setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("todos_phys.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("T3", "T1", "T2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$diam2<-as.factor(datos$diam2)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.2
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.1 ✔ forcats 1.0.0
## Warning: package 'tibble' was built under R version 4.1.2
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'purrr' was built under R version 4.1.2
## Warning: package 'stringr' was built under R version 4.1.2
## Warning: package 'forcats' was built under R version 4.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::arrange() masks plyr::arrange()
## ✖ purrr::compact() masks plyr::compact()
## ✖ dplyr::count() masks plyr::count()
## ✖ dplyr::desc() masks plyr::desc()
## ✖ dplyr::failwith() masks plyr::failwith()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::id() masks plyr::id()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::mutate() masks plyr::mutate()
## ✖ dplyr::rename() masks plyr::rename()
## ✖ dplyr::summarise() masks plyr::summarise()
## ✖ dplyr::summarize() masks plyr::summarize()
library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
##
## mutate
library(rstatix)
##
## Attaching package: 'rstatix'
## The following objects are masked from 'package:plyr':
##
## desc, mutate
## The following object is masked from 'package:stats':
##
## filter
library(emmeans)
##Summary statistics
summ<-datos %>%
group_by(curva, gen, diam2) %>%
get_summary_stats(acidez.grano, type = "mean_sd")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 63 × 7
## curva diam2 gen variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 T3 0 CCN51 acidez.grano 3 0.393 0.025
## 2 T3 1 CCN51 acidez.grano 3 0.363 0.045
## 3 T3 2 CCN51 acidez.grano 3 0.3 0
## 4 T3 3 CCN51 acidez.grano 3 0.577 0.07
## 5 T3 4 CCN51 acidez.grano 3 0.567 0.103
## 6 T3 5 CCN51 acidez.grano 3 0.643 0.046
## 7 T3 6 CCN51 acidez.grano 3 0.32 0.165
## 8 T3 0 ICS95 acidez.grano 3 0.37 0.036
## 9 T3 1 ICS95 acidez.grano 3 0.333 0.04
## 10 T3 2 ICS95 acidez.grano 3 0.457 0.08
## 11 T3 3 ICS95 acidez.grano 3 0.927 0.081
## 12 T3 4 ICS95 acidez.grano 3 0.8 0.436
## 13 T3 5 ICS95 acidez.grano 3 0.623 0.491
## 14 T3 6 ICS95 acidez.grano 3 0.64 0.078
## 15 T3 0 TCS01 acidez.grano 3 0.377 0.086
## 16 T3 1 TCS01 acidez.grano 3 0.387 0.085
## 17 T3 2 TCS01 acidez.grano 3 0.457 0.025
## 18 T3 3 TCS01 acidez.grano 3 1.55 0.081
## 19 T3 4 TCS01 acidez.grano 3 1.82 0.307
## 20 T3 5 TCS01 acidez.grano 3 1.87 0.1
## 21 T3 6 TCS01 acidez.grano 3 1.31 0.258
## 22 T1 0 CCN51 acidez.grano 3 0.29 0.036
## 23 T1 1 CCN51 acidez.grano 3 0.23 0.07
## 24 T1 2 CCN51 acidez.grano 3 0.78 0.036
## 25 T1 3 CCN51 acidez.grano 3 0.643 0.215
## 26 T1 4 CCN51 acidez.grano 3 0.953 0.381
## 27 T1 5 CCN51 acidez.grano 3 0.713 0.231
## 28 T1 6 CCN51 acidez.grano 3 0.727 0.176
## 29 T1 0 ICS95 acidez.grano 3 0.51 0.085
## 30 T1 1 ICS95 acidez.grano 3 0.26 0.02
## 31 T1 2 ICS95 acidez.grano 3 0.82 0.036
## 32 T1 3 ICS95 acidez.grano 3 0.667 0.154
## 33 T1 4 ICS95 acidez.grano 3 0.793 0.126
## 34 T1 5 ICS95 acidez.grano 3 0.447 0.031
## 35 T1 6 ICS95 acidez.grano 3 0.507 0.076
## 36 T1 0 TCS01 acidez.grano 3 0.467 0.049
## 37 T1 1 TCS01 acidez.grano 3 0.36 0.072
## 38 T1 2 TCS01 acidez.grano 3 0.53 0.044
## 39 T1 3 TCS01 acidez.grano 3 0.603 0.029
## 40 T1 4 TCS01 acidez.grano 3 0.493 0.055
## 41 T1 5 TCS01 acidez.grano 3 0.63 0.035
## 42 T1 6 TCS01 acidez.grano 3 0.49 0.07
## 43 T2 0 CCN51 acidez.grano 3 0.376 0.01
## 44 T2 1 CCN51 acidez.grano 3 0.19 0.034
## 45 T2 2 CCN51 acidez.grano 3 0.31 0.096
## 46 T2 3 CCN51 acidez.grano 3 0.931 0.033
## 47 T2 4 CCN51 acidez.grano 3 1.04 0.159
## 48 T2 5 CCN51 acidez.grano 3 0.703 0.133
## 49 T2 6 CCN51 acidez.grano 3 0.513 0.062
## 50 T2 0 ICS95 acidez.grano 3 0.34 0.056
## 51 T2 1 ICS95 acidez.grano 3 0.188 0.007
## 52 T2 2 ICS95 acidez.grano 3 0.2 0.054
## 53 T2 3 ICS95 acidez.grano 3 0.388 0.167
## 54 T2 4 ICS95 acidez.grano 3 0.493 0.307
## 55 T2 5 ICS95 acidez.grano 3 0.457 0.203
## 56 T2 6 ICS95 acidez.grano 3 0.336 0.126
## 57 T2 0 TCS01 acidez.grano 3 0.226 0.044
## 58 T2 1 TCS01 acidez.grano 3 0.19 0.032
## 59 T2 2 TCS01 acidez.grano 3 0.33 0.215
## 60 T2 3 TCS01 acidez.grano 3 0.335 0.063
## 61 T2 4 TCS01 acidez.grano 3 0.611 0.173
## 62 T2 5 TCS01 acidez.grano 3 0.633 0.261
## 63 T2 6 TCS01 acidez.grano 3 0.451 0.185
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "acidez.grano",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(acidez.grano)
## [1] curva diam2 gen time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm<-datos %>%
group_by(curva, gen, diam2) #%>%
#shapiro_test(acidez.grano)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 189 × 10
## curva diam2 time.let gen muestra ph.testa acidez.testa ph.grano
## <fct> <fct> <chr> <fct> <fct> <dbl> <dbl> <dbl>
## 1 T3 0 cero CCN51 14 3.19 0.79 5.58
## 2 T3 0 cero CCN51 25 3.18 0.93 5.63
## 3 T3 0 cero CCN51 36 3.20 0.67 5.59
## 4 T3 0 cero ICS95 14 3.24 1.07 5.66
## 5 T3 0 cero ICS95 25 3.24 0.85 5.60
## 6 T3 0 cero ICS95 36 3.23 0.93 5.55
## 7 T3 0 cero TCS01 14 3.69 0.69 5.67
## 8 T3 0 cero TCS01 25 3.58 0.69 5.70
## 9 T3 0 cero TCS01 36 3.60 0.8 5.70
## 10 T1 0 cero CCN51 14 3.06 0.58 5.49
## 11 T1 0 cero CCN51 25 2.92 0.66 5.38
## 12 T1 0 cero CCN51 36 2.89 0.67 5.36
## 13 T1 0 cero ICS95 14 2.96 0.61 5.04
## 14 T1 0 cero ICS95 25 2.82 0.71 4.94
## 15 T1 0 cero ICS95 36 2.78 0.77 5.15
## 16 T1 0 cero TCS01 14 3.45 0.6 5.69
## 17 T1 0 cero TCS01 25 3.53 0.59 5.42
## 18 T1 0 cero TCS01 36 3.42 0.57 5.51
## 19 T2 0 cero CCN51 14 2.38 0.912 5.31
## 20 T2 0 cero CCN51 25 2.57 0.887 5.37
## 21 T2 0 cero CCN51 36 2.25 0.845 5.32
## 22 T2 0 cero ICS95 14 2.80 0.792 5.37
## 23 T2 0 cero ICS95 25 3.72 0.695 6.52
## 24 T2 0 cero ICS95 36 3.76 0.641 6.45
## 25 T2 0 cero TCS01 14 3.63 0.659 6.12
## 26 T2 0 cero TCS01 25 3.22 0.719 5.86
## 27 T2 0 cero TCS01 36 3.07 0.808 5.42
## 28 T3 1 uno CCN51 14 3.19 0.83 5.60
## 29 T3 1 uno CCN51 25 3.12 0.92 5.52
## 30 T3 1 uno CCN51 36 3.09 1.12 5.46
## 31 T3 1 uno ICS95 14 3.11 0.9 5.45
## 32 T3 1 uno ICS95 25 3.65 1.1 5.48
## 33 T3 1 uno ICS95 36 3.03 1.18 5.57
## 34 T3 1 uno TCS01 14 3.29 0.76 5.36
## 35 T3 1 uno TCS01 25 3.44 0.82 5.55
## 36 T3 1 uno TCS01 36 3.23 1.1 4.79
## 37 T1 1 uno CCN51 14 2.79 1.14 5.73
## 38 T1 1 uno CCN51 25 2.85 0.87 5.55
## 39 T1 1 uno CCN51 36 2.67 1.44 5.44
## 40 T1 1 uno ICS95 14 2.45 1.27 5.39
## 41 T1 1 uno ICS95 25 2.40 1.14 5.32
## 42 T1 1 uno ICS95 36 2.25 1.14 5.35
## 43 T1 1 uno TCS01 14 2.49 0.94 5.19
## 44 T1 1 uno TCS01 25 2.4 1.06 5.64
## 45 T1 1 uno TCS01 36 3.05 0.82 5.01
## 46 T2 1 uno CCN51 14 3.17 1.16 6.55
## 47 T2 1 uno CCN51 25 3.12 1.14 6.48
## 48 T2 1 uno CCN51 36 3.09 1.34 6.02
## 49 T2 1 uno ICS95 14 2.90 1.26 6.6
## 50 T2 1 uno ICS95 25 2.85 1.31 6.88
## 51 T2 1 uno ICS95 36 2.98 1.28 6.74
## 52 T2 1 uno TCS01 14 3.34 0.9 6.57
## 53 T2 1 uno TCS01 25 2.94 1.40 5.97
## 54 T2 1 uno TCS01 36 3.09 1.07 6.40
## 55 T3 2 dos CCN51 14 3.84 0.24 5.36
## 56 T3 2 dos CCN51 25 4.17 0.2 5.46
## 57 T3 2 dos CCN51 36 4.06 0.22 5.54
## 58 T3 2 dos ICS95 14 3.35 0.28 5.16
## 59 T3 2 dos ICS95 25 3.48 0.49 4.51
## 60 T3 2 dos ICS95 36 3.64 0.34 5.13
## 61 T3 2 dos TCS01 14 3.67 0.79 5.17
## 62 T3 2 dos TCS01 25 3.66 0.65 4.46
## 63 T3 2 dos TCS01 36 3.70 0.89 4.67
## 64 T1 2 dos CCN51 14 2.56 0.99 4.41
## 65 T1 2 dos CCN51 25 2.57 1.1 4.15
## 66 T1 2 dos CCN51 36 2.53 0.95 4.36
## 67 T1 2 dos ICS95 14 1.82 1.56 4.35
## 68 T1 2 dos ICS95 25 1.87 1.34 4.05
## 69 T1 2 dos ICS95 36 1.98 0.89 4.21
## 70 T1 2 dos TCS01 14 3.59 0.72 4.96
## 71 T1 2 dos TCS01 25 3.19 0.89 4.91
## 72 T1 2 dos TCS01 36 3.37 0.49 5.10
## 73 T2 2 dos CCN51 14 3.13 1.95 5.72
## 74 T2 2 dos CCN51 25 3.07 2.27 5.32
## 75 T2 2 dos CCN51 36 3.10 2.34 5.20
## 76 T2 2 dos ICS95 14 2.91 1.7 5.99
## 77 T2 2 dos ICS95 25 2.94 1.33 6.12
## 78 T2 2 dos ICS95 36 3.01 1.26 6.04
## 79 T2 2 dos TCS01 14 3.62 0.779 6.15
## 80 T2 2 dos TCS01 25 3.39 1.19 4.39
## 81 T2 2 dos TCS01 36 3.87 0.647 5.81
## 82 T3 3 tres CCN51 14 3.97 0.31 4.69
## 83 T3 3 tres CCN51 25 4.31 0.22 4.84
## 84 T3 3 tres CCN51 36 3.92 0.33 4.56
## 85 T3 3 tres ICS95 14 3.11 0.83 3.77
## 86 T3 3 tres ICS95 25 3.28 0.47 3.91
## 87 T3 3 tres ICS95 36 2.71 1.68 3.18
## 88 T3 3 tres TCS01 14 3.54 2 3.78
## 89 T3 3 tres TCS01 25 3.51 2.07 3.62
## 90 T3 3 tres TCS01 36 3.48 2.33 3.56
## 91 T1 3 tres CCN51 14 3.28 1.58 4.68
## 92 T1 3 tres CCN51 25 3.40 1.03 5.01
## 93 T1 3 tres CCN51 36 3.20 1.8 4.08
## 94 T1 3 tres ICS95 14 2.53 1.14 4.95
## 95 T1 3 tres ICS95 25 2.41 1.11 4.57
## 96 T1 3 tres ICS95 36 2.34 1.19 4.45
## 97 T1 3 tres TCS01 14 4.47 0.41 4.79
## 98 T1 3 tres TCS01 25 4.18 0.48 4.82
## 99 T1 3 tres TCS01 36 3.84 0.59 4.89
## 100 T2 3 tres CCN51 14 3.41 3.26 4.64
## 101 T2 3 tres CCN51 25 3.60 3.17 4.40
## 102 T2 3 tres CCN51 36 3.61 2.46 4.50
## 103 T2 3 tres ICS95 14 3.17 1.99 4.99
## 104 T2 3 tres ICS95 25 3.3 1.69 6.38
## 105 T2 3 tres ICS95 36 3.20 1.80 5.97
## 106 T2 3 tres TCS01 14 4.59 0.575 5.77
## 107 T2 3 tres TCS01 25 3.86 1.17 5.60
## 108 T2 3 tres TCS01 36 4.17 0.899 6.01
## 109 T3 4 cuatro CCN51 14 3.66 0.39 4.44
## 110 T3 4 cuatro CCN51 25 4.12 0.34 4.22
## 111 T3 4 cuatro CCN51 36 3.90 0.47 4.26
## 112 T3 4 cuatro ICS95 14 3.83 0.74 4.19
## 113 T3 4 cuatro ICS95 25 3.86 0.62 4.17
## 114 T3 4 cuatro ICS95 36 3.51 1.26 3.56
## 115 T3 4 cuatro TCS01 14 3.70 2.22 3.79
## 116 T3 4 cuatro TCS01 25 3.65 1.89 3.70
## 117 T3 4 cuatro TCS01 36 3.56 2.27 3.53
## 118 T1 4 cuatro CCN51 14 3.46 1.29 3.81
## 119 T1 4 cuatro CCN51 25 5.50 0.43 4.95
## 120 T1 4 cuatro CCN51 36 4.92 0.7 3.74
## 121 T1 4 cuatro ICS95 14 2.67 1.28 4.36
## 122 T1 4 cuatro ICS95 25 2.51 1.21 4.08
## 123 T1 4 cuatro ICS95 36 2.43 1.23 4.14
## 124 T1 4 cuatro TCS01 14 5.85 0.41 5.32
## 125 T1 4 cuatro TCS01 25 5.47 0.46 3.15
## 126 T1 4 cuatro TCS01 36 5.25 0.36 4.88
## 127 T2 4 cuatro CCN51 14 3.68 2.1 4.46
## 128 T2 4 cuatro CCN51 25 3.71 1.93 4.47
## 129 T2 4 cuatro CCN51 36 3.78 2.10 4.40
## 130 T2 4 cuatro ICS95 14 3.31 2.04 4.71
## 131 T2 4 cuatro ICS95 25 3.46 1.08 5.98
## 132 T2 4 cuatro ICS95 36 3.31 1.53 5.32
## 133 T2 4 cuatro TCS01 14 4.08 1.71 4.82
## 134 T2 4 cuatro TCS01 25 4.20 0.93 4.58
## 135 T2 4 cuatro TCS01 36 5.22 0.413 5.44
## 136 T3 5 cinco CCN51 14 5.18 0.4 4.56
## 137 T3 5 cinco CCN51 25 5.23 0.34 4.26
## 138 T3 5 cinco CCN51 36 5.21 0.35 4.28
## 139 T3 5 cinco ICS95 14 5.95 0.24 5.04
## 140 T3 5 cinco ICS95 25 4.35 0.61 5.33
## 141 T3 5 cinco ICS95 36 4.72 0.48 3.62
## 142 T3 5 cinco TCS01 14 3.26 1.82 3.02
## 143 T3 5 cinco TCS01 25 3.11 1.82 2.67
## 144 T3 5 cinco TCS01 36 3.01 2.05 2.78
## 145 T1 5 cinco CCN51 14 5.46 0.47 4.50
## 146 T1 5 cinco CCN51 25 6.29 0.36 5.49
## 147 T1 5 cinco CCN51 36 5.00 0.78 5.12
## 148 T1 5 cinco ICS95 14 4.42 0.58 5.33
## 149 T1 5 cinco ICS95 25 3.79 0.87 5.44
## 150 T1 5 cinco ICS95 36 4.45 0.49 5.42
## 151 T1 5 cinco TCS01 14 6.52 0.21 4.78
## 152 T1 5 cinco TCS01 25 5.59 0.75 4.96
## 153 T1 5 cinco TCS01 36 6.60 0.24 5.49
## 154 T2 5 cinco CCN51 14 4.32 1.04 5.03
## 155 T2 5 cinco CCN51 25 3.94 1.74 4.7
## 156 T2 5 cinco CCN51 36 3.96 1.59 4.73
## 157 T2 5 cinco ICS95 14 3.72 1.30 5.01
## 158 T2 5 cinco ICS95 25 3.68 1.23 5.88
## 159 T2 5 cinco ICS95 36 3.68 1.25 5.66
## 160 T2 5 cinco TCS01 14 7.00 0.144 5.73
## 161 T2 5 cinco TCS01 25 4.89 0.67 4.89
## 162 T2 5 cinco TCS01 36 5.32 0.605 4.94
## 163 T3 6 seis CCN51 14 6.52 0.12 5.51
## 164 T3 6 seis CCN51 25 6.66 0.12 4.16
## 165 T3 6 seis CCN51 36 6.79 0.11 5.47
## 166 T3 6 seis ICS95 14 6.74 0.17 5.15
## 167 T3 6 seis ICS95 25 6.58 0.21 4.82
## 168 T3 6 seis ICS95 36 6.53 0.13 4.36
## 169 T3 6 seis TCS01 14 4.57 1.11 4.22
## 170 T3 6 seis TCS01 25 4.26 1.07 3.85
## 171 T3 6 seis TCS01 36 4.45 0.92 4.08
## 172 T1 6 seis CCN51 14 5.38 0.96 4.96
## 173 T1 6 seis CCN51 25 5.62 0.48 4.45
## 174 T1 6 seis CCN51 36 5.60 0.29 5.62
## 175 T1 6 seis ICS95 14 5.76 0.03 5.92
## 176 T1 6 seis ICS95 25 4.60 0.61 5.21
## 177 T1 6 seis ICS95 36 4.75 0.56 5.38
## 178 T1 6 seis TCS01 14 7.05 0.12 5.73
## 179 T1 6 seis TCS01 25 6.97 0.2 5.77
## 180 T1 6 seis TCS01 36 6.40 0.44 5.89
## 181 T2 6 seis CCN51 14 5.85 0.672 5.39
## 182 T2 6 seis CCN51 25 5.02 0.714 5.00
## 183 T2 6 seis CCN51 36 5.02 0.846 5.27
## 184 T2 6 seis ICS95 14 4.52 0.899 5.44
## 185 T2 6 seis ICS95 25 4.61 0.629 6.31
## 186 T2 6 seis ICS95 36 4.87 0.551 5.87
## 187 T2 6 seis TCS01 14 7.63 0.126 6.40
## 188 T2 6 seis TCS01 25 5.51 0.534 5.14
## 189 T2 6 seis TCS01 36 6.47 0.431 5.32
## # ℹ 2 more variables: acidez.grano <dbl>, id <fct>
##Create QQ plot for each cell of design:
ggqqplot(datos, "acidez.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
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## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev<-datos %>%
group_by(diam2) %>%
levene_test(acidez.grano ~ curva*gen)
lev %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 8 18 0.606 0.761
## 2 1 8 18 0.683 0.701
## 3 2 8 18 0.925 0.519
## 4 3 8 18 0.692 0.694
## 5 4 8 18 0.859 0.567
## 6 5 8 18 0.687 0.698
## 7 6 8 18 0.378 0.919
##Computation
res.aov <- anova_test(
data = datos, dv = acidez.grano, wid = id,
within = diam2, between = c(curva, gen)
)
res.aov
## ANOVA Table (type II tests)
##
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 curva 2 18 23.528 9.50e-06 * 0.453
## 2 gen 2 18 9.282 2.00e-03 * 0.246
## 3 diam2 6 108 64.315 2.12e-33 * 0.710
## 4 curva:gen 4 18 23.318 6.48e-07 * 0.621
## 5 curva:diam2 12 108 9.215 5.31e-12 * 0.412
## 6 gen:diam2 12 108 5.609 2.12e-07 * 0.299
## 7 curva:gen:diam2 24 108 8.569 9.55e-16 * 0.566
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.014 7.24e-07 *
## 2 curva:diam2 0.014 7.24e-07 *
## 3 gen:diam2 0.014 7.24e-07 *
## 4 curva:gen:diam2 0.014 7.24e-07 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.408 2.45, 44.05 6.01e-15 * 0.477 2.86, 51.5
## 2 curva:diam2 0.408 4.89, 44.05 5.30e-06 * 0.477 5.72, 51.5
## 3 gen:diam2 0.408 4.89, 44.05 4.75e-04 * 0.477 5.72, 51.5
## 4 curva:gen:diam2 0.408 9.79, 44.05 1.67e-07 * 0.477 11.44, 51.5
## p[HF] p[HF]<.05
## 1 4.18e-17 *
## 2 1.04e-06 *
## 3 1.90e-04 *
## 4 1.79e-08 *
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2.00 18.00 23.528 9.50e-06 * 0.453
## 2 gen 2.00 18.00 9.282 2.00e-03 * 0.246
## 3 diam2 2.45 44.05 64.315 6.01e-15 * 0.710
## 4 curva:gen 4.00 18.00 23.318 6.48e-07 * 0.621
## 5 curva:diam2 4.89 44.05 9.215 5.30e-06 * 0.412
## 6 gen:diam2 4.89 44.05 5.609 4.75e-04 * 0.299
## 7 curva:gen:diam2 9.79 44.05 8.569 1.67e-07 * 0.566
#Table by error
res.aov.error <- aov(acidez.grano ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = acidez.grano ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 0.5751481
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 2.446662 0.965281 4.849728 0.935919
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 0.2280252
## 48 out of 56 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 2: id:diam2
##
## Terms:
## diam2 diam2:curva diam2:gen diam2:curva:gen Residuals
## Sum of Squares 7.228258 2.071277 1.260815 3.852067 2.022980
## Deg. of Freedom 6 12 12 24 108
##
## Residual standard error: 0.1368623
## Estimated effects may be unbalanced
## Emmeans
emmip(res.aov.error, gen ~ diam2 | curva)
## Note: re-fitting model with sum-to-zero contrasts
emm_curva <- emmeans(res.aov.error, pairwise ~ curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_curva
## $emmeans
## curva emmean SE df lower.CL upper.CL
## T3 0.718 0.0287 18 0.658 0.779
## T1 0.567 0.0287 18 0.507 0.628
## T2 0.440 0.0287 18 0.380 0.500
##
## Results are averaged over the levels of: diam2, gen
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## T3 - T1 0.151 0.0406 18 3.715 0.0043
## T3 - T2 0.278 0.0406 18 6.852 <.0001
## T1 - T2 0.127 0.0406 18 3.136 0.0150
##
## Results are averaged over the levels of: diam2, gen
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_curva <- emmeans(res.aov.error, pairwise ~ gen | curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_gen_curva
## $emmeans
## curva = T3:
## gen emmean SE df lower.CL upper.CL
## CCN51 0.452 0.0498 18 0.347 0.556
## ICS95 0.593 0.0498 18 0.488 0.697
## TCS01 1.110 0.0498 18 1.005 1.215
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 0.620 0.0498 18 0.515 0.724
## ICS95 0.572 0.0498 18 0.467 0.676
## TCS01 0.510 0.0498 18 0.406 0.615
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 0.580 0.0498 18 0.475 0.684
## ICS95 0.343 0.0498 18 0.239 0.448
## TCS01 0.397 0.0498 18 0.292 0.501
##
## Results are averaged over the levels of: diam2
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -0.1410 0.0704 18 -2.003 0.1402
## CCN51 - TCS01 -0.6581 0.0704 18 -9.352 <.0001
## ICS95 - TCS01 -0.5171 0.0704 18 -7.349 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.0476 0.0704 18 0.677 0.7798
## CCN51 - TCS01 0.1090 0.0704 18 1.550 0.2924
## ICS95 - TCS01 0.0614 0.0704 18 0.873 0.6637
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.2366 0.0704 18 3.362 0.0092
## CCN51 - TCS01 0.1831 0.0704 18 2.603 0.0452
## ICS95 - TCS01 -0.0535 0.0704 18 -0.760 0.7316
##
## Results are averaged over the levels of: diam2
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_diam2 <- emmeans(res.aov.error, pairwise ~ diam2 | curva*gen)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2
## $emmeans
## curva = T3, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.393 0.0885 101 0.2178 0.569
## 1 0.363 0.0885 101 0.1878 0.539
## 2 0.300 0.0885 101 0.1245 0.476
## 3 0.577 0.0885 101 0.4012 0.752
## 4 0.567 0.0885 101 0.3912 0.742
## 5 0.643 0.0885 101 0.4678 0.819
## 6 0.320 0.0885 101 0.1445 0.496
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.290 0.0885 101 0.1145 0.466
## 1 0.230 0.0885 101 0.0545 0.406
## 2 0.780 0.0885 101 0.6045 0.956
## 3 0.643 0.0885 101 0.4678 0.819
## 4 0.953 0.0885 101 0.7778 1.129
## 5 0.713 0.0885 101 0.5378 0.889
## 6 0.727 0.0885 101 0.5512 0.902
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.376 0.0885 101 0.2002 0.551
## 1 0.190 0.0885 101 0.0145 0.366
## 2 0.310 0.0885 101 0.1342 0.485
## 3 0.931 0.0885 101 0.7558 1.107
## 4 1.036 0.0885 101 0.8602 1.211
## 5 0.703 0.0885 101 0.5275 0.879
## 6 0.513 0.0885 101 0.3378 0.689
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.370 0.0885 101 0.1945 0.546
## 1 0.333 0.0885 101 0.1578 0.509
## 2 0.457 0.0885 101 0.2812 0.632
## 3 0.927 0.0885 101 0.7512 1.102
## 4 0.800 0.0885 101 0.6245 0.976
## 5 0.623 0.0885 101 0.4478 0.799
## 6 0.640 0.0885 101 0.4645 0.816
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.510 0.0885 101 0.3345 0.686
## 1 0.260 0.0885 101 0.0845 0.436
## 2 0.820 0.0885 101 0.6445 0.996
## 3 0.667 0.0885 101 0.4912 0.842
## 4 0.793 0.0885 101 0.6178 0.969
## 5 0.447 0.0885 101 0.2712 0.622
## 6 0.507 0.0885 101 0.3312 0.682
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.340 0.0885 101 0.1645 0.516
## 1 0.188 0.0885 101 0.0125 0.364
## 2 0.200 0.0885 101 0.0245 0.376
## 3 0.388 0.0885 101 0.2125 0.564
## 4 0.493 0.0885 101 0.3178 0.669
## 5 0.457 0.0885 101 0.2818 0.633
## 6 0.336 0.0885 101 0.1602 0.511
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.377 0.0885 101 0.2012 0.552
## 1 0.387 0.0885 101 0.2112 0.562
## 2 0.457 0.0885 101 0.2812 0.632
## 3 1.547 0.0885 101 1.3712 1.722
## 4 1.823 0.0885 101 1.6478 1.999
## 5 1.867 0.0885 101 1.6912 2.042
## 6 1.313 0.0885 101 1.1378 1.489
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.467 0.0885 101 0.2912 0.642
## 1 0.360 0.0885 101 0.1845 0.536
## 2 0.530 0.0885 101 0.3545 0.706
## 3 0.603 0.0885 101 0.4278 0.779
## 4 0.493 0.0885 101 0.3178 0.669
## 5 0.630 0.0885 101 0.4545 0.806
## 6 0.490 0.0885 101 0.3145 0.666
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.226 0.0885 101 0.0502 0.401
## 1 0.190 0.0885 101 0.0142 0.365
## 2 0.330 0.0885 101 0.1545 0.506
## 3 0.335 0.0885 101 0.1598 0.511
## 4 0.611 0.0885 101 0.4358 0.787
## 5 0.633 0.0885 101 0.4578 0.809
## 6 0.451 0.0885 101 0.2758 0.627
##
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.03000 0.112 108 0.268 1.0000
## 0 - 2 0.09333 0.112 108 0.835 0.9807
## 0 - 3 -0.18333 0.112 108 -1.641 0.6565
## 0 - 4 -0.17333 0.112 108 -1.551 0.7133
## 0 - 5 -0.25000 0.112 108 -2.237 0.2848
## 0 - 6 0.07333 0.112 108 0.656 0.9946
## 1 - 2 0.06333 0.112 108 0.567 0.9976
## 1 - 3 -0.21333 0.112 108 -1.909 0.4789
## 1 - 4 -0.20333 0.112 108 -1.820 0.5380
## 1 - 5 -0.28000 0.112 108 -2.506 0.1676
## 1 - 6 0.04333 0.112 108 0.388 0.9997
## 2 - 3 -0.27667 0.112 108 -2.476 0.1786
## 2 - 4 -0.26667 0.112 108 -2.386 0.2145
## 2 - 5 -0.34333 0.112 108 -3.072 0.0416
## 2 - 6 -0.02000 0.112 108 -0.179 1.0000
## 3 - 4 0.01000 0.112 108 0.089 1.0000
## 3 - 5 -0.06667 0.112 108 -0.597 0.9968
## 3 - 6 0.25667 0.112 108 2.297 0.2551
## 4 - 5 -0.07667 0.112 108 -0.686 0.9931
## 4 - 6 0.24667 0.112 108 2.207 0.3003
## 5 - 6 0.32333 0.112 108 2.893 0.0670
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.06000 0.112 108 0.537 0.9982
## 0 - 2 -0.49000 0.112 108 -4.385 0.0005
## 0 - 3 -0.35333 0.112 108 -3.162 0.0323
## 0 - 4 -0.66333 0.112 108 -5.936 <.0001
## 0 - 5 -0.42333 0.112 108 -3.788 0.0045
## 0 - 6 -0.43667 0.112 108 -3.908 0.0030
## 1 - 2 -0.55000 0.112 108 -4.922 0.0001
## 1 - 3 -0.41333 0.112 108 -3.699 0.0061
## 1 - 4 -0.72333 0.112 108 -6.473 <.0001
## 1 - 5 -0.48333 0.112 108 -4.325 0.0007
## 1 - 6 -0.49667 0.112 108 -4.445 0.0004
## 2 - 3 0.13667 0.112 108 1.223 0.8838
## 2 - 4 -0.17333 0.112 108 -1.551 0.7133
## 2 - 5 0.06667 0.112 108 0.597 0.9968
## 2 - 6 0.05333 0.112 108 0.477 0.9991
## 3 - 4 -0.31000 0.112 108 -2.774 0.0905
## 3 - 5 -0.07000 0.112 108 -0.626 0.9958
## 3 - 6 -0.08333 0.112 108 -0.746 0.9893
## 4 - 5 0.24000 0.112 108 2.148 0.3329
## 4 - 6 0.22667 0.112 108 2.028 0.4031
## 5 - 6 -0.01333 0.112 108 -0.119 1.0000
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.18567 0.112 108 1.661 0.6429
## 0 - 2 0.06600 0.112 108 0.591 0.9970
## 0 - 3 -0.55567 0.112 108 -4.973 0.0001
## 0 - 4 -0.66000 0.112 108 -5.906 <.0001
## 0 - 5 -0.32733 0.112 108 -2.929 0.0611
## 0 - 6 -0.13767 0.112 108 -1.232 0.8801
## 1 - 2 -0.11967 0.112 108 -1.071 0.9353
## 1 - 3 -0.74133 0.112 108 -6.634 <.0001
## 1 - 4 -0.84567 0.112 108 -7.568 <.0001
## 1 - 5 -0.51300 0.112 108 -4.591 0.0002
## 1 - 6 -0.32333 0.112 108 -2.893 0.0670
## 2 - 3 -0.62167 0.112 108 -5.563 <.0001
## 2 - 4 -0.72600 0.112 108 -6.497 <.0001
## 2 - 5 -0.39333 0.112 108 -3.520 0.0110
## 2 - 6 -0.20367 0.112 108 -1.823 0.5360
## 3 - 4 -0.10433 0.112 108 -0.934 0.9662
## 3 - 5 0.22833 0.112 108 2.043 0.3940
## 3 - 6 0.41800 0.112 108 3.741 0.0053
## 4 - 5 0.33267 0.112 108 2.977 0.0539
## 4 - 6 0.52233 0.112 108 4.674 0.0002
## 5 - 6 0.18967 0.112 108 1.697 0.6193
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.03667 0.112 108 0.328 0.9999
## 0 - 2 -0.08667 0.112 108 -0.776 0.9868
## 0 - 3 -0.55667 0.112 108 -4.981 <.0001
## 0 - 4 -0.43000 0.112 108 -3.848 0.0037
## 0 - 5 -0.25333 0.112 108 -2.267 0.2697
## 0 - 6 -0.27000 0.112 108 -2.416 0.2020
## 1 - 2 -0.12333 0.112 108 -1.104 0.9257
## 1 - 3 -0.59333 0.112 108 -5.310 <.0001
## 1 - 4 -0.46667 0.112 108 -4.176 0.0012
## 1 - 5 -0.29000 0.112 108 -2.595 0.1378
## 1 - 6 -0.30667 0.112 108 -2.744 0.0973
## 2 - 3 -0.47000 0.112 108 -4.206 0.0010
## 2 - 4 -0.34333 0.112 108 -3.072 0.0416
## 2 - 5 -0.16667 0.112 108 -1.491 0.7493
## 2 - 6 -0.18333 0.112 108 -1.641 0.6565
## 3 - 4 0.12667 0.112 108 1.134 0.9163
## 3 - 5 0.30333 0.112 108 2.714 0.1045
## 3 - 6 0.28667 0.112 108 2.565 0.1472
## 4 - 5 0.17667 0.112 108 1.581 0.6947
## 4 - 6 0.16000 0.112 108 1.432 0.7834
## 5 - 6 -0.01667 0.112 108 -0.149 1.0000
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.25000 0.112 108 2.237 0.2848
## 0 - 2 -0.31000 0.112 108 -2.774 0.0905
## 0 - 3 -0.15667 0.112 108 -1.402 0.7997
## 0 - 4 -0.28333 0.112 108 -2.535 0.1572
## 0 - 5 0.06333 0.112 108 0.567 0.9976
## 0 - 6 0.00333 0.112 108 0.030 1.0000
## 1 - 2 -0.56000 0.112 108 -5.011 <.0001
## 1 - 3 -0.40667 0.112 108 -3.639 0.0075
## 1 - 4 -0.53333 0.112 108 -4.773 0.0001
## 1 - 5 -0.18667 0.112 108 -1.670 0.6370
## 1 - 6 -0.24667 0.112 108 -2.207 0.3003
## 2 - 3 0.15333 0.112 108 1.372 0.8153
## 2 - 4 0.02667 0.112 108 0.239 1.0000
## 2 - 5 0.37333 0.112 108 3.341 0.0191
## 2 - 6 0.31333 0.112 108 2.804 0.0841
## 3 - 4 -0.12667 0.112 108 -1.134 0.9163
## 3 - 5 0.22000 0.112 108 1.969 0.4405
## 3 - 6 0.16000 0.112 108 1.432 0.7834
## 4 - 5 0.34667 0.112 108 3.102 0.0383
## 4 - 6 0.28667 0.112 108 2.565 0.1472
## 5 - 6 -0.06000 0.112 108 -0.537 0.9982
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.15200 0.112 108 1.360 0.8214
## 0 - 2 0.14000 0.112 108 1.253 0.8715
## 0 - 3 -0.04800 0.112 108 -0.430 0.9995
## 0 - 4 -0.15333 0.112 108 -1.372 0.8153
## 0 - 5 -0.11733 0.112 108 -1.050 0.9409
## 0 - 6 0.00433 0.112 108 0.039 1.0000
## 1 - 2 -0.01200 0.112 108 -0.107 1.0000
## 1 - 3 -0.20000 0.112 108 -1.790 0.5578
## 1 - 4 -0.30533 0.112 108 -2.732 0.1001
## 1 - 5 -0.26933 0.112 108 -2.410 0.2045
## 1 - 6 -0.14767 0.112 108 -1.321 0.8405
## 2 - 3 -0.18800 0.112 108 -1.682 0.6292
## 2 - 4 -0.29333 0.112 108 -2.625 0.1288
## 2 - 5 -0.25733 0.112 108 -2.303 0.2523
## 2 - 6 -0.13567 0.112 108 -1.214 0.8873
## 3 - 4 -0.10533 0.112 108 -0.943 0.9646
## 3 - 5 -0.06933 0.112 108 -0.620 0.9960
## 3 - 6 0.05233 0.112 108 0.468 0.9992
## 4 - 5 0.03600 0.112 108 0.322 0.9999
## 4 - 6 0.15767 0.112 108 1.411 0.7949
## 5 - 6 0.12167 0.112 108 1.089 0.9302
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.01000 0.112 108 -0.089 1.0000
## 0 - 2 -0.08000 0.112 108 -0.716 0.9914
## 0 - 3 -1.17000 0.112 108 -10.470 <.0001
## 0 - 4 -1.44667 0.112 108 -12.946 <.0001
## 0 - 5 -1.49000 0.112 108 -13.334 <.0001
## 0 - 6 -0.93667 0.112 108 -8.382 <.0001
## 1 - 2 -0.07000 0.112 108 -0.626 0.9958
## 1 - 3 -1.16000 0.112 108 -10.381 <.0001
## 1 - 4 -1.43667 0.112 108 -12.856 <.0001
## 1 - 5 -1.48000 0.112 108 -13.244 <.0001
## 1 - 6 -0.92667 0.112 108 -8.292 <.0001
## 2 - 3 -1.09000 0.112 108 -9.754 <.0001
## 2 - 4 -1.36667 0.112 108 -12.230 <.0001
## 2 - 5 -1.41000 0.112 108 -12.618 <.0001
## 2 - 6 -0.85667 0.112 108 -7.666 <.0001
## 3 - 4 -0.27667 0.112 108 -2.476 0.1786
## 3 - 5 -0.32000 0.112 108 -2.864 0.0724
## 3 - 6 0.23333 0.112 108 2.088 0.3672
## 4 - 5 -0.04333 0.112 108 -0.388 0.9997
## 4 - 6 0.51000 0.112 108 4.564 0.0003
## 5 - 6 0.55333 0.112 108 4.952 0.0001
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.10667 0.112 108 0.955 0.9624
## 0 - 2 -0.06333 0.112 108 -0.567 0.9976
## 0 - 3 -0.13667 0.112 108 -1.223 0.8838
## 0 - 4 -0.02667 0.112 108 -0.239 1.0000
## 0 - 5 -0.16333 0.112 108 -1.462 0.7666
## 0 - 6 -0.02333 0.112 108 -0.209 1.0000
## 1 - 2 -0.17000 0.112 108 -1.521 0.7315
## 1 - 3 -0.24333 0.112 108 -2.178 0.3164
## 1 - 4 -0.13333 0.112 108 -1.193 0.8953
## 1 - 5 -0.27000 0.112 108 -2.416 0.2020
## 1 - 6 -0.13000 0.112 108 -1.163 0.9062
## 2 - 3 -0.07333 0.112 108 -0.656 0.9946
## 2 - 4 0.03667 0.112 108 0.328 0.9999
## 2 - 5 -0.10000 0.112 108 -0.895 0.9726
## 2 - 6 0.04000 0.112 108 0.358 0.9998
## 3 - 4 0.11000 0.112 108 0.984 0.9564
## 3 - 5 -0.02667 0.112 108 -0.239 1.0000
## 3 - 6 0.11333 0.112 108 1.014 0.9497
## 4 - 5 -0.13667 0.112 108 -1.223 0.8838
## 4 - 6 0.00333 0.112 108 0.030 1.0000
## 5 - 6 0.14000 0.112 108 1.253 0.8715
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.03600 0.112 108 0.322 0.9999
## 0 - 2 -0.10433 0.112 108 -0.934 0.9662
## 0 - 3 -0.10967 0.112 108 -0.981 0.9570
## 0 - 4 -0.38567 0.112 108 -3.451 0.0136
## 0 - 5 -0.40767 0.112 108 -3.648 0.0073
## 0 - 6 -0.22567 0.112 108 -2.019 0.4087
## 1 - 2 -0.14033 0.112 108 -1.256 0.8702
## 1 - 3 -0.14567 0.112 108 -1.304 0.8489
## 1 - 4 -0.42167 0.112 108 -3.773 0.0048
## 1 - 5 -0.44367 0.112 108 -3.970 0.0024
## 1 - 6 -0.26167 0.112 108 -2.342 0.2342
## 2 - 3 -0.00533 0.112 108 -0.048 1.0000
## 2 - 4 -0.28133 0.112 108 -2.518 0.1634
## 2 - 5 -0.30333 0.112 108 -2.714 0.1045
## 2 - 6 -0.12133 0.112 108 -1.086 0.9310
## 3 - 4 -0.27600 0.112 108 -2.470 0.1808
## 3 - 5 -0.29800 0.112 108 -2.667 0.1170
## 3 - 6 -0.11600 0.112 108 -1.038 0.9439
## 4 - 5 -0.02200 0.112 108 -0.197 1.0000
## 4 - 6 0.16000 0.112 108 1.432 0.7834
## 5 - 6 0.18200 0.112 108 1.629 0.6642
##
## P value adjustment: tukey method for comparing a family of 7 estimates
emm_gen_diam2_trend <- emmeans(res.aov.error, pairwise ~ diam2*gen | curva)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2_trend
## $emmeans
## curva = T3:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.393 0.0885 101 0.2178 0.569
## 1 CCN51 0.363 0.0885 101 0.1878 0.539
## 2 CCN51 0.300 0.0885 101 0.1245 0.476
## 3 CCN51 0.577 0.0885 101 0.4012 0.752
## 4 CCN51 0.567 0.0885 101 0.3912 0.742
## 5 CCN51 0.643 0.0885 101 0.4678 0.819
## 6 CCN51 0.320 0.0885 101 0.1445 0.496
## 0 ICS95 0.370 0.0885 101 0.1945 0.546
## 1 ICS95 0.333 0.0885 101 0.1578 0.509
## 2 ICS95 0.457 0.0885 101 0.2812 0.632
## 3 ICS95 0.927 0.0885 101 0.7512 1.102
## 4 ICS95 0.800 0.0885 101 0.6245 0.976
## 5 ICS95 0.623 0.0885 101 0.4478 0.799
## 6 ICS95 0.640 0.0885 101 0.4645 0.816
## 0 TCS01 0.377 0.0885 101 0.2012 0.552
## 1 TCS01 0.387 0.0885 101 0.2112 0.562
## 2 TCS01 0.457 0.0885 101 0.2812 0.632
## 3 TCS01 1.547 0.0885 101 1.3712 1.722
## 4 TCS01 1.823 0.0885 101 1.6478 1.999
## 5 TCS01 1.867 0.0885 101 1.6912 2.042
## 6 TCS01 1.313 0.0885 101 1.1378 1.489
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.290 0.0885 101 0.1145 0.466
## 1 CCN51 0.230 0.0885 101 0.0545 0.406
## 2 CCN51 0.780 0.0885 101 0.6045 0.956
## 3 CCN51 0.643 0.0885 101 0.4678 0.819
## 4 CCN51 0.953 0.0885 101 0.7778 1.129
## 5 CCN51 0.713 0.0885 101 0.5378 0.889
## 6 CCN51 0.727 0.0885 101 0.5512 0.902
## 0 ICS95 0.510 0.0885 101 0.3345 0.686
## 1 ICS95 0.260 0.0885 101 0.0845 0.436
## 2 ICS95 0.820 0.0885 101 0.6445 0.996
## 3 ICS95 0.667 0.0885 101 0.4912 0.842
## 4 ICS95 0.793 0.0885 101 0.6178 0.969
## 5 ICS95 0.447 0.0885 101 0.2712 0.622
## 6 ICS95 0.507 0.0885 101 0.3312 0.682
## 0 TCS01 0.467 0.0885 101 0.2912 0.642
## 1 TCS01 0.360 0.0885 101 0.1845 0.536
## 2 TCS01 0.530 0.0885 101 0.3545 0.706
## 3 TCS01 0.603 0.0885 101 0.4278 0.779
## 4 TCS01 0.493 0.0885 101 0.3178 0.669
## 5 TCS01 0.630 0.0885 101 0.4545 0.806
## 6 TCS01 0.490 0.0885 101 0.3145 0.666
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.376 0.0885 101 0.2002 0.551
## 1 CCN51 0.190 0.0885 101 0.0145 0.366
## 2 CCN51 0.310 0.0885 101 0.1342 0.485
## 3 CCN51 0.931 0.0885 101 0.7558 1.107
## 4 CCN51 1.036 0.0885 101 0.8602 1.211
## 5 CCN51 0.703 0.0885 101 0.5275 0.879
## 6 CCN51 0.513 0.0885 101 0.3378 0.689
## 0 ICS95 0.340 0.0885 101 0.1645 0.516
## 1 ICS95 0.188 0.0885 101 0.0125 0.364
## 2 ICS95 0.200 0.0885 101 0.0245 0.376
## 3 ICS95 0.388 0.0885 101 0.2125 0.564
## 4 ICS95 0.493 0.0885 101 0.3178 0.669
## 5 ICS95 0.457 0.0885 101 0.2818 0.633
## 6 ICS95 0.336 0.0885 101 0.1602 0.511
## 0 TCS01 0.226 0.0885 101 0.0502 0.401
## 1 TCS01 0.190 0.0885 101 0.0142 0.365
## 2 TCS01 0.330 0.0885 101 0.1545 0.506
## 3 TCS01 0.335 0.0885 101 0.1598 0.511
## 4 TCS01 0.611 0.0885 101 0.4358 0.787
## 5 TCS01 0.633 0.0885 101 0.4578 0.809
## 6 TCS01 0.451 0.0885 101 0.2758 0.627
##
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 0.030000 0.112 108 0.268 1.0000
## 0 CCN51 - 2 CCN51 0.093333 0.112 108 0.835 1.0000
## 0 CCN51 - 3 CCN51 -0.183333 0.112 108 -1.641 0.9867
## 0 CCN51 - 4 CCN51 -0.173333 0.112 108 -1.551 0.9930
## 0 CCN51 - 5 CCN51 -0.250000 0.112 108 -2.237 0.7913
## 0 CCN51 - 6 CCN51 0.073333 0.112 108 0.656 1.0000
## 0 CCN51 - 0 ICS95 0.023333 0.125 101 0.186 1.0000
## 0 CCN51 - 1 ICS95 0.060000 0.125 101 0.480 1.0000
## 0 CCN51 - 2 ICS95 -0.063333 0.125 101 -0.506 1.0000
## 0 CCN51 - 3 ICS95 -0.533333 0.125 101 -4.263 0.0073
## 0 CCN51 - 4 ICS95 -0.406667 0.125 101 -3.250 0.1532
## 0 CCN51 - 5 ICS95 -0.230000 0.125 101 -1.838 0.9565
## 0 CCN51 - 6 ICS95 -0.246667 0.125 101 -1.971 0.9185
## 0 CCN51 - 0 TCS01 0.016667 0.125 101 0.133 1.0000
## 0 CCN51 - 1 TCS01 0.006667 0.125 101 0.053 1.0000
## 0 CCN51 - 2 TCS01 -0.063333 0.125 101 -0.506 1.0000
## 0 CCN51 - 3 TCS01 -1.153333 0.125 101 -9.218 <.0001
## 0 CCN51 - 4 TCS01 -1.430000 0.125 101 -11.429 <.0001
## 0 CCN51 - 5 TCS01 -1.473333 0.125 101 -11.775 <.0001
## 0 CCN51 - 6 TCS01 -0.920000 0.125 101 -7.353 <.0001
## 1 CCN51 - 2 CCN51 0.063333 0.112 108 0.567 1.0000
## 1 CCN51 - 3 CCN51 -0.213333 0.112 108 -1.909 0.9388
## 1 CCN51 - 4 CCN51 -0.203333 0.112 108 -1.820 0.9609
## 1 CCN51 - 5 CCN51 -0.280000 0.112 108 -2.506 0.6058
## 1 CCN51 - 6 CCN51 0.043333 0.112 108 0.388 1.0000
## 1 CCN51 - 0 ICS95 -0.006667 0.125 101 -0.053 1.0000
## 1 CCN51 - 1 ICS95 0.030000 0.125 101 0.240 1.0000
## 1 CCN51 - 2 ICS95 -0.093333 0.125 101 -0.746 1.0000
## 1 CCN51 - 3 ICS95 -0.563333 0.125 101 -4.502 0.0031
## 1 CCN51 - 4 ICS95 -0.436667 0.125 101 -3.490 0.0826
## 1 CCN51 - 5 ICS95 -0.260000 0.125 101 -2.078 0.8757
## 1 CCN51 - 6 ICS95 -0.276667 0.125 101 -2.211 0.8063
## 1 CCN51 - 0 TCS01 -0.013333 0.125 101 -0.107 1.0000
## 1 CCN51 - 1 TCS01 -0.023333 0.125 101 -0.186 1.0000
## 1 CCN51 - 2 TCS01 -0.093333 0.125 101 -0.746 1.0000
## 1 CCN51 - 3 TCS01 -1.183333 0.125 101 -9.457 <.0001
## 1 CCN51 - 4 TCS01 -1.460000 0.125 101 -11.669 <.0001
## 1 CCN51 - 5 TCS01 -1.503333 0.125 101 -12.015 <.0001
## 1 CCN51 - 6 TCS01 -0.950000 0.125 101 -7.593 <.0001
## 2 CCN51 - 3 CCN51 -0.276667 0.112 108 -2.476 0.6279
## 2 CCN51 - 4 CCN51 -0.266667 0.112 108 -2.386 0.6928
## 2 CCN51 - 5 CCN51 -0.343333 0.112 108 -3.072 0.2290
## 2 CCN51 - 6 CCN51 -0.020000 0.112 108 -0.179 1.0000
## 2 CCN51 - 0 ICS95 -0.070000 0.125 101 -0.559 1.0000
## 2 CCN51 - 1 ICS95 -0.033333 0.125 101 -0.266 1.0000
## 2 CCN51 - 2 ICS95 -0.156667 0.125 101 -1.252 0.9996
## 2 CCN51 - 3 ICS95 -0.626667 0.125 101 -5.008 0.0004
## 2 CCN51 - 4 ICS95 -0.500000 0.125 101 -3.996 0.0179
## 2 CCN51 - 5 ICS95 -0.323333 0.125 101 -2.584 0.5474
## 2 CCN51 - 6 ICS95 -0.340000 0.125 101 -2.717 0.4496
## 2 CCN51 - 0 TCS01 -0.076667 0.125 101 -0.613 1.0000
## 2 CCN51 - 1 TCS01 -0.086667 0.125 101 -0.693 1.0000
## 2 CCN51 - 2 TCS01 -0.156667 0.125 101 -1.252 0.9996
## 2 CCN51 - 3 TCS01 -1.246667 0.125 101 -9.964 <.0001
## 2 CCN51 - 4 TCS01 -1.523333 0.125 101 -12.175 <.0001
## 2 CCN51 - 5 TCS01 -1.566667 0.125 101 -12.521 <.0001
## 2 CCN51 - 6 TCS01 -1.013333 0.125 101 -8.099 <.0001
## 3 CCN51 - 4 CCN51 0.010000 0.112 108 0.089 1.0000
## 3 CCN51 - 5 CCN51 -0.066667 0.112 108 -0.597 1.0000
## 3 CCN51 - 6 CCN51 0.256667 0.112 108 2.297 0.7537
## 3 CCN51 - 0 ICS95 0.206667 0.125 101 1.652 0.9854
## 3 CCN51 - 1 ICS95 0.243333 0.125 101 1.945 0.9275
## 3 CCN51 - 2 ICS95 0.120000 0.125 101 0.959 1.0000
## 3 CCN51 - 3 ICS95 -0.350000 0.125 101 -2.797 0.3938
## 3 CCN51 - 4 ICS95 -0.223333 0.125 101 -1.785 0.9673
## 3 CCN51 - 5 ICS95 -0.046667 0.125 101 -0.373 1.0000
## 3 CCN51 - 6 ICS95 -0.063333 0.125 101 -0.506 1.0000
## 3 CCN51 - 0 TCS01 0.200000 0.125 101 1.598 0.9899
## 3 CCN51 - 1 TCS01 0.190000 0.125 101 1.519 0.9945
## 3 CCN51 - 2 TCS01 0.120000 0.125 101 0.959 1.0000
## 3 CCN51 - 3 TCS01 -0.970000 0.125 101 -7.752 <.0001
## 3 CCN51 - 4 TCS01 -1.246667 0.125 101 -9.964 <.0001
## 3 CCN51 - 5 TCS01 -1.290000 0.125 101 -10.310 <.0001
## 3 CCN51 - 6 TCS01 -0.736667 0.125 101 -5.888 <.0001
## 4 CCN51 - 5 CCN51 -0.076667 0.112 108 -0.686 1.0000
## 4 CCN51 - 6 CCN51 0.246667 0.112 108 2.207 0.8090
## 4 CCN51 - 0 ICS95 0.196667 0.125 101 1.572 0.9917
## 4 CCN51 - 1 ICS95 0.233333 0.125 101 1.865 0.9502
## 4 CCN51 - 2 ICS95 0.110000 0.125 101 0.879 1.0000
## 4 CCN51 - 3 ICS95 -0.360000 0.125 101 -2.877 0.3412
## 4 CCN51 - 4 ICS95 -0.233333 0.125 101 -1.865 0.9502
## 4 CCN51 - 5 ICS95 -0.056667 0.125 101 -0.453 1.0000
## 4 CCN51 - 6 ICS95 -0.073333 0.125 101 -0.586 1.0000
## 4 CCN51 - 0 TCS01 0.190000 0.125 101 1.519 0.9945
## 4 CCN51 - 1 TCS01 0.180000 0.125 101 1.439 0.9972
## 4 CCN51 - 2 TCS01 0.110000 0.125 101 0.879 1.0000
## 4 CCN51 - 3 TCS01 -0.980000 0.125 101 -7.832 <.0001
## 4 CCN51 - 4 TCS01 -1.256667 0.125 101 -10.044 <.0001
## 4 CCN51 - 5 TCS01 -1.300000 0.125 101 -10.390 <.0001
## 4 CCN51 - 6 TCS01 -0.746667 0.125 101 -5.968 <.0001
## 5 CCN51 - 6 CCN51 0.323333 0.112 108 2.893 0.3299
## 5 CCN51 - 0 ICS95 0.273333 0.125 101 2.185 0.8215
## 5 CCN51 - 1 ICS95 0.310000 0.125 101 2.478 0.6267
## 5 CCN51 - 2 ICS95 0.186667 0.125 101 1.492 0.9955
## 5 CCN51 - 3 ICS95 -0.283333 0.125 101 -2.264 0.7740
## 5 CCN51 - 4 ICS95 -0.156667 0.125 101 -1.252 0.9996
## 5 CCN51 - 5 ICS95 0.020000 0.125 101 0.160 1.0000
## 5 CCN51 - 6 ICS95 0.003333 0.125 101 0.027 1.0000
## 5 CCN51 - 0 TCS01 0.266667 0.125 101 2.131 0.8500
## 5 CCN51 - 1 TCS01 0.256667 0.125 101 2.051 0.8875
## 5 CCN51 - 2 TCS01 0.186667 0.125 101 1.492 0.9955
## 5 CCN51 - 3 TCS01 -0.903333 0.125 101 -7.220 <.0001
## 5 CCN51 - 4 TCS01 -1.180000 0.125 101 -9.431 <.0001
## 5 CCN51 - 5 TCS01 -1.223333 0.125 101 -9.777 <.0001
## 5 CCN51 - 6 TCS01 -0.670000 0.125 101 -5.355 0.0001
## 6 CCN51 - 0 ICS95 -0.050000 0.125 101 -0.400 1.0000
## 6 CCN51 - 1 ICS95 -0.013333 0.125 101 -0.107 1.0000
## 6 CCN51 - 2 ICS95 -0.136667 0.125 101 -1.092 0.9999
## 6 CCN51 - 3 ICS95 -0.606667 0.125 101 -4.849 0.0008
## 6 CCN51 - 4 ICS95 -0.480000 0.125 101 -3.836 0.0299
## 6 CCN51 - 5 ICS95 -0.303333 0.125 101 -2.424 0.6655
## 6 CCN51 - 6 ICS95 -0.320000 0.125 101 -2.558 0.5673
## 6 CCN51 - 0 TCS01 -0.056667 0.125 101 -0.453 1.0000
## 6 CCN51 - 1 TCS01 -0.066667 0.125 101 -0.533 1.0000
## 6 CCN51 - 2 TCS01 -0.136667 0.125 101 -1.092 0.9999
## 6 CCN51 - 3 TCS01 -1.226667 0.125 101 -9.804 <.0001
## 6 CCN51 - 4 TCS01 -1.503333 0.125 101 -12.015 <.0001
## 6 CCN51 - 5 TCS01 -1.546667 0.125 101 -12.361 <.0001
## 6 CCN51 - 6 TCS01 -0.993333 0.125 101 -7.939 <.0001
## 0 ICS95 - 1 ICS95 0.036667 0.112 108 0.328 1.0000
## 0 ICS95 - 2 ICS95 -0.086667 0.112 108 -0.776 1.0000
## 0 ICS95 - 3 ICS95 -0.556667 0.112 108 -4.981 0.0004
## 0 ICS95 - 4 ICS95 -0.430000 0.112 108 -3.848 0.0282
## 0 ICS95 - 5 ICS95 -0.253333 0.112 108 -2.267 0.7728
## 0 ICS95 - 6 ICS95 -0.270000 0.112 108 -2.416 0.6715
## 0 ICS95 - 0 TCS01 -0.006667 0.125 101 -0.053 1.0000
## 0 ICS95 - 1 TCS01 -0.016667 0.125 101 -0.133 1.0000
## 0 ICS95 - 2 TCS01 -0.086667 0.125 101 -0.693 1.0000
## 0 ICS95 - 3 TCS01 -1.176667 0.125 101 -9.404 <.0001
## 0 ICS95 - 4 TCS01 -1.453333 0.125 101 -11.615 <.0001
## 0 ICS95 - 5 TCS01 -1.496667 0.125 101 -11.962 <.0001
## 0 ICS95 - 6 TCS01 -0.943333 0.125 101 -7.539 <.0001
## 1 ICS95 - 2 ICS95 -0.123333 0.112 108 -1.104 0.9999
## 1 ICS95 - 3 ICS95 -0.593333 0.112 108 -5.310 0.0001
## 1 ICS95 - 4 ICS95 -0.466667 0.112 108 -4.176 0.0095
## 1 ICS95 - 5 ICS95 -0.290000 0.112 108 -2.595 0.5389
## 1 ICS95 - 6 ICS95 -0.306667 0.112 108 -2.744 0.4297
## 1 ICS95 - 0 TCS01 -0.043333 0.125 101 -0.346 1.0000
## 1 ICS95 - 1 TCS01 -0.053333 0.125 101 -0.426 1.0000
## 1 ICS95 - 2 TCS01 -0.123333 0.125 101 -0.986 1.0000
## 1 ICS95 - 3 TCS01 -1.213333 0.125 101 -9.697 <.0001
## 1 ICS95 - 4 TCS01 -1.490000 0.125 101 -11.908 <.0001
## 1 ICS95 - 5 TCS01 -1.533333 0.125 101 -12.255 <.0001
## 1 ICS95 - 6 TCS01 -0.980000 0.125 101 -7.832 <.0001
## 2 ICS95 - 3 ICS95 -0.470000 0.112 108 -4.206 0.0085
## 2 ICS95 - 4 ICS95 -0.343333 0.112 108 -3.072 0.2290
## 2 ICS95 - 5 ICS95 -0.166667 0.112 108 -1.491 0.9956
## 2 ICS95 - 6 ICS95 -0.183333 0.112 108 -1.641 0.9867
## 2 ICS95 - 0 TCS01 0.080000 0.125 101 0.639 1.0000
## 2 ICS95 - 1 TCS01 0.070000 0.125 101 0.559 1.0000
## 2 ICS95 - 2 TCS01 0.000000 0.125 101 0.000 1.0000
## 2 ICS95 - 3 TCS01 -1.090000 0.125 101 -8.711 <.0001
## 2 ICS95 - 4 TCS01 -1.366667 0.125 101 -10.923 <.0001
## 2 ICS95 - 5 TCS01 -1.410000 0.125 101 -11.269 <.0001
## 2 ICS95 - 6 TCS01 -0.856667 0.125 101 -6.847 <.0001
## 3 ICS95 - 4 ICS95 0.126667 0.112 108 1.134 0.9999
## 3 ICS95 - 5 ICS95 0.303333 0.112 108 2.714 0.4510
## 3 ICS95 - 6 ICS95 0.286667 0.112 108 2.565 0.5612
## 3 ICS95 - 0 TCS01 0.550000 0.125 101 4.396 0.0045
## 3 ICS95 - 1 TCS01 0.540000 0.125 101 4.316 0.0060
## 3 ICS95 - 2 TCS01 0.470000 0.125 101 3.756 0.0383
## 3 ICS95 - 3 TCS01 -0.620000 0.125 101 -4.955 0.0005
## 3 ICS95 - 4 TCS01 -0.896667 0.125 101 -7.166 <.0001
## 3 ICS95 - 5 TCS01 -0.940000 0.125 101 -7.513 <.0001
## 3 ICS95 - 6 TCS01 -0.386667 0.125 101 -3.090 0.2215
## 4 ICS95 - 5 ICS95 0.176667 0.112 108 1.581 0.9912
## 4 ICS95 - 6 ICS95 0.160000 0.112 108 1.432 0.9974
## 4 ICS95 - 0 TCS01 0.423333 0.125 101 3.383 0.1097
## 4 ICS95 - 1 TCS01 0.413333 0.125 101 3.303 0.1344
## 4 ICS95 - 2 TCS01 0.343333 0.125 101 2.744 0.4307
## 4 ICS95 - 3 TCS01 -0.746667 0.125 101 -5.968 <.0001
## 4 ICS95 - 4 TCS01 -1.023333 0.125 101 -8.179 <.0001
## 4 ICS95 - 5 TCS01 -1.066667 0.125 101 -8.525 <.0001
## 4 ICS95 - 6 TCS01 -0.513333 0.125 101 -4.103 0.0126
## 5 ICS95 - 6 ICS95 -0.016667 0.112 108 -0.149 1.0000
## 5 ICS95 - 0 TCS01 0.246667 0.125 101 1.971 0.9185
## 5 ICS95 - 1 TCS01 0.236667 0.125 101 1.891 0.9433
## 5 ICS95 - 2 TCS01 0.166667 0.125 101 1.332 0.9990
## 5 ICS95 - 3 TCS01 -0.923333 0.125 101 -7.379 <.0001
## 5 ICS95 - 4 TCS01 -1.200000 0.125 101 -9.591 <.0001
## 5 ICS95 - 5 TCS01 -1.243333 0.125 101 -9.937 <.0001
## 5 ICS95 - 6 TCS01 -0.690000 0.125 101 -5.515 0.0001
## 6 ICS95 - 0 TCS01 0.263333 0.125 101 2.105 0.8632
## 6 ICS95 - 1 TCS01 0.253333 0.125 101 2.025 0.8985
## 6 ICS95 - 2 TCS01 0.183333 0.125 101 1.465 0.9964
## 6 ICS95 - 3 TCS01 -0.906667 0.125 101 -7.246 <.0001
## 6 ICS95 - 4 TCS01 -1.183333 0.125 101 -9.457 <.0001
## 6 ICS95 - 5 TCS01 -1.226667 0.125 101 -9.804 <.0001
## 6 ICS95 - 6 TCS01 -0.673333 0.125 101 -5.381 0.0001
## 0 TCS01 - 1 TCS01 -0.010000 0.112 108 -0.089 1.0000
## 0 TCS01 - 2 TCS01 -0.080000 0.112 108 -0.716 1.0000
## 0 TCS01 - 3 TCS01 -1.170000 0.112 108 -10.470 <.0001
## 0 TCS01 - 4 TCS01 -1.446667 0.112 108 -12.946 <.0001
## 0 TCS01 - 5 TCS01 -1.490000 0.112 108 -13.334 <.0001
## 0 TCS01 - 6 TCS01 -0.936667 0.112 108 -8.382 <.0001
## 1 TCS01 - 2 TCS01 -0.070000 0.112 108 -0.626 1.0000
## 1 TCS01 - 3 TCS01 -1.160000 0.112 108 -10.381 <.0001
## 1 TCS01 - 4 TCS01 -1.436667 0.112 108 -12.856 <.0001
## 1 TCS01 - 5 TCS01 -1.480000 0.112 108 -13.244 <.0001
## 1 TCS01 - 6 TCS01 -0.926667 0.112 108 -8.292 <.0001
## 2 TCS01 - 3 TCS01 -1.090000 0.112 108 -9.754 <.0001
## 2 TCS01 - 4 TCS01 -1.366667 0.112 108 -12.230 <.0001
## 2 TCS01 - 5 TCS01 -1.410000 0.112 108 -12.618 <.0001
## 2 TCS01 - 6 TCS01 -0.856667 0.112 108 -7.666 <.0001
## 3 TCS01 - 4 TCS01 -0.276667 0.112 108 -2.476 0.6279
## 3 TCS01 - 5 TCS01 -0.320000 0.112 108 -2.864 0.3488
## 3 TCS01 - 6 TCS01 0.233333 0.112 108 2.088 0.8716
## 4 TCS01 - 5 TCS01 -0.043333 0.112 108 -0.388 1.0000
## 4 TCS01 - 6 TCS01 0.510000 0.112 108 4.564 0.0023
## 5 TCS01 - 6 TCS01 0.553333 0.112 108 4.952 0.0005
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 0.060000 0.112 108 0.537 1.0000
## 0 CCN51 - 2 CCN51 -0.490000 0.112 108 -4.385 0.0045
## 0 CCN51 - 3 CCN51 -0.353333 0.112 108 -3.162 0.1873
## 0 CCN51 - 4 CCN51 -0.663333 0.112 108 -5.936 <.0001
## 0 CCN51 - 5 CCN51 -0.423333 0.112 108 -3.788 0.0340
## 0 CCN51 - 6 CCN51 -0.436667 0.112 108 -3.908 0.0233
## 0 CCN51 - 0 ICS95 -0.220000 0.125 101 -1.758 0.9718
## 0 CCN51 - 1 ICS95 0.030000 0.125 101 0.240 1.0000
## 0 CCN51 - 2 ICS95 -0.530000 0.125 101 -4.236 0.0080
## 0 CCN51 - 3 ICS95 -0.376667 0.125 101 -3.010 0.2626
## 0 CCN51 - 4 ICS95 -0.503333 0.125 101 -4.023 0.0164
## 0 CCN51 - 5 ICS95 -0.156667 0.125 101 -1.252 0.9996
## 0 CCN51 - 6 ICS95 -0.216667 0.125 101 -1.732 0.9759
## 0 CCN51 - 0 TCS01 -0.176667 0.125 101 -1.412 0.9978
## 0 CCN51 - 1 TCS01 -0.070000 0.125 101 -0.559 1.0000
## 0 CCN51 - 2 TCS01 -0.240000 0.125 101 -1.918 0.9357
## 0 CCN51 - 3 TCS01 -0.313333 0.125 101 -2.504 0.6070
## 0 CCN51 - 4 TCS01 -0.203333 0.125 101 -1.625 0.9878
## 0 CCN51 - 5 TCS01 -0.340000 0.125 101 -2.717 0.4496
## 0 CCN51 - 6 TCS01 -0.200000 0.125 101 -1.598 0.9899
## 1 CCN51 - 2 CCN51 -0.550000 0.112 108 -4.922 0.0006
## 1 CCN51 - 3 CCN51 -0.413333 0.112 108 -3.699 0.0446
## 1 CCN51 - 4 CCN51 -0.723333 0.112 108 -6.473 <.0001
## 1 CCN51 - 5 CCN51 -0.483333 0.112 108 -4.325 0.0056
## 1 CCN51 - 6 CCN51 -0.496667 0.112 108 -4.445 0.0036
## 1 CCN51 - 0 ICS95 -0.280000 0.125 101 -2.238 0.7904
## 1 CCN51 - 1 ICS95 -0.030000 0.125 101 -0.240 1.0000
## 1 CCN51 - 2 ICS95 -0.590000 0.125 101 -4.715 0.0014
## 1 CCN51 - 3 ICS95 -0.436667 0.125 101 -3.490 0.0826
## 1 CCN51 - 4 ICS95 -0.563333 0.125 101 -4.502 0.0031
## 1 CCN51 - 5 ICS95 -0.216667 0.125 101 -1.732 0.9759
## 1 CCN51 - 6 ICS95 -0.276667 0.125 101 -2.211 0.8063
## 1 CCN51 - 0 TCS01 -0.236667 0.125 101 -1.891 0.9433
## 1 CCN51 - 1 TCS01 -0.130000 0.125 101 -1.039 1.0000
## 1 CCN51 - 2 TCS01 -0.300000 0.125 101 -2.398 0.6846
## 1 CCN51 - 3 TCS01 -0.373333 0.125 101 -2.984 0.2774
## 1 CCN51 - 4 TCS01 -0.263333 0.125 101 -2.105 0.8632
## 1 CCN51 - 5 TCS01 -0.400000 0.125 101 -3.197 0.1739
## 1 CCN51 - 6 TCS01 -0.260000 0.125 101 -2.078 0.8757
## 2 CCN51 - 3 CCN51 0.136667 0.112 108 1.223 0.9997
## 2 CCN51 - 4 CCN51 -0.173333 0.112 108 -1.551 0.9930
## 2 CCN51 - 5 CCN51 0.066667 0.112 108 0.597 1.0000
## 2 CCN51 - 6 CCN51 0.053333 0.112 108 0.477 1.0000
## 2 CCN51 - 0 ICS95 0.270000 0.125 101 2.158 0.8361
## 2 CCN51 - 1 ICS95 0.520000 0.125 101 4.156 0.0105
## 2 CCN51 - 2 ICS95 -0.040000 0.125 101 -0.320 1.0000
## 2 CCN51 - 3 ICS95 0.113333 0.125 101 0.906 1.0000
## 2 CCN51 - 4 ICS95 -0.013333 0.125 101 -0.107 1.0000
## 2 CCN51 - 5 ICS95 0.333333 0.125 101 2.664 0.4882
## 2 CCN51 - 6 ICS95 0.273333 0.125 101 2.185 0.8215
## 2 CCN51 - 0 TCS01 0.313333 0.125 101 2.504 0.6070
## 2 CCN51 - 1 TCS01 0.420000 0.125 101 3.357 0.1175
## 2 CCN51 - 2 TCS01 0.250000 0.125 101 1.998 0.9089
## 2 CCN51 - 3 TCS01 0.176667 0.125 101 1.412 0.9978
## 2 CCN51 - 4 TCS01 0.286667 0.125 101 2.291 0.7570
## 2 CCN51 - 5 TCS01 0.150000 0.125 101 1.199 0.9998
## 2 CCN51 - 6 TCS01 0.290000 0.125 101 2.318 0.7396
## 3 CCN51 - 4 CCN51 -0.310000 0.112 108 -2.774 0.4088
## 3 CCN51 - 5 CCN51 -0.070000 0.112 108 -0.626 1.0000
## 3 CCN51 - 6 CCN51 -0.083333 0.112 108 -0.746 1.0000
## 3 CCN51 - 0 ICS95 0.133333 0.125 101 1.066 1.0000
## 3 CCN51 - 1 ICS95 0.383333 0.125 101 3.064 0.2347
## 3 CCN51 - 2 ICS95 -0.176667 0.125 101 -1.412 0.9978
## 3 CCN51 - 3 ICS95 -0.023333 0.125 101 -0.186 1.0000
## 3 CCN51 - 4 ICS95 -0.150000 0.125 101 -1.199 0.9998
## 3 CCN51 - 5 ICS95 0.196667 0.125 101 1.572 0.9917
## 3 CCN51 - 6 ICS95 0.136667 0.125 101 1.092 0.9999
## 3 CCN51 - 0 TCS01 0.176667 0.125 101 1.412 0.9978
## 3 CCN51 - 1 TCS01 0.283333 0.125 101 2.264 0.7740
## 3 CCN51 - 2 TCS01 0.113333 0.125 101 0.906 1.0000
## 3 CCN51 - 3 TCS01 0.040000 0.125 101 0.320 1.0000
## 3 CCN51 - 4 TCS01 0.150000 0.125 101 1.199 0.9998
## 3 CCN51 - 5 TCS01 0.013333 0.125 101 0.107 1.0000
## 3 CCN51 - 6 TCS01 0.153333 0.125 101 1.225 0.9997
## 4 CCN51 - 5 CCN51 0.240000 0.112 108 2.148 0.8420
## 4 CCN51 - 6 CCN51 0.226667 0.112 108 2.028 0.8976
## 4 CCN51 - 0 ICS95 0.443333 0.125 101 3.543 0.0713
## 4 CCN51 - 1 ICS95 0.693333 0.125 101 5.541 <.0001
## 4 CCN51 - 2 ICS95 0.133333 0.125 101 1.066 1.0000
## 4 CCN51 - 3 ICS95 0.286667 0.125 101 2.291 0.7570
## 4 CCN51 - 4 ICS95 0.160000 0.125 101 1.279 0.9994
## 4 CCN51 - 5 ICS95 0.506667 0.125 101 4.049 0.0151
## 4 CCN51 - 6 ICS95 0.446667 0.125 101 3.570 0.0661
## 4 CCN51 - 0 TCS01 0.486667 0.125 101 3.890 0.0253
## 4 CCN51 - 1 TCS01 0.593333 0.125 101 4.742 0.0012
## 4 CCN51 - 2 TCS01 0.423333 0.125 101 3.383 0.1097
## 4 CCN51 - 3 TCS01 0.350000 0.125 101 2.797 0.3938
## 4 CCN51 - 4 TCS01 0.460000 0.125 101 3.676 0.0486
## 4 CCN51 - 5 TCS01 0.323333 0.125 101 2.584 0.5474
## 4 CCN51 - 6 TCS01 0.463333 0.125 101 3.703 0.0449
## 5 CCN51 - 6 CCN51 -0.013333 0.112 108 -0.119 1.0000
## 5 CCN51 - 0 ICS95 0.203333 0.125 101 1.625 0.9878
## 5 CCN51 - 1 ICS95 0.453333 0.125 101 3.623 0.0568
## 5 CCN51 - 2 ICS95 -0.106667 0.125 101 -0.853 1.0000
## 5 CCN51 - 3 ICS95 0.046667 0.125 101 0.373 1.0000
## 5 CCN51 - 4 ICS95 -0.080000 0.125 101 -0.639 1.0000
## 5 CCN51 - 5 ICS95 0.266667 0.125 101 2.131 0.8500
## 5 CCN51 - 6 ICS95 0.206667 0.125 101 1.652 0.9854
## 5 CCN51 - 0 TCS01 0.246667 0.125 101 1.971 0.9185
## 5 CCN51 - 1 TCS01 0.353333 0.125 101 2.824 0.3758
## 5 CCN51 - 2 TCS01 0.183333 0.125 101 1.465 0.9964
## 5 CCN51 - 3 TCS01 0.110000 0.125 101 0.879 1.0000
## 5 CCN51 - 4 TCS01 0.220000 0.125 101 1.758 0.9718
## 5 CCN51 - 5 TCS01 0.083333 0.125 101 0.666 1.0000
## 5 CCN51 - 6 TCS01 0.223333 0.125 101 1.785 0.9673
## 6 CCN51 - 0 ICS95 0.216667 0.125 101 1.732 0.9759
## 6 CCN51 - 1 ICS95 0.466667 0.125 101 3.730 0.0415
## 6 CCN51 - 2 ICS95 -0.093333 0.125 101 -0.746 1.0000
## 6 CCN51 - 3 ICS95 0.060000 0.125 101 0.480 1.0000
## 6 CCN51 - 4 ICS95 -0.066667 0.125 101 -0.533 1.0000
## 6 CCN51 - 5 ICS95 0.280000 0.125 101 2.238 0.7904
## 6 CCN51 - 6 ICS95 0.220000 0.125 101 1.758 0.9718
## 6 CCN51 - 0 TCS01 0.260000 0.125 101 2.078 0.8757
## 6 CCN51 - 1 TCS01 0.366667 0.125 101 2.930 0.3083
## 6 CCN51 - 2 TCS01 0.196667 0.125 101 1.572 0.9917
## 6 CCN51 - 3 TCS01 0.123333 0.125 101 0.986 1.0000
## 6 CCN51 - 4 TCS01 0.233333 0.125 101 1.865 0.9502
## 6 CCN51 - 5 TCS01 0.096667 0.125 101 0.773 1.0000
## 6 CCN51 - 6 TCS01 0.236667 0.125 101 1.891 0.9433
## 0 ICS95 - 1 ICS95 0.250000 0.112 108 2.237 0.7913
## 0 ICS95 - 2 ICS95 -0.310000 0.112 108 -2.774 0.4088
## 0 ICS95 - 3 ICS95 -0.156667 0.112 108 -1.402 0.9980
## 0 ICS95 - 4 ICS95 -0.283333 0.112 108 -2.535 0.5835
## 0 ICS95 - 5 ICS95 0.063333 0.112 108 0.567 1.0000
## 0 ICS95 - 6 ICS95 0.003333 0.112 108 0.030 1.0000
## 0 ICS95 - 0 TCS01 0.043333 0.125 101 0.346 1.0000
## 0 ICS95 - 1 TCS01 0.150000 0.125 101 1.199 0.9998
## 0 ICS95 - 2 TCS01 -0.020000 0.125 101 -0.160 1.0000
## 0 ICS95 - 3 TCS01 -0.093333 0.125 101 -0.746 1.0000
## 0 ICS95 - 4 TCS01 0.016667 0.125 101 0.133 1.0000
## 0 ICS95 - 5 TCS01 -0.120000 0.125 101 -0.959 1.0000
## 0 ICS95 - 6 TCS01 0.020000 0.125 101 0.160 1.0000
## 1 ICS95 - 2 ICS95 -0.560000 0.112 108 -5.011 0.0004
## 1 ICS95 - 3 ICS95 -0.406667 0.112 108 -3.639 0.0532
## 1 ICS95 - 4 ICS95 -0.533333 0.112 108 -4.773 0.0010
## 1 ICS95 - 5 ICS95 -0.186667 0.112 108 -1.670 0.9837
## 1 ICS95 - 6 ICS95 -0.246667 0.112 108 -2.207 0.8090
## 1 ICS95 - 0 TCS01 -0.206667 0.125 101 -1.652 0.9854
## 1 ICS95 - 1 TCS01 -0.100000 0.125 101 -0.799 1.0000
## 1 ICS95 - 2 TCS01 -0.270000 0.125 101 -2.158 0.8361
## 1 ICS95 - 3 TCS01 -0.343333 0.125 101 -2.744 0.4307
## 1 ICS95 - 4 TCS01 -0.233333 0.125 101 -1.865 0.9502
## 1 ICS95 - 5 TCS01 -0.370000 0.125 101 -2.957 0.2926
## 1 ICS95 - 6 TCS01 -0.230000 0.125 101 -1.838 0.9565
## 2 ICS95 - 3 ICS95 0.153333 0.112 108 1.372 0.9985
## 2 ICS95 - 4 ICS95 0.026667 0.112 108 0.239 1.0000
## 2 ICS95 - 5 ICS95 0.373333 0.112 108 3.341 0.1210
## 2 ICS95 - 6 ICS95 0.313333 0.112 108 2.804 0.3883
## 2 ICS95 - 0 TCS01 0.353333 0.125 101 2.824 0.3758
## 2 ICS95 - 1 TCS01 0.460000 0.125 101 3.676 0.0486
## 2 ICS95 - 2 TCS01 0.290000 0.125 101 2.318 0.7396
## 2 ICS95 - 3 TCS01 0.216667 0.125 101 1.732 0.9759
## 2 ICS95 - 4 TCS01 0.326667 0.125 101 2.611 0.5276
## 2 ICS95 - 5 TCS01 0.190000 0.125 101 1.519 0.9945
## 2 ICS95 - 6 TCS01 0.330000 0.125 101 2.637 0.5078
## 3 ICS95 - 4 ICS95 -0.126667 0.112 108 -1.134 0.9999
## 3 ICS95 - 5 ICS95 0.220000 0.112 108 1.969 0.9200
## 3 ICS95 - 6 ICS95 0.160000 0.112 108 1.432 0.9974
## 3 ICS95 - 0 TCS01 0.200000 0.125 101 1.598 0.9899
## 3 ICS95 - 1 TCS01 0.306667 0.125 101 2.451 0.6462
## 3 ICS95 - 2 TCS01 0.136667 0.125 101 1.092 0.9999
## 3 ICS95 - 3 TCS01 0.063333 0.125 101 0.506 1.0000
## 3 ICS95 - 4 TCS01 0.173333 0.125 101 1.385 0.9983
## 3 ICS95 - 5 TCS01 0.036667 0.125 101 0.293 1.0000
## 3 ICS95 - 6 TCS01 0.176667 0.125 101 1.412 0.9978
## 4 ICS95 - 5 ICS95 0.346667 0.112 108 3.102 0.2144
## 4 ICS95 - 6 ICS95 0.286667 0.112 108 2.565 0.5612
## 4 ICS95 - 0 TCS01 0.326667 0.125 101 2.611 0.5276
## 4 ICS95 - 1 TCS01 0.433333 0.125 101 3.463 0.0887
## 4 ICS95 - 2 TCS01 0.263333 0.125 101 2.105 0.8632
## 4 ICS95 - 3 TCS01 0.190000 0.125 101 1.519 0.9945
## 4 ICS95 - 4 TCS01 0.300000 0.125 101 2.398 0.6846
## 4 ICS95 - 5 TCS01 0.163333 0.125 101 1.305 0.9992
## 4 ICS95 - 6 TCS01 0.303333 0.125 101 2.424 0.6655
## 5 ICS95 - 6 ICS95 -0.060000 0.112 108 -0.537 1.0000
## 5 ICS95 - 0 TCS01 -0.020000 0.125 101 -0.160 1.0000
## 5 ICS95 - 1 TCS01 0.086667 0.125 101 0.693 1.0000
## 5 ICS95 - 2 TCS01 -0.083333 0.125 101 -0.666 1.0000
## 5 ICS95 - 3 TCS01 -0.156667 0.125 101 -1.252 0.9996
## 5 ICS95 - 4 TCS01 -0.046667 0.125 101 -0.373 1.0000
## 5 ICS95 - 5 TCS01 -0.183333 0.125 101 -1.465 0.9964
## 5 ICS95 - 6 TCS01 -0.043333 0.125 101 -0.346 1.0000
## 6 ICS95 - 0 TCS01 0.040000 0.125 101 0.320 1.0000
## 6 ICS95 - 1 TCS01 0.146667 0.125 101 1.172 0.9998
## 6 ICS95 - 2 TCS01 -0.023333 0.125 101 -0.186 1.0000
## 6 ICS95 - 3 TCS01 -0.096667 0.125 101 -0.773 1.0000
## 6 ICS95 - 4 TCS01 0.013333 0.125 101 0.107 1.0000
## 6 ICS95 - 5 TCS01 -0.123333 0.125 101 -0.986 1.0000
## 6 ICS95 - 6 TCS01 0.016667 0.125 101 0.133 1.0000
## 0 TCS01 - 1 TCS01 0.106667 0.112 108 0.955 1.0000
## 0 TCS01 - 2 TCS01 -0.063333 0.112 108 -0.567 1.0000
## 0 TCS01 - 3 TCS01 -0.136667 0.112 108 -1.223 0.9997
## 0 TCS01 - 4 TCS01 -0.026667 0.112 108 -0.239 1.0000
## 0 TCS01 - 5 TCS01 -0.163333 0.112 108 -1.462 0.9966
## 0 TCS01 - 6 TCS01 -0.023333 0.112 108 -0.209 1.0000
## 1 TCS01 - 2 TCS01 -0.170000 0.112 108 -1.521 0.9944
## 1 TCS01 - 3 TCS01 -0.243333 0.112 108 -2.178 0.8259
## 1 TCS01 - 4 TCS01 -0.133333 0.112 108 -1.193 0.9998
## 1 TCS01 - 5 TCS01 -0.270000 0.112 108 -2.416 0.6715
## 1 TCS01 - 6 TCS01 -0.130000 0.112 108 -1.163 0.9999
## 2 TCS01 - 3 TCS01 -0.073333 0.112 108 -0.656 1.0000
## 2 TCS01 - 4 TCS01 0.036667 0.112 108 0.328 1.0000
## 2 TCS01 - 5 TCS01 -0.100000 0.112 108 -0.895 1.0000
## 2 TCS01 - 6 TCS01 0.040000 0.112 108 0.358 1.0000
## 3 TCS01 - 4 TCS01 0.110000 0.112 108 0.984 1.0000
## 3 TCS01 - 5 TCS01 -0.026667 0.112 108 -0.239 1.0000
## 3 TCS01 - 6 TCS01 0.113333 0.112 108 1.014 1.0000
## 4 TCS01 - 5 TCS01 -0.136667 0.112 108 -1.223 0.9997
## 4 TCS01 - 6 TCS01 0.003333 0.112 108 0.030 1.0000
## 5 TCS01 - 6 TCS01 0.140000 0.112 108 1.253 0.9996
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 0.185667 0.112 108 1.661 0.9847
## 0 CCN51 - 2 CCN51 0.066000 0.112 108 0.591 1.0000
## 0 CCN51 - 3 CCN51 -0.555667 0.112 108 -4.973 0.0005
## 0 CCN51 - 4 CCN51 -0.660000 0.112 108 -5.906 <.0001
## 0 CCN51 - 5 CCN51 -0.327333 0.112 108 -2.929 0.3079
## 0 CCN51 - 6 CCN51 -0.137667 0.112 108 -1.232 0.9997
## 0 CCN51 - 0 ICS95 0.035667 0.125 101 0.285 1.0000
## 0 CCN51 - 1 ICS95 0.187667 0.125 101 1.500 0.9952
## 0 CCN51 - 2 ICS95 0.175667 0.125 101 1.404 0.9979
## 0 CCN51 - 3 ICS95 -0.012333 0.125 101 -0.099 1.0000
## 0 CCN51 - 4 ICS95 -0.117667 0.125 101 -0.940 1.0000
## 0 CCN51 - 5 ICS95 -0.081667 0.125 101 -0.653 1.0000
## 0 CCN51 - 6 ICS95 0.040000 0.125 101 0.320 1.0000
## 0 CCN51 - 0 TCS01 0.150000 0.125 101 1.199 0.9998
## 0 CCN51 - 1 TCS01 0.186000 0.125 101 1.487 0.9957
## 0 CCN51 - 2 TCS01 0.045667 0.125 101 0.365 1.0000
## 0 CCN51 - 3 TCS01 0.040333 0.125 101 0.322 1.0000
## 0 CCN51 - 4 TCS01 -0.235667 0.125 101 -1.883 0.9454
## 0 CCN51 - 5 TCS01 -0.257667 0.125 101 -2.059 0.8840
## 0 CCN51 - 6 TCS01 -0.075667 0.125 101 -0.605 1.0000
## 1 CCN51 - 2 CCN51 -0.119667 0.112 108 -1.071 1.0000
## 1 CCN51 - 3 CCN51 -0.741333 0.112 108 -6.634 <.0001
## 1 CCN51 - 4 CCN51 -0.845667 0.112 108 -7.568 <.0001
## 1 CCN51 - 5 CCN51 -0.513000 0.112 108 -4.591 0.0021
## 1 CCN51 - 6 CCN51 -0.323333 0.112 108 -2.893 0.3299
## 1 CCN51 - 0 ICS95 -0.150000 0.125 101 -1.199 0.9998
## 1 CCN51 - 1 ICS95 0.002000 0.125 101 0.016 1.0000
## 1 CCN51 - 2 ICS95 -0.010000 0.125 101 -0.080 1.0000
## 1 CCN51 - 3 ICS95 -0.198000 0.125 101 -1.582 0.9910
## 1 CCN51 - 4 ICS95 -0.303333 0.125 101 -2.424 0.6655
## 1 CCN51 - 5 ICS95 -0.267333 0.125 101 -2.137 0.8472
## 1 CCN51 - 6 ICS95 -0.145667 0.125 101 -1.164 0.9998
## 1 CCN51 - 0 TCS01 -0.035667 0.125 101 -0.285 1.0000
## 1 CCN51 - 1 TCS01 0.000333 0.125 101 0.003 1.0000
## 1 CCN51 - 2 TCS01 -0.140000 0.125 101 -1.119 0.9999
## 1 CCN51 - 3 TCS01 -0.145333 0.125 101 -1.162 0.9999
## 1 CCN51 - 4 TCS01 -0.421333 0.125 101 -3.367 0.1143
## 1 CCN51 - 5 TCS01 -0.443333 0.125 101 -3.543 0.0713
## 1 CCN51 - 6 TCS01 -0.261333 0.125 101 -2.089 0.8708
## 2 CCN51 - 3 CCN51 -0.621667 0.112 108 -5.563 <.0001
## 2 CCN51 - 4 CCN51 -0.726000 0.112 108 -6.497 <.0001
## 2 CCN51 - 5 CCN51 -0.393333 0.112 108 -3.520 0.0749
## 2 CCN51 - 6 CCN51 -0.203667 0.112 108 -1.823 0.9602
## 2 CCN51 - 0 ICS95 -0.030333 0.125 101 -0.242 1.0000
## 2 CCN51 - 1 ICS95 0.121667 0.125 101 0.972 1.0000
## 2 CCN51 - 2 ICS95 0.109667 0.125 101 0.876 1.0000
## 2 CCN51 - 3 ICS95 -0.078333 0.125 101 -0.626 1.0000
## 2 CCN51 - 4 ICS95 -0.183667 0.125 101 -1.468 0.9964
## 2 CCN51 - 5 ICS95 -0.147667 0.125 101 -1.180 0.9998
## 2 CCN51 - 6 ICS95 -0.026000 0.125 101 -0.208 1.0000
## 2 CCN51 - 0 TCS01 0.084000 0.125 101 0.671 1.0000
## 2 CCN51 - 1 TCS01 0.120000 0.125 101 0.959 1.0000
## 2 CCN51 - 2 TCS01 -0.020333 0.125 101 -0.163 1.0000
## 2 CCN51 - 3 TCS01 -0.025667 0.125 101 -0.205 1.0000
## 2 CCN51 - 4 TCS01 -0.301667 0.125 101 -2.411 0.6751
## 2 CCN51 - 5 TCS01 -0.323667 0.125 101 -2.587 0.5454
## 2 CCN51 - 6 TCS01 -0.141667 0.125 101 -1.132 0.9999
## 3 CCN51 - 4 CCN51 -0.104333 0.112 108 -0.934 1.0000
## 3 CCN51 - 5 CCN51 0.228333 0.112 108 2.043 0.8914
## 3 CCN51 - 6 CCN51 0.418000 0.112 108 3.741 0.0393
## 3 CCN51 - 0 ICS95 0.591333 0.125 101 4.726 0.0013
## 3 CCN51 - 1 ICS95 0.743333 0.125 101 5.941 <.0001
## 3 CCN51 - 2 ICS95 0.731333 0.125 101 5.845 <.0001
## 3 CCN51 - 3 ICS95 0.543333 0.125 101 4.342 0.0055
## 3 CCN51 - 4 ICS95 0.438000 0.125 101 3.501 0.0802
## 3 CCN51 - 5 ICS95 0.474000 0.125 101 3.788 0.0347
## 3 CCN51 - 6 ICS95 0.595667 0.125 101 4.761 0.0011
## 3 CCN51 - 0 TCS01 0.705667 0.125 101 5.640 <.0001
## 3 CCN51 - 1 TCS01 0.741667 0.125 101 5.928 <.0001
## 3 CCN51 - 2 TCS01 0.601333 0.125 101 4.806 0.0010
## 3 CCN51 - 3 TCS01 0.596000 0.125 101 4.763 0.0011
## 3 CCN51 - 4 TCS01 0.320000 0.125 101 2.558 0.5673
## 3 CCN51 - 5 TCS01 0.298000 0.125 101 2.382 0.6958
## 3 CCN51 - 6 TCS01 0.480000 0.125 101 3.836 0.0299
## 4 CCN51 - 5 CCN51 0.332667 0.112 108 2.977 0.2800
## 4 CCN51 - 6 CCN51 0.522333 0.112 108 4.674 0.0015
## 4 CCN51 - 0 ICS95 0.695667 0.125 101 5.560 <.0001
## 4 CCN51 - 1 ICS95 0.847667 0.125 101 6.775 <.0001
## 4 CCN51 - 2 ICS95 0.835667 0.125 101 6.679 <.0001
## 4 CCN51 - 3 ICS95 0.647667 0.125 101 5.176 0.0002
## 4 CCN51 - 4 ICS95 0.542333 0.125 101 4.334 0.0056
## 4 CCN51 - 5 ICS95 0.578333 0.125 101 4.622 0.0020
## 4 CCN51 - 6 ICS95 0.700000 0.125 101 5.595 <.0001
## 4 CCN51 - 0 TCS01 0.810000 0.125 101 6.474 <.0001
## 4 CCN51 - 1 TCS01 0.846000 0.125 101 6.761 <.0001
## 4 CCN51 - 2 TCS01 0.705667 0.125 101 5.640 <.0001
## 4 CCN51 - 3 TCS01 0.700333 0.125 101 5.597 <.0001
## 4 CCN51 - 4 TCS01 0.424333 0.125 101 3.391 0.1074
## 4 CCN51 - 5 TCS01 0.402333 0.125 101 3.216 0.1665
## 4 CCN51 - 6 TCS01 0.584333 0.125 101 4.670 0.0016
## 5 CCN51 - 6 CCN51 0.189667 0.112 108 1.697 0.9807
## 5 CCN51 - 0 ICS95 0.363000 0.125 101 2.901 0.3262
## 5 CCN51 - 1 ICS95 0.515000 0.125 101 4.116 0.0120
## 5 CCN51 - 2 ICS95 0.503000 0.125 101 4.020 0.0166
## 5 CCN51 - 3 ICS95 0.315000 0.125 101 2.518 0.5971
## 5 CCN51 - 4 ICS95 0.209667 0.125 101 1.676 0.9830
## 5 CCN51 - 5 ICS95 0.245667 0.125 101 1.963 0.9213
## 5 CCN51 - 6 ICS95 0.367333 0.125 101 2.936 0.3051
## 5 CCN51 - 0 TCS01 0.477333 0.125 101 3.815 0.0320
## 5 CCN51 - 1 TCS01 0.513333 0.125 101 4.103 0.0126
## 5 CCN51 - 2 TCS01 0.373000 0.125 101 2.981 0.2789
## 5 CCN51 - 3 TCS01 0.367667 0.125 101 2.938 0.3035
## 5 CCN51 - 4 TCS01 0.091667 0.125 101 0.733 1.0000
## 5 CCN51 - 5 TCS01 0.069667 0.125 101 0.557 1.0000
## 5 CCN51 - 6 TCS01 0.251667 0.125 101 2.011 0.9038
## 6 CCN51 - 0 ICS95 0.173333 0.125 101 1.385 0.9983
## 6 CCN51 - 1 ICS95 0.325333 0.125 101 2.600 0.5355
## 6 CCN51 - 2 ICS95 0.313333 0.125 101 2.504 0.6070
## 6 CCN51 - 3 ICS95 0.125333 0.125 101 1.002 1.0000
## 6 CCN51 - 4 ICS95 0.020000 0.125 101 0.160 1.0000
## 6 CCN51 - 5 ICS95 0.056000 0.125 101 0.448 1.0000
## 6 CCN51 - 6 ICS95 0.177667 0.125 101 1.420 0.9976
## 6 CCN51 - 0 TCS01 0.287667 0.125 101 2.299 0.7519
## 6 CCN51 - 1 TCS01 0.323667 0.125 101 2.587 0.5454
## 6 CCN51 - 2 TCS01 0.183333 0.125 101 1.465 0.9964
## 6 CCN51 - 3 TCS01 0.178000 0.125 101 1.423 0.9976
## 6 CCN51 - 4 TCS01 -0.098000 0.125 101 -0.783 1.0000
## 6 CCN51 - 5 TCS01 -0.120000 0.125 101 -0.959 1.0000
## 6 CCN51 - 6 TCS01 0.062000 0.125 101 0.496 1.0000
## 0 ICS95 - 1 ICS95 0.152000 0.112 108 1.360 0.9987
## 0 ICS95 - 2 ICS95 0.140000 0.112 108 1.253 0.9996
## 0 ICS95 - 3 ICS95 -0.048000 0.112 108 -0.430 1.0000
## 0 ICS95 - 4 ICS95 -0.153333 0.112 108 -1.372 0.9985
## 0 ICS95 - 5 ICS95 -0.117333 0.112 108 -1.050 1.0000
## 0 ICS95 - 6 ICS95 0.004333 0.112 108 0.039 1.0000
## 0 ICS95 - 0 TCS01 0.114333 0.125 101 0.914 1.0000
## 0 ICS95 - 1 TCS01 0.150333 0.125 101 1.201 0.9998
## 0 ICS95 - 2 TCS01 0.010000 0.125 101 0.080 1.0000
## 0 ICS95 - 3 TCS01 0.004667 0.125 101 0.037 1.0000
## 0 ICS95 - 4 TCS01 -0.271333 0.125 101 -2.169 0.8303
## 0 ICS95 - 5 TCS01 -0.293333 0.125 101 -2.344 0.7216
## 0 ICS95 - 6 TCS01 -0.111333 0.125 101 -0.890 1.0000
## 1 ICS95 - 2 ICS95 -0.012000 0.112 108 -0.107 1.0000
## 1 ICS95 - 3 ICS95 -0.200000 0.112 108 -1.790 0.9667
## 1 ICS95 - 4 ICS95 -0.305333 0.112 108 -2.732 0.4382
## 1 ICS95 - 5 ICS95 -0.269333 0.112 108 -2.410 0.6758
## 1 ICS95 - 6 ICS95 -0.147667 0.112 108 -1.321 0.9991
## 1 ICS95 - 0 TCS01 -0.037667 0.125 101 -0.301 1.0000
## 1 ICS95 - 1 TCS01 -0.001667 0.125 101 -0.013 1.0000
## 1 ICS95 - 2 TCS01 -0.142000 0.125 101 -1.135 0.9999
## 1 ICS95 - 3 TCS01 -0.147333 0.125 101 -1.178 0.9998
## 1 ICS95 - 4 TCS01 -0.423333 0.125 101 -3.383 0.1097
## 1 ICS95 - 5 TCS01 -0.445333 0.125 101 -3.559 0.0681
## 1 ICS95 - 6 TCS01 -0.263333 0.125 101 -2.105 0.8632
## 2 ICS95 - 3 ICS95 -0.188000 0.112 108 -1.682 0.9824
## 2 ICS95 - 4 ICS95 -0.293333 0.112 108 -2.625 0.5166
## 2 ICS95 - 5 ICS95 -0.257333 0.112 108 -2.303 0.7498
## 2 ICS95 - 6 ICS95 -0.135667 0.112 108 -1.214 0.9997
## 2 ICS95 - 0 TCS01 -0.025667 0.125 101 -0.205 1.0000
## 2 ICS95 - 1 TCS01 0.010333 0.125 101 0.083 1.0000
## 2 ICS95 - 2 TCS01 -0.130000 0.125 101 -1.039 1.0000
## 2 ICS95 - 3 TCS01 -0.135333 0.125 101 -1.082 1.0000
## 2 ICS95 - 4 TCS01 -0.411333 0.125 101 -3.287 0.1398
## 2 ICS95 - 5 TCS01 -0.433333 0.125 101 -3.463 0.0887
## 2 ICS95 - 6 TCS01 -0.251333 0.125 101 -2.009 0.9048
## 3 ICS95 - 4 ICS95 -0.105333 0.112 108 -0.943 1.0000
## 3 ICS95 - 5 ICS95 -0.069333 0.112 108 -0.620 1.0000
## 3 ICS95 - 6 ICS95 0.052333 0.112 108 0.468 1.0000
## 3 ICS95 - 0 TCS01 0.162333 0.125 101 1.297 0.9993
## 3 ICS95 - 1 TCS01 0.198333 0.125 101 1.585 0.9908
## 3 ICS95 - 2 TCS01 0.058000 0.125 101 0.464 1.0000
## 3 ICS95 - 3 TCS01 0.052667 0.125 101 0.421 1.0000
## 3 ICS95 - 4 TCS01 -0.223333 0.125 101 -1.785 0.9673
## 3 ICS95 - 5 TCS01 -0.245333 0.125 101 -1.961 0.9222
## 3 ICS95 - 6 TCS01 -0.063333 0.125 101 -0.506 1.0000
## 4 ICS95 - 5 ICS95 0.036000 0.112 108 0.322 1.0000
## 4 ICS95 - 6 ICS95 0.157667 0.112 108 1.411 0.9978
## 4 ICS95 - 0 TCS01 0.267667 0.125 101 2.139 0.8459
## 4 ICS95 - 1 TCS01 0.303667 0.125 101 2.427 0.6636
## 4 ICS95 - 2 TCS01 0.163333 0.125 101 1.305 0.9992
## 4 ICS95 - 3 TCS01 0.158000 0.125 101 1.263 0.9995
## 4 ICS95 - 4 TCS01 -0.118000 0.125 101 -0.943 1.0000
## 4 ICS95 - 5 TCS01 -0.140000 0.125 101 -1.119 0.9999
## 4 ICS95 - 6 TCS01 0.042000 0.125 101 0.336 1.0000
## 5 ICS95 - 6 ICS95 0.121667 0.112 108 1.089 0.9999
## 5 ICS95 - 0 TCS01 0.231667 0.125 101 1.852 0.9534
## 5 ICS95 - 1 TCS01 0.267667 0.125 101 2.139 0.8459
## 5 ICS95 - 2 TCS01 0.127333 0.125 101 1.018 1.0000
## 5 ICS95 - 3 TCS01 0.122000 0.125 101 0.975 1.0000
## 5 ICS95 - 4 TCS01 -0.154000 0.125 101 -1.231 0.9997
## 5 ICS95 - 5 TCS01 -0.176000 0.125 101 -1.407 0.9979
## 5 ICS95 - 6 TCS01 0.006000 0.125 101 0.048 1.0000
## 6 ICS95 - 0 TCS01 0.110000 0.125 101 0.879 1.0000
## 6 ICS95 - 1 TCS01 0.146000 0.125 101 1.167 0.9998
## 6 ICS95 - 2 TCS01 0.005667 0.125 101 0.045 1.0000
## 6 ICS95 - 3 TCS01 0.000333 0.125 101 0.003 1.0000
## 6 ICS95 - 4 TCS01 -0.275667 0.125 101 -2.203 0.8109
## 6 ICS95 - 5 TCS01 -0.297667 0.125 101 -2.379 0.6977
## 6 ICS95 - 6 TCS01 -0.115667 0.125 101 -0.924 1.0000
## 0 TCS01 - 1 TCS01 0.036000 0.112 108 0.322 1.0000
## 0 TCS01 - 2 TCS01 -0.104333 0.112 108 -0.934 1.0000
## 0 TCS01 - 3 TCS01 -0.109667 0.112 108 -0.981 1.0000
## 0 TCS01 - 4 TCS01 -0.385667 0.112 108 -3.451 0.0905
## 0 TCS01 - 5 TCS01 -0.407667 0.112 108 -3.648 0.0519
## 0 TCS01 - 6 TCS01 -0.225667 0.112 108 -2.019 0.9012
## 1 TCS01 - 2 TCS01 -0.140333 0.112 108 -1.256 0.9996
## 1 TCS01 - 3 TCS01 -0.145667 0.112 108 -1.304 0.9993
## 1 TCS01 - 4 TCS01 -0.421667 0.112 108 -3.773 0.0356
## 1 TCS01 - 5 TCS01 -0.443667 0.112 108 -3.970 0.0190
## 1 TCS01 - 6 TCS01 -0.261667 0.112 108 -2.342 0.7238
## 2 TCS01 - 3 TCS01 -0.005333 0.112 108 -0.048 1.0000
## 2 TCS01 - 4 TCS01 -0.281333 0.112 108 -2.518 0.5969
## 2 TCS01 - 5 TCS01 -0.303333 0.112 108 -2.714 0.4510
## 2 TCS01 - 6 TCS01 -0.121333 0.112 108 -1.086 0.9999
## 3 TCS01 - 4 TCS01 -0.276000 0.112 108 -2.470 0.6323
## 3 TCS01 - 5 TCS01 -0.298000 0.112 108 -2.667 0.4857
## 3 TCS01 - 6 TCS01 -0.116000 0.112 108 -1.038 1.0000
## 4 TCS01 - 5 TCS01 -0.022000 0.112 108 -0.197 1.0000
## 4 TCS01 - 6 TCS01 0.160000 0.112 108 1.432 0.9974
## 5 TCS01 - 6 TCS01 0.182000 0.112 108 1.629 0.9877
##
## P value adjustment: tukey method for comparing a family of 21 estimates
##Splitting dataframe by temperature ramp
## Protocol 3 (T3)
datos.curve1<-filter(datos, curva=="T3")
##Check assumptions
##Outliers
datos.curve1 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm1<-datos.curve1 %>%
group_by(gen, diam2) #%>%
#shapiro_test(acidez.grano)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 63 × 10
## curva diam2 time.let gen muestra ph.testa acidez.testa ph.grano
## <fct> <fct> <chr> <fct> <fct> <dbl> <dbl> <dbl>
## 1 T3 0 cero CCN51 14 3.19 0.79 5.58
## 2 T3 0 cero CCN51 25 3.18 0.93 5.63
## 3 T3 0 cero CCN51 36 3.20 0.67 5.59
## 4 T3 0 cero ICS95 14 3.24 1.07 5.66
## 5 T3 0 cero ICS95 25 3.24 0.85 5.60
## 6 T3 0 cero ICS95 36 3.23 0.93 5.55
## 7 T3 0 cero TCS01 14 3.69 0.69 5.67
## 8 T3 0 cero TCS01 25 3.58 0.69 5.70
## 9 T3 0 cero TCS01 36 3.60 0.8 5.70
## 10 T3 1 uno CCN51 14 3.19 0.83 5.60
## 11 T3 1 uno CCN51 25 3.12 0.92 5.52
## 12 T3 1 uno CCN51 36 3.09 1.12 5.46
## 13 T3 1 uno ICS95 14 3.11 0.9 5.45
## 14 T3 1 uno ICS95 25 3.65 1.1 5.48
## 15 T3 1 uno ICS95 36 3.03 1.18 5.57
## 16 T3 1 uno TCS01 14 3.29 0.76 5.36
## 17 T3 1 uno TCS01 25 3.44 0.82 5.55
## 18 T3 1 uno TCS01 36 3.23 1.1 4.79
## 19 T3 2 dos CCN51 14 3.84 0.24 5.36
## 20 T3 2 dos CCN51 25 4.17 0.2 5.46
## 21 T3 2 dos CCN51 36 4.06 0.22 5.54
## 22 T3 2 dos ICS95 14 3.35 0.28 5.16
## 23 T3 2 dos ICS95 25 3.48 0.49 4.51
## 24 T3 2 dos ICS95 36 3.64 0.34 5.13
## 25 T3 2 dos TCS01 14 3.67 0.79 5.17
## 26 T3 2 dos TCS01 25 3.66 0.65 4.46
## 27 T3 2 dos TCS01 36 3.70 0.89 4.67
## 28 T3 3 tres CCN51 14 3.97 0.31 4.69
## 29 T3 3 tres CCN51 25 4.31 0.22 4.84
## 30 T3 3 tres CCN51 36 3.92 0.33 4.56
## 31 T3 3 tres ICS95 14 3.11 0.83 3.77
## 32 T3 3 tres ICS95 25 3.28 0.47 3.91
## 33 T3 3 tres ICS95 36 2.71 1.68 3.18
## 34 T3 3 tres TCS01 14 3.54 2 3.78
## 35 T3 3 tres TCS01 25 3.51 2.07 3.62
## 36 T3 3 tres TCS01 36 3.48 2.33 3.56
## 37 T3 4 cuatro CCN51 14 3.66 0.39 4.44
## 38 T3 4 cuatro CCN51 25 4.12 0.34 4.22
## 39 T3 4 cuatro CCN51 36 3.90 0.47 4.26
## 40 T3 4 cuatro ICS95 14 3.83 0.74 4.19
## 41 T3 4 cuatro ICS95 25 3.86 0.62 4.17
## 42 T3 4 cuatro ICS95 36 3.51 1.26 3.56
## 43 T3 4 cuatro TCS01 14 3.70 2.22 3.79
## 44 T3 4 cuatro TCS01 25 3.65 1.89 3.70
## 45 T3 4 cuatro TCS01 36 3.56 2.27 3.53
## 46 T3 5 cinco CCN51 14 5.18 0.4 4.56
## 47 T3 5 cinco CCN51 25 5.23 0.34 4.26
## 48 T3 5 cinco CCN51 36 5.21 0.35 4.28
## 49 T3 5 cinco ICS95 14 5.95 0.24 5.04
## 50 T3 5 cinco ICS95 25 4.35 0.61 5.33
## 51 T3 5 cinco ICS95 36 4.72 0.48 3.62
## 52 T3 5 cinco TCS01 14 3.26 1.82 3.02
## 53 T3 5 cinco TCS01 25 3.11 1.82 2.67
## 54 T3 5 cinco TCS01 36 3.01 2.05 2.78
## 55 T3 6 seis CCN51 14 6.52 0.12 5.51
## 56 T3 6 seis CCN51 25 6.66 0.12 4.16
## 57 T3 6 seis CCN51 36 6.79 0.11 5.47
## 58 T3 6 seis ICS95 14 6.74 0.17 5.15
## 59 T3 6 seis ICS95 25 6.58 0.21 4.82
## 60 T3 6 seis ICS95 36 6.53 0.13 4.36
## 61 T3 6 seis TCS01 14 4.57 1.11 4.22
## 62 T3 6 seis TCS01 25 4.26 1.07 3.85
## 63 T3 6 seis TCS01 36 4.45 0.92 4.08
## # ℹ 2 more variables: acidez.grano <dbl>, id <fct>
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "acidez.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev1<-datos.curve1 %>%
group_by(diam2) %>%
levene_test(acidez.grano ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 1.06 0.403
## 2 1 2 6 0.646 0.557
## 3 2 2 6 2.72 0.145
## 4 3 2 6 0.0196 0.981
## 5 4 2 6 1.01 0.418
## 6 5 2 6 0.750 0.512
## 7 6 2 6 0.329 0.732
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = acidez.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov1)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 38.530 3.77e-04 * 0.784
## 2 diam2 1.71 10.28 35.709 3.37e-05 * 0.810
## 3 gen:diam2 3.43 10.28 11.257 1.00e-03 * 0.729
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 9.611 0.000532 * 0.788
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 2.721 0.066 0.493
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 61.467 2.6e-08 * 0.956
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve2 %>%
group_by(gen, diam2) %>%
shapiro_test(acidez.grano)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 acidez.grano 0.942 0.537
## 2 1 CCN51 acidez.grano 0.862 0.274
## 3 2 CCN51 acidez.grano 0.942 0.537
## 4 3 CCN51 acidez.grano 0.954 0.587
## 5 4 CCN51 acidez.grano 0.996 0.884
## 6 5 CCN51 acidez.grano 0.990 0.809
## 7 6 CCN51 acidez.grano 0.987 0.780
## 8 0 ICS95 acidez.grano 0.990 0.806
## 9 1 ICS95 acidez.grano 1 1.00
## 10 2 ICS95 acidez.grano 0.942 0.537
## 11 3 ICS95 acidez.grano 0.829 0.187
## 12 4 ICS95 acidez.grano 0.987 0.780
## 13 5 ICS95 acidez.grano 0.964 0.637
## 14 6 ICS95 acidez.grano 0.855 0.253
## 15 0 TCS01 acidez.grano 0.832 0.194
## 16 1 TCS01 acidez.grano 0.942 0.537
## 17 2 TCS01 acidez.grano 0.842 0.220
## 18 3 TCS01 acidez.grano 0.75 0
## 19 4 TCS01 acidez.grano 0.824 0.174
## 20 5 TCS01 acidez.grano 0.75 0
## 21 6 TCS01 acidez.grano 0.862 0.274
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "acidez.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve2 %>%
group_by(diam2) %>%
levene_test(acidez.grano ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.542 0.608
## 2 1 2 6 0.497 0.631
## 3 2 2 6 0.0123 0.988
## 4 3 2 6 0.860 0.469
## 5 4 2 6 2.06 0.208
## 6 5 2 6 2.56 0.157
## 7 6 2 6 0.812 0.487
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = acidez.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.0 6.00 2.737 0.143000 0.149
## 2 diam2 2.4 14.41 14.959 0.000189 * 0.668
## 3 gen:diam2 4.8 14.41 3.539 0.028000 * 0.488
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 5.5 0.006 * 0.689
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 16.787 3.37e-05 * 0.867
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 8.2 0.001 * 0.789
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve3 %>%
group_by(gen, diam2) %>%
shapiro_test(acidez.grano)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 acidez.grano 0.957 0.600
## 2 1 CCN51 acidez.grano 0.936 0.510
## 3 2 CCN51 acidez.grano 0.994 0.856
## 4 3 CCN51 acidez.grano 0.812 0.144
## 5 4 CCN51 acidez.grano 0.953 0.585
## 6 5 CCN51 acidez.grano 0.885 0.340
## 7 6 CCN51 acidez.grano 0.948 0.561
## 8 0 ICS95 acidez.grano 0.837 0.206
## 9 1 ICS95 acidez.grano 0.75 0
## 10 2 ICS95 acidez.grano 0.878 0.317
## 11 3 ICS95 acidez.grano 0.909 0.416
## 12 4 ICS95 acidez.grano 0.913 0.430
## 13 5 ICS95 acidez.grano 0.942 0.537
## 14 6 ICS95 acidez.grano 0.994 0.847
## 15 0 TCS01 acidez.grano 0.903 0.397
## 16 1 TCS01 acidez.grano 0.825 0.177
## 17 2 TCS01 acidez.grano 0.850 0.241
## 18 3 TCS01 acidez.grano 0.898 0.380
## 19 4 TCS01 acidez.grano 0.943 0.539
## 20 5 TCS01 acidez.grano 0.998 0.913
## 21 6 TCS01 acidez.grano 0.941 0.533
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "acidez.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve3 %>%
group_by(diam2) %>%
levene_test(acidez.grano ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.532 0.613
## 2 1 2 6 0.583 0.587
## 3 2 2 6 0.583 0.587
## 4 3 2 6 0.942 0.441
## 5 4 2 6 0.295 0.755
## 6 5 2 6 0.338 0.726
## 7 6 2 6 0.589 0.584
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = acidez.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 4.783 5.7e-02 0.430
## 2 diam2 2.36 14.17 25.870 1.1e-05 * 0.694
## 3 gen:diam2 4.72 14.17 4.682 1.1e-02 * 0.451
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 49.577 8.9e-08 * 0.941
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 2.602 0.075 0.399
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = acidez.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 5.921 0.004 * 0.6
## Gráficas por réplica y genotipo
datos$diam2<-as.numeric(as.character(datos$diam2))
##Gráfica por réplica compuesta
pht<- ggplot(datos, aes(x = diam2)) +
facet_grid(curva~gen*muestra) +
geom_line(aes(y=acidez.grano)) +
geom_point(aes(y=acidez.grano)) +
scale_y_continuous(name = expression("Nib acidity")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
linewidth = 0.25)) +
theme(text = element_text(size = 12))
pht
## Gráfica por genotipo
datos2<-summarySE (datos, measurevar = "acidez.grano", groupvars = c("curva", "gen","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data_out/acidity_mean_grano.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=acidez.grano-ci, ymax=acidez.grano+ci), width=.1) +
geom_line(aes(y=acidez.grano)) +
geom_point(aes(y=acidez.grano)) +
scale_y_continuous(name = expression("Nib acidity")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
linewidth = 0.25)) +
theme(text = element_text(size = 15))
pht2
