library(readxl)
ref_art <- read_excel("~/ADC/Referencias_Articulos.xlsx")
View(ref_art)
library(FactoMineR)
library(ade4)
##
## Attaching package: 'ade4'
## The following object is masked from 'package:FactoMineR':
##
## reconst
library(FactoClass)
## Loading required package: ggplot2
## Loading required package: ggrepel
## Loading required package: xtable
## Loading required package: scatterplot3d
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(missMDA)
library(ggplot2)
#############
#####################################
Sev_relativa<-as.numeric(ref_art$Sev_relativa)
Gen<-as.factor(ref_art$Gen)
Temp<-as.numeric(ref_art$Temp)
Pais<-as.factor(ref_art$Pais)
Genotipo<-as.factor(ref_art$Genotipo)
Cromosoma<-as.numeric(ref_art$Cromosoma)
t<-data.frame(Gen, Pais, Genotipo, Cromosoma, Sev_relativa, Temp)
View(t)
r_mca <- MCA(t,quanti.sup=c(4:6))
summary(r_mca, ncp=3, nbelements=Inf)
##
## Call:
## MCA(X = t, quanti.sup = c(4:6))
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 0.966 0.774 0.702 0.667 0.667 0.667 0.667
## % of var. 5.366 4.300 3.902 3.704 3.704 3.704 3.704
## Cumulative % of var. 5.366 9.666 13.568 17.272 20.975 24.679 28.383
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.667 0.667 0.616 0.544 0.528 0.526 0.526
## % of var. 3.704 3.704 3.424 3.022 2.933 2.921 2.921
## Cumulative % of var. 32.086 35.790 39.214 42.236 45.170 48.091 51.012
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 0.514 0.500 0.490 0.482 0.482 0.480 0.477
## % of var. 2.858 2.778 2.721 2.680 2.680 2.665 2.647
## Cumulative % of var. 53.870 56.647 59.369 62.049 64.729 67.394 70.041
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 0.459 0.356 0.333 0.333 0.333 0.333 0.333
## % of var. 2.552 1.978 1.852 1.852 1.852 1.852 1.852
## Cumulative % of var. 72.593 74.571 76.423 78.275 80.127 81.979 83.831
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 0.333 0.333 0.333 0.207 0.184 0.184 0.175
## % of var. 1.852 1.852 1.852 1.152 1.024 1.024 0.972
## Cumulative % of var. 85.682 87.534 89.386 90.538 91.562 92.585 93.557
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.167 0.166 0.159 0.141 0.141 0.141 0.127
## % of var. 0.926 0.923 0.882 0.785 0.783 0.783 0.708
## Cumulative % of var. 94.483 95.407 96.288 97.074 97.856 98.639 99.347
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 0.064 0.053 0.000 0.000 0.000 0.000 0.000
## % of var. 0.357 0.296 0.000 0.000 0.000 0.000 0.000
## Cumulative % of var. 99.704 100.000 100.000 100.000 100.000 100.000 100.000
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54
## Variance 0.000 0.000 0.000 0.000 0.000
## % of var. 0.000 0.000 0.000 0.000 0.000
## Cumulative % of var. 100.000 100.000 100.000 100.000 100.000
##
## Individuals
## Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | -0.559 0.395 0.062 | 0.002 0.000 0.000 |
## 2 | -0.542 0.371 0.052 | 0.022 0.001 0.000 |
## 3 | -0.542 0.371 0.052 | 0.022 0.001 0.000 |
## 4 | -0.542 0.371 0.052 | 0.022 0.001 0.000 |
## 5 | -0.643 0.522 0.063 | -0.059 0.006 0.001 |
## 6 | -0.644 0.524 0.052 | -0.063 0.006 0.000 |
## 7 | -0.643 0.522 0.063 | -0.059 0.006 0.001 |
## 8 | -0.643 0.522 0.063 | -0.059 0.006 0.001 |
## 9 | -0.644 0.524 0.052 | -0.063 0.006 0.000 |
## 10 | -0.646 0.527 0.083 | -0.071 0.008 0.001 |
## 11 | -0.646 0.527 0.083 | -0.071 0.008 0.001 |
## 12 | -0.557 0.391 0.060 | 0.005 0.000 0.000 |
## 13 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 14 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 15 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 16 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 17 | -0.642 0.520 0.051 | -0.060 0.006 0.000 |
## 18 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 19 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 20 | -0.642 0.520 0.051 | -0.060 0.006 0.000 |
## 21 | -0.643 0.523 0.080 | -0.068 0.007 0.001 |
## 22 | -0.643 0.523 0.080 | -0.068 0.007 0.001 |
## 23 | -0.557 0.391 0.060 | 0.005 0.000 0.000 |
## 24 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 25 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 26 | -0.539 0.367 0.050 | 0.025 0.001 0.000 |
## 27 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 28 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 29 | -0.641 0.518 0.061 | -0.056 0.005 0.000 |
## 30 | -0.643 0.523 0.080 | -0.068 0.007 0.001 |
## 31 | -0.643 0.523 0.080 | -0.068 0.007 0.001 |
## 32 | -0.535 0.361 0.009 | 0.026 0.001 0.000 |
## 33 | -0.509 0.327 0.008 | 0.062 0.006 0.000 |
## 34 | -0.509 0.327 0.008 | 0.062 0.006 0.000 |
## 35 | -0.509 0.327 0.008 | 0.062 0.006 0.000 |
## 36 | -0.663 0.555 0.013 | -0.081 0.010 0.000 |
## 37 | -0.665 0.558 0.013 | -0.087 0.012 0.000 |
## 38 | -0.663 0.555 0.013 | -0.081 0.010 0.000 |
## 39 | -0.663 0.555 0.013 | -0.081 0.010 0.000 |
## 40 | -0.665 0.558 0.013 | -0.087 0.012 0.000 |
## 41 | -0.668 0.563 0.014 | -0.102 0.016 0.000 |
## 42 | -0.668 0.563 0.014 | -0.102 0.016 0.000 |
## 43 | -0.573 0.414 0.046 | -0.044 0.003 0.000 |
## 44 | -0.556 0.390 0.040 | -0.023 0.001 0.000 |
## 45 | -0.556 0.390 0.040 | -0.023 0.001 0.000 |
## 46 | -0.556 0.390 0.040 | -0.023 0.001 0.000 |
## 47 | -0.657 0.545 0.050 | -0.105 0.017 0.001 |
## 48 | -0.658 0.547 0.043 | -0.108 0.018 0.001 |
## 49 | -0.657 0.545 0.050 | -0.105 0.017 0.001 |
## 50 | -0.657 0.545 0.050 | -0.105 0.017 0.001 |
## 51 | -0.658 0.547 0.043 | -0.108 0.018 0.001 |
## 52 | -0.660 0.549 0.061 | -0.117 0.021 0.002 |
## 53 | -0.660 0.549 0.061 | -0.117 0.021 0.002 |
## 54 | -0.670 0.567 0.045 | -0.134 0.028 0.002 |
## 55 | -0.670 0.567 0.045 | -0.134 0.028 0.002 |
## 56 | 1.847 4.306 0.212 | -1.959 6.046 0.239 |
## 57 | 1.929 4.700 0.149 | -3.307 17.235 0.438 |
## 58 | 1.847 4.306 0.212 | -1.959 6.046 0.239 |
## 59 | 1.929 4.700 0.149 | -3.307 17.235 0.438 |
## 60 | 1.679 3.558 0.265 | -0.537 0.455 0.027 |
## 61 | 1.679 3.558 0.265 | -0.537 0.455 0.027 |
## 62 | -0.583 0.430 0.034 | -0.061 0.006 0.000 |
## 63 | -0.670 0.567 0.045 | -0.134 0.028 0.002 |
## 64 | -0.670 0.567 0.045 | -0.134 0.028 0.002 |
## 65 | 1.666 3.504 0.142 | -0.636 0.637 0.021 |
## 66 | 1.641 3.400 0.154 | -0.174 0.048 0.002 |
## 67 | 1.666 3.504 0.142 | -0.636 0.637 0.021 |
## 68 | 1.641 3.400 0.154 | -0.174 0.048 0.002 |
## 69 | 1.634 3.372 0.086 | 0.001 0.000 0.000 |
## 70 | 0.525 0.347 0.009 | 0.558 0.491 0.010 |
## 71 | 0.551 0.383 0.009 | 0.594 0.556 0.011 |
## 72 | 0.551 0.383 0.009 | 0.594 0.556 0.011 |
## 73 | 0.551 0.383 0.009 | 0.594 0.556 0.011 |
## 74 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 75 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 76 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 77 | 1.634 3.372 0.086 | 0.001 0.000 0.000 |
## 78 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 79 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 80 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## 81 | 1.672 3.532 0.084 | -0.810 1.034 0.020 |
## 82 | 1.561 3.076 0.044 | 2.075 6.783 0.078 |
## Dim.3 ctr cos2
## 1 0.417 0.302 0.035 |
## 2 0.730 0.925 0.094 |
## 3 0.730 0.925 0.094 |
## 4 0.730 0.925 0.094 |
## 5 0.740 0.951 0.083 |
## 6 0.641 0.714 0.052 |
## 7 0.740 0.951 0.083 |
## 8 0.740 0.951 0.083 |
## 9 0.641 0.714 0.052 |
## 10 0.193 0.065 0.007 |
## 11 0.193 0.065 0.007 |
## 12 0.409 0.290 0.032 |
## 13 0.722 0.905 0.090 |
## 14 0.722 0.905 0.090 |
## 15 0.722 0.905 0.090 |
## 16 0.732 0.930 0.080 |
## 17 0.633 0.696 0.050 |
## 18 0.732 0.930 0.080 |
## 19 0.732 0.930 0.080 |
## 20 0.633 0.696 0.050 |
## 21 0.185 0.060 0.007 |
## 22 0.185 0.060 0.007 |
## 23 0.409 0.290 0.032 |
## 24 0.722 0.905 0.090 |
## 25 0.722 0.905 0.090 |
## 26 0.722 0.905 0.090 |
## 27 0.732 0.930 0.080 |
## 28 0.732 0.930 0.080 |
## 29 0.732 0.930 0.080 |
## 30 0.185 0.060 0.007 |
## 31 0.185 0.060 0.007 |
## 32 0.268 0.124 0.002 |
## 33 0.863 1.294 0.023 |
## 34 0.863 1.294 0.023 |
## 35 0.863 1.294 0.023 |
## 36 0.883 1.353 0.024 |
## 37 0.694 0.837 0.014 |
## 38 0.883 1.353 0.024 |
## 39 0.883 1.353 0.024 |
## 40 0.694 0.837 0.014 |
## 41 -0.158 0.043 0.001 |
## 42 -0.158 0.043 0.001 |
## 43 -1.494 3.876 0.314 |
## 44 -1.181 2.422 0.180 |
## 45 -1.181 2.422 0.180 |
## 46 -1.181 2.422 0.180 |
## 47 -1.171 2.380 0.158 |
## 48 -1.270 2.799 0.161 |
## 49 -1.171 2.380 0.158 |
## 50 -1.171 2.380 0.158 |
## 51 -1.270 2.799 0.161 |
## 52 -1.718 5.122 0.416 |
## 53 -1.718 5.122 0.416 |
## 54 -2.036 7.197 0.411 |
## 55 -2.036 7.197 0.411 |
## 56 0.016 0.000 0.000 |
## 57 0.049 0.004 0.000 |
## 58 0.016 0.000 0.000 |
## 59 0.049 0.004 0.000 |
## 60 -0.006 0.000 0.000 |
## 61 -0.006 0.000 0.000 |
## 62 -1.812 5.703 0.326 |
## 63 -2.036 7.197 0.411 |
## 64 -2.036 7.197 0.411 |
## 65 0.002 0.000 0.000 |
## 66 -0.010 0.000 0.000 |
## 67 0.002 0.000 0.000 |
## 68 -0.010 0.000 0.000 |
## 69 -0.016 0.000 0.000 |
## 70 -0.281 0.137 0.003 |
## 71 0.315 0.172 0.003 |
## 72 0.315 0.172 0.003 |
## 73 0.315 0.172 0.003 |
## 74 -0.126 0.027 0.000 |
## 75 -0.126 0.027 0.000 |
## 76 -0.126 0.027 0.000 |
## 77 -0.016 0.000 0.000 |
## 78 -0.126 0.027 0.000 |
## 79 -0.126 0.027 0.000 |
## 80 -0.126 0.027 0.000 |
## 81 0.008 0.000 0.000 |
## 82 -0.126 0.027 0.000 |
##
## Categories
## Dim.1 ctr cos2 v.test Dim.2 ctr
## R1 | -0.413 0.502 0.016 -1.135 | 0.080 0.023
## R10 | -0.669 1.318 0.042 -1.839 | -0.113 0.047
## R11 | -0.669 1.318 0.042 -1.839 | -0.113 0.047
## R2 | -0.362 0.331 0.010 -0.915 | 0.134 0.056
## R3 | -0.362 0.331 0.010 -0.915 | 0.134 0.056
## R3a | 1.588 1.062 0.031 1.588 | 2.358 2.921
## R3b | 1.588 1.062 0.031 1.588 | 2.358 2.921
## R4 | -0.362 0.331 0.010 -0.915 | 0.134 0.056
## R5 | -0.660 0.917 0.028 -1.514 | -0.081 0.017
## R6 | -0.664 0.741 0.023 -1.352 | -0.090 0.017
## R7 | -0.660 0.917 0.028 -1.514 | -0.081 0.017
## R8 | -0.660 0.917 0.028 -1.514 | -0.081 0.017
## R9 | -0.664 0.741 0.023 -1.352 | -0.090 0.017
## Rpi-blb1 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Rpi-blb2 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Rpi-blb3 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Rpi-chc1 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Rpi-pta1 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Rpi-sto1 | 1.730 10.077 0.324 5.119 | -0.759 2.418
## Rpi-vnt1.1 | 1.804 6.846 0.211 4.136 | -1.977 10.263
## Bélgica | 1.921 6.214 0.189 3.915 | -2.993 18.818
## Brasil | -0.613 3.164 0.121 -3.134 | -0.032 0.011
## China | -0.620 3.561 0.141 -3.380 | -0.041 0.019
## Escocia | -0.664 0.929 0.029 -1.523 | -0.135 0.048
## PaÃses Bajos | 1.425 17.102 0.655 7.286 | 0.759 6.058
## Polonia | -0.633 1.856 0.062 -2.243 | -0.091 0.048
## A01-20 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A02-33 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A03-142 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A04-33 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A09 | 1.794 5.416 0.165 3.656 | -1.419 4.227
## A09-277 | 1.663 1.164 0.034 1.663 | 0.001 0.000
## A13-13 | 1.702 1.219 0.036 1.702 | -0.921 0.445
## A15 | 1.963 3.244 0.096 2.794 | -3.759 14.845
## A17-27 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A23-43 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## A25-11 | 1.588 1.062 0.031 1.588 | 2.358 2.921
## Atlantic | 1.682 2.383 0.071 2.394 | -0.460 0.222
## Bintje | 1.682 2.383 0.071 2.394 | -0.460 0.222
## Black | -0.643 2.783 0.100 -2.849 | -0.105 0.092
## Black 2182ef(7); CIP 800992 | -0.675 0.192 0.006 -0.675 | -0.092 0.004
## Black 2424a(5); CIP 800993 | -0.675 0.192 0.006 -0.675 | -0.092 0.004
## Black 2573; CIP 800994 | -0.677 0.193 0.006 -0.677 | -0.099 0.005
## Black 3053-18;CIP800990 | -0.675 0.192 0.006 -0.675 | -0.092 0.004
## Black 3618ad(1); CIP 800995 | -0.679 0.194 0.006 -0.679 | -0.116 0.007
## Black 5008ab(6); CIP 800996 | -0.679 0.194 0.006 -0.679 | -0.116 0.007
## Black XD2-21; CIP 800991 | -0.677 0.193 0.006 -0.677 | -0.099 0.005
## CEBECO-43154-5;CIP800986 | -0.544 0.125 0.004 -0.544 | 0.029 0.000
## CEBECO-44158-4;CIP800987 | -0.518 0.113 0.003 -0.518 | 0.070 0.003
## CEBECO-4431-5;CIP800989 | -0.518 0.113 0.003 -0.518 | 0.070 0.003
## CEBECO-4642-1;CIP800988 | -0.518 0.113 0.003 -0.518 | 0.070 0.003
## MaR1: CEBECO 43154-5 | 0.534 0.120 0.004 0.534 | 0.635 0.212
## MaR2: CEBECO 44158-4 | 0.560 0.132 0.004 0.560 | 0.675 0.240
## MaR3: CEBECO 4642-1 | 0.560 0.132 0.004 0.560 | 0.675 0.240
## MaR4: CEBECO 4431-5 | 0.560 0.132 0.004 0.560 | 0.675 0.240
## Potae9 | 1.663 1.164 0.034 1.663 | 0.001 0.000
## Solanum demissum | -0.615 4.938 0.230 -4.317 | -0.035 0.020
## cos2 v.test Dim.3 ctr cos2 v.test
## R1 0.001 0.219 | -0.356 0.512 0.012 -0.978 |
## R10 0.001 -0.309 | -0.918 3.412 0.079 -2.523 |
## R11 0.001 -0.309 | -0.918 3.412 0.079 -2.523 |
## R2 0.001 0.338 | 0.432 0.647 0.015 1.091 |
## R3 0.001 0.338 | 0.432 0.647 0.015 1.091 |
## R3a 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## R3b 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## R4 0.001 0.338 | 0.432 0.647 0.015 1.091 |
## R5 0.000 -0.187 | 0.457 0.605 0.014 1.049 |
## R6 0.000 -0.184 | 0.208 0.101 0.002 0.425 |
## R7 0.000 -0.187 | 0.457 0.605 0.014 1.049 |
## R8 0.000 -0.187 | 0.457 0.605 0.014 1.049 |
## R9 0.000 -0.184 | 0.208 0.101 0.002 0.425 |
## Rpi-blb1 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Rpi-blb2 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Rpi-blb3 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Rpi-chc1 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Rpi-pta1 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Rpi-sto1 0.062 -2.245 | -0.005 0.000 0.000 -0.015 |
## Rpi-vnt1.1 0.254 -4.534 | 0.026 0.002 0.000 0.060 |
## Bélgica 0.459 -6.100 | 0.039 0.003 0.000 0.079 |
## Brasil 0.000 -0.164 | 0.689 5.493 0.153 3.521 |
## China 0.001 -0.222 | 0.709 6.401 0.184 3.864 |
## Escocia 0.001 -0.310 | -2.376 16.335 0.367 -5.449 |
## PaÃses Bajos 0.186 3.882 | -0.016 0.003 0.000 -0.082 |
## Polonia 0.001 -0.322 | -1.575 15.800 0.385 -5.581 |
## A01-20 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A02-33 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A03-142 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A04-33 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A09 0.103 -2.891 | 0.006 0.000 0.000 0.011 |
## A09-277 0.000 0.001 | -0.019 0.000 0.000 -0.019 |
## A13-13 0.010 -0.921 | 0.009 0.000 0.000 0.009 |
## A15 0.353 -5.350 | 0.059 0.004 0.000 0.083 |
## A17-27 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A23-43 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## A25-11 0.069 2.358 | -0.150 0.013 0.000 -0.150 |
## Atlantic 0.005 -0.655 | -0.005 0.000 0.000 -0.007 |
## Bintje 0.005 -0.655 | -0.005 0.000 0.000 -0.007 |
## Black 0.003 -0.464 | -1.826 30.860 0.808 -8.090 |
## Black 2182ef(7); CIP 800992 0.000 -0.092 | 1.053 0.642 0.014 1.053 |
## Black 2424a(5); CIP 800993 0.000 -0.092 | 1.053 0.642 0.014 1.053 |
## Black 2573; CIP 800994 0.000 -0.099 | 0.829 0.397 0.008 0.829 |
## Black 3053-18;CIP800990 0.000 -0.092 | 1.053 0.642 0.014 1.053 |
## Black 3618ad(1); CIP 800995 0.000 -0.116 | -0.188 0.021 0.000 -0.188 |
## Black 5008ab(6); CIP 800996 0.000 -0.116 | -0.188 0.021 0.000 -0.188 |
## Black XD2-21; CIP 800991 0.000 -0.099 | 0.829 0.397 0.008 0.829 |
## CEBECO-43154-5;CIP800986 0.000 0.029 | 0.319 0.059 0.001 0.319 |
## CEBECO-44158-4;CIP800987 0.000 0.070 | 1.030 0.614 0.013 1.030 |
## CEBECO-4431-5;CIP800989 0.000 0.070 | 1.030 0.614 0.013 1.030 |
## CEBECO-4642-1;CIP800988 0.000 0.070 | 1.030 0.614 0.013 1.030 |
## MaR1: CEBECO 43154-5 0.005 0.635 | -0.336 0.065 0.001 -0.336 |
## MaR2: CEBECO 44158-4 0.006 0.675 | 0.375 0.081 0.002 0.375 |
## MaR3: CEBECO 4642-1 0.006 0.675 | 0.375 0.081 0.002 0.375 |
## MaR4: CEBECO 4431-5 0.006 0.675 | 0.375 0.081 0.002 0.375 |
## Potae9 0.000 0.001 | -0.019 0.000 0.000 -0.019 |
## Solanum demissum 0.001 -0.243 | 0.694 8.651 0.293 4.873 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## Gen | 0.948 0.778 0.240 |
## Pais | 0.951 0.581 0.928 |
## Genotipo | 0.998 0.964 0.939 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## Cromosoma | -0.257 | -0.078 | -0.003 |
## Sev_relativa | -0.478 | 0.222 | -0.260 |
## Temp | -0.296 | 0.021 | 0.458 |
dimdesc(r_mca)
## $`Dim 1`
## $quanti
## correlation p.value
## Cromosoma -0.2570890 1.971976e-02
## Temp -0.2964603 6.842087e-03
## Sev_relativa -0.4781230 5.549937e-06
##
## $quali
## R2 p.value
## Genotipo 0.9983032 4.156132e-61
## Pais 0.9510818 2.723682e-48
## Gen 0.9480080 1.321024e-32
##
## $category
## Estimate p.value
## Pais=PaÃses Bajos 1.2671220 3.417011e-20
## Gen=Rpi-sto1 1.2820507 2.472889e-08
## Gen=Rpi-vnt1.1 1.3544474 1.401206e-05
## Pais=Bélgica 1.7543824 4.417644e-05
## Genotipo=A09 1.2113535 1.529742e-04
## Genotipo=A15 1.3779120 4.539176e-03
## Genotipo=Bintje 1.1020288 1.571336e-02
## Genotipo=Atlantic 1.1020288 1.571336e-02
## Pais=Polonia -0.7558807 2.394752e-02
## Genotipo=Black -1.1831386 3.763649e-03
## Pais=Brasil -0.7360911 1.348244e-03
## Pais=China -0.7430877 5.079578e-04
## Genotipo=Solanum demissum -1.1559197 5.131492e-06
##
## attr(,"class")
## [1] "condes" "list"
##
## $`Dim 2`
## $quanti
## correlation p.value
## Sev_relativa 0.2218534 0.04515974
##
## $quali
## R2 p.value
## Genotipo 0.9635949 2.321139e-27
## Gen 0.7778697 9.311604e-14
## Pais 0.5805449 3.862879e-13
##
## $category
## Estimate p.value
## Pais=PaÃses Bajos 1.039460 5.221175e-05
## Genotipo=A04-33 1.747174 1.739982e-02
## Gen=R3a 1.476405 1.739982e-02
## Genotipo=A02-33 1.747174 1.739982e-02
## Gen=Rpi-blb2 1.476405 1.739982e-02
## Genotipo=A25-11 1.747174 1.739982e-02
## Genotipo=A17-27 1.747174 1.739982e-02
## Gen=Rpi-chc1 1.476405 1.739982e-02
## Gen=R3b 1.476405 1.739982e-02
## Genotipo=A23-43 1.747174 1.739982e-02
## Gen=Rpi-pta1 1.476405 1.739982e-02
## Genotipo=A03-142 1.747174 1.739982e-02
## Gen=Rpi-blb3 1.476405 1.739982e-02
## Genotipo=A01-20 1.747174 1.739982e-02
## Gen=Rpi-blb1 1.476405 1.739982e-02
## Gen=Rpi-sto1 -1.265784 2.383717e-02
## Genotipo=A09 -1.575651 3.253486e-03
## Gen=Rpi-vnt1.1 -2.337672 1.402629e-06
## Genotipo=A15 -3.635020 3.920839e-09
## Pais=Bélgica -2.261774 2.678217e-12
##
## attr(,"class")
## [1] "condes" "list"
##
## $`Dim 3`
## $quanti
## correlation p.value
## Temp 0.4580641 1.510083e-05
## Sev_relativa -0.2595649 1.852812e-02
##
## $quali
## R2 p.value
## Pais 0.9279547 6.437428e-42
## Genotipo 0.9394268 7.178640e-22
##
## $category
## Estimate p.value
## Genotipo=Solanum demissum 0.4066636 1.504178e-07
## Pais=China 0.9478027 5.696649e-05
## Pais=Brasil 0.9309103 2.783069e-04
## Gen=R10 -0.7635350 1.073076e-02
## Gen=R11 -0.7635350 1.073076e-02
## Pais=Escocia -1.6378057 1.682434e-09
## Pais=Polonia -0.9668700 5.203866e-10
## Genotipo=Black -1.7054259 2.130520e-30
##
## attr(,"class")
## [1] "condes" "list"
##
## $call
## $call$num.var
## [1] 1
##
## $call$proba
## [1] 0.05
##
## $call$weights
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [77] 1 1 1 1 1 1
##
## $call$X
## Dim 1 Gen Pais
## 1 -0.5589812 Gen=R1 Pais=China
## 2 -0.5417682 Gen=R2 Pais=China
## 3 -0.5417682 Gen=R3 Pais=China
## 4 -0.5417682 Gen=R4 Pais=China
## 5 -0.6429357 Gen=R5 Pais=China
## 6 -0.6441042 Gen=R6 Pais=China
## 7 -0.6429357 Gen=R7 Pais=China
## 8 -0.6429357 Gen=R8 Pais=China
## 9 -0.6441042 Gen=R9 Pais=China
## 10 -0.6458399 Gen=R10 Pais=China
## 11 -0.6458399 Gen=R11 Pais=China
## 12 -0.5565664 Gen=R1 Pais=Brasil
## 13 -0.5393534 Gen=R2 Pais=Brasil
## 14 -0.5393534 Gen=R3 Pais=Brasil
## 15 -0.5393534 Gen=R4 Pais=Brasil
## 16 -0.6405209 Gen=R5 Pais=Brasil
## 17 -0.6416894 Gen=R6 Pais=Brasil
## 18 -0.6405209 Gen=R7 Pais=Brasil
## 19 -0.6405209 Gen=R8 Pais=Brasil
## 20 -0.6416894 Gen=R9 Pais=Brasil
## 21 -0.6434251 Gen=R10 Pais=Brasil
## 22 -0.6434251 Gen=R11 Pais=Brasil
## 23 -0.5565664 Gen=R1 Pais=Brasil
## 24 -0.5393534 Gen=R2 Pais=Brasil
## 25 -0.5393534 Gen=R3 Pais=Brasil
## 26 -0.5393534 Gen=R4 Pais=Brasil
## 27 -0.6405209 Gen=R5 Pais=Brasil
## 28 -0.6405209 Gen=R7 Pais=Brasil
## 29 -0.6405209 Gen=R8 Pais=Brasil
## 30 -0.6434251 Gen=R10 Pais=Brasil
## 31 -0.6434251 Gen=R11 Pais=Brasil
## 32 -0.5349411 Gen=R1 Pais=China
## 33 -0.5086561 Gen=R2 Pais=China
## 34 -0.5086561 Gen=R3 Pais=China
## 35 -0.5086561 Gen=R4 Pais=China
## 36 -0.6631429 Gen=R5 Pais=China
## 37 -0.6649271 Gen=R6 Pais=China
## 38 -0.6631429 Gen=R7 Pais=China
## 39 -0.6631429 Gen=R8 Pais=China
## 40 -0.6649271 Gen=R9 Pais=China
## 41 -0.6675777 Gen=R10 Pais=China
## 42 -0.6675777 Gen=R11 Pais=China
## 43 -0.5727908 Gen=R1 Pais=Polonia
## 44 -0.5555778 Gen=R2 Pais=Polonia
## 45 -0.5555778 Gen=R3 Pais=Polonia
## 46 -0.5555778 Gen=R4 Pais=Polonia
## 47 -0.6567453 Gen=R5 Pais=Polonia
## 48 -0.6579138 Gen=R6 Pais=Polonia
## 49 -0.6567453 Gen=R7 Pais=Polonia
## 50 -0.6567453 Gen=R8 Pais=Polonia
## 51 -0.6579138 Gen=R9 Pais=Polonia
## 52 -0.6596495 Gen=R10 Pais=Polonia
## 53 -0.6596495 Gen=R11 Pais=Polonia
## 54 -0.6701984 Gen=R10 Pais=Escocia
## 55 -0.6701984 Gen=R11 Pais=Escocia
## 56 1.8467643 Gen=Rpi-sto1 Pais=Bélgica
## 57 1.9292368 Gen=Rpi-vnt1.1 Pais=Bélgica
## 58 1.8467643 Gen=Rpi-sto1 Pais=Bélgica
## 59 1.9292368 Gen=Rpi-vnt1.1 Pais=Bélgica
## 60 1.6785923 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 61 1.6785923 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 62 -0.5833397 Gen=R1 Pais=Escocia
## 63 -0.6701984 Gen=R10 Pais=Escocia
## 64 -0.6701984 Gen=R11 Pais=Escocia
## 65 1.6658471 Gen=Rpi-vnt1.1 Pais=PaÃses Bajos
## 66 1.6408603 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 67 1.6658471 Gen=Rpi-vnt1.1 Pais=PaÃses Bajos
## 68 1.6408603 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 69 1.6342757 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 70 0.5245177 Gen=R1 Pais=PaÃses Bajos
## 71 0.5508027 Gen=R2 Pais=PaÃses Bajos
## 72 0.5508027 Gen=R3 Pais=PaÃses Bajos
## 73 0.5508027 Gen=R4 Pais=PaÃses Bajos
## 74 1.5608994 Gen=Rpi-blb3 Pais=PaÃses Bajos
## 75 1.5608994 Gen=R3a Pais=PaÃses Bajos
## 76 1.5608994 Gen=R3b Pais=PaÃses Bajos
## 77 1.6342757 Gen=Rpi-sto1 Pais=PaÃses Bajos
## 78 1.5608994 Gen=Rpi-blb1 Pais=PaÃses Bajos
## 79 1.5608994 Gen=Rpi-pta1 Pais=PaÃses Bajos
## 80 1.5608994 Gen=Rpi-blb2 Pais=PaÃses Bajos
## 81 1.6724316 Gen=Rpi-vnt1.1 Pais=PaÃses Bajos
## 82 1.5608994 Gen=Rpi-chc1 Pais=PaÃses Bajos
## Genotipo Cromosoma Sev_relativa Temp
## 1 Genotipo=Solanum demissum 5 31.20 18
## 2 Genotipo=Solanum demissum 4 18.00 18
## 3 Genotipo=Solanum demissum 11 98.40 18
## 4 Genotipo=Solanum demissum 11 65.60 18
## 5 Genotipo=Solanum demissum 11 13.10 18
## 6 Genotipo=Solanum demissum 11 42.60 18
## 7 Genotipo=Solanum demissum 11 83.60 18
## 8 Genotipo=Solanum demissum 11 36.10 18
## 9 Genotipo=Solanum demissum 11 9.80 18
## 10 Genotipo=Solanum demissum 11 40.90 18
## 11 Genotipo=Solanum demissum 11 54.10 18
## 12 Genotipo=Solanum demissum 5 73.40 18
## 13 Genotipo=Solanum demissum 4 30.50 18
## 14 Genotipo=Solanum demissum 11 80.50 18
## 15 Genotipo=Solanum demissum 11 65.60 18
## 16 Genotipo=Solanum demissum 11 9.30 18
## 17 Genotipo=Solanum demissum 11 35.90 18
## 18 Genotipo=Solanum demissum 11 89.00 18
## 19 Genotipo=Solanum demissum 11 41.40 18
## 20 Genotipo=Solanum demissum 11 31.20 18
## 21 Genotipo=Solanum demissum 11 43.70 18
## 22 Genotipo=Solanum demissum 11 72.70 18
## 23 Genotipo=Solanum demissum 5 41.40 18
## 24 Genotipo=Solanum demissum 4 37.90 18
## 25 Genotipo=Solanum demissum 11 96.60 18
## 26 Genotipo=Solanum demissum 11 96.60 18
## 27 Genotipo=Solanum demissum 11 13.80 18
## 28 Genotipo=Solanum demissum 11 80.00 18
## 29 Genotipo=Solanum demissum 11 26.40 18
## 30 Genotipo=Solanum demissum 11 62.00 18
## 31 Genotipo=Solanum demissum 11 64.00 18
## 32 Genotipo=CEBECO-43154-5;CIP800986 5 20.90 18
## 33 Genotipo=CEBECO-44158-4;CIP800987 4 18.60 18
## 34 Genotipo=CEBECO-4642-1;CIP800988 11 93.00 18
## 35 Genotipo=CEBECO-4431-5;CIP800989 11 76.70 18
## 36 Genotipo=Black 3053-18;CIP800990 11 20.90 18
## 37 Genotipo=Black XD2-21; CIP 800991 11 53.50 18
## 38 Genotipo=Black 2182ef(7); CIP 800992 11 95.30 18
## 39 Genotipo=Black 2424a(5); CIP 800993 11 51.20 18
## 40 Genotipo=Black 2573; CIP 800994 11 23.30 18
## 41 Genotipo=Black 3618ad(1); CIP 800995 11 90.60 18
## 42 Genotipo=Black 5008ab(6); CIP 800996 11 30.20 18
## 43 Genotipo=Black 5 100.00 16
## 44 Genotipo=Black 4 58.00 16
## 45 Genotipo=Black 11 100.00 16
## 46 Genotipo=Black 11 100.00 16
## 47 Genotipo=Black 11 81.00 16
## 48 Genotipo=Black 11 67.00 16
## 49 Genotipo=Black 11 100.00 16
## 50 Genotipo=Black 11 60.00 16
## 51 Genotipo=Black 11 17.00 16
## 52 Genotipo=Black 11 100.00 16
## 53 Genotipo=Black 11 100.00 16
## 54 Genotipo=Black 11 79.30 15
## 55 Genotipo=Black 11 89.00 15
## 56 Genotipo=A09 8 3.79 15
## 57 Genotipo=A15 9 0.14 15
## 58 Genotipo=A09 8 8.19 15
## 59 Genotipo=A15 9 1.00 15
## 60 Genotipo=A09 8 1.15 15
## 61 Genotipo=A09 8 0.16 15
## 62 Genotipo=Black 5 56.40 18
## 63 Genotipo=Black 11 43.00 18
## 64 Genotipo=Black 11 51.30 18
## 65 Genotipo=Atlantic 9 20.00 20
## 66 Genotipo=Atlantic 8 20.00 20
## 67 Genotipo=Bintje 9 20.00 20
## 68 Genotipo=Bintje 8 20.00 20
## 69 Genotipo=Potae9 8 20.00 20
## 70 Genotipo=MaR1: CEBECO 43154-5 5 75.00 16
## 71 Genotipo=MaR2: CEBECO 44158-4 4 39.00 16
## 72 Genotipo=MaR3: CEBECO 4642-1 11 73.00 16
## 73 Genotipo=MaR4: CEBECO 4431-5 11 59.00 16
## 74 Genotipo=A03-142 4 37.50 16
## 75 Genotipo=A04-33 11 75.00 16
## 76 Genotipo=A25-11 11 100.00 16
## 77 Genotipo=A09-277 11 25.00 16
## 78 Genotipo=A01-20 8 25.00 16
## 79 Genotipo=A23-43 8 28.20 16
## 80 Genotipo=A02-33 6 12.50 16
## 81 Genotipo=A13-13 9 12.50 16
## 82 Genotipo=A17-27 10 9.40 16
plot(r_mca, label=c("var", "quali.sup"), xlim = c(-3,3), ylim = c(-2,3.5))
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_text).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_text).
######################################
#library(lattice)
#library(ggplot2)
#Confidence ellipses around the categories for the variables
FactoMineR::plotellipses(r_mca, keepvar = 1:3, autoLab = "auto", cex=0.7)
?plotellipses
## starting httpd help server ...
## done
library(Factoshiny)
## Loading required package: shiny
## Loading required package: FactoInvestigate
#outMCA<-MCAshiny(ref_art)
#outMCA<-MCAshiny(t)
res.MCA<-MCA(t[,-c(6)],quanti.sup=c(4,5),graph=FALSE)
plot.MCA(res.MCA, choix='var',axes=c(2,3))
plot.MCA(res.MCA,axes=c(2,3),invisible= 'ind',select= 'cos2 0.15',selectMod= 'cos2 0.2',habillage='cos2',label =c('var'))
plot.MCA(res.MCA, choix='quanti.sup',axes=c(2,3),title="Supplementary quantitatives variables",col.quanti.sup='#FF0099')
#Cos 1
res.MCA1<-MCA(t,quanti.sup=c(4,5,6),graph=FALSE)
summary(res.MCA1)
##
## Call:
## MCA(X = t, quanti.sup = c(4, 5, 6), graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 0.966 0.774 0.702 0.667 0.667 0.667 0.667
## % of var. 5.366 4.300 3.902 3.704 3.704 3.704 3.704
## Cumulative % of var. 5.366 9.666 13.568 17.272 20.975 24.679 28.383
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.667 0.667 0.616 0.544 0.528 0.526 0.526
## % of var. 3.704 3.704 3.424 3.022 2.933 2.921 2.921
## Cumulative % of var. 32.086 35.790 39.214 42.236 45.170 48.091 51.012
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 0.514 0.500 0.490 0.482 0.482 0.480 0.477
## % of var. 2.858 2.778 2.721 2.680 2.680 2.665 2.647
## Cumulative % of var. 53.870 56.647 59.369 62.049 64.729 67.394 70.041
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 0.459 0.356 0.333 0.333 0.333 0.333 0.333
## % of var. 2.552 1.978 1.852 1.852 1.852 1.852 1.852
## Cumulative % of var. 72.593 74.571 76.423 78.275 80.127 81.979 83.831
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 0.333 0.333 0.333 0.207 0.184 0.184 0.175
## % of var. 1.852 1.852 1.852 1.152 1.024 1.024 0.972
## Cumulative % of var. 85.682 87.534 89.386 90.538 91.562 92.585 93.557
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 0.167 0.166 0.159 0.141 0.141 0.141 0.127
## % of var. 0.926 0.923 0.882 0.785 0.783 0.783 0.708
## Cumulative % of var. 94.483 95.407 96.288 97.074 97.856 98.639 99.347
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 0.064 0.053 0.000 0.000 0.000 0.000 0.000
## % of var. 0.357 0.296 0.000 0.000 0.000 0.000 0.000
## Cumulative % of var. 99.704 100.000 100.000 100.000 100.000 100.000 100.000
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54
## Variance 0.000 0.000 0.000 0.000 0.000
## % of var. 0.000 0.000 0.000 0.000 0.000
## Cumulative % of var. 100.000 100.000 100.000 100.000 100.000
##
## Individuals (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 1 | -0.559 0.395 0.062 | 0.002 0.000 0.000 | 0.417 0.302
## 2 | -0.542 0.371 0.052 | 0.022 0.001 0.000 | 0.730 0.925
## 3 | -0.542 0.371 0.052 | 0.022 0.001 0.000 | 0.730 0.925
## 4 | -0.542 0.371 0.052 | 0.022 0.001 0.000 | 0.730 0.925
## 5 | -0.643 0.522 0.063 | -0.059 0.006 0.001 | 0.740 0.951
## 6 | -0.644 0.524 0.052 | -0.063 0.006 0.000 | 0.641 0.714
## 7 | -0.643 0.522 0.063 | -0.059 0.006 0.001 | 0.740 0.951
## 8 | -0.643 0.522 0.063 | -0.059 0.006 0.001 | 0.740 0.951
## 9 | -0.644 0.524 0.052 | -0.063 0.006 0.000 | 0.641 0.714
## 10 | -0.646 0.527 0.083 | -0.071 0.008 0.001 | 0.193 0.065
## cos2
## 1 0.035 |
## 2 0.094 |
## 3 0.094 |
## 4 0.094 |
## 5 0.083 |
## 6 0.052 |
## 7 0.083 |
## 8 0.083 |
## 9 0.052 |
## 10 0.007 |
##
## Categories (the 10 first)
## Dim.1 ctr cos2 v.test Dim.2 ctr cos2 v.test
## R1 | -0.413 0.502 0.016 -1.135 | 0.080 0.023 0.001 0.219 |
## R10 | -0.669 1.318 0.042 -1.839 | -0.113 0.047 0.001 -0.309 |
## R11 | -0.669 1.318 0.042 -1.839 | -0.113 0.047 0.001 -0.309 |
## R2 | -0.362 0.331 0.010 -0.915 | 0.134 0.056 0.001 0.338 |
## R3 | -0.362 0.331 0.010 -0.915 | 0.134 0.056 0.001 0.338 |
## R3a | 1.588 1.062 0.031 1.588 | 2.358 2.921 0.069 2.358 |
## R3b | 1.588 1.062 0.031 1.588 | 2.358 2.921 0.069 2.358 |
## R4 | -0.362 0.331 0.010 -0.915 | 0.134 0.056 0.001 0.338 |
## R5 | -0.660 0.917 0.028 -1.514 | -0.081 0.017 0.000 -0.187 |
## R6 | -0.664 0.741 0.023 -1.352 | -0.090 0.017 0.000 -0.184 |
## Dim.3 ctr cos2 v.test
## R1 -0.356 0.512 0.012 -0.978 |
## R10 -0.918 3.412 0.079 -2.523 |
## R11 -0.918 3.412 0.079 -2.523 |
## R2 0.432 0.647 0.015 1.091 |
## R3 0.432 0.647 0.015 1.091 |
## R3a -0.150 0.013 0.000 -0.150 |
## R3b -0.150 0.013 0.000 -0.150 |
## R4 0.432 0.647 0.015 1.091 |
## R5 0.457 0.605 0.014 1.049 |
## R6 0.208 0.101 0.002 0.425 |
##
## Categorical variables (eta2)
## Dim.1 Dim.2 Dim.3
## Gen | 0.948 0.778 0.240 |
## Pais | 0.951 0.581 0.928 |
## Genotipo | 0.998 0.964 0.939 |
##
## Supplementary continuous variables
## Dim.1 Dim.2 Dim.3
## Cromosoma | -0.257 | -0.078 | -0.003 |
## Sev_relativa | -0.478 | 0.222 | -0.260 |
## Temp | -0.296 | 0.021 | 0.458 |
plot.MCA(res.MCA1, choix='var',axes=c(2,3))
plot.MCA(res.MCA1,axes=c(2,3),invisible= 'ind',select= 'cos2 0.05',selectMod= 'cos2 0.05',habillage='cos2',cex=0.9,cex.main=0.9,cex.axis=0.9,label =c('var'))
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
plot.MCA(res.MCA1, choix='quanti.sup',axes=c(2,3),title="Supplementary quantitatives variables")
################################33
fviz_mca_var(res.MCA1, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())
## Warning: ggrepel: 42 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_cos2(res.MCA1, choice = "var", axes = 1:2)
# Contributions of rows to dimension 1
fviz_contrib(res.MCA1, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.MCA1, choice = "var", axes = 2, top = 15)
fviz_mca_ind(res.MCA1, habillage = 2, addEllipses = TRUE)
## Warning: Computation failed in `stat_ellipse()`:
## the leading minor of order 2 is not positive definite
# habillage = external grouping variable
fviz_mca_ind(res.MCA1, habillage = t$Gen, addEllipses = TRUE)
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
# Biplot of individuals and variable categories
fviz_mca_biplot(res.MCA1, repel = TRUE,
ggtheme = theme_minimal())
## Warning: ggrepel: 65 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 42 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
##################################3
FactoMineR::plotellipses(res.MCA1, keepvar = 1:3, autoLab = "auto", cex=0.7)
#COS2 SIN TEMP
res.MCA<-MCA(t[,-c(6)],quanti.sup=c(4,5),graph=FALSE)
plot.MCA(res.MCA, choix='var',axes=c(2,3),col.var=c(1,2,3),cex=1.05,cex.main=1.05,cex.axis=1.05)
plot.MCA(res.MCA,axes=c(2,3),invisible= 'ind',select= 'cos2 0.05',selectMod= 'cos2 0.05',habillage='cos2',col.ind='#1B1BDB92',label =c('var'))
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
plot.MCA(res.MCA, choix='quanti.sup',axes=c(2,3),title="Supplementary quantitatives variables",cex=1.15,cex.main=1.15,cex.axis=1.15)
library(FactoInvestigate)
#FactoInvestigate
#graphCA(res.MCA)
ggplot(t, aes(Gen, Sev_relativa))+geom_boxplot()
ggplot(data = t, aes(x = Gen, y = Sev_relativa, group = 1))+
geom_point(aes(color = Genotipo))+
theme(legend.title=element_blank()) +
facet_wrap(vars(Pais),scales = "free_y", nrow = 5)
p <- ggplot(t, aes(Gen, Sev_relativa)) + geom_point()
p + facet_wrap(vars(Genotipo))
