Paper extra Proyecto Final
Angélica Reyes
10/28/2021
Extraído de: https://dialnet.unirioja.es/servlet/articulo?codigo=5833456
Se evaluó la prevalencia del Aspergillus spp. (ufc/g) y su principal metabolito la aflatoxina (ppb/g), en maíz (Zea mays, L) almacenado en los dos silos de la Unidad Nacional de Almacenamiento en Quevedo - Ecuador, durante el primer semestre del 2014. Se muestreó los lados lateral oeste, lateral este y medio de cada silo con tres repeticiones cada muestra, las extracciones se realizaron a los 30, 60 y 90 días. El crecimiento del hongo fue de 2.06 105 a 4.60 105 ufc/g. La determinación de aflatoxinas se efectuó por la técnica de micro Elisa, reportando a a 90 días el nivel más alto de 55,71 ppb. Se realizó el análisis de varianza (ANOVA) y se comparó las medias con la prueba de Duncan (P<0.05).
Ojetivos del trabajo fueron: Evaluar la prevalencia de Aspergillus spp.en el maíz almacenado en los silos; identificar y cuantificar la cantidad de aflatoxinas presentes, con fines de determinar si existe riesgos a la seguridad alimentaria relacionada directamente con la calidad del grano.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(agricolae)df1<-read.csv("https://raw.githubusercontent.com/A14Reyes/Diseno_Experimental/main/problema%20Ang%C3%A9lica.csv")
df1## T U S REP Y1 Y2
## 1 30 1 1 1 17.10 316000
## 2 30 1 1 1 18.50 118000
## 3 30 1 1 2 61.30 13500
## 4 30 1 1 2 22.25 129000
## 5 30 1 1 3 24.61 131000
## 6 30 1 1 3 25.19 134000
## 7 60 1 1 1 19.05 316000
## 8 60 1 1 1 15.05 99800
## 9 60 1 1 2 39.65 2260
## 10 60 1 1 2 24.20 287000
## 11 60 1 1 3 25.39 141000
## 12 60 1 1 3 26.53 146000
## 13 90 1 1 1 15.20 319000
## 14 90 1 1 1 15.85 95100
## 15 90 1 1 2 67.25 8030
## 16 90 1 1 2 15.20 131000
## 17 90 1 1 3 27.60 155000
## 18 90 1 1 3 29.05 162000
## 19 30 2 1 1 14.75 7430
## 20 30 2 1 1 69.00 207000
## 21 30 2 1 2 137.35 214000
## 22 30 2 1 2 37.95 95200
## 23 30 2 1 3 45.43 134000
## 24 30 2 1 3 47.30 137000
## 25 60 2 1 1 19.65 8950
## 26 60 2 1 1 38.75 185000
## 27 60 2 1 2 94.85 273000
## 28 60 2 1 2 42.85 96700
## 29 60 2 2 3 48.80 144000
## 30 60 2 2 3 51.47 150000
## 31 90 2 2 1 12.65 69300
## 32 90 2 2 1 46.35 198000
## 33 90 2 2 2 116.20 378000
## 34 90 2 2 2 35.85 157000
## 35 90 2 2 3 54.09 159000
## 36 90 2 2 3 57.19 166000
## 37 30 3 2 1 50.05 245000
## 38 30 3 2 1 42.30 55800
## 39 30 3 2 2 59.25 453000
## 40 30 3 2 2 57.20 1780000
## 41 30 3 2 3 27.72 509000
## 42 30 3 2 3 34.58 536000
## 43 60 3 2 1 18.60 206000
## 44 60 3 2 1 40.90 96600
## 45 60 3 2 2 37.20 447000
## 46 60 3 2 2 25.75 1740000
## 47 60 3 2 3 35.35 568000
## 48 60 3 2 3 37.10 600000
## 49 90 3 2 1 27.90 330000
## 50 90 3 2 1 47.55 80800
## 51 90 3 2 2 40.45 447000
## 52 90 3 2 2 35.05 2050000
## 53 90 3 2 3 38.80 637000
## 54 90 3 2 3 40.90 673000Las variables analizadas fueron: Cantidad de Aflatoxinas (ppb/g), cantidad de Aspergillus spp.(UFC/g) por silo (dos) por ubicación (lateral oeste, media y lateral este). Se analizó 3 repeticiones por muestra.
df<- df1 %>% gather(key="mycotoxins",value="score",Y1,Y2) %>% convert_as_factor(T,U,S,REP)
df## T U S REP mycotoxins score
## 1 30 1 1 1 Y1 17.10
## 2 30 1 1 1 Y1 18.50
## 3 30 1 1 2 Y1 61.30
## 4 30 1 1 2 Y1 22.25
## 5 30 1 1 3 Y1 24.61
## 6 30 1 1 3 Y1 25.19
## 7 60 1 1 1 Y1 19.05
## 8 60 1 1 1 Y1 15.05
## 9 60 1 1 2 Y1 39.65
## 10 60 1 1 2 Y1 24.20
## 11 60 1 1 3 Y1 25.39
## 12 60 1 1 3 Y1 26.53
## 13 90 1 1 1 Y1 15.20
## 14 90 1 1 1 Y1 15.85
## 15 90 1 1 2 Y1 67.25
## 16 90 1 1 2 Y1 15.20
## 17 90 1 1 3 Y1 27.60
## 18 90 1 1 3 Y1 29.05
## 19 30 2 1 1 Y1 14.75
## 20 30 2 1 1 Y1 69.00
## 21 30 2 1 2 Y1 137.35
## 22 30 2 1 2 Y1 37.95
## 23 30 2 1 3 Y1 45.43
## 24 30 2 1 3 Y1 47.30
## 25 60 2 1 1 Y1 19.65
## 26 60 2 1 1 Y1 38.75
## 27 60 2 1 2 Y1 94.85
## 28 60 2 1 2 Y1 42.85
## 29 60 2 2 3 Y1 48.80
## 30 60 2 2 3 Y1 51.47
## 31 90 2 2 1 Y1 12.65
## 32 90 2 2 1 Y1 46.35
## 33 90 2 2 2 Y1 116.20
## 34 90 2 2 2 Y1 35.85
## 35 90 2 2 3 Y1 54.09
## 36 90 2 2 3 Y1 57.19
## 37 30 3 2 1 Y1 50.05
## 38 30 3 2 1 Y1 42.30
## 39 30 3 2 2 Y1 59.25
## 40 30 3 2 2 Y1 57.20
## 41 30 3 2 3 Y1 27.72
## 42 30 3 2 3 Y1 34.58
## 43 60 3 2 1 Y1 18.60
## 44 60 3 2 1 Y1 40.90
## 45 60 3 2 2 Y1 37.20
## 46 60 3 2 2 Y1 25.75
## 47 60 3 2 3 Y1 35.35
## 48 60 3 2 3 Y1 37.10
## 49 90 3 2 1 Y1 27.90
## 50 90 3 2 1 Y1 47.55
## 51 90 3 2 2 Y1 40.45
## 52 90 3 2 2 Y1 35.05
## 53 90 3 2 3 Y1 38.80
## 54 90 3 2 3 Y1 40.90
## 55 30 1 1 1 Y2 316000.00
## 56 30 1 1 1 Y2 118000.00
## 57 30 1 1 2 Y2 13500.00
## 58 30 1 1 2 Y2 129000.00
## 59 30 1 1 3 Y2 131000.00
## 60 30 1 1 3 Y2 134000.00
## 61 60 1 1 1 Y2 316000.00
## 62 60 1 1 1 Y2 99800.00
## 63 60 1 1 2 Y2 2260.00
## 64 60 1 1 2 Y2 287000.00
## 65 60 1 1 3 Y2 141000.00
## 66 60 1 1 3 Y2 146000.00
## 67 90 1 1 1 Y2 319000.00
## 68 90 1 1 1 Y2 95100.00
## 69 90 1 1 2 Y2 8030.00
## 70 90 1 1 2 Y2 131000.00
## 71 90 1 1 3 Y2 155000.00
## 72 90 1 1 3 Y2 162000.00
## 73 30 2 1 1 Y2 7430.00
## 74 30 2 1 1 Y2 207000.00
## 75 30 2 1 2 Y2 214000.00
## 76 30 2 1 2 Y2 95200.00
## 77 30 2 1 3 Y2 134000.00
## 78 30 2 1 3 Y2 137000.00
## 79 60 2 1 1 Y2 8950.00
## 80 60 2 1 1 Y2 185000.00
## 81 60 2 1 2 Y2 273000.00
## 82 60 2 1 2 Y2 96700.00
## 83 60 2 2 3 Y2 144000.00
## 84 60 2 2 3 Y2 150000.00
## 85 90 2 2 1 Y2 69300.00
## 86 90 2 2 1 Y2 198000.00
## 87 90 2 2 2 Y2 378000.00
## 88 90 2 2 2 Y2 157000.00
## 89 90 2 2 3 Y2 159000.00
## 90 90 2 2 3 Y2 166000.00
## 91 30 3 2 1 Y2 245000.00
## 92 30 3 2 1 Y2 55800.00
## 93 30 3 2 2 Y2 453000.00
## 94 30 3 2 2 Y2 1780000.00
## 95 30 3 2 3 Y2 509000.00
## 96 30 3 2 3 Y2 536000.00
## 97 60 3 2 1 Y2 206000.00
## 98 60 3 2 1 Y2 96600.00
## 99 60 3 2 2 Y2 447000.00
## 100 60 3 2 2 Y2 1740000.00
## 101 60 3 2 3 Y2 568000.00
## 102 60 3 2 3 Y2 600000.00
## 103 90 3 2 1 Y2 330000.00
## 104 90 3 2 1 Y2 80800.00
## 105 90 3 2 2 Y2 447000.00
## 106 90 3 2 2 Y2 2050000.00
## 107 90 3 2 3 Y2 637000.00
## 108 90 3 2 3 Y2 673000.00NOTA:
Tiempo (T) = 30, 60, 90;
Ubicación (U) = 1 lateral Oeste, 2 media, 3 lateral este;
Silos (S) = 1 y 2
Repeticiones (REP) = 1, 2, 3.
Mycotoxins ( Y1 y Y2) Y1 = Aflatoxinas (ppb/g) Y2 = Aspergillus (ufc/g)
Resumen de datos del Tiempo con respecto a las Micotoxinas
df %>% group_by(T,mycotoxins) %>% get_summary_stats(score,type="mean_sd")## # A tibble: 6 x 6
## T mycotoxins variable n mean sd
## <fct> <chr> <chr> <dbl> <dbl> <dbl>
## 1 30 Y1 score 18 44.0 28.6
## 2 30 Y2 score 18 289718. 403840.
## 3 60 Y1 score 18 35.6 18.3
## 4 60 Y2 score 18 305962. 396424.
## 5 90 Y1 score 18 40.2 24.5
## 6 90 Y2 score 18 345291. 464825.df## T U S REP mycotoxins score
## 1 30 1 1 1 Y1 17.10
## 2 30 1 1 1 Y1 18.50
## 3 30 1 1 2 Y1 61.30
## 4 30 1 1 2 Y1 22.25
## 5 30 1 1 3 Y1 24.61
## 6 30 1 1 3 Y1 25.19
## 7 60 1 1 1 Y1 19.05
## 8 60 1 1 1 Y1 15.05
## 9 60 1 1 2 Y1 39.65
## 10 60 1 1 2 Y1 24.20
## 11 60 1 1 3 Y1 25.39
## 12 60 1 1 3 Y1 26.53
## 13 90 1 1 1 Y1 15.20
## 14 90 1 1 1 Y1 15.85
## 15 90 1 1 2 Y1 67.25
## 16 90 1 1 2 Y1 15.20
## 17 90 1 1 3 Y1 27.60
## 18 90 1 1 3 Y1 29.05
## 19 30 2 1 1 Y1 14.75
## 20 30 2 1 1 Y1 69.00
## 21 30 2 1 2 Y1 137.35
## 22 30 2 1 2 Y1 37.95
## 23 30 2 1 3 Y1 45.43
## 24 30 2 1 3 Y1 47.30
## 25 60 2 1 1 Y1 19.65
## 26 60 2 1 1 Y1 38.75
## 27 60 2 1 2 Y1 94.85
## 28 60 2 1 2 Y1 42.85
## 29 60 2 2 3 Y1 48.80
## 30 60 2 2 3 Y1 51.47
## 31 90 2 2 1 Y1 12.65
## 32 90 2 2 1 Y1 46.35
## 33 90 2 2 2 Y1 116.20
## 34 90 2 2 2 Y1 35.85
## 35 90 2 2 3 Y1 54.09
## 36 90 2 2 3 Y1 57.19
## 37 30 3 2 1 Y1 50.05
## 38 30 3 2 1 Y1 42.30
## 39 30 3 2 2 Y1 59.25
## 40 30 3 2 2 Y1 57.20
## 41 30 3 2 3 Y1 27.72
## 42 30 3 2 3 Y1 34.58
## 43 60 3 2 1 Y1 18.60
## 44 60 3 2 1 Y1 40.90
## 45 60 3 2 2 Y1 37.20
## 46 60 3 2 2 Y1 25.75
## 47 60 3 2 3 Y1 35.35
## 48 60 3 2 3 Y1 37.10
## 49 90 3 2 1 Y1 27.90
## 50 90 3 2 1 Y1 47.55
## 51 90 3 2 2 Y1 40.45
## 52 90 3 2 2 Y1 35.05
## 53 90 3 2 3 Y1 38.80
## 54 90 3 2 3 Y1 40.90
## 55 30 1 1 1 Y2 316000.00
## 56 30 1 1 1 Y2 118000.00
## 57 30 1 1 2 Y2 13500.00
## 58 30 1 1 2 Y2 129000.00
## 59 30 1 1 3 Y2 131000.00
## 60 30 1 1 3 Y2 134000.00
## 61 60 1 1 1 Y2 316000.00
## 62 60 1 1 1 Y2 99800.00
## 63 60 1 1 2 Y2 2260.00
## 64 60 1 1 2 Y2 287000.00
## 65 60 1 1 3 Y2 141000.00
## 66 60 1 1 3 Y2 146000.00
## 67 90 1 1 1 Y2 319000.00
## 68 90 1 1 1 Y2 95100.00
## 69 90 1 1 2 Y2 8030.00
## 70 90 1 1 2 Y2 131000.00
## 71 90 1 1 3 Y2 155000.00
## 72 90 1 1 3 Y2 162000.00
## 73 30 2 1 1 Y2 7430.00
## 74 30 2 1 1 Y2 207000.00
## 75 30 2 1 2 Y2 214000.00
## 76 30 2 1 2 Y2 95200.00
## 77 30 2 1 3 Y2 134000.00
## 78 30 2 1 3 Y2 137000.00
## 79 60 2 1 1 Y2 8950.00
## 80 60 2 1 1 Y2 185000.00
## 81 60 2 1 2 Y2 273000.00
## 82 60 2 1 2 Y2 96700.00
## 83 60 2 2 3 Y2 144000.00
## 84 60 2 2 3 Y2 150000.00
## 85 90 2 2 1 Y2 69300.00
## 86 90 2 2 1 Y2 198000.00
## 87 90 2 2 2 Y2 378000.00
## 88 90 2 2 2 Y2 157000.00
## 89 90 2 2 3 Y2 159000.00
## 90 90 2 2 3 Y2 166000.00
## 91 30 3 2 1 Y2 245000.00
## 92 30 3 2 1 Y2 55800.00
## 93 30 3 2 2 Y2 453000.00
## 94 30 3 2 2 Y2 1780000.00
## 95 30 3 2 3 Y2 509000.00
## 96 30 3 2 3 Y2 536000.00
## 97 60 3 2 1 Y2 206000.00
## 98 60 3 2 1 Y2 96600.00
## 99 60 3 2 2 Y2 447000.00
## 100 60 3 2 2 Y2 1740000.00
## 101 60 3 2 3 Y2 568000.00
## 102 60 3 2 3 Y2 600000.00
## 103 90 3 2 1 Y2 330000.00
## 104 90 3 2 1 Y2 80800.00
## 105 90 3 2 2 Y2 447000.00
## 106 90 3 2 2 Y2 2050000.00
## 107 90 3 2 3 Y2 637000.00
## 108 90 3 2 3 Y2 673000.00bxp<-ggboxplot(df,x="mycotoxins",y="score",color="T",palette="reds")
bxp
En la gráfica se observa que para el hongo Aspergillus, hay valores fuera del conjunto de datos para los diversos tiempos de almacenaje.
df %>% group_by(T,mycotoxins) %>% identify_outliers(score)## # A tibble: 6 x 8
## T mycotoxins U S REP score is.outlier is.extreme
## <fct> <chr> <fct> <fct> <fct> <dbl> <lgl> <lgl>
## 1 30 Y1 2 1 2 137. TRUE FALSE
## 2 30 Y2 3 2 2 1780000 TRUE TRUE
## 3 60 Y1 2 1 2 94.8 TRUE TRUE
## 4 60 Y2 3 2 2 1740000 TRUE TRUE
## 5 90 Y1 2 2 2 116. TRUE TRUE
## 6 90 Y2 3 2 2 2050000 TRUE TRUELa prueba nos muestra valores atipicos extremos 2 para Aflatoxina y 3 para Aspergillus.
Resumen de datos de la Ubicación con respecto a las Micotoxinas
df %>% group_by(U,mycotoxins) %>% get_summary_stats(score,type="mean_sd")## # A tibble: 6 x 6
## U mycotoxins variable n mean sd
## <fct> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 Y1 score 18 27.2 14.9
## 2 1 Y2 score 18 150205 100381.
## 3 2 Y1 score 18 53.9 32.8
## 4 2 Y2 score 18 154421. 87803.
## 5 3 Y1 score 18 38.7 10.4
## 6 3 Y2 score 18 636344. 595698.df## T U S REP mycotoxins score
## 1 30 1 1 1 Y1 17.10
## 2 30 1 1 1 Y1 18.50
## 3 30 1 1 2 Y1 61.30
## 4 30 1 1 2 Y1 22.25
## 5 30 1 1 3 Y1 24.61
## 6 30 1 1 3 Y1 25.19
## 7 60 1 1 1 Y1 19.05
## 8 60 1 1 1 Y1 15.05
## 9 60 1 1 2 Y1 39.65
## 10 60 1 1 2 Y1 24.20
## 11 60 1 1 3 Y1 25.39
## 12 60 1 1 3 Y1 26.53
## 13 90 1 1 1 Y1 15.20
## 14 90 1 1 1 Y1 15.85
## 15 90 1 1 2 Y1 67.25
## 16 90 1 1 2 Y1 15.20
## 17 90 1 1 3 Y1 27.60
## 18 90 1 1 3 Y1 29.05
## 19 30 2 1 1 Y1 14.75
## 20 30 2 1 1 Y1 69.00
## 21 30 2 1 2 Y1 137.35
## 22 30 2 1 2 Y1 37.95
## 23 30 2 1 3 Y1 45.43
## 24 30 2 1 3 Y1 47.30
## 25 60 2 1 1 Y1 19.65
## 26 60 2 1 1 Y1 38.75
## 27 60 2 1 2 Y1 94.85
## 28 60 2 1 2 Y1 42.85
## 29 60 2 2 3 Y1 48.80
## 30 60 2 2 3 Y1 51.47
## 31 90 2 2 1 Y1 12.65
## 32 90 2 2 1 Y1 46.35
## 33 90 2 2 2 Y1 116.20
## 34 90 2 2 2 Y1 35.85
## 35 90 2 2 3 Y1 54.09
## 36 90 2 2 3 Y1 57.19
## 37 30 3 2 1 Y1 50.05
## 38 30 3 2 1 Y1 42.30
## 39 30 3 2 2 Y1 59.25
## 40 30 3 2 2 Y1 57.20
## 41 30 3 2 3 Y1 27.72
## 42 30 3 2 3 Y1 34.58
## 43 60 3 2 1 Y1 18.60
## 44 60 3 2 1 Y1 40.90
## 45 60 3 2 2 Y1 37.20
## 46 60 3 2 2 Y1 25.75
## 47 60 3 2 3 Y1 35.35
## 48 60 3 2 3 Y1 37.10
## 49 90 3 2 1 Y1 27.90
## 50 90 3 2 1 Y1 47.55
## 51 90 3 2 2 Y1 40.45
## 52 90 3 2 2 Y1 35.05
## 53 90 3 2 3 Y1 38.80
## 54 90 3 2 3 Y1 40.90
## 55 30 1 1 1 Y2 316000.00
## 56 30 1 1 1 Y2 118000.00
## 57 30 1 1 2 Y2 13500.00
## 58 30 1 1 2 Y2 129000.00
## 59 30 1 1 3 Y2 131000.00
## 60 30 1 1 3 Y2 134000.00
## 61 60 1 1 1 Y2 316000.00
## 62 60 1 1 1 Y2 99800.00
## 63 60 1 1 2 Y2 2260.00
## 64 60 1 1 2 Y2 287000.00
## 65 60 1 1 3 Y2 141000.00
## 66 60 1 1 3 Y2 146000.00
## 67 90 1 1 1 Y2 319000.00
## 68 90 1 1 1 Y2 95100.00
## 69 90 1 1 2 Y2 8030.00
## 70 90 1 1 2 Y2 131000.00
## 71 90 1 1 3 Y2 155000.00
## 72 90 1 1 3 Y2 162000.00
## 73 30 2 1 1 Y2 7430.00
## 74 30 2 1 1 Y2 207000.00
## 75 30 2 1 2 Y2 214000.00
## 76 30 2 1 2 Y2 95200.00
## 77 30 2 1 3 Y2 134000.00
## 78 30 2 1 3 Y2 137000.00
## 79 60 2 1 1 Y2 8950.00
## 80 60 2 1 1 Y2 185000.00
## 81 60 2 1 2 Y2 273000.00
## 82 60 2 1 2 Y2 96700.00
## 83 60 2 2 3 Y2 144000.00
## 84 60 2 2 3 Y2 150000.00
## 85 90 2 2 1 Y2 69300.00
## 86 90 2 2 1 Y2 198000.00
## 87 90 2 2 2 Y2 378000.00
## 88 90 2 2 2 Y2 157000.00
## 89 90 2 2 3 Y2 159000.00
## 90 90 2 2 3 Y2 166000.00
## 91 30 3 2 1 Y2 245000.00
## 92 30 3 2 1 Y2 55800.00
## 93 30 3 2 2 Y2 453000.00
## 94 30 3 2 2 Y2 1780000.00
## 95 30 3 2 3 Y2 509000.00
## 96 30 3 2 3 Y2 536000.00
## 97 60 3 2 1 Y2 206000.00
## 98 60 3 2 1 Y2 96600.00
## 99 60 3 2 2 Y2 447000.00
## 100 60 3 2 2 Y2 1740000.00
## 101 60 3 2 3 Y2 568000.00
## 102 60 3 2 3 Y2 600000.00
## 103 90 3 2 1 Y2 330000.00
## 104 90 3 2 1 Y2 80800.00
## 105 90 3 2 2 Y2 447000.00
## 106 90 3 2 2 Y2 2050000.00
## 107 90 3 2 3 Y2 637000.00
## 108 90 3 2 3 Y2 673000.00bxp<-ggboxplot(df,x="mycotoxins",y="score",color="U",palette="Blues")
bxp
En cuanto al análisis gráfico observamos que en la ubicación lateral este los valores atipicos se alejan bastante del conjunto de datos para el Aspergillus.
df %>% group_by(U,mycotoxins) %>% identify_outliers(score)## # A tibble: 19 x 8
## U mycotoxins T S REP score is.outlier is.extreme
## <fct> <chr> <fct> <fct> <fct> <dbl> <lgl> <lgl>
## 1 1 Y1 30 1 2 61.3 TRUE TRUE
## 2 1 Y1 90 1 2 67.2 TRUE TRUE
## 3 1 Y2 30 1 1 316000 TRUE FALSE
## 4 1 Y2 30 1 2 13500 TRUE FALSE
## 5 1 Y2 60 1 1 316000 TRUE FALSE
## 6 1 Y2 60 1 2 2260 TRUE FALSE
## 7 1 Y2 60 1 2 287000 TRUE FALSE
## 8 1 Y2 90 1 1 319000 TRUE FALSE
## 9 1 Y2 90 1 2 8030 TRUE FALSE
## 10 2 Y1 30 1 2 137. TRUE TRUE
## 11 2 Y1 60 1 2 94.8 TRUE FALSE
## 12 2 Y1 90 2 2 116. TRUE TRUE
## 13 2 Y2 90 2 2 378000 TRUE FALSE
## 14 3 Y1 30 2 2 59.2 TRUE FALSE
## 15 3 Y1 30 2 2 57.2 TRUE FALSE
## 16 3 Y1 60 2 1 18.6 TRUE FALSE
## 17 3 Y2 30 2 2 1780000 TRUE TRUE
## 18 3 Y2 60 2 2 1740000 TRUE TRUE
## 19 3 Y2 90 2 2 2050000 TRUE TRUEEl estadístico de valores atipicos nos muestra que los valores observados en el gráfico qq no son extremos, sin embargo, para las aflatoxinas si representan ser extremos, en el gráfico no fue posible verlos por las disparidad de los valores.
Resumen de datos de los Silos con respecto a las Micotoxinas
df %>% group_by(S,mycotoxins) %>% get_summary_stats(score,type="mean_sd")## # A tibble: 4 x 6
## S mycotoxins variable n mean sd
## <fct> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 Y1 score 28 37.0 27.9
## 2 1 Y2 score 28 145070. 94440.
## 3 2 Y1 score 26 43.0 18.9
## 4 2 Y2 score 26 495212. 538623.df## T U S REP mycotoxins score
## 1 30 1 1 1 Y1 17.10
## 2 30 1 1 1 Y1 18.50
## 3 30 1 1 2 Y1 61.30
## 4 30 1 1 2 Y1 22.25
## 5 30 1 1 3 Y1 24.61
## 6 30 1 1 3 Y1 25.19
## 7 60 1 1 1 Y1 19.05
## 8 60 1 1 1 Y1 15.05
## 9 60 1 1 2 Y1 39.65
## 10 60 1 1 2 Y1 24.20
## 11 60 1 1 3 Y1 25.39
## 12 60 1 1 3 Y1 26.53
## 13 90 1 1 1 Y1 15.20
## 14 90 1 1 1 Y1 15.85
## 15 90 1 1 2 Y1 67.25
## 16 90 1 1 2 Y1 15.20
## 17 90 1 1 3 Y1 27.60
## 18 90 1 1 3 Y1 29.05
## 19 30 2 1 1 Y1 14.75
## 20 30 2 1 1 Y1 69.00
## 21 30 2 1 2 Y1 137.35
## 22 30 2 1 2 Y1 37.95
## 23 30 2 1 3 Y1 45.43
## 24 30 2 1 3 Y1 47.30
## 25 60 2 1 1 Y1 19.65
## 26 60 2 1 1 Y1 38.75
## 27 60 2 1 2 Y1 94.85
## 28 60 2 1 2 Y1 42.85
## 29 60 2 2 3 Y1 48.80
## 30 60 2 2 3 Y1 51.47
## 31 90 2 2 1 Y1 12.65
## 32 90 2 2 1 Y1 46.35
## 33 90 2 2 2 Y1 116.20
## 34 90 2 2 2 Y1 35.85
## 35 90 2 2 3 Y1 54.09
## 36 90 2 2 3 Y1 57.19
## 37 30 3 2 1 Y1 50.05
## 38 30 3 2 1 Y1 42.30
## 39 30 3 2 2 Y1 59.25
## 40 30 3 2 2 Y1 57.20
## 41 30 3 2 3 Y1 27.72
## 42 30 3 2 3 Y1 34.58
## 43 60 3 2 1 Y1 18.60
## 44 60 3 2 1 Y1 40.90
## 45 60 3 2 2 Y1 37.20
## 46 60 3 2 2 Y1 25.75
## 47 60 3 2 3 Y1 35.35
## 48 60 3 2 3 Y1 37.10
## 49 90 3 2 1 Y1 27.90
## 50 90 3 2 1 Y1 47.55
## 51 90 3 2 2 Y1 40.45
## 52 90 3 2 2 Y1 35.05
## 53 90 3 2 3 Y1 38.80
## 54 90 3 2 3 Y1 40.90
## 55 30 1 1 1 Y2 316000.00
## 56 30 1 1 1 Y2 118000.00
## 57 30 1 1 2 Y2 13500.00
## 58 30 1 1 2 Y2 129000.00
## 59 30 1 1 3 Y2 131000.00
## 60 30 1 1 3 Y2 134000.00
## 61 60 1 1 1 Y2 316000.00
## 62 60 1 1 1 Y2 99800.00
## 63 60 1 1 2 Y2 2260.00
## 64 60 1 1 2 Y2 287000.00
## 65 60 1 1 3 Y2 141000.00
## 66 60 1 1 3 Y2 146000.00
## 67 90 1 1 1 Y2 319000.00
## 68 90 1 1 1 Y2 95100.00
## 69 90 1 1 2 Y2 8030.00
## 70 90 1 1 2 Y2 131000.00
## 71 90 1 1 3 Y2 155000.00
## 72 90 1 1 3 Y2 162000.00
## 73 30 2 1 1 Y2 7430.00
## 74 30 2 1 1 Y2 207000.00
## 75 30 2 1 2 Y2 214000.00
## 76 30 2 1 2 Y2 95200.00
## 77 30 2 1 3 Y2 134000.00
## 78 30 2 1 3 Y2 137000.00
## 79 60 2 1 1 Y2 8950.00
## 80 60 2 1 1 Y2 185000.00
## 81 60 2 1 2 Y2 273000.00
## 82 60 2 1 2 Y2 96700.00
## 83 60 2 2 3 Y2 144000.00
## 84 60 2 2 3 Y2 150000.00
## 85 90 2 2 1 Y2 69300.00
## 86 90 2 2 1 Y2 198000.00
## 87 90 2 2 2 Y2 378000.00
## 88 90 2 2 2 Y2 157000.00
## 89 90 2 2 3 Y2 159000.00
## 90 90 2 2 3 Y2 166000.00
## 91 30 3 2 1 Y2 245000.00
## 92 30 3 2 1 Y2 55800.00
## 93 30 3 2 2 Y2 453000.00
## 94 30 3 2 2 Y2 1780000.00
## 95 30 3 2 3 Y2 509000.00
## 96 30 3 2 3 Y2 536000.00
## 97 60 3 2 1 Y2 206000.00
## 98 60 3 2 1 Y2 96600.00
## 99 60 3 2 2 Y2 447000.00
## 100 60 3 2 2 Y2 1740000.00
## 101 60 3 2 3 Y2 568000.00
## 102 60 3 2 3 Y2 600000.00
## 103 90 3 2 1 Y2 330000.00
## 104 90 3 2 1 Y2 80800.00
## 105 90 3 2 2 Y2 447000.00
## 106 90 3 2 2 Y2 2050000.00
## 107 90 3 2 3 Y2 637000.00
## 108 90 3 2 3 Y2 673000.00bxp<-ggboxplot(df,x="mycotoxins",y="score",color="S",palette="jco")
bxp
df %>% group_by(S,mycotoxins) %>% identify_outliers(score)## # A tibble: 7 x 8
## S mycotoxins T U REP score is.outlier is.extreme
## <fct> <chr> <fct> <fct> <fct> <dbl> <lgl> <lgl>
## 1 1 Y1 30 2 2 137. TRUE TRUE
## 2 1 Y1 60 2 2 94.8 TRUE FALSE
## 3 2 Y1 90 2 1 12.6 TRUE FALSE
## 4 2 Y1 90 2 2 116. TRUE TRUE
## 5 2 Y2 30 3 2 1780000 TRUE TRUE
## 6 2 Y2 60 3 2 1740000 TRUE FALSE
## 7 2 Y2 90 3 2 2050000 TRUE TRUEHay valores atipicos extremos tanto en las Aflatoxinas como para el Aspergillus mayoritariamente en el silo 2.
Resumen de datos de las Repeticiones con respecto a las Micotoxinas
df %>% group_by(REP,mycotoxins) %>% get_summary_stats(score,type="mean_sd")## # A tibble: 6 x 6
## REP mycotoxins variable n mean sd
## <fct> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 Y1 score 18 29.4 16.6
## 2 1 Y2 score 18 164099. 108801.
## 3 2 Y1 score 18 52.8 33.1
## 4 2 Y2 score 18 483427. 651667
## 5 3 Y1 score 18 37.6 10.9
## 6 3 Y2 score 18 293444. 216538.df## T U S REP mycotoxins score
## 1 30 1 1 1 Y1 17.10
## 2 30 1 1 1 Y1 18.50
## 3 30 1 1 2 Y1 61.30
## 4 30 1 1 2 Y1 22.25
## 5 30 1 1 3 Y1 24.61
## 6 30 1 1 3 Y1 25.19
## 7 60 1 1 1 Y1 19.05
## 8 60 1 1 1 Y1 15.05
## 9 60 1 1 2 Y1 39.65
## 10 60 1 1 2 Y1 24.20
## 11 60 1 1 3 Y1 25.39
## 12 60 1 1 3 Y1 26.53
## 13 90 1 1 1 Y1 15.20
## 14 90 1 1 1 Y1 15.85
## 15 90 1 1 2 Y1 67.25
## 16 90 1 1 2 Y1 15.20
## 17 90 1 1 3 Y1 27.60
## 18 90 1 1 3 Y1 29.05
## 19 30 2 1 1 Y1 14.75
## 20 30 2 1 1 Y1 69.00
## 21 30 2 1 2 Y1 137.35
## 22 30 2 1 2 Y1 37.95
## 23 30 2 1 3 Y1 45.43
## 24 30 2 1 3 Y1 47.30
## 25 60 2 1 1 Y1 19.65
## 26 60 2 1 1 Y1 38.75
## 27 60 2 1 2 Y1 94.85
## 28 60 2 1 2 Y1 42.85
## 29 60 2 2 3 Y1 48.80
## 30 60 2 2 3 Y1 51.47
## 31 90 2 2 1 Y1 12.65
## 32 90 2 2 1 Y1 46.35
## 33 90 2 2 2 Y1 116.20
## 34 90 2 2 2 Y1 35.85
## 35 90 2 2 3 Y1 54.09
## 36 90 2 2 3 Y1 57.19
## 37 30 3 2 1 Y1 50.05
## 38 30 3 2 1 Y1 42.30
## 39 30 3 2 2 Y1 59.25
## 40 30 3 2 2 Y1 57.20
## 41 30 3 2 3 Y1 27.72
## 42 30 3 2 3 Y1 34.58
## 43 60 3 2 1 Y1 18.60
## 44 60 3 2 1 Y1 40.90
## 45 60 3 2 2 Y1 37.20
## 46 60 3 2 2 Y1 25.75
## 47 60 3 2 3 Y1 35.35
## 48 60 3 2 3 Y1 37.10
## 49 90 3 2 1 Y1 27.90
## 50 90 3 2 1 Y1 47.55
## 51 90 3 2 2 Y1 40.45
## 52 90 3 2 2 Y1 35.05
## 53 90 3 2 3 Y1 38.80
## 54 90 3 2 3 Y1 40.90
## 55 30 1 1 1 Y2 316000.00
## 56 30 1 1 1 Y2 118000.00
## 57 30 1 1 2 Y2 13500.00
## 58 30 1 1 2 Y2 129000.00
## 59 30 1 1 3 Y2 131000.00
## 60 30 1 1 3 Y2 134000.00
## 61 60 1 1 1 Y2 316000.00
## 62 60 1 1 1 Y2 99800.00
## 63 60 1 1 2 Y2 2260.00
## 64 60 1 1 2 Y2 287000.00
## 65 60 1 1 3 Y2 141000.00
## 66 60 1 1 3 Y2 146000.00
## 67 90 1 1 1 Y2 319000.00
## 68 90 1 1 1 Y2 95100.00
## 69 90 1 1 2 Y2 8030.00
## 70 90 1 1 2 Y2 131000.00
## 71 90 1 1 3 Y2 155000.00
## 72 90 1 1 3 Y2 162000.00
## 73 30 2 1 1 Y2 7430.00
## 74 30 2 1 1 Y2 207000.00
## 75 30 2 1 2 Y2 214000.00
## 76 30 2 1 2 Y2 95200.00
## 77 30 2 1 3 Y2 134000.00
## 78 30 2 1 3 Y2 137000.00
## 79 60 2 1 1 Y2 8950.00
## 80 60 2 1 1 Y2 185000.00
## 81 60 2 1 2 Y2 273000.00
## 82 60 2 1 2 Y2 96700.00
## 83 60 2 2 3 Y2 144000.00
## 84 60 2 2 3 Y2 150000.00
## 85 90 2 2 1 Y2 69300.00
## 86 90 2 2 1 Y2 198000.00
## 87 90 2 2 2 Y2 378000.00
## 88 90 2 2 2 Y2 157000.00
## 89 90 2 2 3 Y2 159000.00
## 90 90 2 2 3 Y2 166000.00
## 91 30 3 2 1 Y2 245000.00
## 92 30 3 2 1 Y2 55800.00
## 93 30 3 2 2 Y2 453000.00
## 94 30 3 2 2 Y2 1780000.00
## 95 30 3 2 3 Y2 509000.00
## 96 30 3 2 3 Y2 536000.00
## 97 60 3 2 1 Y2 206000.00
## 98 60 3 2 1 Y2 96600.00
## 99 60 3 2 2 Y2 447000.00
## 100 60 3 2 2 Y2 1740000.00
## 101 60 3 2 3 Y2 568000.00
## 102 60 3 2 3 Y2 600000.00
## 103 90 3 2 1 Y2 330000.00
## 104 90 3 2 1 Y2 80800.00
## 105 90 3 2 2 Y2 447000.00
## 106 90 3 2 2 Y2 2050000.00
## 107 90 3 2 3 Y2 637000.00
## 108 90 3 2 3 Y2 673000.00bxp<-ggboxplot(df,x="mycotoxins",y="score",color="REP",palette="Accent")
bxp
Graficamente la repetición 2 es la que muestra valores atipicos para ambos.
Identificación de Valores Atípicos a gran escala
df %>% group_by(T,U,S,REP) %>% identify_outliers(score)## # A tibble: 5 x 8
## T U S REP mycotoxins score is.outlier is.extreme
## <fct> <fct> <fct> <fct> <chr> <dbl> <lgl> <lgl>
## 1 30 1 1 2 Y2 129000 TRUE FALSE
## 2 30 2 1 1 Y2 207000 TRUE FALSE
## 3 60 1 1 2 Y2 287000 TRUE FALSE
## 4 60 2 1 1 Y2 185000 TRUE FALSE
## 5 90 1 1 2 Y2 131000 TRUE FALSEEn este modelo amplio que usa todos los factores se identifico que si habian valores atípicos sin embargo, la prueba no los considera extremos y como visualizamos en las pruebas y gráficas individuales, los outilers más marcados se encontraban en la micotoxina Aspergillus (Y2).
El que se demostrara que eran extremos en las pruebas individuales se debe a que se está tomando cada muestra con respecto a un solo factor, o sea que se reducen los límites del espectro de revisión entre valores lo que hace más evidente cualquier valor que salga de lo que considera la prueba.
df %>% group_by(mycotoxins) %>% shapiro_test(score)## # A tibble: 2 x 4
## mycotoxins variable statistic p
## <chr> <chr> <dbl> <dbl>
## 1 Y1 score 0.817 1.09e- 6
## 2 Y2 score 0.604 7.88e-11ggqqplot(df,"score",ggtheme = theme_bw()) + facet_grid(mycotoxins~T,labeller = "label_both")
df %>% group_by(mycotoxins) %>% levene_test(score~T)## # A tibble: 2 x 5
## mycotoxins df1 df2 statistic p
## <chr> <int> <int> <dbl> <dbl>
## 1 Y1 2 51 0.693 0.505
## 2 Y2 2 51 0.0344 0.966Análisis de Varianza ANOVA
anova<-aov(score~T*S*U*REP,data=df)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## T 2 1.470e+10 7.348e+09 0.067 0.93511
## S 1 8.304e+11 8.304e+11 7.588 0.00726 **
## U 2 5.754e+11 2.877e+11 2.629 0.07830 .
## REP 2 4.648e+11 2.324e+11 2.124 0.12619
## T:S 2 9.162e+09 4.581e+09 0.042 0.95903
## T:U 2 3.223e+09 1.612e+09 0.015 0.98538
## T:REP 4 2.565e+09 6.412e+08 0.006 0.99993
## S:REP 2 7.984e+11 3.992e+11 3.648 0.03042 *
## U:REP 4 2.332e+11 5.830e+10 0.533 0.71206
## T:S:REP 4 5.508e+09 1.377e+09 0.013 0.99968
## T:U:REP 1 2.204e+08 2.204e+08 0.002 0.96432
## Residuals 81 8.864e+12 1.094e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de Tukey HSD
tk<-TukeyHSD(anova)
tk## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = score ~ T * S * U * REP, data = df)
##
## $T
## diff lwr upr p adj
## 60-30 8117.481 -178048.2 194283.2 0.9940431
## 90-30 27784.203 -158381.5 213949.9 0.9324660
## 90-60 19666.722 -166499.0 205832.4 0.9655537
##
## $S
## diff lwr upr p adj
## 2-1 168605.3 41844.57 295366 0.0097694
##
## $U
## diff lwr upr p adj
## 2-1 -79058.90 -265224.57 107106.8 0.5702714
## 3-1 60419.76 -125745.92 246585.4 0.7194496
## 3-2 139478.65 -46687.02 325644.3 0.1797806
##
## $REP
## diff lwr upr p adj
## 2-1 159675.85 -26489.83 345841.53 0.1074596
## 3-1 64489.10 -121676.58 250654.77 0.6874001
## 3-2 -95186.75 -281352.43 90978.92 0.4444606
##
## $`T:S`
## diff lwr upr p adj
## 60:1-30:1 -12082.177 -304451.6 280287.3 0.9999963
## 90:1-30:1 -54607.835 -396021.8 286806.1 0.9971374
## 30:2-30:1 168932.179 -172481.8 510346.1 0.6999953
## 60:2-30:1 160066.187 -151600.7 471733.1 0.6657683
## 90:2-30:1 153446.311 -125317.0 432209.6 0.5965256
## 90:1-60:1 -42525.658 -395136.5 310085.2 0.9992631
## 30:2-60:1 181014.355 -171596.5 533625.2 0.6661864
## 60:2-60:1 172148.363 -151745.3 496042.1 0.6323695
## 90:2-60:1 165528.488 -126841.0 457897.9 0.5666459
## 30:2-90:1 223540.014 -170690.9 617770.9 0.5649974
## 60:2-90:1 214674.022 -154095.2 583443.2 0.5362799
## 90:2-90:1 208054.146 -133359.8 549468.1 0.4849986
## 60:2-30:2 -8865.992 -377635.2 359903.2 0.9999998
## 90:2-30:2 -15485.868 -356899.8 325928.1 0.9999941
## 90:2-60:2 -6619.876 -318286.7 305047.0 0.9999999
##
## $`T:U`
## diff lwr upr p adj
## 60:1-30:1 34087.794 -396358.1 464533.6 0.9999994
## 90:1-30:1 91037.593 -339408.3 521483.4 0.9989819
## 30:2-30:1 7233.298 -423212.6 437679.2 1.0000000
## 60:2-30:1 -24726.686 -455172.5 405719.2 1.0000000
## 90:2-30:1 -94557.911 -525003.8 335887.9 0.9986626
## 30:3-30:1 63351.267 -367094.6 493797.1 0.9999314
## 60:3-30:1 85575.900 -344870.0 516021.8 0.9993505
## 90:3-30:1 157457.491 -272988.4 587903.3 0.9613140
## 90:1-60:1 56949.799 -373496.1 487395.7 0.9999696
## 30:2-60:1 -26854.496 -457300.4 403591.4 0.9999999
## 60:2-60:1 -58814.480 -489260.3 371631.4 0.9999611
## 90:2-60:1 -128645.705 -559091.6 301800.1 0.9889396
## 30:3-60:1 29263.473 -401182.4 459709.3 0.9999998
## 60:3-60:1 51488.106 -378957.7 481934.0 0.9999861
## 90:3-60:1 123369.698 -307076.2 553815.6 0.9915970
## 30:2-90:1 -83804.295 -514250.1 346641.6 0.9994427
## 60:2-90:1 -115764.279 -546210.1 314681.6 0.9945118
## 90:2-90:1 -185595.504 -616041.4 244850.4 0.9041954
## 30:3-90:1 -27686.326 -458132.2 402759.5 0.9999999
## 60:3-90:1 -5461.693 -435907.5 424984.2 1.0000000
## 90:3-90:1 66419.899 -364026.0 496865.8 0.9999017
## 60:2-30:2 -31959.984 -462405.8 398485.9 0.9999997
## 90:2-30:2 -101791.209 -532237.1 328654.6 0.9977430
## 30:3-30:2 56117.969 -374327.9 486563.8 0.9999729
## 60:3-30:2 78342.602 -352103.3 508788.5 0.9996610
## 90:3-30:2 150224.194 -280221.7 580670.0 0.9707393
## 90:2-60:2 -69831.225 -500277.1 360614.6 0.9998565
## 30:3-60:2 88077.953 -342367.9 518523.8 0.9991987
## 60:3-60:2 110302.586 -320143.3 540748.4 0.9960537
## 90:3-60:2 182184.178 -248261.7 612630.0 0.9130009
## 30:3-90:2 157909.178 -272536.7 588355.0 0.9606607
## 60:3-90:2 180133.811 -250312.0 610579.7 0.9180349
## 90:3-90:2 252015.403 -178430.5 682461.3 0.6385093
## 60:3-30:3 22224.633 -408221.2 452670.5 1.0000000
## 90:3-30:3 94106.224 -336339.6 524552.1 0.9987077
## 90:3-60:3 71881.592 -358564.3 502327.4 0.9998217
##
## $`T:REP`
## diff lwr upr p adj
## 60:1-30:1 -1500.314 -431946.2 428945.5 1.0000000
## 90:1-30:1 11910.317 -418535.5 442356.2 1.0000000
## 30:2-30:1 144636.133 -285809.7 575082.0 0.9767693
## 60:2-30:1 159643.227 -270802.6 590089.1 0.9580792
## 90:2-30:1 185158.192 -245287.7 615604.0 0.9053543
## 30:3-30:1 54037.133 -376408.7 484483.0 0.9999797
## 60:3-30:1 64882.796 -365563.1 495328.7 0.9999177
## 90:3-30:1 84957.367 -345488.5 515403.2 0.9993840
## 90:1-60:1 13410.631 -417035.2 443856.5 1.0000000
## 30:2-60:1 146136.448 -284309.4 576582.3 0.9752511
## 60:2-60:1 161143.542 -269302.3 591589.4 0.9557500
## 90:2-60:1 186658.506 -243787.3 617104.4 0.9013413
## 30:3-60:1 55537.448 -374908.4 485983.3 0.9999750
## 60:3-60:1 66383.110 -364062.7 496829.0 0.9999021
## 90:3-60:1 86457.681 -343988.2 516903.5 0.9993000
## 30:2-90:1 132725.817 -297720.0 563171.7 0.9864669
## 60:2-90:1 147732.911 -282712.9 578178.8 0.9735555
## 90:2-90:1 173247.875 -257198.0 603693.7 0.9335382
## 30:3-90:1 42126.817 -388319.0 472572.7 0.9999971
## 60:3-90:1 52972.479 -377473.4 483418.3 0.9999826
## 90:3-90:1 73047.050 -357398.8 503492.9 0.9997988
## 60:2-30:2 15007.094 -415438.8 445452.9 1.0000000
## 90:2-30:2 40522.058 -389923.8 470967.9 0.9999978
## 30:3-30:2 -90599.000 -521044.9 339846.9 0.9990168
## 60:3-30:2 -79753.337 -510199.2 350692.5 0.9996131
## 90:3-30:2 -59678.767 -490124.6 370767.1 0.9999565
## 90:2-60:2 25514.964 -404930.9 455960.8 0.9999999
## 30:3-60:2 -105606.094 -536051.9 324839.8 0.9970799
## 60:3-60:2 -94760.431 -525206.3 335685.4 0.9986419
## 90:3-60:2 -74685.861 -505131.7 355760.0 0.9997625
## 30:3-90:2 -131121.058 -561566.9 299324.8 0.9874859
## 60:3-90:2 -120275.396 -550721.2 310170.5 0.9929027
## 90:3-90:2 -100200.825 -530646.7 330245.0 0.9979801
## 60:3-30:3 10845.663 -419600.2 441291.5 1.0000000
## 90:3-30:3 30920.233 -399525.6 461366.1 0.9999997
## 90:3-60:3 20074.571 -410371.3 450520.4 1.0000000
##
## $`S:REP`
## diff lwr upr p adj
## 2:1-1:1 -22091.215 -345984.93 301802.50 0.9999552
## 1:2-1:1 -18400.098 -323770.02 286969.82 0.9999758
## 2:2-1:1 360179.569 36285.86 684073.28 0.0203911
## 1:3-1:1 -10291.764 -334185.48 313601.95 0.9999990
## 2:3-1:1 143171.963 -162197.96 448541.88 0.7454488
## 1:2-2:1 3691.117 -320202.60 327584.83 1.0000000
## 2:2-2:1 382270.784 40856.83 723684.74 0.0191178
## 1:3-2:1 11799.451 -329614.50 353213.40 0.9999985
## 2:3-2:1 165263.177 -158630.54 489156.89 0.6719263
## 2:2-1:2 378579.667 54685.95 702473.38 0.0125044
## 1:3-1:2 8108.333 -315785.38 332002.05 0.9999997
## 2:3-1:2 161572.060 -143797.86 466941.98 0.6368646
## 1:3-2:2 -370471.334 -711885.28 -29057.38 0.0255095
## 2:3-2:2 -217007.607 -540901.32 106886.11 0.3766790
## 2:3-1:3 153463.727 -170429.99 477357.44 0.7369553
##
## $`U:REP`
## diff lwr upr p adj
## 2:1-1:1 -39930.944 -470376.8 390514.9 0.9999981
## 3:1-1:1 7814.518 -422631.3 438260.4 1.0000000
## 1:2-1:1 140113.756 -290332.1 570559.6 0.9809243
## 2:2-1:1 54408.255 -376037.6 484854.1 0.9999786
## 3:2-1:1 252389.114 -178056.7 682835.0 0.6366612
## 1:3-1:1 50466.611 -379979.2 480912.5 0.9999881
## 2:3-1:1 -61073.630 -491519.5 369372.2 0.9999481
## 3:3-1:1 111636.006 -318809.8 542081.9 0.9957144
## 3:1-2:1 47745.463 -382700.4 478191.3 0.9999922
## 1:2-2:1 180044.700 -250401.2 610490.6 0.9182493
## 2:2-2:1 94339.199 -336106.7 524785.1 0.9986846
## 3:2-2:1 292320.058 -138125.8 722765.9 0.4383785
## 1:3-2:1 90397.556 -340048.3 520843.4 0.9990325
## 2:3-2:1 -21142.685 -451588.5 409303.2 1.0000000
## 3:3-2:1 151566.951 -278878.9 582012.8 0.9691328
## 1:2-3:1 132299.237 -298146.6 562745.1 0.9867439
## 2:2-3:1 46593.736 -383852.1 477039.6 0.9999936
## 3:2-3:1 244574.595 -185871.3 675020.4 0.6748956
## 1:3-3:1 42652.093 -387793.8 473097.9 0.9999968
## 2:3-3:1 -68888.148 -499334.0 361557.7 0.9998705
## 3:3-3:1 103821.488 -326624.4 534267.3 0.9974075
## 2:2-1:2 -85705.501 -516151.4 344740.4 0.9993433
## 3:2-1:2 112275.358 -318170.5 542721.2 0.9955436
## 1:3-1:2 -89647.144 -520093.0 340798.7 0.9990892
## 2:3-1:2 -201187.385 -631633.2 229258.5 0.8571020
## 3:3-1:2 -28477.749 -458923.6 401968.1 0.9999999
## 3:2-2:2 197980.859 -232465.0 628426.7 0.8676960
## 1:3-2:2 -3941.644 -434387.5 426504.2 1.0000000
## 2:3-2:2 -115481.885 -545927.7 314964.0 0.9946019
## 3:3-2:2 57227.751 -373218.1 487673.6 0.9999685
## 1:3-3:2 -201922.502 -632368.4 228523.4 0.8546084
## 2:3-3:2 -313462.743 -743908.6 116983.1 0.3422151
## 3:3-3:2 -140753.107 -571199.0 289692.7 0.9803740
## 2:3-1:3 -111540.241 -541986.1 318905.6 0.9957395
## 3:3-1:3 61169.395 -369276.5 491615.2 0.9999475
## 3:3-2:3 172709.636 -257736.2 603155.5 0.9346602
##
## $`T:S:REP`
## diff lwr upr p adj
## 60:1:1-30:1:1 -24632.09495 -620739.3 571475.1 1.0000000
## 90:1:1-30:1:1 7586.61343 -722492.6 737665.8 1.0000000
## 30:2:1-30:1:1 -39787.77247 -769867.0 690291.4 1.0000000
## 60:2:1-30:1:1 -51681.25406 -781760.5 678397.9 1.0000000
## 90:2:1-30:1:1 -5821.71795 -601928.9 590285.5 1.0000000
## 30:1:2-30:1:1 -5794.38802 -601901.6 590312.8 1.0000000
## 60:1:2-30:1:1 312.21078 -595795.0 596419.4 1.0000000
## 90:1:2-30:1:1 -136285.73449 -866364.9 593793.5 0.9999997
## 30:2:2-30:1:1 405709.40357 -324369.8 1135788.6 0.8590892
## 60:2:2-30:1:1 381860.75948 -348218.4 1111940.0 0.9089710
## 90:2:2-30:1:1 325986.26851 -270120.9 922093.4 0.8739122
## 30:1:3-30:1:1 -4238.13894 -600345.3 591869.0 1.0000000
## 60:1:3-30:1:1 -19887.34730 -749966.5 710191.9 1.0000000
## 90:1:3-30:1:1 -47249.93283 -777329.1 682829.3 1.0000000
## 30:2:3-30:1:1 135028.42150 -595050.8 865107.6 0.9999998
## 60:2:3-30:1:1 146144.96820 -449962.2 742252.1 0.9999845
## 90:2:3-30:1:1 133281.38823 -462825.8 729388.6 0.9999959
## 90:1:1-60:1:1 32218.70838 -697860.5 762297.9 1.0000000
## 30:2:1-60:1:1 -15155.67752 -745234.9 714923.5 1.0000000
## 60:2:1-60:1:1 -27049.15911 -757128.4 703030.0 1.0000000
## 90:2:1-60:1:1 18810.37700 -577296.8 614917.5 1.0000000
## 30:1:2-60:1:1 18837.70693 -577269.5 614944.9 1.0000000
## 60:1:2-60:1:1 24944.30573 -571162.9 621051.5 1.0000000
## 90:1:2-60:1:1 -111653.63954 -841732.8 618425.6 1.0000000
## 30:2:2-60:1:1 430341.49852 -299737.7 1160420.7 0.7940994
## 60:2:2-60:1:1 406492.85443 -323586.3 1136572.1 0.8572262
## 90:2:2-60:1:1 350618.36346 -245488.8 946725.5 0.7967626
## 30:1:3-60:1:1 20393.95601 -575713.2 616501.1 1.0000000
## 60:1:3-60:1:1 4744.74765 -725334.5 734823.9 1.0000000
## 90:1:3-60:1:1 -22617.83788 -752697.0 707461.4 1.0000000
## 30:2:3-60:1:1 159660.51645 -570418.7 889739.7 0.9999971
## 60:2:3-60:1:1 170777.06315 -425330.1 766884.2 0.9998655
## 90:2:3-60:1:1 157913.48318 -438193.7 754020.7 0.9999538
## 30:2:1-90:1:1 -47374.38590 -890397.2 795648.5 1.0000000
## 60:2:1-90:1:1 -59267.86749 -902290.7 783755.0 1.0000000
## 90:2:1-90:1:1 -13408.33138 -743487.5 716670.9 1.0000000
## 30:1:2-90:1:1 -13381.00145 -743460.2 716698.2 1.0000000
## 60:1:2-90:1:1 -7274.40265 -737353.6 722804.8 1.0000000
## 90:1:2-90:1:1 -143872.34792 -986895.2 699150.5 0.9999999
## 30:2:2-90:1:1 398122.79014 -444900.1 1241145.6 0.9607431
## 60:2:2-90:1:1 374274.14605 -468748.7 1217297.0 0.9777787
## 90:2:2-90:1:1 318399.65508 -411679.5 1048478.9 0.9812977
## 30:1:3-90:1:1 -11824.75237 -741904.0 718254.4 1.0000000
## 60:1:3-90:1:1 -27473.96073 -870496.8 815548.9 1.0000000
## 90:1:3-90:1:1 -54836.54626 -897859.4 788186.3 1.0000000
## 30:2:3-90:1:1 127441.80807 -715581.0 970464.7 1.0000000
## 60:2:3-90:1:1 138558.35477 -591520.8 868637.6 0.9999997
## 90:2:3-90:1:1 125694.77480 -604384.4 855774.0 0.9999999
## 60:2:1-30:2:1 -11893.48159 -854916.3 831129.4 1.0000000
## 90:2:1-30:2:1 33966.05452 -696113.1 764045.3 1.0000000
## 30:1:2-30:2:1 33993.38445 -696085.8 764072.6 1.0000000
## 60:1:2-30:2:1 40099.98325 -689979.2 770179.2 1.0000000
## 90:1:2-30:2:1 -96497.96201 -939520.8 746524.9 1.0000000
## 30:2:2-30:2:1 445497.17604 -397525.7 1288520.0 0.9016291
## 60:2:2-30:2:1 421648.53195 -421374.3 1264671.4 0.9360135
## 90:2:2-30:2:1 365774.04098 -364305.2 1095853.2 0.9351290
## 30:1:3-30:2:1 35549.63354 -694529.6 765628.8 1.0000000
## 60:1:3-30:2:1 19900.42518 -823122.4 862923.3 1.0000000
## 90:1:3-30:2:1 -7462.16036 -850485.0 835560.7 1.0000000
## 30:2:3-30:2:1 174816.19398 -668206.7 1017839.0 0.9999987
## 60:2:3-30:2:1 185932.74067 -544146.5 916011.9 0.9999733
## 90:2:3-30:2:1 173069.16070 -557010.0 903148.4 0.9999904
## 90:2:1-60:2:1 45859.53611 -684219.7 775938.7 1.0000000
## 30:1:2-60:2:1 45886.86604 -684192.3 775966.1 1.0000000
## 60:1:2-60:2:1 51993.46484 -678085.7 782072.7 1.0000000
## 90:1:2-60:2:1 -84604.48042 -927627.3 758418.4 1.0000000
## 30:2:2-60:2:1 457390.65763 -385632.2 1300413.5 0.8807640
## 60:2:2-60:2:1 433542.01354 -409480.8 1276564.9 0.9200934
## 90:2:2-60:2:1 377667.52257 -352411.7 1107746.7 0.9163633
## 30:1:3-60:2:1 47443.11513 -682636.1 777522.3 1.0000000
## 60:1:3-60:2:1 31793.90677 -811228.9 874816.8 1.0000000
## 90:1:3-60:2:1 4431.32123 -838591.5 847454.2 1.0000000
## 30:2:3-60:2:1 186709.67557 -656313.2 1029732.5 0.9999964
## 60:2:3-60:2:1 197826.22226 -532253.0 927905.4 0.9999368
## 90:2:3-60:2:1 184962.64229 -545116.6 915041.8 0.9999752
## 30:1:2-90:2:1 27.32993 -596079.8 596134.5 1.0000000
## 60:1:2-90:2:1 6133.92873 -589973.2 602241.1 1.0000000
## 90:1:2-90:2:1 -130464.01654 -860543.2 599615.2 0.9999999
## 30:2:2-90:2:1 411531.12152 -318548.1 1141610.3 0.8449133
## 60:2:2-90:2:1 387682.47743 -342396.7 1117761.7 0.8980253
## 90:2:2-90:2:1 331807.98646 -264299.2 927915.2 0.8574951
## 30:1:3-90:2:1 1583.57901 -594523.6 597690.7 1.0000000
## 60:1:3-90:2:1 -14065.62935 -744144.8 716013.6 1.0000000
## 90:1:3-90:2:1 -41428.21488 -771507.4 688651.0 1.0000000
## 30:2:3-90:2:1 140850.13945 -589229.1 870929.3 0.9999996
## 60:2:3-90:2:1 151966.68615 -444140.5 748073.9 0.9999730
## 90:2:3-90:2:1 139103.10618 -457004.1 735210.3 0.9999924
## 60:1:2-30:1:2 6106.59880 -590000.6 602213.8 1.0000000
## 90:1:2-30:1:2 -130491.34647 -860570.5 599587.9 0.9999999
## 30:2:2-30:1:2 411503.79159 -318575.4 1141583.0 0.8449816
## 60:2:2-30:1:2 387655.14750 -342424.1 1117734.3 0.8980785
## 90:2:2-30:1:2 331780.65653 -264326.5 927887.8 0.8575749
## 30:1:3-30:1:2 1556.24908 -594550.9 597663.4 1.0000000
## 60:1:3-30:1:2 -14092.95928 -744172.2 715986.2 1.0000000
## 90:1:3-30:1:2 -41455.54481 -771534.7 688623.7 1.0000000
## 30:2:3-30:1:2 140822.80952 -589256.4 870902.0 0.9999996
## 60:2:3-30:1:2 151939.35622 -444167.8 748046.5 0.9999730
## 90:2:3-30:1:2 139075.77625 -457031.4 735182.9 0.9999924
## 90:1:2-60:1:2 -136597.94527 -866677.1 593481.3 0.9999997
## 30:2:2-60:1:2 405397.19279 -324682.0 1135476.4 0.8598278
## 60:2:2-60:1:2 381548.54870 -348530.7 1111627.7 0.9095355
## 90:2:2-60:1:2 325674.05773 -270433.1 921781.2 0.8747594
## 30:1:3-60:1:2 -4550.34972 -600657.5 591556.8 1.0000000
## 60:1:3-60:1:2 -20199.55808 -750278.8 709879.6 1.0000000
## 90:1:3-60:1:2 -47562.14361 -777641.3 682517.1 1.0000000
## 30:2:3-60:1:2 134716.21072 -595363.0 864795.4 0.9999998
## 60:2:3-60:1:2 145832.75742 -450274.4 741939.9 0.9999849
## 90:2:3-60:1:2 132969.17745 -463138.0 729076.3 0.9999961
## 30:2:2-90:1:2 541995.13806 -301027.7 1385018.0 0.6688787
## 60:2:2-90:1:2 518146.49397 -324876.4 1361169.3 0.7378909
## 90:2:2-90:1:2 462272.00300 -267807.2 1192351.2 0.6931953
## 30:1:3-90:1:2 132047.59555 -598031.6 862126.8 0.9999998
## 60:1:3-90:1:2 116398.38719 -726624.5 959421.2 1.0000000
## 90:1:3-90:1:2 89035.80166 -753987.0 932058.6 1.0000000
## 30:2:3-90:1:2 271314.15599 -571708.7 1114337.0 0.9993894
## 60:2:3-90:1:2 282430.70269 -447648.5 1012509.9 0.9946211
## 90:2:3-90:1:2 269567.12272 -460512.1 999646.3 0.9968121
## 60:2:2-30:2:2 -23848.64409 -866871.5 819174.2 1.0000000
## 90:2:2-30:2:2 -79723.13506 -809802.3 650356.1 1.0000000
## 30:1:3-30:2:2 -409947.54250 -1140026.7 320131.7 0.8488448
## 60:1:3-30:2:2 -425596.75087 -1268619.6 417426.1 0.9309943
## 90:1:3-30:2:2 -452959.33640 -1295982.2 390063.5 0.8888296
## 30:2:3-30:2:2 -270680.98207 -1113703.8 572341.9 0.9994071
## 60:2:3-30:2:2 -259564.43537 -989643.6 470514.8 0.9979448
## 90:2:3-30:2:2 -272428.01534 -1002507.2 457651.2 0.9964048
## 90:2:2-60:2:2 -55874.49097 -785953.7 674204.7 1.0000000
## 30:1:3-60:2:2 -386098.89841 -1116178.1 343980.3 0.9010815
## 60:1:3-60:2:2 -401748.10678 -1244771.0 441274.7 0.9574832
## 90:1:3-60:2:2 -429110.69231 -1272133.5 413912.2 0.9263062
## 30:2:3-60:2:2 -246832.33798 -1089855.2 596190.5 0.9998202
## 60:2:3-60:2:2 -235715.79128 -965795.0 494363.4 0.9993644
## 90:2:3-60:2:2 -248579.37125 -978658.6 481499.8 0.9987747
## 30:1:3-90:2:2 -330224.40744 -926331.6 265882.8 0.8620766
## 60:1:3-90:2:2 -345873.61580 -1075952.8 384205.6 0.9596337
## 90:1:3-90:2:2 -373236.20134 -1103315.4 356843.0 0.9237321
## 30:2:3-90:2:2 -190957.84701 -921037.0 539121.4 0.9999613
## 60:2:3-90:2:2 -179841.30031 -775948.5 416265.9 0.9997334
## 90:2:3-90:2:2 -192704.88028 -788812.1 403402.3 0.9993542
## 60:1:3-30:1:3 -15649.20836 -745728.4 714430.0 1.0000000
## 90:1:3-30:1:3 -43011.79390 -773091.0 687067.4 1.0000000
## 30:2:3-30:1:3 139266.56044 -590812.6 869345.8 0.9999996
## 60:2:3-30:1:3 150383.10714 -445724.1 746490.3 0.9999767
## 90:2:3-30:1:3 137519.52717 -458587.6 733626.7 0.9999935
## 90:1:3-60:1:3 -27362.58554 -870385.4 815660.3 1.0000000
## 30:2:3-60:1:3 154915.76880 -688107.1 997938.6 0.9999998
## 60:2:3-60:1:3 166032.31550 -564046.9 896111.5 0.9999948
## 90:2:3-60:1:3 153168.73553 -576910.5 883247.9 0.9999984
## 30:2:3-90:1:3 182278.35433 -660744.5 1025301.2 0.9999975
## 60:2:3-90:1:3 193394.90103 -536684.3 923474.1 0.9999538
## 90:2:3-90:1:3 180531.32106 -549547.9 910610.5 0.9999824
## 60:2:3-30:2:3 11116.54670 -718962.7 741195.7 1.0000000
## 90:2:3-30:2:3 -1747.03327 -731826.2 728332.2 1.0000000
## 90:2:3-60:2:3 -12863.57997 -608970.7 583243.6 1.0000000
##
## $`T:U:REP`
## diff lwr upr p adj
## 60:1:1-30:1:1 17488.9349 -878467.3 913445.2 1.0000000
## 90:1:1-30:1:1 -1125.5768 -897081.8 894830.7 1.0000000
## 30:2:1-30:1:1 -54431.3997 -950387.7 841524.9 1.0000000
## 60:2:1-30:1:1 -35372.6827 -931328.9 860583.6 1.0000000
## 90:2:1-30:1:1 -22142.1169 -918098.4 873814.1 1.0000000
## 30:3:1-30:1:1 8121.8393 -887834.4 904078.1 1.0000000
## 60:3:1-30:1:1 18995.9909 -876960.3 914952.3 1.0000000
## 90:3:1-30:1:1 12689.0832 -883267.2 908645.3 1.0000000
## 30:1:2-30:1:1 72485.7646 -823470.5 968442.0 1.0000000
## 60:1:2-30:1:1 116978.8640 -778977.4 1012935.1 1.0000000
## 90:1:2-30:1:1 247239.9961 -648716.3 1143196.3 0.9999982
## 30:2:2-30:1:1 124796.2534 -771160.0 1020752.5 1.0000000
## 60:2:2-30:1:1 149865.6225 -746090.6 1045821.9 1.0000000
## 90:2:2-30:1:1 -103590.4773 -999546.7 792365.8 1.0000000
## 30:3:2-30:1:1 190316.8216 -705639.4 1086273.1 1.0000000
## 60:3:2-30:1:1 217698.3815 -678257.9 1113654.6 0.9999999
## 90:3:2-30:1:1 365515.4957 -530440.8 1261471.8 0.9981736
## 30:1:3-30:1:1 9284.3615 -886671.9 905240.6 1.0000000
## 60:1:3-30:1:1 47472.6871 -848483.6 943428.9 1.0000000
## 90:1:3-30:1:1 102489.4194 -793466.8 998445.7 1.0000000
## 30:2:3-30:1:1 24733.0783 -871223.2 920689.3 1.0000000
## 60:2:3-30:1:1 -111088.9162 -1007045.2 784867.3 1.0000000
## 90:2:3-30:1:1 -71984.9693 -967941.2 823971.3 1.0000000
## 30:3:3-30:1:1 73385.2661 -822571.0 969341.5 1.0000000
## 60:3:3-30:1:1 99710.4304 -796245.8 995666.7 1.0000000
## 90:3:3-30:1:1 169658.9558 -726297.3 1065615.2 1.0000000
## 90:1:1-60:1:1 -18614.5117 -914570.8 877341.7 1.0000000
## 30:2:1-60:1:1 -71920.3346 -967876.6 824035.9 1.0000000
## 60:2:1-60:1:1 -52861.6176 -948817.9 843094.6 1.0000000
## 90:2:1-60:1:1 -39631.0517 -935587.3 856325.2 1.0000000
## 30:3:1-60:1:1 -9367.0956 -905323.4 886589.2 1.0000000
## 60:3:1-60:1:1 1507.0561 -894449.2 897463.3 1.0000000
## 90:3:1-60:1:1 -4799.8516 -900756.1 891156.4 1.0000000
## 30:1:2-60:1:1 54996.8297 -840959.4 950953.1 1.0000000
## 60:1:2-60:1:1 99489.9292 -796466.3 995446.2 1.0000000
## 90:1:2-60:1:1 229751.0612 -666205.2 1125707.3 0.9999996
## 30:2:2-60:1:1 107307.3185 -788648.9 1003263.6 1.0000000
## 60:2:2-60:1:1 132376.6876 -763579.6 1028332.9 1.0000000
## 90:2:2-60:1:1 -121079.4121 -1017035.7 774876.8 1.0000000
## 30:3:2-60:1:1 172827.8867 -723128.4 1068784.1 1.0000000
## 60:3:2-60:1:1 200209.4467 -695746.8 1096165.7 1.0000000
## 90:3:2-60:1:1 348026.5609 -547929.7 1243982.8 0.9991376
## 30:1:3-60:1:1 -8204.5734 -904160.8 887751.7 1.0000000
## 60:1:3-60:1:1 29983.7522 -865972.5 925940.0 1.0000000
## 90:1:3-60:1:1 85000.4845 -810955.8 980956.7 1.0000000
## 30:2:3-60:1:1 7244.1434 -888712.1 903200.4 1.0000000
## 60:2:3-60:1:1 -128577.8510 -1024534.1 767378.4 1.0000000
## 90:2:3-60:1:1 -89473.9042 -985430.2 806482.4 1.0000000
## 30:3:3-60:1:1 55896.3313 -840059.9 951852.6 1.0000000
## 60:3:3-60:1:1 82221.4955 -813734.8 978177.8 1.0000000
## 90:3:3-60:1:1 152170.0209 -743786.2 1048126.3 1.0000000
## 30:2:1-90:1:1 -53305.8229 -949262.1 842650.4 1.0000000
## 60:2:1-90:1:1 -34247.1059 -930203.4 861709.2 1.0000000
## 90:2:1-90:1:1 -21016.5400 -916972.8 874939.7 1.0000000
## 30:3:1-90:1:1 9247.4161 -886708.8 905203.7 1.0000000
## 60:3:1-90:1:1 20121.5678 -875834.7 916077.8 1.0000000
## 90:3:1-90:1:1 13814.6601 -882141.6 909770.9 1.0000000
## 30:1:2-90:1:1 73611.3414 -822344.9 969567.6 1.0000000
## 60:1:2-90:1:1 118104.4409 -777851.8 1014060.7 1.0000000
## 90:1:2-90:1:1 248365.5729 -647590.7 1144321.8 0.9999980
## 30:2:2-90:1:1 125921.8302 -770034.4 1021878.1 1.0000000
## 60:2:2-90:1:1 150991.1993 -744965.1 1046947.5 1.0000000
## 90:2:2-90:1:1 -102464.9004 -998421.2 793491.4 1.0000000
## 30:3:2-90:1:1 191442.3984 -704513.9 1087398.7 1.0000000
## 60:3:2-90:1:1 218823.9584 -677132.3 1114780.2 0.9999999
## 90:3:2-90:1:1 366641.0726 -529315.2 1262597.3 0.9980881
## 30:1:3-90:1:1 10409.9383 -885546.3 906366.2 1.0000000
## 60:1:3-90:1:1 48598.2639 -847358.0 944554.5 1.0000000
## 90:1:3-90:1:1 103614.9963 -792341.3 999571.3 1.0000000
## 30:2:3-90:1:1 25858.6551 -870097.6 921814.9 1.0000000
## 60:2:3-90:1:1 -109963.3393 -1005919.6 785992.9 1.0000000
## 90:2:3-90:1:1 -70859.3924 -966815.7 825096.9 1.0000000
## 30:3:3-90:1:1 74510.8430 -821445.4 970467.1 1.0000000
## 60:3:3-90:1:1 100836.0072 -795120.3 996792.3 1.0000000
## 90:3:3-90:1:1 170784.5326 -725171.7 1066740.8 1.0000000
## 60:2:1-30:2:1 19058.7170 -876897.5 915015.0 1.0000000
## 90:2:1-30:2:1 32289.2829 -863667.0 928245.5 1.0000000
## 30:3:1-30:2:1 62553.2390 -833403.0 958509.5 1.0000000
## 60:3:1-30:2:1 73427.3907 -822528.9 969383.7 1.0000000
## 90:3:1-30:2:1 67120.4830 -828835.8 963076.7 1.0000000
## 30:1:2-30:2:1 126917.1643 -769039.1 1022873.4 1.0000000
## 60:1:2-30:2:1 171410.2638 -724546.0 1067366.5 1.0000000
## 90:1:2-30:2:1 301671.3958 -594284.9 1197627.7 0.9999213
## 30:2:2-30:2:1 179227.6531 -716728.6 1075183.9 1.0000000
## 60:2:2-30:2:1 204297.0223 -691659.2 1100253.3 1.0000000
## 90:2:2-30:2:1 -49159.0775 -945115.3 846797.2 1.0000000
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## 30:2:3-60:1:3 -22739.6088 -918695.9 873216.7 1.0000000
## 60:2:3-60:1:3 -158561.6033 -1054517.9 737394.7 1.0000000
## 90:2:3-60:1:3 -119457.6564 -1015413.9 776498.6 1.0000000
## 30:3:3-60:1:3 25912.5790 -870043.7 921868.8 1.0000000
## 60:3:3-60:1:3 52237.7433 -843718.5 948194.0 1.0000000
## 90:3:3-60:1:3 122186.2687 -773770.0 1018142.5 1.0000000
## 30:2:3-90:1:3 -77756.3411 -973712.6 818199.9 1.0000000
## 60:2:3-90:1:3 -213578.3356 -1109534.6 682377.9 0.9999999
## 90:2:3-90:1:3 -174474.3887 -1070430.6 721481.9 1.0000000
## 30:3:3-90:1:3 -29104.1533 -925060.4 866852.1 1.0000000
## 60:3:3-90:1:3 -2778.9890 -898735.2 893177.3 1.0000000
## 90:3:3-90:1:3 67169.5364 -828786.7 963125.8 1.0000000
## 60:2:3-30:2:3 -135821.9945 -1031778.3 760134.3 1.0000000
## 90:2:3-30:2:3 -96718.0476 -992674.3 799238.2 1.0000000
## 30:3:3-30:2:3 48652.1878 -847304.1 944608.4 1.0000000
## 60:3:3-30:2:3 74977.3521 -820978.9 970933.6 1.0000000
## 90:3:3-30:2:3 144925.8775 -751030.4 1040882.1 1.0000000
## 90:2:3-60:2:3 39103.9469 -856852.3 935060.2 1.0000000
## 30:3:3-60:2:3 184474.1823 -711482.1 1080430.4 1.0000000
## 60:3:3-60:2:3 210799.3466 -685156.9 1106755.6 0.9999999
## 90:3:3-60:2:3 280747.8720 -615208.4 1176704.1 0.9999788
## 30:3:3-90:2:3 145370.2354 -750586.0 1041326.5 1.0000000
## 60:3:3-90:2:3 171695.3997 -724260.9 1067651.7 1.0000000
## 90:3:3-90:2:3 241643.9251 -654312.3 1137600.2 0.9999989
## 60:3:3-30:3:3 26325.1643 -869631.1 922281.4 1.0000000
## 90:3:3-30:3:3 96273.6897 -799682.6 992229.9 1.0000000
## 90:3:3-60:3:3 69948.5254 -826007.7 965904.8 1.0000000plot(tk)
SUPUESTOS
Prueba de normalidad mediante el estadístico de Shapiro-Wilk
Hipótesis
Ho: Los residuos siguen la distribución normal
Ha: Los residuos no siguen la distribución normal
shapiro.test(anova$residuals)##
## Shapiro-Wilk normality test
##
## data: anova$residuals
## W = 0.75512, p-value = 3.924e-12Prueba de Normalidad
qqnorm(anova$residuals)
qqline(anova$residuals)
Prueba de Homogeneidad de Varianzas
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somelibrary(carData)leveneTest(df$score~df$T)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.066 0.9361
## 105Prueba de comparaciones múltiples LSD
LSD<-LSD.test(anova,"score",group=T,console=T)##
## Study: anova ~ "score"
##
## LSD t Test for score
##
## Mean Square Error: 109437914178
##
## score, means and individual ( 95 %) CI
##
## score std r LCL UCL Min Max
## 12.65 12.65 NA 1 -658203.48 658228.8 12.65 12.65
## 14.75 14.75 NA 1 -658201.38 658230.9 14.75 14.75
## 15.05 15.05 NA 1 -658201.08 658231.2 15.05 15.05
## 15.2 15.20 0 2 -465413.89 465444.3 15.20 15.20
## 15.85 15.85 NA 1 -658200.28 658232.0 15.85 15.85
## 17.1 17.10 NA 1 -658199.03 658233.2 17.10 17.10
## 18.5 18.50 NA 1 -658197.63 658234.6 18.50 18.50
## 18.6 18.60 NA 1 -658197.53 658234.7 18.60 18.60
## 19.05 19.05 NA 1 -658197.08 658235.2 19.05 19.05
## 19.65 19.65 NA 1 -658196.48 658235.8 19.65 19.65
## 22.25 22.25 NA 1 -658193.88 658238.4 22.25 22.25
## 24.2 24.20 NA 1 -658191.93 658240.3 24.20 24.20
## 24.61 24.61 NA 1 -658191.52 658240.7 24.61 24.61
## 25.19 25.19 NA 1 -658190.94 658241.3 25.19 25.19
## 25.39 25.39 NA 1 -658190.74 658241.5 25.39 25.39
## 25.75 25.75 NA 1 -658190.38 658241.9 25.75 25.75
## 26.53 26.53 NA 1 -658189.60 658242.7 26.53 26.53
## 27.6 27.60 NA 1 -658188.53 658243.7 27.60 27.60
## 27.72 27.72 NA 1 -658188.41 658243.8 27.72 27.72
## 27.9 27.90 NA 1 -658188.23 658244.0 27.90 27.90
## 29.05 29.05 NA 1 -658187.08 658245.2 29.05 29.05
## 34.58 34.58 NA 1 -658181.55 658250.7 34.58 34.58
## 35.05 35.05 NA 1 -658181.08 658251.2 35.05 35.05
## 35.35 35.35 NA 1 -658180.78 658251.5 35.35 35.35
## 35.85 35.85 NA 1 -658180.28 658252.0 35.85 35.85
## 37.1 37.10 NA 1 -658179.03 658253.2 37.10 37.10
## 37.2 37.20 NA 1 -658178.93 658253.3 37.20 37.20
## 37.95 37.95 NA 1 -658178.18 658254.1 37.95 37.95
## 38.75 38.75 NA 1 -658177.38 658254.9 38.75 38.75
## 38.8 38.80 NA 1 -658177.33 658254.9 38.80 38.80
## 39.65 39.65 NA 1 -658176.48 658255.8 39.65 39.65
## 40.45 40.45 NA 1 -658175.68 658256.6 40.45 40.45
## 40.9 40.90 0 2 -465388.19 465470.0 40.90 40.90
## 42.3 42.30 NA 1 -658173.83 658258.4 42.30 42.30
## 42.85 42.85 NA 1 -658173.28 658259.0 42.85 42.85
## 45.43 45.43 NA 1 -658170.70 658261.6 45.43 45.43
## 46.35 46.35 NA 1 -658169.78 658262.5 46.35 46.35
## 47.3 47.30 NA 1 -658168.83 658263.4 47.30 47.30
## 47.55 47.55 NA 1 -658168.58 658263.7 47.55 47.55
## 48.8 48.80 NA 1 -658167.33 658264.9 48.80 48.80
## 50.05 50.05 NA 1 -658166.08 658266.2 50.05 50.05
## 51.47 51.47 NA 1 -658164.66 658267.6 51.47 51.47
## 54.09 54.09 NA 1 -658162.04 658270.2 54.09 54.09
## 57.19 57.19 NA 1 -658158.94 658273.3 57.19 57.19
## 57.2 57.20 NA 1 -658158.93 658273.3 57.20 57.20
## 59.25 59.25 NA 1 -658156.88 658275.4 59.25 59.25
## 61.3 61.30 NA 1 -658154.83 658277.4 61.30 61.30
## 67.25 67.25 NA 1 -658148.88 658283.4 67.25 67.25
## 69 69.00 NA 1 -658147.13 658285.1 69.00 69.00
## 94.85 94.85 NA 1 -658121.28 658311.0 94.85 94.85
## 116.2 116.20 NA 1 -658099.93 658332.3 116.20 116.20
## 137.35 137.35 NA 1 -658078.78 658353.5 137.35 137.35
## 2260 2260.00 NA 1 -655956.13 660476.1 2260.00 2260.00
## 7430 7430.00 NA 1 -650786.13 665646.1 7430.00 7430.00
## 8030 8030.00 NA 1 -650186.13 666246.1 8030.00 8030.00
## 8950 8950.00 NA 1 -649266.13 667166.1 8950.00 8950.00
## 13500 13500.00 NA 1 -644716.13 671716.1 13500.00 13500.00
## 55800 55800.00 NA 1 -602416.13 714016.1 55800.00 55800.00
## 69300 69300.00 NA 1 -588916.13 727516.1 69300.00 69300.00
## 80800 80800.00 NA 1 -577416.13 739016.1 80800.00 80800.00
## 95100 95100.00 NA 1 -563116.13 753316.1 95100.00 95100.00
## 95200 95200.00 NA 1 -563016.13 753416.1 95200.00 95200.00
## 96600 96600.00 NA 1 -561616.13 754816.1 96600.00 96600.00
## 96700 96700.00 NA 1 -561516.13 754916.1 96700.00 96700.00
## 99800 99800.00 NA 1 -558416.13 758016.1 99800.00 99800.00
## 118000 118000.00 NA 1 -540216.13 776216.1 118000.00 118000.00
## 129000 129000.00 NA 1 -529216.13 787216.1 129000.00 129000.00
## 131000 131000.00 0 2 -334429.09 596429.1 131000.00 131000.00
## 134000 134000.00 0 2 -331429.09 599429.1 134000.00 134000.00
## 137000 137000.00 NA 1 -521216.13 795216.1 137000.00 137000.00
## 141000 141000.00 NA 1 -517216.13 799216.1 141000.00 141000.00
## 144000 144000.00 NA 1 -514216.13 802216.1 144000.00 144000.00
## 146000 146000.00 NA 1 -512216.13 804216.1 146000.00 146000.00
## 150000 150000.00 NA 1 -508216.13 808216.1 150000.00 150000.00
## 155000 155000.00 NA 1 -503216.13 813216.1 155000.00 155000.00
## 157000 157000.00 NA 1 -501216.13 815216.1 157000.00 157000.00
## 159000 159000.00 NA 1 -499216.13 817216.1 159000.00 159000.00
## 162000 162000.00 NA 1 -496216.13 820216.1 162000.00 162000.00
## 166000 166000.00 NA 1 -492216.13 824216.1 166000.00 166000.00
## 185000 185000.00 NA 1 -473216.13 843216.1 185000.00 185000.00
## 198000 198000.00 NA 1 -460216.13 856216.1 198000.00 198000.00
## 206000 206000.00 NA 1 -452216.13 864216.1 206000.00 206000.00
## 207000 207000.00 NA 1 -451216.13 865216.1 207000.00 207000.00
## 214000 214000.00 NA 1 -444216.13 872216.1 214000.00 214000.00
## 245000 245000.00 NA 1 -413216.13 903216.1 245000.00 245000.00
## 273000 273000.00 NA 1 -385216.13 931216.1 273000.00 273000.00
## 287000 287000.00 NA 1 -371216.13 945216.1 287000.00 287000.00
## 316000 316000.00 0 2 -149429.09 781429.1 316000.00 316000.00
## 319000 319000.00 NA 1 -339216.13 977216.1 319000.00 319000.00
## 330000 330000.00 NA 1 -328216.13 988216.1 330000.00 330000.00
## 378000 378000.00 NA 1 -280216.13 1036216.1 378000.00 378000.00
## 447000 447000.00 0 2 -18429.09 912429.1 447000.00 447000.00
## 453000 453000.00 NA 1 -205216.13 1111216.1 453000.00 453000.00
## 509000 509000.00 NA 1 -149216.13 1167216.1 509000.00 509000.00
## 536000 536000.00 NA 1 -122216.13 1194216.1 536000.00 536000.00
## 568000 568000.00 NA 1 -90216.13 1226216.1 568000.00 568000.00
## 6e+05 600000.00 NA 1 -58216.13 1258216.1 600000.00 600000.00
## 637000 637000.00 NA 1 -21216.13 1295216.1 637000.00 637000.00
## 673000 673000.00 NA 1 14783.87 1331216.1 673000.00 673000.00
## 1740000 1740000.00 NA 1 1081783.87 2398216.1 1740000.00 1740000.00
## 1780000 1780000.00 NA 1 1121783.87 2438216.1 1780000.00 1780000.00
## 2050000 2050000.00 NA 1 1391783.87 2708216.1 2050000.00 2050000.00
##
## Alpha: 0.05 ; DF Error: 81
## Critical Value of t: 1.989686
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## score groups
## 2050000 2050000.00 a
## 1780000 1780000.00 a
## 1740000 1740000.00 a
## 673000 673000.00 b
## 637000 637000.00 b
## 6e+05 600000.00 b
## 568000 568000.00 b
## 536000 536000.00 b
## 509000 509000.00 b
## 453000 453000.00 b
## 447000 447000.00 b
## 378000 378000.00 b
## 330000 330000.00 b
## 319000 319000.00 b
## 316000 316000.00 b
## 287000 287000.00 b
## 273000 273000.00 b
## 245000 245000.00 b
## 214000 214000.00 b
## 207000 207000.00 b
## 206000 206000.00 b
## 198000 198000.00 b
## 185000 185000.00 b
## 166000 166000.00 b
## 162000 162000.00 b
## 159000 159000.00 b
## 157000 157000.00 b
## 155000 155000.00 b
## 150000 150000.00 b
## 146000 146000.00 b
## 144000 144000.00 b
## 141000 141000.00 b
## 137000 137000.00 b
## 134000 134000.00 b
## 131000 131000.00 b
## 129000 129000.00 b
## 118000 118000.00 b
## 99800 99800.00 b
## 96700 96700.00 b
## 96600 96600.00 b
## 95200 95200.00 b
## 95100 95100.00 b
## 80800 80800.00 b
## 69300 69300.00 b
## 55800 55800.00 b
## 13500 13500.00 b
## 8950 8950.00 b
## 8030 8030.00 b
## 7430 7430.00 b
## 2260 2260.00 b
## 137.35 137.35 b
## 116.2 116.20 b
## 94.85 94.85 b
## 69 69.00 b
## 67.25 67.25 b
## 61.3 61.30 b
## 59.25 59.25 b
## 57.2 57.20 b
## 57.19 57.19 b
## 54.09 54.09 b
## 51.47 51.47 b
## 50.05 50.05 b
## 48.8 48.80 b
## 47.55 47.55 b
## 47.3 47.30 b
## 46.35 46.35 b
## 45.43 45.43 b
## 42.85 42.85 b
## 42.3 42.30 b
## 40.9 40.90 b
## 40.45 40.45 b
## 39.65 39.65 b
## 38.8 38.80 b
## 38.75 38.75 b
## 37.95 37.95 b
## 37.2 37.20 b
## 37.1 37.10 b
## 35.85 35.85 b
## 35.35 35.35 b
## 35.05 35.05 b
## 34.58 34.58 b
## 29.05 29.05 b
## 27.9 27.90 b
## 27.72 27.72 b
## 27.6 27.60 b
## 26.53 26.53 b
## 25.75 25.75 b
## 25.39 25.39 b
## 25.19 25.19 b
## 24.61 24.61 b
## 24.2 24.20 b
## 22.25 22.25 b
## 19.65 19.65 b
## 19.05 19.05 b
## 18.6 18.60 b
## 18.5 18.50 b
## 17.1 17.10 b
## 15.85 15.85 b
## 15.2 15.20 b
## 15.05 15.05 b
## 14.75 14.75 b
## 12.65 12.65 bbar.group(x=LSD$groups,horiz=T,col="red",
xlab="micotoxinas",ylab="tiempos (T)",main="Micotoxinas vs Tiempos")
#Prueba de comparaciones múltiples Duncan
Duncan<-duncan.test(anova,"score",group=T,console=T,alpha=0.01)##
## Study: anova ~ "score"
##
## Duncan's new multiple range test
## for score
##
## Mean Square Error: 109437914178
##
## score, means
##
## score std r Min Max
## 12.65 12.65 NA 1 12.65 12.65
## 14.75 14.75 NA 1 14.75 14.75
## 15.05 15.05 NA 1 15.05 15.05
## 15.2 15.20 0 2 15.20 15.20
## 15.85 15.85 NA 1 15.85 15.85
## 17.1 17.10 NA 1 17.10 17.10
## 18.5 18.50 NA 1 18.50 18.50
## 18.6 18.60 NA 1 18.60 18.60
## 19.05 19.05 NA 1 19.05 19.05
## 19.65 19.65 NA 1 19.65 19.65
## 22.25 22.25 NA 1 22.25 22.25
## 24.2 24.20 NA 1 24.20 24.20
## 24.61 24.61 NA 1 24.61 24.61
## 25.19 25.19 NA 1 25.19 25.19
## 25.39 25.39 NA 1 25.39 25.39
## 25.75 25.75 NA 1 25.75 25.75
## 26.53 26.53 NA 1 26.53 26.53
## 27.6 27.60 NA 1 27.60 27.60
## 27.72 27.72 NA 1 27.72 27.72
## 27.9 27.90 NA 1 27.90 27.90
## 29.05 29.05 NA 1 29.05 29.05
## 34.58 34.58 NA 1 34.58 34.58
## 35.05 35.05 NA 1 35.05 35.05
## 35.35 35.35 NA 1 35.35 35.35
## 35.85 35.85 NA 1 35.85 35.85
## 37.1 37.10 NA 1 37.10 37.10
## 37.2 37.20 NA 1 37.20 37.20
## 37.95 37.95 NA 1 37.95 37.95
## 38.75 38.75 NA 1 38.75 38.75
## 38.8 38.80 NA 1 38.80 38.80
## 39.65 39.65 NA 1 39.65 39.65
## 40.45 40.45 NA 1 40.45 40.45
## 40.9 40.90 0 2 40.90 40.90
## 42.3 42.30 NA 1 42.30 42.30
## 42.85 42.85 NA 1 42.85 42.85
## 45.43 45.43 NA 1 45.43 45.43
## 46.35 46.35 NA 1 46.35 46.35
## 47.3 47.30 NA 1 47.30 47.30
## 47.55 47.55 NA 1 47.55 47.55
## 48.8 48.80 NA 1 48.80 48.80
## 50.05 50.05 NA 1 50.05 50.05
## 51.47 51.47 NA 1 51.47 51.47
## 54.09 54.09 NA 1 54.09 54.09
## 57.19 57.19 NA 1 57.19 57.19
## 57.2 57.20 NA 1 57.20 57.20
## 59.25 59.25 NA 1 59.25 59.25
## 61.3 61.30 NA 1 61.30 61.30
## 67.25 67.25 NA 1 67.25 67.25
## 69 69.00 NA 1 69.00 69.00
## 94.85 94.85 NA 1 94.85 94.85
## 116.2 116.20 NA 1 116.20 116.20
## 137.35 137.35 NA 1 137.35 137.35
## 2260 2260.00 NA 1 2260.00 2260.00
## 7430 7430.00 NA 1 7430.00 7430.00
## 8030 8030.00 NA 1 8030.00 8030.00
## 8950 8950.00 NA 1 8950.00 8950.00
## 13500 13500.00 NA 1 13500.00 13500.00
## 55800 55800.00 NA 1 55800.00 55800.00
## 69300 69300.00 NA 1 69300.00 69300.00
## 80800 80800.00 NA 1 80800.00 80800.00
## 95100 95100.00 NA 1 95100.00 95100.00
## 95200 95200.00 NA 1 95200.00 95200.00
## 96600 96600.00 NA 1 96600.00 96600.00
## 96700 96700.00 NA 1 96700.00 96700.00
## 99800 99800.00 NA 1 99800.00 99800.00
## 118000 118000.00 NA 1 118000.00 118000.00
## 129000 129000.00 NA 1 129000.00 129000.00
## 131000 131000.00 0 2 131000.00 131000.00
## 134000 134000.00 0 2 134000.00 134000.00
## 137000 137000.00 NA 1 137000.00 137000.00
## 141000 141000.00 NA 1 141000.00 141000.00
## 144000 144000.00 NA 1 144000.00 144000.00
## 146000 146000.00 NA 1 146000.00 146000.00
## 150000 150000.00 NA 1 150000.00 150000.00
## 155000 155000.00 NA 1 155000.00 155000.00
## 157000 157000.00 NA 1 157000.00 157000.00
## 159000 159000.00 NA 1 159000.00 159000.00
## 162000 162000.00 NA 1 162000.00 162000.00
## 166000 166000.00 NA 1 166000.00 166000.00
## 185000 185000.00 NA 1 185000.00 185000.00
## 198000 198000.00 NA 1 198000.00 198000.00
## 206000 206000.00 NA 1 206000.00 206000.00
## 207000 207000.00 NA 1 207000.00 207000.00
## 214000 214000.00 NA 1 214000.00 214000.00
## 245000 245000.00 NA 1 245000.00 245000.00
## 273000 273000.00 NA 1 273000.00 273000.00
## 287000 287000.00 NA 1 287000.00 287000.00
## 316000 316000.00 0 2 316000.00 316000.00
## 319000 319000.00 NA 1 319000.00 319000.00
## 330000 330000.00 NA 1 330000.00 330000.00
## 378000 378000.00 NA 1 378000.00 378000.00
## 447000 447000.00 0 2 447000.00 447000.00
## 453000 453000.00 NA 1 453000.00 453000.00
## 509000 509000.00 NA 1 509000.00 509000.00
## 536000 536000.00 NA 1 536000.00 536000.00
## 568000 568000.00 NA 1 568000.00 568000.00
## 6e+05 600000.00 NA 1 600000.00 600000.00
## 637000 637000.00 NA 1 637000.00 637000.00
## 673000 673000.00 NA 1 673000.00 673000.00
## 1740000 1740000.00 NA 1 1740000.00 1740000.00
## 1780000 1780000.00 NA 1 1780000.00 1780000.00
## 2050000 2050000.00 NA 1 2050000.00 2050000.00
##
## Groups according to probability of means differences and alpha level( 0.01 )
##
## Means with the same letter are not significantly different.
##
## score groups
## 2050000 2050000.00 a
## 1780000 1780000.00 ab
## 1740000 1740000.00 abc
## 673000 673000.00 bcd
## 637000 637000.00 bcd
## 6e+05 600000.00 bcd
## 568000 568000.00 bcd
## 536000 536000.00 bcd
## 509000 509000.00 bcd
## 453000 453000.00 bcd
## 447000 447000.00 bcd
## 378000 378000.00 bcd
## 330000 330000.00 cd
## 319000 319000.00 d
## 316000 316000.00 d
## 287000 287000.00 d
## 273000 273000.00 d
## 245000 245000.00 d
## 214000 214000.00 d
## 207000 207000.00 d
## 206000 206000.00 d
## 198000 198000.00 d
## 185000 185000.00 d
## 166000 166000.00 d
## 162000 162000.00 d
## 159000 159000.00 d
## 157000 157000.00 d
## 155000 155000.00 d
## 150000 150000.00 d
## 146000 146000.00 d
## 144000 144000.00 d
## 141000 141000.00 d
## 137000 137000.00 d
## 134000 134000.00 d
## 131000 131000.00 d
## 129000 129000.00 d
## 118000 118000.00 d
## 99800 99800.00 d
## 96700 96700.00 d
## 96600 96600.00 d
## 95200 95200.00 d
## 95100 95100.00 d
## 80800 80800.00 d
## 69300 69300.00 d
## 55800 55800.00 d
## 13500 13500.00 d
## 8950 8950.00 d
## 8030 8030.00 d
## 7430 7430.00 d
## 2260 2260.00 d
## 137.35 137.35 d
## 116.2 116.20 d
## 94.85 94.85 d
## 69 69.00 d
## 67.25 67.25 d
## 61.3 61.30 d
## 59.25 59.25 d
## 57.2 57.20 d
## 57.19 57.19 d
## 54.09 54.09 d
## 51.47 51.47 d
## 50.05 50.05 d
## 48.8 48.80 d
## 47.55 47.55 d
## 47.3 47.30 d
## 46.35 46.35 d
## 45.43 45.43 d
## 42.85 42.85 d
## 42.3 42.30 d
## 40.9 40.90 d
## 40.45 40.45 d
## 39.65 39.65 d
## 38.8 38.80 d
## 38.75 38.75 d
## 37.95 37.95 d
## 37.2 37.20 d
## 37.1 37.10 d
## 35.85 35.85 d
## 35.35 35.35 d
## 35.05 35.05 d
## 34.58 34.58 d
## 29.05 29.05 d
## 27.9 27.90 d
## 27.72 27.72 d
## 27.6 27.60 d
## 26.53 26.53 d
## 25.75 25.75 d
## 25.39 25.39 d
## 25.19 25.19 d
## 24.61 24.61 d
## 24.2 24.20 d
## 22.25 22.25 d
## 19.65 19.65 d
## 19.05 19.05 d
## 18.6 18.60 d
## 18.5 18.50 d
## 17.1 17.10 d
## 15.85 15.85 d
## 15.2 15.20 d
## 15.05 15.05 d
## 14.75 14.75 d
## 12.65 12.65 dbar.group(x=Duncan$groups,horiz=T,col="blue",
xlab="micotoxinas",ylab="T",main="Micotoxinas vs Tiempos")
CONCLUSION
En las pruebas no se encontró diferencia significativa para Arpergillus y Aflatoxinas
Los resultados fueron consistentes con los obtenidos por los investigaodres, presentaron homocedasticidad de las varianzas para la ubicación en el silo para los niveles de Aspergillus y aflatoxinas (Test de Levene P<0.05) también se observó una distribución normal (Prueba de Shapiro) mientras que ellos usaron la prueba de Kolmogorov y se obtuvo igual resultados estadístico por lo tanto se puede inferir que los datos son confiables y permiten un análisis razonable. En cuanto a los valores de Aspergillus y aflatoxinas no se encontró diferencia estadística (P<0.05) frente al tiempo de monitoreo (30, 60 y 90 días) sin embargo en relación a la ubicación en el silo (lateral oeste, media y lateral este) donde se tomó la muestra, si existió significancia.