 23434 976.4
## Total 47 53109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 8.23712 %
## CV 2 = 26.34646 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## Teste de Tukey
## ------------------------------------------------------------------------
## Grupos Tratamentos Medias
## a V 155.25
## b BR 119.25
## c E 100.5833
## c P 99.33333
## ------------------------------------------------------------------------
##
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 126.7500
## 2 G 111.8333
## 3 N 118.1667
## 4 SA 117.6667
## ------------------------------------------------------------------------
psub2.dbc(Genotipo, Acolchado, REP, ppf, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 45878 15292.6 27.1591 0.000687 ***
## Bloco 2 744 372.2 0.6609 0.550287
## Erro a 6 3378 563.1
## Acolchado 3 2748 916.1 1.1238 0.359150
## Genotipo*Acolchado 9 2963 329.2 0.4038 0.920673
## Erro b 24 19564 815.2
## Total 47 75276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 12.97673 %
## CV 2 = 15.6138 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## Teste de Tukey
## ------------------------------------------------------------------------
## Grupos Tratamentos Medias
## a E 209.773
## a BR 197.2349
## a P 194.1511
## b V 130.2794
## ------------------------------------------------------------------------
##
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 195.4849
## 2 G 175.5065
## 3 N 179.3138
## 4 SA 181.1333
## ------------------------------------------------------------------------
psub2.dbc(Genotipo, Acolchado, REP, REND, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 117.08 39.026 4.6962 0.05131 .
## Bloco 2 12.46 6.231 0.7499 0.51206
## Erro a 6 49.86 8.310
## Acolchado 3 159.31 53.104 2.0396 0.13511
## Genotipo*Acolchado 9 54.34 6.038 0.2319 0.98620
## Erro b 24 624.87 26.036
## Total 47 1017.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 13.82144 %
## CV 2 = 24.46438 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
## Niveis Medias
## 1 BR 23.35590
## 2 E 20.83957
## 3 P 19.15058
## 4 V 20.08247
## ------------------------------------------------------------------------
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 23.82641
## 2 G 18.86742
## 3 N 20.26598
## 4 SA 20.46872
## ------------------------------------------------------------------------
psub2.dbc(Genotipo, Acolchado, REP, RP, quali = c(TRUE, FALSE), mcomp = "tukey", fac.names = c("Genotipo", "Acolchado"), sigT = 0.05, sigF = 0.05)
## ------------------------------------------------------------------------
## Legenda:
## FATOR 1 (parcela): Genotipo
## FATOR 2 (subparcela): Acolchado
## ------------------------------------------------------------------------
##
## ------------------------------------------------------------------------
## $`Quadro da analise de variancia\n------------------------------------------------------------------------\n`
## GL SQ QM Fc Pr(>Fc)
## Genotipo 3 3.2522 1.08407 4.6961 0.05131 .
## Bloco 2 0.3462 0.17310 0.7499 0.51206
## Erro a 6 1.3850 0.23084
## Acolchado 3 4.4253 1.47510 2.0396 0.13511
## Genotipo*Acolchado 9 1.5095 0.16772 0.2319 0.98620
## Erro b 24 17.3575 0.72323
## Total 47 28.2757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------------
## CV 1 = 13.82144 %
## CV 2 = 24.46438 %
##
## Interacao nao significativa: analisando os efeitos simples
## ------------------------------------------------------------------------
## Genotipo
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
## Niveis Medias
## 1 BR 3.892651
## 2 E 3.473262
## 3 P 3.191763
## 4 V 3.347079
## ------------------------------------------------------------------------
## Acolchado
## De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
##
## Niveis Medias
## 1 B 3.971069
## 2 G 3.144569
## 3 N 3.377663
## 4 SA 3.411454
## ------------------------------------------------------------------------
#MULTIVARIADO BIPLOT ACOLCHADO BLANCO
BLC <- read.delim("~/R/BLC.txt")
## Warning in read.table(file = file, header = header, sep = sep, quote =
## quote, : incomplete final line found by readTableHeader on '~/R/BLC.txt'
attach (BLC)
## The following objects are masked from ALVARO:
##
## Acolchado, AP, DT, Genotipo, HR, REND, TEMP
data <- BLC
cor(BLC[,3:9])
## DT AP NF PPF REND TEMP
## DT 1.0000000 -0.7183364 0.975990995 -0.8513627 0.104773255 -0.6321726
## AP -0.7183364 1.0000000 -0.760565753 0.5799985 -0.517049602 0.3506701
## NF 0.9759910 -0.7605658 1.000000000 -0.9317390 0.003223192 -0.4519454
## PPF -0.8513627 0.5799985 -0.931739000 1.0000000 0.339594274 0.1475469
## REND 0.1047733 -0.5170496 0.003223192 0.3395943 1.000000000 -0.5538056
## TEMP -0.6321726 0.3506701 -0.451945384 0.1475469 -0.553805631 1.0000000
## HR 0.6795609 -0.2051140 0.506166908 -0.2779735 0.280066571 -0.9543120
## HR
## DT 0.6795609
## AP -0.2051140
## NF 0.5061669
## PPF -0.2779735
## REND 0.2800666
## TEMP -0.9543120
## HR 1.0000000
PCs <- prcomp(data [,3:9],scale=T)
summary(PCs)
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 2.034 1.3462 1.0251 3.392e-16
## Proportion of Variance 0.591 0.2589 0.1501 0.000e+00
## Cumulative Proportion 0.591 0.8499 1.0000 1.000e+00
PCs
## Standard deviations:
## [1] 2.033995e+00 1.346151e+00 1.025057e+00 3.391787e-16
##
## Rotation:
## PC1 PC2 PC3 PC4
## DT 0.4843762 -0.11808698 0.06227491 -0.01554998
## AP -0.3766977 0.02076069 0.62629969 0.25883904
## NF 0.4615796 -0.25314219 -0.04816137 0.00292791
## PPF -0.3721549 0.48496970 -0.02770328 -0.27894872
## REND 0.1331571 0.60298145 -0.50482656 0.45607828
## TEMP -0.3558650 -0.46494920 -0.28330264 0.64850805
## HR 0.3571455 0.32647126 0.51543506 0.47578303
#Vectores de carga
PCs$rotation
## PC1 PC2 PC3 PC4
## DT 0.4843762 -0.11808698 0.06227491 -0.01554998
## AP -0.3766977 0.02076069 0.62629969 0.25883904
## NF 0.4615796 -0.25314219 -0.04816137 0.00292791
## PPF -0.3721549 0.48496970 -0.02770328 -0.27894872
## REND 0.1331571 0.60298145 -0.50482656 0.45607828
## TEMP -0.3558650 -0.46494920 -0.28330264 0.64850805
## HR 0.3571455 0.32647126 0.51543506 0.47578303
#Despliegue los primeros renglones de los vectores de récords
head(PCs$x)
## PC1 PC2 PC3 PC4
## [1,] -0.3199878 0.4652248 -1.4875032 -5.551115e-17
## [2,] -0.6854891 1.6116453 0.8595050 3.386180e-15
## [3,] -1.8777683 -1.5210537 0.3565363 -2.220446e-15
## [4,] 2.8832452 -0.5558164 0.2714619 1.110223e-16
#Grafica con los porcentajes de varianzas explicadas para los componentes
PCs_var <- PCs$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 (59.10 %)",ylab="PC2 (25.89 %)", cex= c(1,.8))
abline(h=0, v=0 , col="black",cex= c(1,.7))
#MULTIVARIADO BIPLOT ACOLCHADO GRIS
GRIS <- read.delim("~/R/GRIS.txt")
## Warning in read.table(file = file, header = header, sep = sep, quote =
## quote, : incomplete final line found by readTableHeader on '~/R/GRIS.txt'
attach (GRIS)
## The following objects are masked from BLC:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from ALVARO:
##
## Acolchado, AP, DT, Genotipo, HR, REND, TEMP
data <- GRIS
cor(GRIS[,3:9])
## DT AP NF PPF REND TEMP
## DT 1.0000000 -0.8471964 0.9956653 -0.9696208 0.3613849 -0.5907301
## AP -0.8471964 1.0000000 -0.8926072 0.7477128 -0.6603103 0.2014117
## NF 0.9956653 -0.8926072 1.0000000 -0.9547572 0.4143993 -0.5299562
## PPF -0.9696208 0.7477128 -0.9547572 1.0000000 -0.1265627 0.5344825
## REND 0.3613849 -0.6603103 0.4143993 -0.1265627 1.0000000 -0.2151588
## TEMP -0.5907301 0.2014117 -0.5299562 0.5344825 -0.2151588 1.0000000
## HR 0.3876257 0.1294500 0.3028137 -0.4295621 -0.2210233 -0.9032437
## HR
## DT 0.3876257
## AP 0.1294500
## NF 0.3028137
## PPF -0.4295621
## REND -0.2210233
## TEMP -0.9032437
## HR 1.0000000
PCs <- prcomp(data [,3:9],scale=T)
summary(PCs)
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 2.0861 1.3540 0.9027 1.458e-15
## Proportion of Variance 0.6217 0.2619 0.1164 0.000e+00
## Cumulative Proportion 0.6217 0.8836 1.0000 1.000e+00
PCs
## Standard deviations:
## [1] 2.086065e+00 1.354045e+00 9.027166e-01 1.458159e-15
##
## Rotation:
## PC1 PC2 PC3 PC4
## DT 0.4743397 -0.02602200 0.15525288 0.29770296
## AP -0.3979507 0.41046982 -0.04881665 0.52598094
## NF 0.4708640 -0.09454008 0.15185904 0.27140906
## PPF -0.4470768 -0.05588716 -0.39082810 0.10060470
## REND 0.2114247 -0.43083173 -0.75552948 -0.08379719
## TEMP -0.3241680 -0.45461760 0.44830515 -0.41109770
## HR 0.2127467 0.65305669 -0.16094532 -0.61229856
#Vectores de carga
PCs$rotation
## PC1 PC2 PC3 PC4
## DT 0.4743397 -0.02602200 0.15525288 0.29770296
## AP -0.3979507 0.41046982 -0.04881665 0.52598094
## NF 0.4708640 -0.09454008 0.15185904 0.27140906
## PPF -0.4470768 -0.05588716 -0.39082810 0.10060470
## REND 0.2114247 -0.43083173 -0.75552948 -0.08379719
## TEMP -0.3241680 -0.45461760 0.44830515 -0.41109770
## HR 0.2127467 0.65305669 -0.16094532 -0.61229856
#Despliegue los primeros renglones de los vectores de récords
head(PCs$x)
## PC1 PC2 PC3 PC4
## [1,] 0.04061678 -1.467631150 -0.9358740 1.110223e-16
## [2,] -0.41458645 1.798086802 -0.6035956 -6.661338e-16
## [3,] -2.34397722 -0.336479518 0.8685288 1.609823e-14
## [4,] 2.71794689 0.006023865 0.6709408 -1.110223e-14
#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 (62.17 %)",ylab="PC2 (26.19 %)", cex= c(1,.8))
abline(h=0, v=0 , col="black",cex= c(1,.7))
#MULTIVARIADO BIPLOT ACOLCHADO SIN ACOLCHAR
SA <- read.delim("~/R/SA.txt")
## Warning in read.table(file = file, header = header, sep = sep, quote =
## quote, : incomplete final line found by readTableHeader on '~/R/SA.txt'
attach (SA)
## The following objects are masked from GRIS:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from BLC:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from ALVARO:
##
## Acolchado, AP, DT, Genotipo, HR, REND, TEMP
data <- SA
cor(SA[,3:9])
## DT AP NF PPF REND TEMP
## DT 1.0000000 -0.9380604 0.9497552 -0.9475164 -0.6913257 0.8304548
## AP -0.9380604 1.0000000 -0.9646288 0.9531898 0.6487132 -0.8092101
## NF 0.9497552 -0.9646288 1.0000000 -0.8796825 -0.4908226 0.9335980
## PPF -0.9475164 0.9531898 -0.8796825 1.0000000 0.8431598 -0.6598887
## REND -0.6913257 0.6487132 -0.4908226 0.8431598 1.0000000 -0.1766038
## TEMP 0.8304548 -0.8092101 0.9335980 -0.6598887 -0.1766038 1.0000000
## HR -0.8273134 0.8156136 -0.9363083 0.6619784 0.1745955 -0.9996309
## HR
## DT -0.8273134
## AP 0.8156136
## NF -0.9363083
## PPF 0.6619784
## REND 0.1745955
## TEMP -0.9996309
## HR 1.0000000
PCs <- prcomp(data [,3:9],scale=T)
summary(PCs)
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 2.3887 1.1021 0.28173 3.348e-15
## Proportion of Variance 0.8152 0.1735 0.01134 0.000e+00
## Cumulative Proportion 0.8152 0.9887 1.00000 1.000e+00
PCs
## Standard deviations:
## [1] 2.388737e+00 1.102073e+00 2.817279e-01 3.347941e-15
##
## Rotation:
## PC1 PC2 PC3 PC4
## DT -0.4117170 0.08550980 0.5485071 -0.25053542
## AP 0.4091054 -0.07128118 0.6993636 -0.02321038
## NF -0.4136025 -0.13159272 -0.1894409 -0.23699621
## PPF 0.3927272 -0.31118704 0.1706675 0.08651300
## REND 0.2578620 -0.71153763 -0.2672876 -0.27496856
## TEMP -0.3680845 -0.42807625 0.2338076 -0.38592919
## HR 0.3685665 0.42885660 -0.1375016 -0.80529034
#Vectores de carga
PCs$rotation
## PC1 PC2 PC3 PC4
## DT -0.4117170 0.08550980 0.5485071 -0.25053542
## AP 0.4091054 -0.07128118 0.6993636 -0.02321038
## NF -0.4136025 -0.13159272 -0.1894409 -0.23699621
## PPF 0.3927272 -0.31118704 0.1706675 0.08651300
## REND 0.2578620 -0.71153763 -0.2672876 -0.27496856
## TEMP -0.3680845 -0.42807625 0.2338076 -0.38592919
## HR 0.3685665 0.42885660 -0.1375016 -0.80529034
#Despliegue los primeros renglones de los vectores de récords
head(PCs$x)
## PC1 PC2 PC3 PC4
## [1,] -0.5843991 -1.4193623 -0.2053826 2.997602e-15
## [2,] 1.9603343 -0.2952514 0.3455905 2.775558e-15
## [3,] 1.7602112 1.0470226 -0.2526798 2.886580e-15
## [4,] -3.1361464 0.6675911 0.1124719 2.886580e-15
#Grafica con los porcentajes de varianzas explicadas para los componentes
PCs_var <- PCs$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 (81.52 %)",ylab="PC2 (17.35 %)", cex= c(1,.8))
abline(h=0, v=0 , col="black",cex= c(1,.7))
#MULTIVARIADO BIPLOT ACOLCHADO NEGRO
NEGRO <- read.delim("~/R/NEGRO.txt")
## Warning in read.table(file = file, header = header, sep = sep, quote =
## quote, : incomplete final line found by readTableHeader on '~/R/NEGRO.txt'
attach (NEGRO)
## The following objects are masked from SA:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from GRIS:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from BLC:
##
## Acolchado, AP, DT, Genotipo, HR, NF, PPF, REND, TEMP
##
## The following objects are masked from ALVARO:
##
## Acolchado, AP, DT, Genotipo, HR, REND, TEMP
data <- NEGRO
cor(NEGRO[,3:9])
## DT AP NF PPF REND TEMP
## DT 1.00000000 -0.8661857 0.99961681 -0.92321839 0.34936702 -0.1822574
## AP -0.86618571 1.0000000 -0.87800112 0.66627660 -0.70362790 -0.3005973
## NF 0.99961681 -0.8780011 1.00000000 -0.91271525 0.37514512 -0.1645695
## PPF -0.92321839 0.6662766 -0.91271525 1.00000000 0.02538596 0.3154172
## REND 0.34936702 -0.7036279 0.37514512 0.02538596 1.00000000 0.5014645
## TEMP -0.18225738 -0.3005973 -0.16456952 0.31541722 0.50146452 1.0000000
## HR 0.01669306 0.4644307 -0.00283108 -0.19239225 -0.62520851 -0.9831173
## HR
## DT 0.01669306
## AP 0.46443068
## NF -0.00283108
## PPF -0.19239225
## REND -0.62520851
## TEMP -0.98311729
## HR 1.00000000
PCs <- prcomp(data [,3:9],scale=T)
summary(PCs)
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.960 1.6275 0.71320 6.57e-16
## Proportion of Variance 0.549 0.3784 0.07266 0.00e+00
## Cumulative Proportion 0.549 0.9273 1.00000 1.00e+00
PCs
## Standard deviations:
## [1] 1.960290e+00 1.627455e+00 7.131986e-01 6.570123e-16
##
## Rotation:
## PC1 PC2 PC3 PC4
## DT -0.49157248 0.1641936 -0.006584516 -0.3684095
## AP 0.49385511 0.1530823 -0.037507580 -0.3371108
## NF -0.49461406 0.1502598 0.014051407 0.2462420
## PPF 0.41915679 -0.2990323 0.415988779 0.1980571
## REND -0.27808317 -0.3988085 0.744034473 -0.2380893
## TEMP -0.03752098 -0.5852628 -0.414413002 -0.5753087
## HR 0.12405677 0.5796785 0.316196593 -0.5129351
#Vectores de carga
PCs$rotation
## PC1 PC2 PC3 PC4
## DT -0.49157248 0.1641936 -0.006584516 -0.3684095
## AP 0.49385511 0.1530823 -0.037507580 -0.3371108
## NF -0.49461406 0.1502598 0.014051407 0.2462420
## PPF 0.41915679 -0.2990323 0.415988779 0.1980571
## REND -0.27808317 -0.3988085 0.744034473 -0.2380893
## TEMP -0.03752098 -0.5852628 -0.414413002 -0.5753087
## HR 0.12405677 0.5796785 0.316196593 -0.5129351
#Despliegue los primeros renglones de los vectores de récords
head(PCs$x)
## PC1 PC2 PC3 PC4
## [1,] -0.5665487 -2.0913297 0.5118996 5.551115e-16
## [2,] 1.9233982 1.3148341 0.5681332 -1.110223e-16
## [3,] 1.1364106 -0.4897921 -0.9630441 2.775558e-15
## [4,] -2.4932602 1.2662877 -0.1169887 -6.661338e-16
#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 (54.90 %)",ylab="PC2 (37.84 %)", cex= c(1,.8))
abline(h=0, v=0 , col="black",cex= c(1,.7))
