Proyecto multivariado
Pedro Lizarazo, Geraldyne Rozo, Jimer Lozano
2023-09-02
Cargamos la data proveniente de la tesis
library(readxl)
Datos_tesis <- read_excel("Datos tesis.xlsx", sheet = "DATOS")
head(Datos_tesis)## # A tibble: 6 × 18
## G TTO R `ajus aer` pf.a ps.a ms.a `ajus rai` pf.r ps.r ms.r
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 93 CONTROL 2 1 193 15 0.0777 1 26.5 7.07 0.267
## 2 59 ESTRÉS 3 0 186. 16 0.0862 0 11 4.8 0.436
## 3 131 ESTRÉS 3 1 77.1 7 0.0908 0 22.3 6 0.269
## 4 43 ESTRÉS 3 1 51.9 5 0.0963 0 8.7 3.5 0.402
## 5 36 ESTRÉS 1 0 75.7 7.3 0.0964 0 9.7 3.8 0.392
## 6 137 ESTRÉS 1 0 269. 26.6 0.0989 0 22 6 0.273
## # ℹ 7 more variables: pf.tu <dbl>, ps.tu <dbl>, ms.tu <dbl>, NT <dbl>,
## # ppt <dbl>, ps.to <dbl>, Estado <chr>library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnames(Datos_tesis)## [1] "G" "TTO" "R" "ajus aer" "pf.a" "ps.a"
## [7] "ms.a" "ajus rai" "pf.r" "ps.r" "ms.r" "pf.tu"
## [13] "ps.tu" "ms.tu" "NT" "ppt" "ps.to" "Estado"TES1 <- Datos_tesis %>%
select(., c("G","TTO","ps.a","ps.r","ps.tu","NT","ppt") ) %>%
mutate(.,
G=as.factor(G),
TTO=as.factor(TTO),
ps.a=round(ps.a, digits = 3),
ps.r=round(ps.r, digits = 3),
ps.tu=round(ps.tu, digits = 3),
ps.to=(ps.a+ps.r+ps.tu),
NT=round(NT, digits = 0),
ppt=round(ppt, digits = 3),
IC=round( (ps.tu/ps.to)*100, digits = 3) )
head(TES1)## # A tibble: 6 × 9
## G TTO ps.a ps.r ps.tu NT ppt ps.to IC
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 93 CONTROL 15 7.07 60.8 8 31.2 82.9 73.4
## 2 59 ESTRÉS 16 4.8 7.59 7 4.44 28.4 26.7
## 3 131 ESTRÉS 7 6 7.1 15 1.94 20.1 35.3
## 4 43 ESTRÉS 5 3.5 13.4 12 4.58 21.9 61.2
## 5 36 ESTRÉS 7.3 3.8 28.3 7 16.6 39.4 71.8
## 6 137 ESTRÉS 26.6 6 13.7 16 3.52 46.3 29.6Partimos la data e los dos grupos (estres y control)
A <- split(TES1, f=TES1$TTO)
estres <- as.data.frame(A$ESTRÉS)
class(estres)## [1] "data.frame"control <- as.data.frame(A$CONTROL)
class(control)## [1] "data.frame"Tomamos cuatro datos al azar por genotipo del grupo estres
# Obtén los valores únicos en la columna "G"
valores_unicos_G_E <- unique(estres$G)
# Establece la cantidad de muestras al azar por grupo
muestras_por_grupo <- 5
# Inicializa un dataframe para almacenar los resultados
datos_al_azar_E <- data.frame()
# Itera a través de los valores únicos en "G" y selecciona datos al azar para cada grupo
for (valor in valores_unicos_G_E) {
# Filtra el dataframe para el grupo actual
grupo_E <- estres[estres$G == valor, , drop = FALSE]
# Selecciona datos al azar con reemplazo
muestras_E <- grupo_E[sample(nrow(grupo_E), muestras_por_grupo, replace = TRUE), ]
# Combina las muestras en el dataframe de resultados
datos_al_azar_E <- rbind(datos_al_azar_E, muestras_E)
}
str(datos_al_azar_E)## 'data.frame': 515 obs. of 9 variables:
## $ G : Factor w/ 103 levels "4","5","6","7",..: 39 39 39 39 39 91 91 91 91 91 ...
## $ TTO : Factor w/ 2 levels "CONTROL","ESTRÉS": 2 2 2 2 2 2 2 2 2 2 ...
## $ ps.a : num 16 10.9 16 16 16 20.3 8 20.3 8 20.3 ...
## $ ps.r : num 4.8 3.1 4.8 4.8 4.8 4.9 4.9 4.9 4.9 4.9 ...
## $ ps.tu: num 7.59 1.05 7.59 7.59 7.59 ...
## $ NT : num 7 4 7 7 7 11 13 11 13 11 ...
## $ ppt : num 4.44 1.07 4.44 4.44 4.44 ...
## $ ps.to: num 28.4 15 28.4 28.4 28.4 ...
## $ IC : num 26.73 6.97 26.73 26.73 26.73 ...Tomamos cuatro datos al azar por genotipo del grupo control
# Obtén los valores únicos en la columna "G"
valores_unicos_G_C <- unique(control$G)
# Establece la cantidad de muestras al azar por grupo
muestras_por_grupo <- 5
# Inicializa un dataframe para almacenar los resultados
datos_al_azar_C <- data.frame()
# Itera a través de los valores únicos en "G" y selecciona datos al azar para cada grupo
for (valor in valores_unicos_G_C) {
# Filtra el dataframe para el grupo actual
grupo_C <- control[control$G == valor, , drop = FALSE]
# Selecciona datos al azar con reemplazo
muestras_C <- grupo_C[sample(nrow(grupo_C), muestras_por_grupo, replace = TRUE), ]
# Combina las muestras en el dataframe de resultados
datos_al_azar_C <- rbind(datos_al_azar_C, muestras_C)
}
str(datos_al_azar_C)## 'data.frame': 515 obs. of 9 variables:
## $ G : Factor w/ 103 levels "4","5","6","7",..: 61 61 61 61 61 23 23 23 23 23 ...
## $ TTO : Factor w/ 2 levels "CONTROL","ESTRÉS": 1 1 1 1 1 1 1 1 1 1 ...
## $ ps.a : num 21.6 17.7 21.6 17 21.6 ...
## $ ps.r : num 4.19 5.7 4.19 5.79 4.19 ...
## $ ps.tu: num 56.6 63 56.6 53.7 56.6 ...
## $ NT : num 11 9 11 16 11 4 6 7 4 7 ...
## $ ppt : num 21.1 28.7 21.1 13.8 21.1 ...
## $ ps.to: num 82.4 86.4 82.4 76.5 82.4 ...
## $ IC : num 68.7 72.9 68.7 70.2 68.7 ...Comprobamos que tenemos los mismos genotipos y el mismo numero de datos por cada grupo
sort(valores_unicos_G_E)==sort(valores_unicos_G_C)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEnrow(datos_al_azar_E)==nrow(datos_al_azar_C)## [1] TRUEUnimos los dataframes
# Renombramos variables segun el grupo
names(datos_al_azar_E) <- c("G","TTO","E_ps.a","E_ps.r","E_ps.tu","E_NT","E_ppt","E_ps.to","E_IC")
names(datos_al_azar_C) <- c("G","TTO","C_ps.a","C_ps.r","C_ps.tu","C_NT","C_ppt","C_ps.to","C_IC")
# Ordenamos los dataframes por el genotipo y tamaño de las plantas
ordenados_E <- datos_al_azar_E[order(datos_al_azar_E$G, datos_al_azar_E$E_ps.to), ]
ordenados_C <- datos_al_azar_C[order(datos_al_azar_C$G, datos_al_azar_C$C_ps.to), ]
# Calculamos la media por genotipo para el control
medias_C <- ordenados_C %>%
group_by(., G) %>%
summarise(.,
M_ps.a=mean(C_ps.a),
M_ps.r=mean(C_ps.r),
M_ps.tu=mean(C_ps.tu),
M_ps.to=mean(C_ps.to),
M_NT=mean(C_NT),
M_ppt=mean(C_ppt),
M_IC=mean(C_IC) )
names(ordenados_C)## [1] "G" "TTO" "C_ps.a" "C_ps.r" "C_ps.tu" "C_NT" "C_ppt"
## [8] "C_ps.to" "C_IC"medias_C## # A tibble: 103 × 8
## G M_ps.a M_ps.r M_ps.tu M_ps.to M_NT M_ppt M_IC
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 46.0 5.51 28.2 79.8 12 10.3 35.1
## 2 5 27.8 8.89 12.7 49.4 5.6 9.81 25.6
## 3 6 26.1 4.66 19.6 50.3 4 24.0 39.1
## 4 7 17.3 4.47 18.1 39.9 5.8 14.8 44.1
## 5 8 14.3 2.40 17.5 34.1 8.2 7.64 45.4
## 6 9 30.2 4.61 21.7 56.5 9.8 9.80 39.0
## 7 11 18.3 2.21 14.5 35.0 6.6 10.6 41.2
## 8 13 34.1 2.82 24.7 61.6 4.2 24.4 39.6
## 9 14 19.8 3.65 15.0 38.4 9.2 6.55 39.5
## 10 15 26.7 3.79 26.1 56.5 5.2 25.2 46.4
## # ℹ 93 more rowsCalculamos el indice de estres (DTI: drought tolerance index)
indices <- data.frame(cbind(ordenados_E, ordenados_C[ ,3:9]) ) %>%
left_join(., medias_C, by = "G") %>%
mutate(.,
I_ps.a = (C_ps.a * E_ps.a) / (M_ps.a^2),
I_ps.r = (C_ps.r * E_ps.r) / (M_ps.r^2),
I_ps.tu = (C_ps.tu * E_ps.tu) / (M_ps.tu^2),
I_NT = (C_NT * E_NT) / (M_NT^2),
I_ppt = (C_ppt * E_ppt) / (M_ppt^2),
I_ps.to = (C_ps.to * E_ps.to) / (M_ps.to^2),
I_IC = (C_IC * E_IC) / (M_IC^2) )
names(indices)## [1] "G" "TTO" "E_ps.a" "E_ps.r" "E_ps.tu" "E_NT" "E_ppt"
## [8] "E_ps.to" "E_IC" "C_ps.a" "C_ps.r" "C_ps.tu" "C_NT" "C_ppt"
## [15] "C_ps.to" "C_IC" "M_ps.a" "M_ps.r" "M_ps.tu" "M_ps.to" "M_NT"
## [22] "M_ppt" "M_IC" "I_ps.a" "I_ps.r" "I_ps.tu" "I_NT" "I_ppt"
## [29] "I_ps.to" "I_IC"View(indices)Agrupamos por la media del DTI de cada genotipo
med_indic_tes <- indices %>%
group_by(., G) %>%
summarise(.,
ps.a=mean(I_ps.a),
ps.r=mean(I_ps.r),
ps.tu=mean(I_ps.tu),
ps.to=mean(I_ps.to),
NT=mean(I_NT),
ppt=mean(I_ppt),
IC=mean(I_IC) )
med_indic_tes## # A tibble: 103 × 8
## G ps.a ps.r ps.tu ps.to NT ppt IC
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 0.227 0.792 0.534 0.371 0.583 0.830 1.33
## 2 5 0.577 0.601 0.569 0.579 0.938 0.591 0.932
## 3 6 1.04 1.86 0.496 0.905 1.78 0.228 0.554
## 4 7 0.971 0.896 0.444 0.734 0.951 0.364 0.633
## 5 8 0.824 1.97 0.683 0.849 0.958 0.529 0.600
## 6 9 0.784 1.22 0.489 0.711 0.848 0.517 0.708
## 7 11 0.712 1.91 0.488 0.699 1.19 0.316 0.718
## 8 13 0.749 1.74 0.614 0.740 0.998 0.614 0.801
## 9 14 0.591 0.971 0.163 0.462 0.227 0.976 0.331
## 10 15 0.749 1.55 0.574 0.725 2.40 0.245 0.786
## # ℹ 93 more rowsCargamos la data proveniente del articulo
TOTAL <- med_indic_art[ ,-1] %>%
mutate(., G=as.factor(G)) %>%
inner_join(., med_indic_tes, by = "G")
TOTAL## # A tibble: 101 × 17
## G SAC GLU FRU FC SPAD NT.x PT RWC GR ps.a
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 1528 1.44e+3 1.03e+3 0.937 9.91e-1 1.30e+3 7.5 e-1 8.66e-1 2 0.227
## 2 5 1338 1.49e+3 1.32e+3 0.977 8.06e-1 7.62e-1 2.1 e-1 8.24e-1 2 0.577
## 3 6 2585 1.52e+3 1.74e+3 0.935 9.77e-1 5.59e-1 4.32e-1 9.12e-1 2 1.04
## 4 7 1605 5.39e+3 8.52e+3 0.962 9.02e-1 6.1 e-1 3.46e-1 1.03e+3 4 0.971
## 5 8 1315 4.37e-1 4.67e-1 0.975 7.9 e-1 1.03e+3 6.25e-1 7.83e-1 2 0.824
## 6 9 3718 3.12e+3 4.49e+3 0.977 8.05e-1 1.23e+3 5.3 e-1 8.75e-1 2 0.784
## 7 11 1325 3.11e+3 1.35e+3 0.977 8.17e-1 1.27e+3 2.69e-1 6.48e-1 2 0.712
## 8 13 3278 2.73e+3 2.35e+3 0.967 1.20e+3 3.45e-1 8.54e-1 8.99e-1 2 0.749
## 9 14 1583 8.23e+3 1.39e+4 0.835 9.55e-1 4.71e-1 1.48e-1 7.88e-1 4 0.591
## 10 15 2937 2.34e+3 2.44e+3 0.962 1.30e+3 6.27e-1 1.08e+3 5.88e-1 2 0.749
## # ℹ 91 more rows
## # ℹ 6 more variables: ps.r <dbl>, ps.tu <dbl>, ps.to <dbl>, NT.y <dbl>,
## # ppt <dbl>, IC <dbl>library(corrplot)## corrplot 0.92 loadednames(TOTAL)## [1] "G" "SAC" "GLU" "FRU" "FC" "SPAD" "NT.x" "PT" "RWC"
## [10] "GR" "ps.a" "ps.r" "ps.tu" "ps.to" "NT.y" "ppt" "IC"M = cor(TOTAL[,-c(1)], method = "spearman")
M## SAC GLU FRU FC SPAD
## SAC 1.000000000 0.525445841 0.45068405 0.021529213 0.166003669
## GLU 0.525445841 1.000000000 0.70482647 -0.044756644 -0.007819098
## FRU 0.450684046 0.704826470 1.00000000 -0.118635729 -0.063938589
## FC 0.021529213 -0.044756644 -0.11863573 1.000000000 0.134187802
## SPAD 0.166003669 -0.007819098 -0.06393859 0.134187802 1.000000000
## NT.x -0.282142379 -0.190614862 -0.22002004 -0.190457888 0.084164269
## PT 0.067205992 -0.016144814 0.02471214 -0.153910468 0.319407314
## RWC 0.216368751 0.178044533 0.06687479 0.299742793 0.060085037
## GR 0.375032416 0.512144282 0.47980064 -0.016241477 -0.287564462
## ps.a -0.115947093 -0.057600801 0.02221316 -0.004847030 0.056891250
## ps.r 0.088783278 0.099645310 0.07721607 0.169477116 0.174418740
## ps.tu 0.075096535 0.282896231 0.16227140 0.083215122 0.070182182
## ps.to -0.006336671 0.154170962 0.11412930 0.090870168 0.057590159
## NT.y 0.126424732 0.194613830 0.21156669 0.122236047 -0.057823129
## ppt -0.066692293 0.081741886 -0.03500291 -0.102906183 0.113136008
## IC 0.111870191 0.247887291 0.13727432 0.002184659 0.021765213
## NT.x PT RWC GR ps.a
## SAC -0.28214238 0.067205992 0.216368751 0.37503242 -0.115947093
## GLU -0.19061486 -0.016144814 0.178044533 0.51214428 -0.057600801
## FRU -0.22002004 0.024712138 0.066874789 0.47980064 0.022213162
## FC -0.19045789 -0.153910468 0.299742793 -0.01624148 -0.004847030
## SPAD 0.08416427 0.319407314 0.060085037 -0.28756446 0.056891250
## NT.x 1.00000000 0.317009814 -0.175447895 -0.35513664 -0.132960198
## PT 0.31700981 1.000000000 -0.006899022 -0.30538570 0.009289621
## RWC -0.17544790 -0.006899022 1.000000000 0.04827384 0.040630424
## GR -0.35513664 -0.305385697 0.048273835 1.00000000 -0.034869961
## ps.a -0.13296020 0.009289621 0.040630424 -0.03486996 1.000000000
## ps.r -0.07103170 0.072453217 0.198294641 -0.06862797 0.360349447
## ps.tu 0.06461345 0.133083280 0.124949037 0.03218404 0.097367501
## ps.to -0.15485911 -0.081562287 0.156260557 0.05532366 0.781025044
## NT.y -0.06198674 0.094084346 0.106555849 0.18211000 0.176167734
## ppt 0.07014642 -0.042877860 0.084767086 -0.09508779 -0.009959231
## IC 0.20596629 0.245781813 0.032878260 0.07893313 -0.454793244
## ps.r ps.tu ps.to NT.y ppt IC
## SAC 0.08878328 0.07509654 -0.006336671 0.12642473 -0.066692293 0.111870191
## GLU 0.09964531 0.28289623 0.154170962 0.19461383 0.081741886 0.247887291
## FRU 0.07721607 0.16227140 0.114129295 0.21156669 -0.035002912 0.137274316
## FC 0.16947712 0.08321512 0.090870168 0.12223605 -0.102906183 0.002184659
## SPAD 0.17441874 0.07018218 0.057590159 -0.05782313 0.113136008 0.021765213
## NT.x -0.07103170 0.06461345 -0.154859113 -0.06198674 0.070146420 0.205966290
## PT 0.07245322 0.13308328 -0.081562287 0.09408435 -0.042877860 0.245781813
## RWC 0.19829464 0.12494904 0.156260557 0.10655585 0.084767086 0.032878260
## GR -0.06862797 0.03218404 0.055323664 0.18211000 -0.095087792 0.078933126
## ps.a 0.36034945 0.09736750 0.781025044 0.17616773 -0.009959231 -0.454793244
## ps.r 1.00000000 0.04068725 0.428211998 0.25125218 -0.177623762 -0.285230052
## ps.tu 0.04068725 1.00000000 0.572300524 0.33170646 0.497740245 0.722317997
## ps.to 0.42821200 0.57230052 1.000000000 0.34735003 0.221630751 -0.018427490
## NT.y 0.25125218 0.33170646 0.347350029 1.00000000 -0.509994176 0.178578917
## ppt -0.17762376 0.49774024 0.221630751 -0.50999418 1.000000000 0.406639487
## IC -0.28523005 0.72231800 -0.018427490 0.17857892 0.406639487 1.000000000corrplot(M, method = 'number', order = 'hclust', addrect = 2)
# Cluster Kmeans
library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(cluster)
# Se reviso previamente cuales tenian mas correlaciones y se quitaron variables que no aportaban
names(TOTAL)## [1] "G" "SAC" "GLU" "FRU" "FC" "SPAD" "NT.x" "PT" "RWC"
## [10] "GR" "ps.a" "ps.r" "ps.tu" "ps.to" "NT.y" "ppt" "IC"crec <- data.frame(scale(TOTAL[,-c(1,5,6,7,8,9,10,11,12,14,15) ]))
rownames(crec) <- TOTAL$G
crec## SAC GLU FRU ps.tu ppt
## 4 -0.208524687 -0.504352875 -0.575310907 -0.319746339 0.559227305
## 5 -0.322215730 -0.486041376 -0.497922739 -0.195710614 -0.150831184
## 6 0.423956534 -0.472307751 -0.383475448 -0.455834807 -1.233367987
## 7 -0.162449896 1.004056930 1.464303311 -0.639110211 -0.826810676
## 8 -0.335978330 -1.053531162 -0.856941982 0.212159947 -0.337304180
## 9 1.101914173 0.135786642 0.365336826 -0.477712787 -0.372000631
## 11 -0.329994590 0.132353236 -0.488112971 -0.483920478 -0.971397432
## 13 0.838629653 -0.011849826 -0.217799370 -0.035260332 -0.083787413
## 14 -0.175614122 2.087105852 2.931136087 -1.637246101 0.992656247
## 15 0.634584150 -0.159486294 -0.192729963 -0.177531904 -1.180980897
## 16 -0.152875914 -0.038554096 -0.149130995 -1.127712119 -1.248147098
## 17 0.124171206 0.031258497 0.086848418 -0.358790244 0.091803850
## 19 -0.191770218 -0.502445428 -0.117521744 -0.681152062 0.138014768
## 20 0.253419970 1.031524180 1.585562940 -0.118987106 -0.595254295
## 21 2.102395348 1.954728968 0.734838079 -0.006670423 1.149945482
## 23 1.234753181 -0.262869970 -0.856807098 0.407129220 1.550908004
## 24 -0.188778348 -0.373501949 -0.291645122 -1.231174993 -0.378053339
## 27 1.711657186 2.119532467 0.584694133 -0.177279126 0.139926462
## 30 0.137933806 0.288763965 1.320154223 0.903091078 0.872504559
## 31 0.069719180 -0.157960335 -0.216709396 -1.321504560 -1.514028124
## 32 0.847006888 0.672542484 -0.054303240 -0.103552047 0.346184364
## 33 0.140925676 -0.486041376 -0.429526858 -1.384679848 -1.010016703
## 35 1.639253943 -0.437592199 -0.856807916 0.681578163 -0.438407996
## 37 2.573913988 0.930810931 0.929125980 1.111118626 -0.595138029
## 38 -0.341962069 -0.190005460 -0.181830221 -0.456342406 0.426844123
## 41 -0.448472624 0.104123007 2.023187580 -0.559175351 -0.838296317
## 42 -0.321617356 -1.053441130 -0.856873041 -0.002307483 -0.561432919
## 43 1.645836056 1.181831075 0.362884384 -0.287461812 -0.983463708
## 44 -0.167835261 -0.415084313 -0.451053848 -0.355593165 -0.164449485
## 45 -0.009266175 -0.603540167 -0.856861869 -0.812075199 2.373080022
## 47 -0.255796226 -0.444459011 0.295850971 -0.170981990 0.202319248
## 51 -1.122389444 -0.669156375 -0.506370039 -0.453512707 -0.582119760
## 52 -0.468218963 -1.053425108 -0.558961294 -0.494324890 -0.977552742
## 53 0.598083342 0.730910390 0.570251975 1.089698880 0.239764748
## 56 -0.498137659 -0.641307635 -0.856846065 0.457271175 0.192204324
## 57 0.545426438 -0.148423096 -0.129511460 0.179491515 -0.173424622
## 59 0.004496424 -0.370450032 -0.856946342 -0.287715939 0.902944453
## 61 0.273764683 -1.053369029 0.686334227 -1.535358048 -0.822700304
## 62 0.243845988 0.338739100 -0.010431779 0.680926433 1.161604035
## 63 -0.443087259 -1.053332024 -0.462226084 -0.041203059 -0.411312673
## 65 -1.122315844 -1.053378566 -0.422987013 -1.190823275 -0.479393160
## 66 0.411390682 -0.392957918 -0.276112989 -0.066720426 0.429842942
## 67 0.245042736 -0.442933053 0.176498796 -1.024594529 -0.669133950
## 69 0.760242671 -0.208316960 -0.567136100 0.024640332 -0.149385339
## 70 -0.063119827 -0.467729876 0.847105421 -0.110002610 -0.541539560
## 71 -0.055939340 -0.172075450 1.021501293 0.259382352 -0.080060181
## 72 2.984996863 0.519183672 0.416020626 1.483210778 2.640738667
## 73 0.473023195 3.973953323 -0.071470334 -0.191078122 -0.653411290
## 74 -0.304264512 -0.586373135 -0.856809823 -0.800420382 -0.676224307
## 76 4.474349522 2.925619953 6.721521359 2.095721609 -0.382840030
## 79 1.133627990 1.834178260 0.413568184 2.325264443 -0.161679906
## 80 -0.203139322 0.420759360 -0.289192680 1.119521843 -0.047720535
## 81 -0.334183208 -0.080517950 -0.255403480 -1.102271028 -0.575613303
## 83 0.356938657 -0.254477200 -0.508822481 -0.583504286 0.636456856
## 86 -0.371880764 -0.448655397 -0.500647674 0.267518499 0.713159934
## 87 -0.329396217 -0.457811146 -0.190822509 0.713064942 0.332443487
## 88 -0.400004338 1.424458449 0.289583619 0.008562290 -0.567513731
## 91 -0.523867737 -0.101499877 -0.467948448 0.521824634 -0.189733535
## 93 0.230681762 1.778480781 0.626385646 0.628895509 1.182323082
## 96 -1.122555792 -0.631388906 -0.083187557 0.209376339 0.219095036
## 98 -1.122524676 -1.053499880 -0.857020188 -1.686546845 1.671974090
## 99 -0.206131191 -0.598962292 -0.441516574 -1.117118231 -0.979752906
## 101 -1.122604858 -0.073651138 -0.370395758 0.371233466 2.312703172
## 102 -0.045168610 -0.253714220 -0.426256935 -1.330951127 -1.184420410
## 103 -0.395217346 0.006843164 0.447357384 3.517652117 2.768826915
## 104 0.045185850 0.061777664 0.148976948 -0.023551825 -0.448131248
## 106 -0.231861269 0.988034368 -0.033866224 0.011758464 0.730196137
## 108 0.144515919 -0.043513461 0.008097783 -1.527011754 -1.791918244
## 109 -0.102014131 -0.238073147 -0.536616823 -0.880094769 -0.255989476
## 110 -0.398807590 -0.580650792 -0.036318666 -1.175891563 0.019137486
## 112 -1.122584514 -0.395246855 -0.537979290 -0.195159660 -0.514166212
## 113 -1.122538439 -0.629862948 -0.573675945 -0.645005647 -1.364605191
## 114 -1.122340975 2.023397092 0.874627270 0.496149068 -0.062992395
## 115 -1.122329606 -0.183520137 -0.399552567 3.628946401 1.708918159
## 116 -1.122476208 -0.482226480 -0.478303203 1.188773417 2.399562096
## 117 -1.122332598 -0.361675772 -0.157305802 0.550014355 -0.209456760
## 118 -0.267165330 -0.436829220 -0.340421467 0.512418050 1.018193761
## 119 -0.347347434 -0.187716523 -0.530076978 0.397483756 1.580415421
## 120 -0.399405964 -1.053383907 -0.238508880 -0.752009236 -0.589080623
## 121 -1.122253015 -0.376935355 -0.533346900 -0.490712650 -0.533320290
## 122 -0.479588068 -0.523427355 -0.082097582 0.558160830 -0.545711310
## 123 -1.122486380 -1.053496446 -0.856983401 -0.782829360 -1.201912548
## 124 -1.122456462 -0.041987503 -0.010159285 -0.849438153 -1.096617535
## 125 -0.289903539 -0.590569521 -0.856843067 0.497659450 0.003054377
## 126 -0.341363695 -0.357479387 -0.491927881 0.439153185 1.158449685
## 127 -0.419152303 -1.053363688 -0.856961602 0.141162329 -0.900181957
## 128 -1.122384058 -1.053390011 -0.271753093 -0.378160406 0.018268216
## 129 -0.256394600 -1.053365977 -0.856938712 -0.134067154 -0.026729657
## 131 -0.252804356 -0.606973573 -0.426801922 0.769575505 0.447173074
## 132 -0.041578367 -0.541738854 -0.366035861 -0.817926746 -1.357620492
## 133 -1.122348156 -0.160249273 -0.001984479 0.584913679 1.936866650
## 135 -0.066111697 0.090389382 0.532102878 3.118012601 1.634802764
## 136 3.729374005 0.591666692 1.570575795 -0.348867217 -1.605564846
## 137 -1.122444494 -1.053618905 -0.856808188 -1.142154842 -0.705065047
## 138 -0.393422225 -0.408980480 -0.546154097 0.591258113 -1.098587321
## 140 -1.122547414 -1.053594108 -0.857037628 2.046773821 1.503138806
## 141 1.471709248 3.829750261 2.514220956 0.876418317 -0.127901308
## 142 -0.468817337 -1.053363688 -0.857018826 -0.451711114 0.774408723
## 143 0.210935423 0.256337350 0.683064304 0.681390962 -0.186629354
## 144 -0.203139322 0.526813464 0.420653016 0.024053143 -0.819293844
## 145 -0.400004338 1.499993386 -0.562503710 -0.194158529 0.050094785
## IC
## 4 1.7950368845
## 5 0.4648752208
## 6 -0.8092140305
## 7 -0.5419190664
## 8 -0.6551521023
## 9 -0.2906118482
## 11 -0.2561103374
## 13 0.0232895770
## 14 -1.5592809267
## 15 -0.0285322168
## 16 -0.9625533513
## 17 0.2045414447
## 19 -0.2354624118
## 20 0.9110546098
## 21 -0.2332550703
## 23 0.1141170860
## 24 -1.0444227048
## 27 -0.2884029640
## 30 1.1353882574
## 31 -0.8882851483
## 32 -0.4750925518
## 33 -0.9228336294
## 35 0.2863884452
## 37 0.5631154599
## 38 -0.2530083203
## 41 -0.5735556202
## 42 -0.8622495949
## 43 0.7015705340
## 44 0.0008098151
## 45 -0.7095642069
## 47 0.0736853630
## 51 -0.4201113047
## 52 -0.7764883967
## 53 -0.1798046508
## 56 -1.0735342117
## 57 0.5784065518
## 59 0.2750806935
## 61 -0.7642558794
## 62 -0.7334657913
## 63 0.0377625916
## 65 -0.9114552376
## 66 0.4965477006
## 67 -1.4748374869
## 69 0.8425109933
## 70 -0.1857675867
## 71 0.3830844906
## 72 1.0599161967
## 73 -0.2925723763
## 74 -0.6474443614
## 76 2.4517407167
## 79 1.4584883268
## 80 0.5219491329
## 81 -1.2180822672
## 83 -0.8884732476
## 86 0.2266012753
## 87 0.4205598847
## 88 -0.4465061311
## 91 -0.1665470311
## 93 0.3720159760
## 96 0.0406832572
## 98 -2.0994163074
## 99 -0.8353776378
## 101 -0.0422082325
## 102 -1.2358532288
## 103 -0.2310034233
## 104 0.5877474038
## 106 -0.0560155556
## 108 -1.6600035634
## 109 -0.8625177944
## 110 -1.4690620502
## 112 0.0433734614
## 113 -0.6543765970
## 114 0.8370053387
## 115 4.0256405532
## 116 3.7581226016
## 117 0.0683960166
## 118 1.0964811759
## 119 0.4313315946
## 120 -0.3292884433
## 121 0.4346202607
## 122 -0.0847860699
## 123 -0.0379008303
## 124 -0.8359843979
## 125 -0.0199301177
## 126 0.8955009524
## 127 1.2545816904
## 128 -0.1830433433
## 129 -0.0775887624
## 131 1.2957073928
## 132 -0.7351890066
## 133 1.8312049497
## 135 2.3512315510
## 136 0.0753695052
## 137 -1.3187989225
## 138 -0.7719423383
## 140 0.2857817317
## 141 0.3046171739
## 142 -0.2189987179
## 143 0.6791624108
## 144 -0.3644477513
## 145 0.1974889051# Revisamos correlaciones de nuevo
M = cor(crec, method = "spearman")
M## SAC GLU FRU ps.tu ppt IC
## SAC 1.00000000 0.52544584 0.45068405 0.07509654 -0.06669229 0.1118702
## GLU 0.52544584 1.00000000 0.70482647 0.28289623 0.08174189 0.2478873
## FRU 0.45068405 0.70482647 1.00000000 0.16227140 -0.03500291 0.1372743
## ps.tu 0.07509654 0.28289623 0.16227140 1.00000000 0.49774024 0.7223180
## ppt -0.06669229 0.08174189 -0.03500291 0.49774024 1.00000000 0.4066395
## IC 0.11187019 0.24788729 0.13727432 0.72231800 0.40663949 1.0000000corrplot(M, method = 'number', order = 'hclust', addrect = 2)
# Grafico de codo
fviz_nbclust(x=crec, FUNcluster=kmeans, method="wss", k.max=15,
diss=get_dist(crec, method="euclidean"), nstart=50)
### Sel 4 grupos
km_clusters<- kmeans(x=crec, centers=3, nstart = 50)
fviz_cluster(object=km_clusters, data=crec)
# Cluster mas bonito
fviz_cluster(object=km_clusters, data=crec, show.clust.cent = TRUE,
ellipse.type="euclid", star.plot=T, repel=T,
pointsize=0.5, outlier.color="darkred")+
labs(title="Resultados agrupaciento cluster K-means")+
xlab(label = "Componente principal 1 (36%)")+
ylab(label = "Componente principal 2 (36%)")+
theme_bw()+
theme(legend.position="none")