setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("temp.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(temp, 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 temp 3 25 0
## 2 T3 1 CCN51 temp 3 37.3 0.675
## 3 T3 2 CCN51 temp 3 40.9 0.153
## 4 T3 3 CCN51 temp 3 46.1 0.941
## 5 T3 4 CCN51 temp 3 45.2 0.252
## 6 T3 5 CCN51 temp 3 43.6 0.862
## 7 T3 6 CCN51 temp 3 38.2 0.705
## 8 T3 0 ICS95 temp 3 25 0
## 9 T3 1 ICS95 temp 3 40.3 0.486
## 10 T3 2 ICS95 temp 3 41.2 0.477
## 11 T3 3 ICS95 temp 3 47.4 0.522
## 12 T3 4 ICS95 temp 3 46.4 0.492
## 13 T3 5 ICS95 temp 3 45.3 1.10
## 14 T3 6 ICS95 temp 3 39.2 0.568
## 15 T3 0 TCS01 temp 3 25 0
## 16 T3 1 TCS01 temp 3 38.4 0.333
## 17 T3 2 TCS01 temp 3 41.8 0.577
## 18 T3 3 TCS01 temp 3 46.6 0.275
## 19 T3 4 TCS01 temp 3 42.5 1.43
## 20 T3 5 TCS01 temp 3 40.2 5.06
## 21 T3 6 TCS01 temp 3 35.8 1.63
## 22 T1 0 CCN51 temp 3 23.6 0.361
## 23 T1 1 CCN51 temp 3 36.3 0.275
## 24 T1 2 CCN51 temp 3 39.7 0.401
## 25 T1 3 CCN51 temp 3 42.9 1.28
## 26 T1 4 CCN51 temp 3 44.7 0.284
## 27 T1 5 CCN51 temp 3 44.0 1.13
## 28 T1 6 CCN51 temp 3 42.8 0.355
## 29 T1 0 ICS95 temp 3 23.6 0.265
## 30 T1 1 ICS95 temp 3 37.9 0.126
## 31 T1 2 ICS95 temp 3 39.6 0.451
## 32 T1 3 ICS95 temp 3 42.9 1.00
## 33 T1 4 ICS95 temp 3 43.0 0.161
## 34 T1 5 ICS95 temp 3 43.5 0.765
## 35 T1 6 ICS95 temp 3 41.4 0.176
## 36 T1 0 TCS01 temp 3 23.5 0.231
## 37 T1 1 TCS01 temp 3 37.1 0.5
## 38 T1 2 TCS01 temp 3 40.1 0.404
## 39 T1 3 TCS01 temp 3 43.7 1.25
## 40 T1 4 TCS01 temp 3 44.7 0.284
## 41 T1 5 TCS01 temp 3 43.4 0.444
## 42 T1 6 TCS01 temp 3 42.5 0.765
## 43 T2 0 CCN51 temp 3 25.9 0.202
## 44 T2 1 CCN51 temp 3 34.5 0.275
## 45 T2 2 CCN51 temp 3 38.1 0.301
## 46 T2 3 CCN51 temp 3 42.7 0.605
## 47 T2 4 CCN51 temp 3 44.0 0.306
## 48 T2 5 CCN51 temp 3 44.0 0.835
## 49 T2 6 CCN51 temp 3 39.0 1.42
## 50 T2 0 ICS95 temp 3 25.8 0.25
## 51 T2 1 ICS95 temp 3 37.6 0.1
## 52 T2 2 ICS95 temp 3 36.6 0.202
## 53 T2 3 ICS95 temp 3 43.7 0.729
## 54 T2 4 ICS95 temp 3 44.2 0.293
## 55 T2 5 ICS95 temp 3 43.7 0.52
## 56 T2 6 ICS95 temp 3 42.4 0.679
## 57 T2 0 TCS01 temp 3 25.9 0.218
## 58 T2 1 TCS01 temp 3 37.2 0.304
## 59 T2 2 TCS01 temp 3 38.0 0.608
## 60 T2 3 TCS01 temp 3 43.4 0.681
## 61 T2 4 TCS01 temp 3 44.9 0.939
## 62 T2 5 TCS01 temp 3 45.0 0.832
## 63 T2 6 TCS01 temp 3 41.5 1.45
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "temp",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(temp)
## [1] curva diam2 gen tiem.let dia progamada
## [7] muestra temp id 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(temp)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 189 × 9
## curva tiem.let diam2 dia gen progamada muestra temp id
## <fct> <chr> <fct> <int> <fct> <int> <fct> <dbl> <fct>
## 1 T3 cero 0 0 CCN51 35 14 25 1
## 2 T3 cero 0 0 CCN51 35 25 25 2
## 3 T3 cero 0 0 CCN51 35 36 25 3
## 4 T3 cero 0 0 ICS95 35 14 25 4
## 5 T3 cero 0 0 ICS95 35 25 25 5
## 6 T3 cero 0 0 ICS95 35 36 25 6
## 7 T3 cero 0 0 TCS01 35 14 25 7
## 8 T3 cero 0 0 TCS01 35 25 25 8
## 9 T3 cero 0 0 TCS01 35 36 25 9
## 10 T1 cero 0 0 CCN51 35 14 24 10
## 11 T1 cero 0 0 CCN51 35 25 23.3 11
## 12 T1 cero 0 0 CCN51 35 36 23.5 12
## 13 T1 cero 0 0 ICS95 35 14 23.2 13
## 14 T1 cero 0 0 ICS95 35 25 23.6 14
## 15 T1 cero 0 0 ICS95 35 36 23.8 15
## 16 T1 cero 0 0 TCS01 35 14 23.4 16
## 17 T1 cero 0 0 TCS01 35 25 23.8 17
## 18 T1 cero 0 0 TCS01 35 36 23.4 18
## 19 T2 cero 0 0 CCN51 35 14 25.7 19
## 20 T2 cero 0 0 CCN51 35 25 26.1 20
## 21 T2 cero 0 0 CCN51 35 36 25.8 21
## 22 T2 cero 0 0 ICS95 35 14 25.8 22
## 23 T2 cero 0 0 ICS95 35 25 25.6 23
## 24 T2 cero 0 0 ICS95 35 36 26.1 24
## 25 T2 cero 0 0 TCS01 35 14 26.2 25
## 26 T2 cero 0 0 TCS01 35 25 25.8 26
## 27 T2 cero 0 0 TCS01 35 36 25.8 27
## 28 T3 uno 1 1 CCN51 35 14 36.6 1
## 29 T3 uno 1 1 CCN51 35 25 38.0 2
## 30 T3 uno 1 1 CCN51 35 36 37.3 3
## 31 T3 uno 1 1 ICS95 35 14 39.8 4
## 32 T3 uno 1 1 ICS95 35 25 40.8 5
## 33 T3 uno 1 1 ICS95 35 36 40.2 6
## 34 T3 uno 1 1 TCS01 35 14 38 7
## 35 T3 uno 1 1 TCS01 35 25 38.4 8
## 36 T3 uno 1 1 TCS01 35 36 38.6 9
## 37 T1 uno 1 1 CCN51 35 14 36.6 10
## 38 T1 uno 1 1 CCN51 35 25 36.2 11
## 39 T1 uno 1 1 CCN51 35 36 36.1 12
## 40 T1 uno 1 1 ICS95 35 14 37.9 13
## 41 T1 uno 1 1 ICS95 35 25 38.0 14
## 42 T1 uno 1 1 ICS95 35 36 37.8 15
## 43 T1 uno 1 1 TCS01 35 14 37.1 16
## 44 T1 uno 1 1 TCS01 35 25 37.6 17
## 45 T1 uno 1 1 TCS01 35 36 36.6 18
## 46 T2 uno 1 1 CCN51 35 14 34.4 19
## 47 T2 uno 1 1 CCN51 35 25 34.4 20
## 48 T2 uno 1 1 CCN51 35 36 34.8 21
## 49 T2 uno 1 1 ICS95 35 14 37.7 22
## 50 T2 uno 1 1 ICS95 35 25 37.5 23
## 51 T2 uno 1 1 ICS95 35 36 37.6 24
## 52 T2 uno 1 1 TCS01 35 14 37.1 25
## 53 T2 uno 1 1 TCS01 35 25 37.6 26
## 54 T2 uno 1 1 TCS01 35 36 37.0 27
## 55 T3 dos 2 2 CCN51 40 14 41 1
## 56 T3 dos 2 2 CCN51 40 25 40.9 2
## 57 T3 dos 2 2 CCN51 40 36 40.7 3
## 58 T3 dos 2 2 ICS95 40 14 41.2 4
## 59 T3 dos 2 2 ICS95 40 25 40.7 5
## 60 T3 dos 2 2 ICS95 40 36 41.6 6
## 61 T3 dos 2 2 TCS01 40 14 41.2 7
## 62 T3 dos 2 2 TCS01 40 25 42.4 8
## 63 T3 dos 2 2 TCS01 40 36 41.7 9
## 64 T1 dos 2 2 CCN51 37 14 40.1 10
## 65 T1 dos 2 2 CCN51 37 25 39.6 11
## 66 T1 dos 2 2 CCN51 37 36 39.3 12
## 67 T1 dos 2 2 ICS95 37 14 39.6 13
## 68 T1 dos 2 2 ICS95 37 25 40.0 14
## 69 T1 dos 2 2 ICS95 37 36 39.2 15
## 70 T1 dos 2 2 TCS01 37 14 40.3 16
## 71 T1 dos 2 2 TCS01 37 25 40.3 17
## 72 T1 dos 2 2 TCS01 37 36 39.6 18
## 73 T2 dos 2 2 CCN51 38 14 37.8 19
## 74 T2 dos 2 2 CCN51 38 25 38.0 20
## 75 T2 dos 2 2 CCN51 38 36 38.4 21
## 76 T2 dos 2 2 ICS95 38 14 36.8 22
## 77 T2 dos 2 2 ICS95 38 25 36.4 23
## 78 T2 dos 2 2 ICS95 38 36 36.6 24
## 79 T2 dos 2 2 TCS01 38 14 37.4 25
## 80 T2 dos 2 2 TCS01 38 25 38.4 26
## 81 T2 dos 2 2 TCS01 38 36 38.4 27
## 82 T3 tres 3 3 CCN51 44 14 46.7 1
## 83 T3 tres 3 3 CCN51 44 25 46.6 2
## 84 T3 tres 3 3 CCN51 44 36 45 3
## 85 T3 tres 3 3 ICS95 44 14 47.8 4
## 86 T3 tres 3 3 ICS95 44 25 47.6 5
## 87 T3 tres 3 3 ICS95 44 36 46.8 6
## 88 T3 tres 3 3 TCS01 44 14 46.8 7
## 89 T3 tres 3 3 TCS01 44 25 46.8 8
## 90 T3 tres 3 3 TCS01 44 36 46.3 9
## 91 T1 tres 3 3 CCN51 40 14 43.6 10
## 92 T1 tres 3 3 CCN51 40 25 43.6 11
## 93 T1 tres 3 3 CCN51 40 36 41.4 12
## 94 T1 tres 3 3 ICS95 40 14 43.2 13
## 95 T1 tres 3 3 ICS95 40 25 43.6 14
## 96 T1 tres 3 3 ICS95 40 36 41.8 15
## 97 T1 tres 3 3 TCS01 40 14 44.4 16
## 98 T1 tres 3 3 TCS01 40 25 44.6 17
## 99 T1 tres 3 3 TCS01 40 36 42.3 18
## 100 T2 tres 3 3 CCN51 42 14 43.2 19
## 101 T2 tres 3 3 CCN51 42 25 43.0 20
## 102 T2 tres 3 3 CCN51 42 36 42.0 21
## 103 T2 tres 3 3 ICS95 42 14 43.2 22
## 104 T2 tres 3 3 ICS95 42 25 44.6 23
## 105 T2 tres 3 3 ICS95 42 36 43.4 24
## 106 T2 tres 3 3 TCS01 42 14 43.6 25
## 107 T2 tres 3 3 TCS01 42 25 43.9 26
## 108 T2 tres 3 3 TCS01 42 36 42.6 27
## 109 T3 cuatro 4 4 CCN51 46 14 45.4 1
## 110 T3 cuatro 4 4 CCN51 46 25 45.2 2
## 111 T3 cuatro 4 4 CCN51 46 36 45.0 3
## 112 T3 cuatro 4 4 ICS95 46 14 47.0 4
## 113 T3 cuatro 4 4 ICS95 46 25 46.2 5
## 114 T3 cuatro 4 4 ICS95 46 36 46 6
## 115 T3 cuatro 4 4 TCS01 46 14 41.0 7
## 116 T3 cuatro 4 4 TCS01 46 25 43.8 8
## 117 T3 cuatro 4 4 TCS01 46 36 42.6 9
## 118 T1 cuatro 4 4 CCN51 44 14 44.8 10
## 119 T1 cuatro 4 4 CCN51 44 25 45.0 11
## 120 T1 cuatro 4 4 CCN51 44 36 44.4 12
## 121 T1 cuatro 4 4 ICS95 44 14 42.9 13
## 122 T1 cuatro 4 4 ICS95 44 25 43.2 14
## 123 T1 cuatro 4 4 ICS95 44 36 43.0 15
## 124 T1 cuatro 4 4 TCS01 44 14 44.8 16
## 125 T1 cuatro 4 4 TCS01 44 25 44.9 17
## 126 T1 cuatro 4 4 TCS01 44 36 44.4 18
## 127 T2 cuatro 4 4 CCN51 44 14 43.7 19
## 128 T2 cuatro 4 4 CCN51 44 25 44.3 20
## 129 T2 cuatro 4 4 CCN51 44 36 44.1 21
## 130 T2 cuatro 4 4 ICS95 44 14 44.0 22
## 131 T2 cuatro 4 4 ICS95 44 25 44.5 23
## 132 T2 cuatro 4 4 ICS95 44 36 44.0 24
## 133 T2 cuatro 4 4 TCS01 44 14 45.1 25
## 134 T2 cuatro 4 4 TCS01 44 25 45.8 26
## 135 T2 cuatro 4 4 TCS01 44 36 43.9 27
## 136 T3 cinco 5 5 CCN51 48 14 43.4 1
## 137 T3 cinco 5 5 CCN51 48 25 44.5 2
## 138 T3 cinco 5 5 CCN51 48 36 42.8 3
## 139 T3 cinco 5 5 ICS95 48 14 44.2 4
## 140 T3 cinco 5 5 ICS95 48 25 45.2 5
## 141 T3 cinco 5 5 ICS95 48 36 46.4 6
## 142 T3 cinco 5 5 TCS01 48 14 34.4 7
## 143 T3 cinco 5 5 TCS01 48 25 42.9 8
## 144 T3 cinco 5 5 TCS01 48 36 43.4 9
## 145 T1 cinco 5 5 CCN51 44 14 42.6 10
## 146 T1 cinco 5 5 CCN51 44 25 44.6 11
## 147 T1 cinco 5 5 CCN51 44 36 44.6 12
## 148 T1 cinco 5 5 ICS95 44 14 43.1 13
## 149 T1 cinco 5 5 ICS95 44 25 44.4 14
## 150 T1 cinco 5 5 ICS95 44 36 43.0 15
## 151 T1 cinco 5 5 TCS01 44 14 43.8 16
## 152 T1 cinco 5 5 TCS01 44 25 43.6 17
## 153 T1 cinco 5 5 TCS01 44 36 43.0 18
## 154 T2 cinco 5 5 CCN51 46 14 44.2 19
## 155 T2 cinco 5 5 CCN51 46 25 44.8 20
## 156 T2 cinco 5 5 CCN51 46 36 43.2 21
## 157 T2 cinco 5 5 ICS95 46 14 43.4 22
## 158 T2 cinco 5 5 ICS95 46 25 44.3 23
## 159 T2 cinco 5 5 ICS95 46 36 43.4 24
## 160 T2 cinco 5 5 TCS01 46 14 45.3 25
## 161 T2 cinco 5 5 TCS01 46 25 45.6 26
## 162 T2 cinco 5 5 TCS01 46 36 44 27
## 163 T3 Seis 6 6 CCN51 47 14 37.4 1
## 164 T3 Seis 6 6 CCN51 47 25 38.8 2
## 165 T3 Seis 6 6 CCN51 47 36 38.3 3
## 166 T3 Seis 6 6 ICS95 47 14 38.8 4
## 167 T3 Seis 6 6 ICS95 47 25 39.0 5
## 168 T3 Seis 6 6 ICS95 47 36 39.8 6
## 169 T3 Seis 6 6 TCS01 47 14 36.7 7
## 170 T3 Seis 6 6 TCS01 47 25 34.0 8
## 171 T3 Seis 6 6 TCS01 47 36 36.8 9
## 172 T1 Seis 6 6 CCN51 44 14 42.4 10
## 173 T1 Seis 6 6 CCN51 44 25 42.8 11
## 174 T1 Seis 6 6 CCN51 44 36 43.1 12
## 175 T1 Seis 6 6 ICS95 44 14 41.2 13
## 176 T1 Seis 6 6 ICS95 44 25 41.4 14
## 177 T1 Seis 6 6 ICS95 44 36 41.6 15
## 178 T1 Seis 6 6 TCS01 44 14 41.6 16
## 179 T1 Seis 6 6 TCS01 44 25 42.8 17
## 180 T1 Seis 6 6 TCS01 44 36 43.1 18
## 181 T2 Seis 6 6 CCN51 46 14 38.0 19
## 182 T2 Seis 6 6 CCN51 46 25 38.4 20
## 183 T2 Seis 6 6 CCN51 46 36 40.6 21
## 184 T2 Seis 6 6 ICS95 46 14 42.8 22
## 185 T2 Seis 6 6 ICS95 46 25 42.8 23
## 186 T2 Seis 6 6 ICS95 46 36 41.6 24
## 187 T2 Seis 6 6 TCS01 46 14 41 25
## 188 T2 Seis 6 6 TCS01 46 25 43.2 26
## 189 T2 Seis 6 6 TCS01 46 36 40.4 27
##Create QQ plot for each cell of design:
ggqqplot(datos, "temp", 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.
## 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?
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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## variable into a factor?
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## the data.
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## variable into a factor?
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## 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.
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## 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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## the data.
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## variable into a factor?
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## the data.
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## variable into a factor?
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## the data.
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## 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?
## 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
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## 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
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## the data.
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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
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## the data.
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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
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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 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
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## 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
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## 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
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## 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
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## 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
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## 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?
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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?
## 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:
lev<-datos %>%
group_by(diam2) %>%
levene_test(temp ~ 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 0.864 0.563
## 2 1 8 18 0.700 0.688
## 3 2 8 18 0.308 0.953
## 4 3 8 18 0.198 0.988
## 5 4 8 18 1.45 0.242
## 6 5 8 18 0.728 0.666
## 7 6 8 18 0.402 0.905
##Computation
#General table
res.aov <- anova_test(
data = datos, dv = temp, wid = id,
within = diam2, between = c(curva, gen),detailed = T, type = 3
)
res.aov
## ANOVA Table (type III tests)
##
## $ANOVA
## Effect DFn DFd SSn SSd F p p<.05 ges
## 1 (Intercept) 1 18 290620.993 23.255 224953.128 2.49e-38 * 1.000
## 2 curva 2 18 13.274 23.255 5.137 1.70e-02 * 0.110
## 3 gen 2 18 11.868 23.255 4.593 2.40e-02 * 0.099
## 4 diam2 6 108 7697.935 84.357 1642.574 1.17e-103 * 0.986
## 5 curva:gen 4 18 49.426 23.255 9.564 2.50e-04 * 0.315
## 6 curva:diam2 12 108 273.329 84.357 29.161 1.77e-28 * 0.718
## 7 gen:diam2 12 108 40.928 84.357 4.367 1.20e-05 * 0.276
## 8 curva:gen:diam2 24 108 57.620 84.357 3.074 3.78e-05 * 0.349
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.004 4.03e-10 *
## 2 curva:diam2 0.004 4.03e-10 *
## 3 gen:diam2 0.004 4.03e-10 *
## 4 curva:gen:diam2 0.004 4.03e-10 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.362 2.17, 39.1 5.21e-39 * 0.414 2.48, 44.66
## 2 curva:diam2 0.362 4.34, 39.1 1.29e-11 * 0.414 4.96, 44.66
## 3 gen:diam2 0.362 4.34, 39.1 4.00e-03 * 0.414 4.96, 44.66
## 4 curva:gen:diam2 0.362 8.69, 39.1 7.00e-03 * 0.414 9.92, 44.66
## p[HF] p[HF]<.05
## 1 3.09e-44 *
## 2 5.50e-13 *
## 3 3.00e-03 *
## 4 5.00e-03 *
get_anova_table(res.aov)
## ANOVA Table (type III tests)
##
## Effect DFn DFd SSn SSd F p p<.05 ges
## 1 (Intercept) 1.00 18.0 290620.993 23.255 224953.128 2.49e-38 * 1.000
## 2 curva 2.00 18.0 13.274 23.255 5.137 1.70e-02 * 0.110
## 3 gen 2.00 18.0 11.868 23.255 4.593 2.40e-02 * 0.099
## 4 diam2 2.17 39.1 7697.935 84.357 1642.574 5.21e-39 * 0.986
## 5 curva:gen 4.00 18.0 49.426 23.255 9.564 2.50e-04 * 0.315
## 6 curva:diam2 4.34 39.1 273.329 84.357 29.161 1.29e-11 * 0.718
## 7 gen:diam2 4.34 39.1 40.928 84.357 4.367 4.00e-03 * 0.276
## 8 curva:gen:diam2 8.69 39.1 57.620 84.357 3.074 7.00e-03 * 0.349
#Table by error
res.aov.error <- aov(temp ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = temp ~ diam2 * curva * gen + Error(id/diam2), data = datos)
##
## Grand Mean: 39.21323
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 13.27415 11.86765 49.42561 23.25452
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 1.136626
## 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 7697.935 273.329 40.928 57.620 84.357
## Deg. of Freedom 6 12 12 24 108
##
## Residual standard error: 0.8837899
## 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 39.6 0.143 18 39.3 39.9
## T1 39.1 0.143 18 38.8 39.4
## T2 39.0 0.143 18 38.7 39.3
##
## 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 0.494 0.203 18 2.438 0.0626
## T3 - T2 0.612 0.203 18 3.021 0.0191
## T1 - T2 0.118 0.203 18 0.584 0.8304
##
## 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 39.5 0.248 18 38.9 40.0
## ICS95 40.7 0.248 18 40.2 41.2
## TCS01 38.6 0.248 18 38.1 39.1
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 39.1 0.248 18 38.6 39.6
## ICS95 38.8 0.248 18 38.3 39.4
## TCS01 39.3 0.248 18 38.8 39.8
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 38.3 0.248 18 37.8 38.9
## ICS95 39.2 0.248 18 38.6 39.7
## TCS01 39.4 0.248 18 38.9 39.9
##
## 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 -1.226 0.351 18 -3.496 0.0069
## CCN51 - TCS01 0.845 0.351 18 2.410 0.0661
## ICS95 - TCS01 2.071 0.351 18 5.905 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.288 0.351 18 0.821 0.6950
## CCN51 - TCS01 -0.167 0.351 18 -0.475 0.8838
## ICS95 - TCS01 -0.455 0.351 18 -1.296 0.4152
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -0.821 0.351 18 -2.342 0.0753
## CCN51 - TCS01 -1.088 0.351 18 -3.102 0.0161
## ICS95 - TCS01 -0.267 0.351 18 -0.760 0.7314
##
## 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 25.0 0.534 121 23.9 26.1
## 1 37.3 0.534 121 36.2 38.3
## 2 40.9 0.534 121 39.8 41.9
## 3 46.1 0.534 121 45.0 47.1
## 4 45.2 0.534 121 44.1 46.2
## 5 43.6 0.534 121 42.5 44.6
## 6 38.2 0.534 121 37.1 39.3
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 23.6 0.534 121 22.5 24.7
## 1 36.3 0.534 121 35.2 37.3
## 2 39.7 0.534 121 38.6 40.7
## 3 42.9 0.534 121 41.8 43.9
## 4 44.7 0.534 121 43.7 45.8
## 5 44.0 0.534 121 42.9 45.0
## 6 42.8 0.534 121 41.7 43.8
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 25.9 0.534 121 24.8 26.9
## 1 34.5 0.534 121 33.5 35.6
## 2 38.1 0.534 121 37.0 39.1
## 3 42.7 0.534 121 41.7 43.8
## 4 44.0 0.534 121 43.0 45.1
## 5 44.0 0.534 121 43.0 45.1
## 6 39.0 0.534 121 38.0 40.1
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 25.0 0.534 121 23.9 26.1
## 1 40.3 0.534 121 39.2 41.3
## 2 41.2 0.534 121 40.1 42.3
## 3 47.4 0.534 121 46.3 48.5
## 4 46.4 0.534 121 45.3 47.5
## 5 45.3 0.534 121 44.2 46.4
## 6 39.2 0.534 121 38.1 40.3
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 23.6 0.534 121 22.5 24.6
## 1 37.9 0.534 121 36.9 39.0
## 2 39.6 0.534 121 38.5 40.6
## 3 42.9 0.534 121 41.8 43.9
## 4 43.0 0.534 121 42.0 44.1
## 5 43.5 0.534 121 42.5 44.6
## 6 41.4 0.534 121 40.4 42.5
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 25.9 0.534 121 24.8 26.9
## 1 37.6 0.534 121 36.5 38.7
## 2 36.6 0.534 121 35.6 37.7
## 3 43.7 0.534 121 42.7 44.8
## 4 44.2 0.534 121 43.1 45.2
## 5 43.7 0.534 121 42.6 44.8
## 6 42.4 0.534 121 41.4 43.5
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 25.0 0.534 121 23.9 26.1
## 1 38.4 0.534 121 37.3 39.4
## 2 41.8 0.534 121 40.7 42.8
## 3 46.6 0.534 121 45.6 47.7
## 4 42.5 0.534 121 41.4 43.5
## 5 40.2 0.534 121 39.2 41.3
## 6 35.8 0.534 121 34.8 36.9
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 23.5 0.534 121 22.5 24.6
## 1 37.1 0.534 121 36.0 38.2
## 2 40.1 0.534 121 39.0 41.1
## 3 43.7 0.534 121 42.7 44.8
## 4 44.7 0.534 121 43.6 45.7
## 5 43.5 0.534 121 42.4 44.5
## 6 42.5 0.534 121 41.5 43.6
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 25.9 0.534 121 24.8 27.0
## 1 37.2 0.534 121 36.2 38.3
## 2 38.0 0.534 121 37.0 39.1
## 3 43.4 0.534 121 42.3 44.4
## 4 44.9 0.534 121 43.9 46.0
## 5 45.0 0.534 121 43.9 46.0
## 6 41.5 0.534 121 40.5 42.6
##
## 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 -12.2833 0.722 108 -17.022 <.0001
## 0 - 2 -15.8667 0.722 108 -21.988 <.0001
## 0 - 3 -21.0833 0.722 108 -29.217 <.0001
## 0 - 4 -20.1833 0.722 108 -27.970 <.0001
## 0 - 5 -18.5667 0.722 108 -25.729 <.0001
## 0 - 6 -13.2000 0.722 108 -18.292 <.0001
## 1 - 2 -3.5833 0.722 108 -4.966 0.0001
## 1 - 3 -8.8000 0.722 108 -12.195 <.0001
## 1 - 4 -7.9000 0.722 108 -10.948 <.0001
## 1 - 5 -6.2833 0.722 108 -8.707 <.0001
## 1 - 6 -0.9167 0.722 108 -1.270 0.8639
## 2 - 3 -5.2167 0.722 108 -7.229 <.0001
## 2 - 4 -4.3167 0.722 108 -5.982 <.0001
## 2 - 5 -2.7000 0.722 108 -3.742 0.0053
## 2 - 6 2.6667 0.722 108 3.695 0.0062
## 3 - 4 0.9000 0.722 108 1.247 0.8738
## 3 - 5 2.5167 0.722 108 3.488 0.0122
## 3 - 6 7.8833 0.722 108 10.925 <.0001
## 4 - 5 1.6167 0.722 108 2.240 0.2831
## 4 - 6 6.9833 0.722 108 9.677 <.0001
## 5 - 6 5.3667 0.722 108 7.437 <.0001
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -12.6833 0.722 108 -17.576 <.0001
## 0 - 2 -16.0833 0.722 108 -22.288 <.0001
## 0 - 3 -19.2833 0.722 108 -26.723 <.0001
## 0 - 4 -21.1167 0.722 108 -29.263 <.0001
## 0 - 5 -20.3500 0.722 108 -28.201 <.0001
## 0 - 6 -19.1833 0.722 108 -26.584 <.0001
## 1 - 2 -3.4000 0.722 108 -4.712 0.0001
## 1 - 3 -6.6000 0.722 108 -9.146 <.0001
## 1 - 4 -8.4333 0.722 108 -11.687 <.0001
## 1 - 5 -7.6667 0.722 108 -10.624 <.0001
## 1 - 6 -6.5000 0.722 108 -9.008 <.0001
## 2 - 3 -3.2000 0.722 108 -4.435 0.0004
## 2 - 4 -5.0333 0.722 108 -6.975 <.0001
## 2 - 5 -4.2667 0.722 108 -5.913 <.0001
## 2 - 6 -3.1000 0.722 108 -4.296 0.0007
## 3 - 4 -1.8333 0.722 108 -2.541 0.1554
## 3 - 5 -1.0667 0.722 108 -1.478 0.7571
## 3 - 6 0.1000 0.722 108 0.139 1.0000
## 4 - 5 0.7667 0.722 108 1.062 0.9376
## 4 - 6 1.9333 0.722 108 2.679 0.1136
## 5 - 6 1.1667 0.722 108 1.617 0.6719
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -8.6500 0.722 108 -11.987 <.0001
## 0 - 2 -12.2000 0.722 108 -16.907 <.0001
## 0 - 3 -16.8500 0.722 108 -23.351 <.0001
## 0 - 4 -18.1500 0.722 108 -25.152 <.0001
## 0 - 5 -18.1667 0.722 108 -25.175 <.0001
## 0 - 6 -13.1333 0.722 108 -18.200 <.0001
## 1 - 2 -3.5500 0.722 108 -4.920 0.0001
## 1 - 3 -8.2000 0.722 108 -11.363 <.0001
## 1 - 4 -9.5000 0.722 108 -13.165 <.0001
## 1 - 5 -9.5167 0.722 108 -13.188 <.0001
## 1 - 6 -4.4833 0.722 108 -6.213 <.0001
## 2 - 3 -4.6500 0.722 108 -6.444 <.0001
## 2 - 4 -5.9500 0.722 108 -8.245 <.0001
## 2 - 5 -5.9667 0.722 108 -8.269 <.0001
## 2 - 6 -0.9333 0.722 108 -1.293 0.8536
## 3 - 4 -1.3000 0.722 108 -1.802 0.5500
## 3 - 5 -1.3167 0.722 108 -1.825 0.5346
## 3 - 6 3.7167 0.722 108 5.151 <.0001
## 4 - 5 -0.0167 0.722 108 -0.023 1.0000
## 4 - 6 5.0167 0.722 108 6.952 <.0001
## 5 - 6 5.0333 0.722 108 6.975 <.0001
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -15.2667 0.722 108 -21.156 <.0001
## 0 - 2 -16.2000 0.722 108 -22.450 <.0001
## 0 - 3 -22.4000 0.722 108 -31.042 <.0001
## 0 - 4 -21.4000 0.722 108 -29.656 <.0001
## 0 - 5 -20.3000 0.722 108 -28.131 <.0001
## 0 - 6 -14.2000 0.722 108 -19.678 <.0001
## 1 - 2 -0.9333 0.722 108 -1.293 0.8536
## 1 - 3 -7.1333 0.722 108 -9.885 <.0001
## 1 - 4 -6.1333 0.722 108 -8.499 <.0001
## 1 - 5 -5.0333 0.722 108 -6.975 <.0001
## 1 - 6 1.0667 0.722 108 1.478 0.7571
## 2 - 3 -6.2000 0.722 108 -8.592 <.0001
## 2 - 4 -5.2000 0.722 108 -7.206 <.0001
## 2 - 5 -4.1000 0.722 108 -5.682 <.0001
## 2 - 6 2.0000 0.722 108 2.772 0.0911
## 3 - 4 1.0000 0.722 108 1.386 0.8082
## 3 - 5 2.1000 0.722 108 2.910 0.0642
## 3 - 6 8.2000 0.722 108 11.363 <.0001
## 4 - 5 1.1000 0.722 108 1.524 0.7296
## 4 - 6 7.2000 0.722 108 9.978 <.0001
## 5 - 6 6.1000 0.722 108 8.453 <.0001
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -14.3667 0.722 108 -19.909 <.0001
## 0 - 2 -16.0333 0.722 108 -22.219 <.0001
## 0 - 3 -19.3333 0.722 108 -26.792 <.0001
## 0 - 4 -19.4667 0.722 108 -26.977 <.0001
## 0 - 5 -19.9667 0.722 108 -27.670 <.0001
## 0 - 6 -17.8667 0.722 108 -24.759 <.0001
## 1 - 2 -1.6667 0.722 108 -2.310 0.2490
## 1 - 3 -4.9667 0.722 108 -6.883 <.0001
## 1 - 4 -5.1000 0.722 108 -7.068 <.0001
## 1 - 5 -5.6000 0.722 108 -7.760 <.0001
## 1 - 6 -3.5000 0.722 108 -4.850 0.0001
## 2 - 3 -3.3000 0.722 108 -4.573 0.0003
## 2 - 4 -3.4333 0.722 108 -4.758 0.0001
## 2 - 5 -3.9333 0.722 108 -5.451 <.0001
## 2 - 6 -1.8333 0.722 108 -2.541 0.1554
## 3 - 4 -0.1333 0.722 108 -0.185 1.0000
## 3 - 5 -0.6333 0.722 108 -0.878 0.9752
## 3 - 6 1.4667 0.722 108 2.032 0.4006
## 4 - 5 -0.5000 0.722 108 -0.693 0.9928
## 4 - 6 1.6000 0.722 108 2.217 0.2951
## 5 - 6 2.1000 0.722 108 2.910 0.0642
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -11.7500 0.722 108 -16.283 <.0001
## 0 - 2 -10.7667 0.722 108 -14.920 <.0001
## 0 - 3 -17.8667 0.722 108 -24.759 <.0001
## 0 - 4 -18.3167 0.722 108 -25.383 <.0001
## 0 - 5 -17.8500 0.722 108 -24.736 <.0001
## 0 - 6 -16.5833 0.722 108 -22.981 <.0001
## 1 - 2 0.9833 0.722 108 1.363 0.8202
## 1 - 3 -6.1167 0.722 108 -8.476 <.0001
## 1 - 4 -6.5667 0.722 108 -9.100 <.0001
## 1 - 5 -6.1000 0.722 108 -8.453 <.0001
## 1 - 6 -4.8333 0.722 108 -6.698 <.0001
## 2 - 3 -7.1000 0.722 108 -9.839 <.0001
## 2 - 4 -7.5500 0.722 108 -10.463 <.0001
## 2 - 5 -7.0833 0.722 108 -9.816 <.0001
## 2 - 6 -5.8167 0.722 108 -8.061 <.0001
## 3 - 4 -0.4500 0.722 108 -0.624 0.9959
## 3 - 5 0.0167 0.722 108 0.023 1.0000
## 3 - 6 1.2833 0.722 108 1.778 0.5654
## 4 - 5 0.4667 0.722 108 0.647 0.9950
## 4 - 6 1.7333 0.722 108 2.402 0.2079
## 5 - 6 1.2667 0.722 108 1.755 0.5808
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -13.3667 0.722 108 -18.523 <.0001
## 0 - 2 -16.7500 0.722 108 -23.212 <.0001
## 0 - 3 -21.6167 0.722 108 -29.956 <.0001
## 0 - 4 -17.4667 0.722 108 -24.205 <.0001
## 0 - 5 -15.2333 0.722 108 -21.110 <.0001
## 0 - 6 -10.8333 0.722 108 -15.013 <.0001
## 1 - 2 -3.3833 0.722 108 -4.689 0.0002
## 1 - 3 -8.2500 0.722 108 -11.433 <.0001
## 1 - 4 -4.1000 0.722 108 -5.682 <.0001
## 1 - 5 -1.8667 0.722 108 -2.587 0.1404
## 1 - 6 2.5333 0.722 108 3.511 0.0113
## 2 - 3 -4.8667 0.722 108 -6.744 <.0001
## 2 - 4 -0.7167 0.722 108 -0.993 0.9545
## 2 - 5 1.5167 0.722 108 2.102 0.3591
## 2 - 6 5.9167 0.722 108 8.199 <.0001
## 3 - 4 4.1500 0.722 108 5.751 <.0001
## 3 - 5 6.3833 0.722 108 8.846 <.0001
## 3 - 6 10.7833 0.722 108 14.943 <.0001
## 4 - 5 2.2333 0.722 108 3.095 0.0390
## 4 - 6 6.6333 0.722 108 9.192 <.0001
## 5 - 6 4.4000 0.722 108 6.097 <.0001
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -13.5667 0.722 108 -18.801 <.0001
## 0 - 2 -16.5333 0.722 108 -22.912 <.0001
## 0 - 3 -20.2000 0.722 108 -27.993 <.0001
## 0 - 4 -21.1333 0.722 108 -29.286 <.0001
## 0 - 5 -19.9167 0.722 108 -27.600 <.0001
## 0 - 6 -18.9833 0.722 108 -26.307 <.0001
## 1 - 2 -2.9667 0.722 108 -4.111 0.0015
## 1 - 3 -6.6333 0.722 108 -9.192 <.0001
## 1 - 4 -7.5667 0.722 108 -10.486 <.0001
## 1 - 5 -6.3500 0.722 108 -8.800 <.0001
## 1 - 6 -5.4167 0.722 108 -7.506 <.0001
## 2 - 3 -3.6667 0.722 108 -5.081 <.0001
## 2 - 4 -4.6000 0.722 108 -6.375 <.0001
## 2 - 5 -3.3833 0.722 108 -4.689 0.0002
## 2 - 6 -2.4500 0.722 108 -3.395 0.0162
## 3 - 4 -0.9333 0.722 108 -1.293 0.8536
## 3 - 5 0.2833 0.722 108 0.393 0.9997
## 3 - 6 1.2167 0.722 108 1.686 0.6268
## 4 - 5 1.2167 0.722 108 1.686 0.6268
## 4 - 6 2.1500 0.722 108 2.979 0.0535
## 5 - 6 0.9333 0.722 108 1.293 0.8536
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -11.3500 0.722 108 -15.729 <.0001
## 0 - 2 -12.1500 0.722 108 -16.837 <.0001
## 0 - 3 -17.4667 0.722 108 -24.205 <.0001
## 0 - 4 -19.0167 0.722 108 -26.353 <.0001
## 0 - 5 -19.0500 0.722 108 -26.399 <.0001
## 0 - 6 -15.6167 0.722 108 -21.641 <.0001
## 1 - 2 -0.8000 0.722 108 -1.109 0.9242
## 1 - 3 -6.1167 0.722 108 -8.476 <.0001
## 1 - 4 -7.6667 0.722 108 -10.624 <.0001
## 1 - 5 -7.7000 0.722 108 -10.671 <.0001
## 1 - 6 -4.2667 0.722 108 -5.913 <.0001
## 2 - 3 -5.3167 0.722 108 -7.368 <.0001
## 2 - 4 -6.8667 0.722 108 -9.516 <.0001
## 2 - 5 -6.9000 0.722 108 -9.562 <.0001
## 2 - 6 -3.4667 0.722 108 -4.804 0.0001
## 3 - 4 -1.5500 0.722 108 -2.148 0.3327
## 3 - 5 -1.5833 0.722 108 -2.194 0.3073
## 3 - 6 1.8500 0.722 108 2.564 0.1478
## 4 - 5 -0.0333 0.722 108 -0.046 1.0000
## 4 - 6 3.4000 0.722 108 4.712 0.0001
## 5 - 6 3.4333 0.722 108 4.758 0.0001
##
## 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 25.0 0.534 121 23.9 26.1
## 1 CCN51 37.3 0.534 121 36.2 38.3
## 2 CCN51 40.9 0.534 121 39.8 41.9
## 3 CCN51 46.1 0.534 121 45.0 47.1
## 4 CCN51 45.2 0.534 121 44.1 46.2
## 5 CCN51 43.6 0.534 121 42.5 44.6
## 6 CCN51 38.2 0.534 121 37.1 39.3
## 0 ICS95 25.0 0.534 121 23.9 26.1
## 1 ICS95 40.3 0.534 121 39.2 41.3
## 2 ICS95 41.2 0.534 121 40.1 42.3
## 3 ICS95 47.4 0.534 121 46.3 48.5
## 4 ICS95 46.4 0.534 121 45.3 47.5
## 5 ICS95 45.3 0.534 121 44.2 46.4
## 6 ICS95 39.2 0.534 121 38.1 40.3
## 0 TCS01 25.0 0.534 121 23.9 26.1
## 1 TCS01 38.4 0.534 121 37.3 39.4
## 2 TCS01 41.8 0.534 121 40.7 42.8
## 3 TCS01 46.6 0.534 121 45.6 47.7
## 4 TCS01 42.5 0.534 121 41.4 43.5
## 5 TCS01 40.2 0.534 121 39.2 41.3
## 6 TCS01 35.8 0.534 121 34.8 36.9
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 23.6 0.534 121 22.5 24.7
## 1 CCN51 36.3 0.534 121 35.2 37.3
## 2 CCN51 39.7 0.534 121 38.6 40.7
## 3 CCN51 42.9 0.534 121 41.8 43.9
## 4 CCN51 44.7 0.534 121 43.7 45.8
## 5 CCN51 44.0 0.534 121 42.9 45.0
## 6 CCN51 42.8 0.534 121 41.7 43.8
## 0 ICS95 23.6 0.534 121 22.5 24.6
## 1 ICS95 37.9 0.534 121 36.9 39.0
## 2 ICS95 39.6 0.534 121 38.5 40.6
## 3 ICS95 42.9 0.534 121 41.8 43.9
## 4 ICS95 43.0 0.534 121 42.0 44.1
## 5 ICS95 43.5 0.534 121 42.5 44.6
## 6 ICS95 41.4 0.534 121 40.4 42.5
## 0 TCS01 23.5 0.534 121 22.5 24.6
## 1 TCS01 37.1 0.534 121 36.0 38.2
## 2 TCS01 40.1 0.534 121 39.0 41.1
## 3 TCS01 43.7 0.534 121 42.7 44.8
## 4 TCS01 44.7 0.534 121 43.6 45.7
## 5 TCS01 43.5 0.534 121 42.4 44.5
## 6 TCS01 42.5 0.534 121 41.5 43.6
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 25.9 0.534 121 24.8 26.9
## 1 CCN51 34.5 0.534 121 33.5 35.6
## 2 CCN51 38.1 0.534 121 37.0 39.1
## 3 CCN51 42.7 0.534 121 41.7 43.8
## 4 CCN51 44.0 0.534 121 43.0 45.1
## 5 CCN51 44.0 0.534 121 43.0 45.1
## 6 CCN51 39.0 0.534 121 38.0 40.1
## 0 ICS95 25.9 0.534 121 24.8 26.9
## 1 ICS95 37.6 0.534 121 36.5 38.7
## 2 ICS95 36.6 0.534 121 35.6 37.7
## 3 ICS95 43.7 0.534 121 42.7 44.8
## 4 ICS95 44.2 0.534 121 43.1 45.2
## 5 ICS95 43.7 0.534 121 42.6 44.8
## 6 ICS95 42.4 0.534 121 41.4 43.5
## 0 TCS01 25.9 0.534 121 24.8 27.0
## 1 TCS01 37.2 0.534 121 36.2 38.3
## 2 TCS01 38.0 0.534 121 37.0 39.1
## 3 TCS01 43.4 0.534 121 42.3 44.4
## 4 TCS01 44.9 0.534 121 43.9 46.0
## 5 TCS01 45.0 0.534 121 43.9 46.0
## 6 TCS01 41.5 0.534 121 40.5 42.6
##
## 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 -12.2833 0.722 108 -17.022 <.0001
## 0 CCN51 - 2 CCN51 -15.8667 0.722 108 -21.988 <.0001
## 0 CCN51 - 3 CCN51 -21.0833 0.722 108 -29.217 <.0001
## 0 CCN51 - 4 CCN51 -20.1833 0.722 108 -27.970 <.0001
## 0 CCN51 - 5 CCN51 -18.5667 0.722 108 -25.729 <.0001
## 0 CCN51 - 6 CCN51 -13.2000 0.722 108 -18.292 <.0001
## 0 CCN51 - 0 ICS95 0.0000 0.755 121 0.000 1.0000
## 0 CCN51 - 1 ICS95 -15.2667 0.755 121 -20.232 <.0001
## 0 CCN51 - 2 ICS95 -16.2000 0.755 121 -21.469 <.0001
## 0 CCN51 - 3 ICS95 -22.4000 0.755 121 -29.686 <.0001
## 0 CCN51 - 4 ICS95 -21.4000 0.755 121 -28.361 <.0001
## 0 CCN51 - 5 ICS95 -20.3000 0.755 121 -26.903 <.0001
## 0 CCN51 - 6 ICS95 -14.2000 0.755 121 -18.819 <.0001
## 0 CCN51 - 0 TCS01 0.0000 0.755 121 0.000 1.0000
## 0 CCN51 - 1 TCS01 -13.3667 0.755 121 -17.714 <.0001
## 0 CCN51 - 2 TCS01 -16.7500 0.755 121 -22.198 <.0001
## 0 CCN51 - 3 TCS01 -21.6167 0.755 121 -28.648 <.0001
## 0 CCN51 - 4 TCS01 -17.4667 0.755 121 -23.148 <.0001
## 0 CCN51 - 5 TCS01 -15.2333 0.755 121 -20.188 <.0001
## 0 CCN51 - 6 TCS01 -10.8333 0.755 121 -14.357 <.0001
## 1 CCN51 - 2 CCN51 -3.5833 0.722 108 -4.966 0.0005
## 1 CCN51 - 3 CCN51 -8.8000 0.722 108 -12.195 <.0001
## 1 CCN51 - 4 CCN51 -7.9000 0.722 108 -10.948 <.0001
## 1 CCN51 - 5 CCN51 -6.2833 0.722 108 -8.707 <.0001
## 1 CCN51 - 6 CCN51 -0.9167 0.722 108 -1.270 0.9995
## 1 CCN51 - 0 ICS95 12.2833 0.755 121 16.279 <.0001
## 1 CCN51 - 1 ICS95 -2.9833 0.755 121 -3.954 0.0192
## 1 CCN51 - 2 ICS95 -3.9167 0.755 121 -5.191 0.0002
## 1 CCN51 - 3 ICS95 -10.1167 0.755 121 -13.407 <.0001
## 1 CCN51 - 4 ICS95 -9.1167 0.755 121 -12.082 <.0001
## 1 CCN51 - 5 ICS95 -8.0167 0.755 121 -10.624 <.0001
## 1 CCN51 - 6 ICS95 -1.9167 0.755 121 -2.540 0.5798
## 1 CCN51 - 0 TCS01 12.2833 0.755 121 16.279 <.0001
## 1 CCN51 - 1 TCS01 -1.0833 0.755 121 -1.436 0.9974
## 1 CCN51 - 2 TCS01 -4.4667 0.755 121 -5.919 <.0001
## 1 CCN51 - 3 TCS01 -9.3333 0.755 121 -12.369 <.0001
## 1 CCN51 - 4 TCS01 -5.1833 0.755 121 -6.869 <.0001
## 1 CCN51 - 5 TCS01 -2.9500 0.755 121 -3.910 0.0222
## 1 CCN51 - 6 TCS01 1.4500 0.755 121 1.922 0.9358
## 2 CCN51 - 3 CCN51 -5.2167 0.722 108 -7.229 <.0001
## 2 CCN51 - 4 CCN51 -4.3167 0.722 108 -5.982 <.0001
## 2 CCN51 - 5 CCN51 -2.7000 0.722 108 -3.742 0.0392
## 2 CCN51 - 6 CCN51 2.6667 0.722 108 3.695 0.0451
## 2 CCN51 - 0 ICS95 15.8667 0.755 121 21.027 <.0001
## 2 CCN51 - 1 ICS95 0.6000 0.755 121 0.795 1.0000
## 2 CCN51 - 2 ICS95 -0.3333 0.755 121 -0.442 1.0000
## 2 CCN51 - 3 ICS95 -6.5333 0.755 121 -8.658 <.0001
## 2 CCN51 - 4 ICS95 -5.5333 0.755 121 -7.333 <.0001
## 2 CCN51 - 5 ICS95 -4.4333 0.755 121 -5.875 <.0001
## 2 CCN51 - 6 ICS95 1.6667 0.755 121 2.209 0.8089
## 2 CCN51 - 0 TCS01 15.8667 0.755 121 21.027 <.0001
## 2 CCN51 - 1 TCS01 2.5000 0.755 121 3.313 0.1278
## 2 CCN51 - 2 TCS01 -0.8833 0.755 121 -1.171 0.9998
## 2 CCN51 - 3 TCS01 -5.7500 0.755 121 -7.620 <.0001
## 2 CCN51 - 4 TCS01 -1.6000 0.755 121 -2.120 0.8568
## 2 CCN51 - 5 TCS01 0.6333 0.755 121 0.839 1.0000
## 2 CCN51 - 6 TCS01 5.0333 0.755 121 6.670 <.0001
## 3 CCN51 - 4 CCN51 0.9000 0.722 108 1.247 0.9996
## 3 CCN51 - 5 CCN51 2.5167 0.722 108 3.488 0.0819
## 3 CCN51 - 6 CCN51 7.8833 0.722 108 10.925 <.0001
## 3 CCN51 - 0 ICS95 21.0833 0.755 121 27.941 <.0001
## 3 CCN51 - 1 ICS95 5.8167 0.755 121 7.709 <.0001
## 3 CCN51 - 2 ICS95 4.8833 0.755 121 6.472 <.0001
## 3 CCN51 - 3 ICS95 -1.3167 0.755 121 -1.745 0.9746
## 3 CCN51 - 4 ICS95 -0.3167 0.755 121 -0.420 1.0000
## 3 CCN51 - 5 ICS95 0.7833 0.755 121 1.038 1.0000
## 3 CCN51 - 6 ICS95 6.8833 0.755 121 9.122 <.0001
## 3 CCN51 - 0 TCS01 21.0833 0.755 121 27.941 <.0001
## 3 CCN51 - 1 TCS01 7.7167 0.755 121 10.227 <.0001
## 3 CCN51 - 2 TCS01 4.3333 0.755 121 5.743 <.0001
## 3 CCN51 - 3 TCS01 -0.5333 0.755 121 -0.707 1.0000
## 3 CCN51 - 4 TCS01 3.6167 0.755 121 4.793 0.0009
## 3 CCN51 - 5 TCS01 5.8500 0.755 121 7.753 <.0001
## 3 CCN51 - 6 TCS01 10.2500 0.755 121 13.584 <.0001
## 4 CCN51 - 5 CCN51 1.6167 0.722 108 2.240 0.7893
## 4 CCN51 - 6 CCN51 6.9833 0.722 108 9.677 <.0001
## 4 CCN51 - 0 ICS95 20.1833 0.755 121 26.748 <.0001
## 4 CCN51 - 1 ICS95 4.9167 0.755 121 6.516 <.0001
## 4 CCN51 - 2 ICS95 3.9833 0.755 121 5.279 0.0001
## 4 CCN51 - 3 ICS95 -2.2167 0.755 121 -2.938 0.3010
## 4 CCN51 - 4 ICS95 -1.2167 0.755 121 -1.612 0.9893
## 4 CCN51 - 5 ICS95 -0.1167 0.755 121 -0.155 1.0000
## 4 CCN51 - 6 ICS95 5.9833 0.755 121 7.929 <.0001
## 4 CCN51 - 0 TCS01 20.1833 0.755 121 26.748 <.0001
## 4 CCN51 - 1 TCS01 6.8167 0.755 121 9.034 <.0001
## 4 CCN51 - 2 TCS01 3.4333 0.755 121 4.550 0.0022
## 4 CCN51 - 3 TCS01 -1.4333 0.755 121 -1.900 0.9422
## 4 CCN51 - 4 TCS01 2.7167 0.755 121 3.600 0.0581
## 4 CCN51 - 5 TCS01 4.9500 0.755 121 6.560 <.0001
## 4 CCN51 - 6 TCS01 9.3500 0.755 121 12.391 <.0001
## 5 CCN51 - 6 CCN51 5.3667 0.722 108 7.437 <.0001
## 5 CCN51 - 0 ICS95 18.5667 0.755 121 24.606 <.0001
## 5 CCN51 - 1 ICS95 3.3000 0.755 121 4.373 0.0044
## 5 CCN51 - 2 ICS95 2.3667 0.755 121 3.136 0.1964
## 5 CCN51 - 3 ICS95 -3.8333 0.755 121 -5.080 0.0003
## 5 CCN51 - 4 ICS95 -2.8333 0.755 121 -3.755 0.0364
## 5 CCN51 - 5 ICS95 -1.7333 0.755 121 -2.297 0.7541
## 5 CCN51 - 6 ICS95 4.3667 0.755 121 5.787 <.0001
## 5 CCN51 - 0 TCS01 18.5667 0.755 121 24.606 <.0001
## 5 CCN51 - 1 TCS01 5.2000 0.755 121 6.891 <.0001
## 5 CCN51 - 2 TCS01 1.8167 0.755 121 2.408 0.6779
## 5 CCN51 - 3 TCS01 -3.0500 0.755 121 -4.042 0.0143
## 5 CCN51 - 4 TCS01 1.1000 0.755 121 1.458 0.9968
## 5 CCN51 - 5 TCS01 3.3333 0.755 121 4.418 0.0037
## 5 CCN51 - 6 TCS01 7.7333 0.755 121 10.249 <.0001
## 6 CCN51 - 0 ICS95 13.2000 0.755 121 17.493 <.0001
## 6 CCN51 - 1 ICS95 -2.0667 0.755 121 -2.739 0.4324
## 6 CCN51 - 2 ICS95 -3.0000 0.755 121 -3.976 0.0178
## 6 CCN51 - 3 ICS95 -9.2000 0.755 121 -12.192 <.0001
## 6 CCN51 - 4 ICS95 -8.2000 0.755 121 -10.867 <.0001
## 6 CCN51 - 5 ICS95 -7.1000 0.755 121 -9.409 <.0001
## 6 CCN51 - 6 ICS95 -1.0000 0.755 121 -1.325 0.9991
## 6 CCN51 - 0 TCS01 13.2000 0.755 121 17.493 <.0001
## 6 CCN51 - 1 TCS01 -0.1667 0.755 121 -0.221 1.0000
## 6 CCN51 - 2 TCS01 -3.5500 0.755 121 -4.705 0.0012
## 6 CCN51 - 3 TCS01 -8.4167 0.755 121 -11.154 <.0001
## 6 CCN51 - 4 TCS01 -4.2667 0.755 121 -5.654 <.0001
## 6 CCN51 - 5 TCS01 -2.0333 0.755 121 -2.695 0.4643
## 6 CCN51 - 6 TCS01 2.3667 0.755 121 3.136 0.1964
## 0 ICS95 - 1 ICS95 -15.2667 0.722 108 -21.156 <.0001
## 0 ICS95 - 2 ICS95 -16.2000 0.722 108 -22.450 <.0001
## 0 ICS95 - 3 ICS95 -22.4000 0.722 108 -31.042 <.0001
## 0 ICS95 - 4 ICS95 -21.4000 0.722 108 -29.656 <.0001
## 0 ICS95 - 5 ICS95 -20.3000 0.722 108 -28.131 <.0001
## 0 ICS95 - 6 ICS95 -14.2000 0.722 108 -19.678 <.0001
## 0 ICS95 - 0 TCS01 0.0000 0.755 121 0.000 1.0000
## 0 ICS95 - 1 TCS01 -13.3667 0.755 121 -17.714 <.0001
## 0 ICS95 - 2 TCS01 -16.7500 0.755 121 -22.198 <.0001
## 0 ICS95 - 3 TCS01 -21.6167 0.755 121 -28.648 <.0001
## 0 ICS95 - 4 TCS01 -17.4667 0.755 121 -23.148 <.0001
## 0 ICS95 - 5 TCS01 -15.2333 0.755 121 -20.188 <.0001
## 0 ICS95 - 6 TCS01 -10.8333 0.755 121 -14.357 <.0001
## 1 ICS95 - 2 ICS95 -0.9333 0.722 108 -1.293 0.9993
## 1 ICS95 - 3 ICS95 -7.1333 0.722 108 -9.885 <.0001
## 1 ICS95 - 4 ICS95 -6.1333 0.722 108 -8.499 <.0001
## 1 ICS95 - 5 ICS95 -5.0333 0.722 108 -6.975 <.0001
## 1 ICS95 - 6 ICS95 1.0667 0.722 108 1.478 0.9961
## 1 ICS95 - 0 TCS01 15.2667 0.755 121 20.232 <.0001
## 1 ICS95 - 1 TCS01 1.9000 0.755 121 2.518 0.5964
## 1 ICS95 - 2 TCS01 -1.4833 0.755 121 -1.966 0.9217
## 1 ICS95 - 3 TCS01 -6.3500 0.755 121 -8.415 <.0001
## 1 ICS95 - 4 TCS01 -2.2000 0.755 121 -2.916 0.3144
## 1 ICS95 - 5 TCS01 0.0333 0.755 121 0.044 1.0000
## 1 ICS95 - 6 TCS01 4.4333 0.755 121 5.875 <.0001
## 2 ICS95 - 3 ICS95 -6.2000 0.722 108 -8.592 <.0001
## 2 ICS95 - 4 ICS95 -5.2000 0.722 108 -7.206 <.0001
## 2 ICS95 - 5 ICS95 -4.1000 0.722 108 -5.682 <.0001
## 2 ICS95 - 6 ICS95 2.0000 0.722 108 2.772 0.4106
## 2 ICS95 - 0 TCS01 16.2000 0.755 121 21.469 <.0001
## 2 ICS95 - 1 TCS01 2.8333 0.755 121 3.755 0.0364
## 2 ICS95 - 2 TCS01 -0.5500 0.755 121 -0.729 1.0000
## 2 ICS95 - 3 TCS01 -5.4167 0.755 121 -7.178 <.0001
## 2 ICS95 - 4 TCS01 -1.2667 0.755 121 -1.679 0.9832
## 2 ICS95 - 5 TCS01 0.9667 0.755 121 1.281 0.9994
## 2 ICS95 - 6 TCS01 5.3667 0.755 121 7.112 <.0001
## 3 ICS95 - 4 ICS95 1.0000 0.722 108 1.386 0.9983
## 3 ICS95 - 5 ICS95 2.1000 0.722 108 2.910 0.3195
## 3 ICS95 - 6 ICS95 8.2000 0.722 108 11.363 <.0001
## 3 ICS95 - 0 TCS01 22.4000 0.755 121 29.686 <.0001
## 3 ICS95 - 1 TCS01 9.0333 0.755 121 11.972 <.0001
## 3 ICS95 - 2 TCS01 5.6500 0.755 121 7.488 <.0001
## 3 ICS95 - 3 TCS01 0.7833 0.755 121 1.038 1.0000
## 3 ICS95 - 4 TCS01 4.9333 0.755 121 6.538 <.0001
## 3 ICS95 - 5 TCS01 7.1667 0.755 121 9.498 <.0001
## 3 ICS95 - 6 TCS01 11.5667 0.755 121 15.329 <.0001
## 4 ICS95 - 5 ICS95 1.1000 0.722 108 1.524 0.9943
## 4 ICS95 - 6 ICS95 7.2000 0.722 108 9.978 <.0001
## 4 ICS95 - 0 TCS01 21.4000 0.755 121 28.361 <.0001
## 4 ICS95 - 1 TCS01 8.0333 0.755 121 10.646 <.0001
## 4 ICS95 - 2 TCS01 4.6500 0.755 121 6.162 <.0001
## 4 ICS95 - 3 TCS01 -0.2167 0.755 121 -0.287 1.0000
## 4 ICS95 - 4 TCS01 3.9333 0.755 121 5.213 0.0001
## 4 ICS95 - 5 TCS01 6.1667 0.755 121 8.172 <.0001
## 4 ICS95 - 6 TCS01 10.5667 0.755 121 14.004 <.0001
## 5 ICS95 - 6 ICS95 6.1000 0.722 108 8.453 <.0001
## 5 ICS95 - 0 TCS01 20.3000 0.755 121 26.903 <.0001
## 5 ICS95 - 1 TCS01 6.9333 0.755 121 9.188 <.0001
## 5 ICS95 - 2 TCS01 3.5500 0.755 121 4.705 0.0012
## 5 ICS95 - 3 TCS01 -1.3167 0.755 121 -1.745 0.9746
## 5 ICS95 - 4 TCS01 2.8333 0.755 121 3.755 0.0364
## 5 ICS95 - 5 TCS01 5.0667 0.755 121 6.715 <.0001
## 5 ICS95 - 6 TCS01 9.4667 0.755 121 12.546 <.0001
## 6 ICS95 - 0 TCS01 14.2000 0.755 121 18.819 <.0001
## 6 ICS95 - 1 TCS01 0.8333 0.755 121 1.104 0.9999
## 6 ICS95 - 2 TCS01 -2.5500 0.755 121 -3.379 0.1076
## 6 ICS95 - 3 TCS01 -7.4167 0.755 121 -9.829 <.0001
## 6 ICS95 - 4 TCS01 -3.2667 0.755 121 -4.329 0.0052
## 6 ICS95 - 5 TCS01 -1.0333 0.755 121 -1.369 0.9986
## 6 ICS95 - 6 TCS01 3.3667 0.755 121 4.462 0.0031
## 0 TCS01 - 1 TCS01 -13.3667 0.722 108 -18.523 <.0001
## 0 TCS01 - 2 TCS01 -16.7500 0.722 108 -23.212 <.0001
## 0 TCS01 - 3 TCS01 -21.6167 0.722 108 -29.956 <.0001
## 0 TCS01 - 4 TCS01 -17.4667 0.722 108 -24.205 <.0001
## 0 TCS01 - 5 TCS01 -15.2333 0.722 108 -21.110 <.0001
## 0 TCS01 - 6 TCS01 -10.8333 0.722 108 -15.013 <.0001
## 1 TCS01 - 2 TCS01 -3.3833 0.722 108 -4.689 0.0014
## 1 TCS01 - 3 TCS01 -8.2500 0.722 108 -11.433 <.0001
## 1 TCS01 - 4 TCS01 -4.1000 0.722 108 -5.682 <.0001
## 1 TCS01 - 5 TCS01 -1.8667 0.722 108 -2.587 0.5451
## 1 TCS01 - 6 TCS01 2.5333 0.722 108 3.511 0.0769
## 2 TCS01 - 3 TCS01 -4.8667 0.722 108 -6.744 <.0001
## 2 TCS01 - 4 TCS01 -0.7167 0.722 108 -0.993 1.0000
## 2 TCS01 - 5 TCS01 1.5167 0.722 108 2.102 0.8651
## 2 TCS01 - 6 TCS01 5.9167 0.722 108 8.199 <.0001
## 3 TCS01 - 4 TCS01 4.1500 0.722 108 5.751 <.0001
## 3 TCS01 - 5 TCS01 6.3833 0.722 108 8.846 <.0001
## 3 TCS01 - 6 TCS01 10.7833 0.722 108 14.943 <.0001
## 4 TCS01 - 5 TCS01 2.2333 0.722 108 3.095 0.2179
## 4 TCS01 - 6 TCS01 6.6333 0.722 108 9.192 <.0001
## 5 TCS01 - 6 TCS01 4.4000 0.722 108 6.097 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -12.6833 0.722 108 -17.576 <.0001
## 0 CCN51 - 2 CCN51 -16.0833 0.722 108 -22.288 <.0001
## 0 CCN51 - 3 CCN51 -19.2833 0.722 108 -26.723 <.0001
## 0 CCN51 - 4 CCN51 -21.1167 0.722 108 -29.263 <.0001
## 0 CCN51 - 5 CCN51 -20.3500 0.722 108 -28.201 <.0001
## 0 CCN51 - 6 CCN51 -19.1833 0.722 108 -26.584 <.0001
## 0 CCN51 - 0 ICS95 0.0500 0.755 121 0.066 1.0000
## 0 CCN51 - 1 ICS95 -14.3167 0.755 121 -18.973 <.0001
## 0 CCN51 - 2 ICS95 -15.9833 0.755 121 -21.182 <.0001
## 0 CCN51 - 3 ICS95 -19.2833 0.755 121 -25.555 <.0001
## 0 CCN51 - 4 ICS95 -19.4167 0.755 121 -25.732 <.0001
## 0 CCN51 - 5 ICS95 -19.9167 0.755 121 -26.395 <.0001
## 0 CCN51 - 6 ICS95 -17.8167 0.755 121 -23.612 <.0001
## 0 CCN51 - 0 TCS01 0.0667 0.755 121 0.088 1.0000
## 0 CCN51 - 1 TCS01 -13.5000 0.755 121 -17.891 <.0001
## 0 CCN51 - 2 TCS01 -16.4667 0.755 121 -21.823 <.0001
## 0 CCN51 - 3 TCS01 -20.1333 0.755 121 -26.682 <.0001
## 0 CCN51 - 4 TCS01 -21.0667 0.755 121 -27.919 <.0001
## 0 CCN51 - 5 TCS01 -19.8500 0.755 121 -26.306 <.0001
## 0 CCN51 - 6 TCS01 -18.9167 0.755 121 -25.070 <.0001
## 1 CCN51 - 2 CCN51 -3.4000 0.722 108 -4.712 0.0013
## 1 CCN51 - 3 CCN51 -6.6000 0.722 108 -9.146 <.0001
## 1 CCN51 - 4 CCN51 -8.4333 0.722 108 -11.687 <.0001
## 1 CCN51 - 5 CCN51 -7.6667 0.722 108 -10.624 <.0001
## 1 CCN51 - 6 CCN51 -6.5000 0.722 108 -9.008 <.0001
## 1 CCN51 - 0 ICS95 12.7333 0.755 121 16.875 <.0001
## 1 CCN51 - 1 ICS95 -1.6333 0.755 121 -2.165 0.8338
## 1 CCN51 - 2 ICS95 -3.3000 0.755 121 -4.373 0.0044
## 1 CCN51 - 3 ICS95 -6.6000 0.755 121 -8.747 <.0001
## 1 CCN51 - 4 ICS95 -6.7333 0.755 121 -8.923 <.0001
## 1 CCN51 - 5 ICS95 -7.2333 0.755 121 -9.586 <.0001
## 1 CCN51 - 6 ICS95 -5.1333 0.755 121 -6.803 <.0001
## 1 CCN51 - 0 TCS01 12.7500 0.755 121 16.897 <.0001
## 1 CCN51 - 1 TCS01 -0.8167 0.755 121 -1.082 1.0000
## 1 CCN51 - 2 TCS01 -3.7833 0.755 121 -5.014 0.0003
## 1 CCN51 - 3 TCS01 -7.4500 0.755 121 -9.873 <.0001
## 1 CCN51 - 4 TCS01 -8.3833 0.755 121 -11.110 <.0001
## 1 CCN51 - 5 TCS01 -7.1667 0.755 121 -9.498 <.0001
## 1 CCN51 - 6 TCS01 -6.2333 0.755 121 -8.261 <.0001
## 2 CCN51 - 3 CCN51 -3.2000 0.722 108 -4.435 0.0037
## 2 CCN51 - 4 CCN51 -5.0333 0.722 108 -6.975 <.0001
## 2 CCN51 - 5 CCN51 -4.2667 0.722 108 -5.913 <.0001
## 2 CCN51 - 6 CCN51 -3.1000 0.722 108 -4.296 0.0062
## 2 CCN51 - 0 ICS95 16.1333 0.755 121 21.381 <.0001
## 2 CCN51 - 1 ICS95 1.7667 0.755 121 2.341 0.7245
## 2 CCN51 - 2 ICS95 0.1000 0.755 121 0.133 1.0000
## 2 CCN51 - 3 ICS95 -3.2000 0.755 121 -4.241 0.0071
## 2 CCN51 - 4 ICS95 -3.3333 0.755 121 -4.418 0.0037
## 2 CCN51 - 5 ICS95 -3.8333 0.755 121 -5.080 0.0003
## 2 CCN51 - 6 ICS95 -1.7333 0.755 121 -2.297 0.7541
## 2 CCN51 - 0 TCS01 16.1500 0.755 121 21.403 <.0001
## 2 CCN51 - 1 TCS01 2.5833 0.755 121 3.424 0.0956
## 2 CCN51 - 2 TCS01 -0.3833 0.755 121 -0.508 1.0000
## 2 CCN51 - 3 TCS01 -4.0500 0.755 121 -5.367 0.0001
## 2 CCN51 - 4 TCS01 -4.9833 0.755 121 -6.604 <.0001
## 2 CCN51 - 5 TCS01 -3.7667 0.755 121 -4.992 0.0004
## 2 CCN51 - 6 TCS01 -2.8333 0.755 121 -3.755 0.0364
## 3 CCN51 - 4 CCN51 -1.8333 0.722 108 -2.541 0.5797
## 3 CCN51 - 5 CCN51 -1.0667 0.722 108 -1.478 0.9961
## 3 CCN51 - 6 CCN51 0.1000 0.722 108 0.139 1.0000
## 3 CCN51 - 0 ICS95 19.3333 0.755 121 25.622 <.0001
## 3 CCN51 - 1 ICS95 4.9667 0.755 121 6.582 <.0001
## 3 CCN51 - 2 ICS95 3.3000 0.755 121 4.373 0.0044
## 3 CCN51 - 3 ICS95 0.0000 0.755 121 0.000 1.0000
## 3 CCN51 - 4 ICS95 -0.1333 0.755 121 -0.177 1.0000
## 3 CCN51 - 5 ICS95 -0.6333 0.755 121 -0.839 1.0000
## 3 CCN51 - 6 ICS95 1.4667 0.755 121 1.944 0.9290
## 3 CCN51 - 0 TCS01 19.3500 0.755 121 25.644 <.0001
## 3 CCN51 - 1 TCS01 5.7833 0.755 121 7.664 <.0001
## 3 CCN51 - 2 TCS01 2.8167 0.755 121 3.733 0.0390
## 3 CCN51 - 3 TCS01 -0.8500 0.755 121 -1.126 0.9999
## 3 CCN51 - 4 TCS01 -1.7833 0.755 121 -2.363 0.7092
## 3 CCN51 - 5 TCS01 -0.5667 0.755 121 -0.751 1.0000
## 3 CCN51 - 6 TCS01 0.3667 0.755 121 0.486 1.0000
## 4 CCN51 - 5 CCN51 0.7667 0.722 108 1.062 1.0000
## 4 CCN51 - 6 CCN51 1.9333 0.722 108 2.679 0.4766
## 4 CCN51 - 0 ICS95 21.1667 0.755 121 28.051 <.0001
## 4 CCN51 - 1 ICS95 6.8000 0.755 121 9.012 <.0001
## 4 CCN51 - 2 ICS95 5.1333 0.755 121 6.803 <.0001
## 4 CCN51 - 3 ICS95 1.8333 0.755 121 2.430 0.6619
## 4 CCN51 - 4 ICS95 1.7000 0.755 121 2.253 0.7823
## 4 CCN51 - 5 ICS95 1.2000 0.755 121 1.590 0.9908
## 4 CCN51 - 6 ICS95 3.3000 0.755 121 4.373 0.0044
## 4 CCN51 - 0 TCS01 21.1833 0.755 121 28.073 <.0001
## 4 CCN51 - 1 TCS01 7.6167 0.755 121 10.094 <.0001
## 4 CCN51 - 2 TCS01 4.6500 0.755 121 6.162 <.0001
## 4 CCN51 - 3 TCS01 0.9833 0.755 121 1.303 0.9993
## 4 CCN51 - 4 TCS01 0.0500 0.755 121 0.066 1.0000
## 4 CCN51 - 5 TCS01 1.2667 0.755 121 1.679 0.9832
## 4 CCN51 - 6 TCS01 2.2000 0.755 121 2.916 0.3144
## 5 CCN51 - 6 CCN51 1.1667 0.722 108 1.617 0.9887
## 5 CCN51 - 0 ICS95 20.4000 0.755 121 27.035 <.0001
## 5 CCN51 - 1 ICS95 6.0333 0.755 121 7.996 <.0001
## 5 CCN51 - 2 ICS95 4.3667 0.755 121 5.787 <.0001
## 5 CCN51 - 3 ICS95 1.0667 0.755 121 1.414 0.9979
## 5 CCN51 - 4 ICS95 0.9333 0.755 121 1.237 0.9997
## 5 CCN51 - 5 ICS95 0.4333 0.755 121 0.574 1.0000
## 5 CCN51 - 6 ICS95 2.5333 0.755 121 3.357 0.1140
## 5 CCN51 - 0 TCS01 20.4167 0.755 121 27.057 <.0001
## 5 CCN51 - 1 TCS01 6.8500 0.755 121 9.078 <.0001
## 5 CCN51 - 2 TCS01 3.8833 0.755 121 5.146 0.0002
## 5 CCN51 - 3 TCS01 0.2167 0.755 121 0.287 1.0000
## 5 CCN51 - 4 TCS01 -0.7167 0.755 121 -0.950 1.0000
## 5 CCN51 - 5 TCS01 0.5000 0.755 121 0.663 1.0000
## 5 CCN51 - 6 TCS01 1.4333 0.755 121 1.900 0.9422
## 6 CCN51 - 0 ICS95 19.2333 0.755 121 25.489 <.0001
## 6 CCN51 - 1 ICS95 4.8667 0.755 121 6.450 <.0001
## 6 CCN51 - 2 ICS95 3.2000 0.755 121 4.241 0.0071
## 6 CCN51 - 3 ICS95 -0.1000 0.755 121 -0.133 1.0000
## 6 CCN51 - 4 ICS95 -0.2333 0.755 121 -0.309 1.0000
## 6 CCN51 - 5 ICS95 -0.7333 0.755 121 -0.972 1.0000
## 6 CCN51 - 6 ICS95 1.3667 0.755 121 1.811 0.9631
## 6 CCN51 - 0 TCS01 19.2500 0.755 121 25.511 <.0001
## 6 CCN51 - 1 TCS01 5.6833 0.755 121 7.532 <.0001
## 6 CCN51 - 2 TCS01 2.7167 0.755 121 3.600 0.0581
## 6 CCN51 - 3 TCS01 -0.9500 0.755 121 -1.259 0.9996
## 6 CCN51 - 4 TCS01 -1.8833 0.755 121 -2.496 0.6129
## 6 CCN51 - 5 TCS01 -0.6667 0.755 121 -0.884 1.0000
## 6 CCN51 - 6 TCS01 0.2667 0.755 121 0.353 1.0000
## 0 ICS95 - 1 ICS95 -14.3667 0.722 108 -19.909 <.0001
## 0 ICS95 - 2 ICS95 -16.0333 0.722 108 -22.219 <.0001
## 0 ICS95 - 3 ICS95 -19.3333 0.722 108 -26.792 <.0001
## 0 ICS95 - 4 ICS95 -19.4667 0.722 108 -26.977 <.0001
## 0 ICS95 - 5 ICS95 -19.9667 0.722 108 -27.670 <.0001
## 0 ICS95 - 6 ICS95 -17.8667 0.722 108 -24.759 <.0001
## 0 ICS95 - 0 TCS01 0.0167 0.755 121 0.022 1.0000
## 0 ICS95 - 1 TCS01 -13.5500 0.755 121 -17.957 <.0001
## 0 ICS95 - 2 TCS01 -16.5167 0.755 121 -21.889 <.0001
## 0 ICS95 - 3 TCS01 -20.1833 0.755 121 -26.748 <.0001
## 0 ICS95 - 4 TCS01 -21.1167 0.755 121 -27.985 <.0001
## 0 ICS95 - 5 TCS01 -19.9000 0.755 121 -26.373 <.0001
## 0 ICS95 - 6 TCS01 -18.9667 0.755 121 -25.136 <.0001
## 1 ICS95 - 2 ICS95 -1.6667 0.722 108 -2.310 0.7453
## 1 ICS95 - 3 ICS95 -4.9667 0.722 108 -6.883 <.0001
## 1 ICS95 - 4 ICS95 -5.1000 0.722 108 -7.068 <.0001
## 1 ICS95 - 5 ICS95 -5.6000 0.722 108 -7.760 <.0001
## 1 ICS95 - 6 ICS95 -3.5000 0.722 108 -4.850 0.0008
## 1 ICS95 - 0 TCS01 14.3833 0.755 121 19.062 <.0001
## 1 ICS95 - 1 TCS01 0.8167 0.755 121 1.082 1.0000
## 1 ICS95 - 2 TCS01 -2.1500 0.755 121 -2.849 0.3565
## 1 ICS95 - 3 TCS01 -5.8167 0.755 121 -7.709 <.0001
## 1 ICS95 - 4 TCS01 -6.7500 0.755 121 -8.946 <.0001
## 1 ICS95 - 5 TCS01 -5.5333 0.755 121 -7.333 <.0001
## 1 ICS95 - 6 TCS01 -4.6000 0.755 121 -6.096 <.0001
## 2 ICS95 - 3 ICS95 -3.3000 0.722 108 -4.573 0.0022
## 2 ICS95 - 4 ICS95 -3.4333 0.722 108 -4.758 0.0011
## 2 ICS95 - 5 ICS95 -3.9333 0.722 108 -5.451 0.0001
## 2 ICS95 - 6 ICS95 -1.8333 0.722 108 -2.541 0.5797
## 2 ICS95 - 0 TCS01 16.0500 0.755 121 21.270 <.0001
## 2 ICS95 - 1 TCS01 2.4833 0.755 121 3.291 0.1352
## 2 ICS95 - 2 TCS01 -0.4833 0.755 121 -0.641 1.0000
## 2 ICS95 - 3 TCS01 -4.1500 0.755 121 -5.500 <.0001
## 2 ICS95 - 4 TCS01 -5.0833 0.755 121 -6.737 <.0001
## 2 ICS95 - 5 TCS01 -3.8667 0.755 121 -5.124 0.0002
## 2 ICS95 - 6 TCS01 -2.9333 0.755 121 -3.887 0.0239
## 3 ICS95 - 4 ICS95 -0.1333 0.722 108 -0.185 1.0000
## 3 ICS95 - 5 ICS95 -0.6333 0.722 108 -0.878 1.0000
## 3 ICS95 - 6 ICS95 1.4667 0.722 108 2.032 0.8959
## 3 ICS95 - 0 TCS01 19.3500 0.755 121 25.644 <.0001
## 3 ICS95 - 1 TCS01 5.7833 0.755 121 7.664 <.0001
## 3 ICS95 - 2 TCS01 2.8167 0.755 121 3.733 0.0390
## 3 ICS95 - 3 TCS01 -0.8500 0.755 121 -1.126 0.9999
## 3 ICS95 - 4 TCS01 -1.7833 0.755 121 -2.363 0.7092
## 3 ICS95 - 5 TCS01 -0.5667 0.755 121 -0.751 1.0000
## 3 ICS95 - 6 TCS01 0.3667 0.755 121 0.486 1.0000
## 4 ICS95 - 5 ICS95 -0.5000 0.722 108 -0.693 1.0000
## 4 ICS95 - 6 ICS95 1.6000 0.722 108 2.217 0.8032
## 4 ICS95 - 0 TCS01 19.4833 0.755 121 25.820 <.0001
## 4 ICS95 - 1 TCS01 5.9167 0.755 121 7.841 <.0001
## 4 ICS95 - 2 TCS01 2.9500 0.755 121 3.910 0.0222
## 4 ICS95 - 3 TCS01 -0.7167 0.755 121 -0.950 1.0000
## 4 ICS95 - 4 TCS01 -1.6500 0.755 121 -2.187 0.8216
## 4 ICS95 - 5 TCS01 -0.4333 0.755 121 -0.574 1.0000
## 4 ICS95 - 6 TCS01 0.5000 0.755 121 0.663 1.0000
## 5 ICS95 - 6 ICS95 2.1000 0.722 108 2.910 0.3195
## 5 ICS95 - 0 TCS01 19.9833 0.755 121 26.483 <.0001
## 5 ICS95 - 1 TCS01 6.4167 0.755 121 8.504 <.0001
## 5 ICS95 - 2 TCS01 3.4500 0.755 121 4.572 0.0021
## 5 ICS95 - 3 TCS01 -0.2167 0.755 121 -0.287 1.0000
## 5 ICS95 - 4 TCS01 -1.1500 0.755 121 -1.524 0.9945
## 5 ICS95 - 5 TCS01 0.0667 0.755 121 0.088 1.0000
## 5 ICS95 - 6 TCS01 1.0000 0.755 121 1.325 0.9991
## 6 ICS95 - 0 TCS01 17.8833 0.755 121 23.700 <.0001
## 6 ICS95 - 1 TCS01 4.3167 0.755 121 5.721 <.0001
## 6 ICS95 - 2 TCS01 1.3500 0.755 121 1.789 0.9673
## 6 ICS95 - 3 TCS01 -2.3167 0.755 121 -3.070 0.2280
## 6 ICS95 - 4 TCS01 -3.2500 0.755 121 -4.307 0.0056
## 6 ICS95 - 5 TCS01 -2.0333 0.755 121 -2.695 0.4643
## 6 ICS95 - 6 TCS01 -1.1000 0.755 121 -1.458 0.9968
## 0 TCS01 - 1 TCS01 -13.5667 0.722 108 -18.801 <.0001
## 0 TCS01 - 2 TCS01 -16.5333 0.722 108 -22.912 <.0001
## 0 TCS01 - 3 TCS01 -20.2000 0.722 108 -27.993 <.0001
## 0 TCS01 - 4 TCS01 -21.1333 0.722 108 -29.286 <.0001
## 0 TCS01 - 5 TCS01 -19.9167 0.722 108 -27.600 <.0001
## 0 TCS01 - 6 TCS01 -18.9833 0.722 108 -26.307 <.0001
## 1 TCS01 - 2 TCS01 -2.9667 0.722 108 -4.111 0.0118
## 1 TCS01 - 3 TCS01 -6.6333 0.722 108 -9.192 <.0001
## 1 TCS01 - 4 TCS01 -7.5667 0.722 108 -10.486 <.0001
## 1 TCS01 - 5 TCS01 -6.3500 0.722 108 -8.800 <.0001
## 1 TCS01 - 6 TCS01 -5.4167 0.722 108 -7.506 <.0001
## 2 TCS01 - 3 TCS01 -3.6667 0.722 108 -5.081 0.0003
## 2 TCS01 - 4 TCS01 -4.6000 0.722 108 -6.375 <.0001
## 2 TCS01 - 5 TCS01 -3.3833 0.722 108 -4.689 0.0014
## 2 TCS01 - 6 TCS01 -2.4500 0.722 108 -3.395 0.1051
## 3 TCS01 - 4 TCS01 -0.9333 0.722 108 -1.293 0.9993
## 3 TCS01 - 5 TCS01 0.2833 0.722 108 0.393 1.0000
## 3 TCS01 - 6 TCS01 1.2167 0.722 108 1.686 0.9820
## 4 TCS01 - 5 TCS01 1.2167 0.722 108 1.686 0.9820
## 4 TCS01 - 6 TCS01 2.1500 0.722 108 2.979 0.2785
## 5 TCS01 - 6 TCS01 0.9333 0.722 108 1.293 0.9993
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -8.6500 0.722 108 -11.987 <.0001
## 0 CCN51 - 2 CCN51 -12.2000 0.722 108 -16.907 <.0001
## 0 CCN51 - 3 CCN51 -16.8500 0.722 108 -23.351 <.0001
## 0 CCN51 - 4 CCN51 -18.1500 0.722 108 -25.152 <.0001
## 0 CCN51 - 5 CCN51 -18.1667 0.722 108 -25.175 <.0001
## 0 CCN51 - 6 CCN51 -13.1333 0.722 108 -18.200 <.0001
## 0 CCN51 - 0 ICS95 0.0333 0.755 121 0.044 1.0000
## 0 CCN51 - 1 ICS95 -11.7167 0.755 121 -15.528 <.0001
## 0 CCN51 - 2 ICS95 -10.7333 0.755 121 -14.224 <.0001
## 0 CCN51 - 3 ICS95 -17.8333 0.755 121 -23.634 <.0001
## 0 CCN51 - 4 ICS95 -18.2833 0.755 121 -24.230 <.0001
## 0 CCN51 - 5 ICS95 -17.8167 0.755 121 -23.612 <.0001
## 0 CCN51 - 6 ICS95 -16.5500 0.755 121 -21.933 <.0001
## 0 CCN51 - 0 TCS01 -0.0167 0.755 121 -0.022 1.0000
## 0 CCN51 - 1 TCS01 -11.3667 0.755 121 -15.064 <.0001
## 0 CCN51 - 2 TCS01 -12.1667 0.755 121 -16.124 <.0001
## 0 CCN51 - 3 TCS01 -17.4833 0.755 121 -23.170 <.0001
## 0 CCN51 - 4 TCS01 -19.0333 0.755 121 -25.224 <.0001
## 0 CCN51 - 5 TCS01 -19.0667 0.755 121 -25.268 <.0001
## 0 CCN51 - 6 TCS01 -15.6333 0.755 121 -20.718 <.0001
## 1 CCN51 - 2 CCN51 -3.5500 0.722 108 -4.920 0.0006
## 1 CCN51 - 3 CCN51 -8.2000 0.722 108 -11.363 <.0001
## 1 CCN51 - 4 CCN51 -9.5000 0.722 108 -13.165 <.0001
## 1 CCN51 - 5 CCN51 -9.5167 0.722 108 -13.188 <.0001
## 1 CCN51 - 6 CCN51 -4.4833 0.722 108 -6.213 <.0001
## 1 CCN51 - 0 ICS95 8.6833 0.755 121 11.508 <.0001
## 1 CCN51 - 1 ICS95 -3.0667 0.755 121 -4.064 0.0132
## 1 CCN51 - 2 ICS95 -2.0833 0.755 121 -2.761 0.4167
## 1 CCN51 - 3 ICS95 -9.1833 0.755 121 -12.170 <.0001
## 1 CCN51 - 4 ICS95 -9.6333 0.755 121 -12.767 <.0001
## 1 CCN51 - 5 ICS95 -9.1667 0.755 121 -12.148 <.0001
## 1 CCN51 - 6 ICS95 -7.9000 0.755 121 -10.470 <.0001
## 1 CCN51 - 0 TCS01 8.6333 0.755 121 11.441 <.0001
## 1 CCN51 - 1 TCS01 -2.7167 0.755 121 -3.600 0.0581
## 1 CCN51 - 2 TCS01 -3.5167 0.755 121 -4.660 0.0015
## 1 CCN51 - 3 TCS01 -8.8333 0.755 121 -11.706 <.0001
## 1 CCN51 - 4 TCS01 -10.3833 0.755 121 -13.761 <.0001
## 1 CCN51 - 5 TCS01 -10.4167 0.755 121 -13.805 <.0001
## 1 CCN51 - 6 TCS01 -6.9833 0.755 121 -9.255 <.0001
## 2 CCN51 - 3 CCN51 -4.6500 0.722 108 -6.444 <.0001
## 2 CCN51 - 4 CCN51 -5.9500 0.722 108 -8.245 <.0001
## 2 CCN51 - 5 CCN51 -5.9667 0.722 108 -8.269 <.0001
## 2 CCN51 - 6 CCN51 -0.9333 0.722 108 -1.293 0.9993
## 2 CCN51 - 0 ICS95 12.2333 0.755 121 16.212 <.0001
## 2 CCN51 - 1 ICS95 0.4833 0.755 121 0.641 1.0000
## 2 CCN51 - 2 ICS95 1.4667 0.755 121 1.944 0.9290
## 2 CCN51 - 3 ICS95 -5.6333 0.755 121 -7.466 <.0001
## 2 CCN51 - 4 ICS95 -6.0833 0.755 121 -8.062 <.0001
## 2 CCN51 - 5 ICS95 -5.6167 0.755 121 -7.444 <.0001
## 2 CCN51 - 6 ICS95 -4.3500 0.755 121 -5.765 <.0001
## 2 CCN51 - 0 TCS01 12.1833 0.755 121 16.146 <.0001
## 2 CCN51 - 1 TCS01 0.8333 0.755 121 1.104 0.9999
## 2 CCN51 - 2 TCS01 0.0333 0.755 121 0.044 1.0000
## 2 CCN51 - 3 TCS01 -5.2833 0.755 121 -7.002 <.0001
## 2 CCN51 - 4 TCS01 -6.8333 0.755 121 -9.056 <.0001
## 2 CCN51 - 5 TCS01 -6.8667 0.755 121 -9.100 <.0001
## 2 CCN51 - 6 TCS01 -3.4333 0.755 121 -4.550 0.0022
## 3 CCN51 - 4 CCN51 -1.3000 0.722 108 -1.802 0.9645
## 3 CCN51 - 5 CCN51 -1.3167 0.722 108 -1.825 0.9598
## 3 CCN51 - 6 CCN51 3.7167 0.722 108 5.151 0.0002
## 3 CCN51 - 0 ICS95 16.8833 0.755 121 22.375 <.0001
## 3 CCN51 - 1 ICS95 5.1333 0.755 121 6.803 <.0001
## 3 CCN51 - 2 ICS95 6.1167 0.755 121 8.106 <.0001
## 3 CCN51 - 3 ICS95 -0.9833 0.755 121 -1.303 0.9993
## 3 CCN51 - 4 ICS95 -1.4333 0.755 121 -1.900 0.9422
## 3 CCN51 - 5 ICS95 -0.9667 0.755 121 -1.281 0.9994
## 3 CCN51 - 6 ICS95 0.3000 0.755 121 0.398 1.0000
## 3 CCN51 - 0 TCS01 16.8333 0.755 121 22.309 <.0001
## 3 CCN51 - 1 TCS01 5.4833 0.755 121 7.267 <.0001
## 3 CCN51 - 2 TCS01 4.6833 0.755 121 6.207 <.0001
## 3 CCN51 - 3 TCS01 -0.6333 0.755 121 -0.839 1.0000
## 3 CCN51 - 4 TCS01 -2.1833 0.755 121 -2.893 0.3281
## 3 CCN51 - 5 TCS01 -2.2167 0.755 121 -2.938 0.3010
## 3 CCN51 - 6 TCS01 1.2167 0.755 121 1.612 0.9893
## 4 CCN51 - 5 CCN51 -0.0167 0.722 108 -0.023 1.0000
## 4 CCN51 - 6 CCN51 5.0167 0.722 108 6.952 <.0001
## 4 CCN51 - 0 ICS95 18.1833 0.755 121 24.098 <.0001
## 4 CCN51 - 1 ICS95 6.4333 0.755 121 8.526 <.0001
## 4 CCN51 - 2 ICS95 7.4167 0.755 121 9.829 <.0001
## 4 CCN51 - 3 ICS95 0.3167 0.755 121 0.420 1.0000
## 4 CCN51 - 4 ICS95 -0.1333 0.755 121 -0.177 1.0000
## 4 CCN51 - 5 ICS95 0.3333 0.755 121 0.442 1.0000
## 4 CCN51 - 6 ICS95 1.6000 0.755 121 2.120 0.8568
## 4 CCN51 - 0 TCS01 18.1333 0.755 121 24.031 <.0001
## 4 CCN51 - 1 TCS01 6.7833 0.755 121 8.990 <.0001
## 4 CCN51 - 2 TCS01 5.9833 0.755 121 7.929 <.0001
## 4 CCN51 - 3 TCS01 0.6667 0.755 121 0.884 1.0000
## 4 CCN51 - 4 TCS01 -0.8833 0.755 121 -1.171 0.9998
## 4 CCN51 - 5 TCS01 -0.9167 0.755 121 -1.215 0.9997
## 4 CCN51 - 6 TCS01 2.5167 0.755 121 3.335 0.1208
## 5 CCN51 - 6 CCN51 5.0333 0.722 108 6.975 <.0001
## 5 CCN51 - 0 ICS95 18.2000 0.755 121 24.120 <.0001
## 5 CCN51 - 1 ICS95 6.4500 0.755 121 8.548 <.0001
## 5 CCN51 - 2 ICS95 7.4333 0.755 121 9.851 <.0001
## 5 CCN51 - 3 ICS95 0.3333 0.755 121 0.442 1.0000
## 5 CCN51 - 4 ICS95 -0.1167 0.755 121 -0.155 1.0000
## 5 CCN51 - 5 ICS95 0.3500 0.755 121 0.464 1.0000
## 5 CCN51 - 6 ICS95 1.6167 0.755 121 2.143 0.8455
## 5 CCN51 - 0 TCS01 18.1500 0.755 121 24.053 <.0001
## 5 CCN51 - 1 TCS01 6.8000 0.755 121 9.012 <.0001
## 5 CCN51 - 2 TCS01 6.0000 0.755 121 7.952 <.0001
## 5 CCN51 - 3 TCS01 0.6833 0.755 121 0.906 1.0000
## 5 CCN51 - 4 TCS01 -0.8667 0.755 121 -1.149 0.9999
## 5 CCN51 - 5 TCS01 -0.9000 0.755 121 -1.193 0.9998
## 5 CCN51 - 6 TCS01 2.5333 0.755 121 3.357 0.1140
## 6 CCN51 - 0 ICS95 13.1667 0.755 121 17.449 <.0001
## 6 CCN51 - 1 ICS95 1.4167 0.755 121 1.877 0.9481
## 6 CCN51 - 2 ICS95 2.4000 0.755 121 3.181 0.1772
## 6 CCN51 - 3 ICS95 -4.7000 0.755 121 -6.229 <.0001
## 6 CCN51 - 4 ICS95 -5.1500 0.755 121 -6.825 <.0001
## 6 CCN51 - 5 ICS95 -4.6833 0.755 121 -6.207 <.0001
## 6 CCN51 - 6 ICS95 -3.4167 0.755 121 -4.528 0.0024
## 6 CCN51 - 0 TCS01 13.1167 0.755 121 17.383 <.0001
## 6 CCN51 - 1 TCS01 1.7667 0.755 121 2.341 0.7245
## 6 CCN51 - 2 TCS01 0.9667 0.755 121 1.281 0.9994
## 6 CCN51 - 3 TCS01 -4.3500 0.755 121 -5.765 <.0001
## 6 CCN51 - 4 TCS01 -5.9000 0.755 121 -7.819 <.0001
## 6 CCN51 - 5 TCS01 -5.9333 0.755 121 -7.863 <.0001
## 6 CCN51 - 6 TCS01 -2.5000 0.755 121 -3.313 0.1278
## 0 ICS95 - 1 ICS95 -11.7500 0.722 108 -16.283 <.0001
## 0 ICS95 - 2 ICS95 -10.7667 0.722 108 -14.920 <.0001
## 0 ICS95 - 3 ICS95 -17.8667 0.722 108 -24.759 <.0001
## 0 ICS95 - 4 ICS95 -18.3167 0.722 108 -25.383 <.0001
## 0 ICS95 - 5 ICS95 -17.8500 0.722 108 -24.736 <.0001
## 0 ICS95 - 6 ICS95 -16.5833 0.722 108 -22.981 <.0001
## 0 ICS95 - 0 TCS01 -0.0500 0.755 121 -0.066 1.0000
## 0 ICS95 - 1 TCS01 -11.4000 0.755 121 -15.108 <.0001
## 0 ICS95 - 2 TCS01 -12.2000 0.755 121 -16.168 <.0001
## 0 ICS95 - 3 TCS01 -17.5167 0.755 121 -23.214 <.0001
## 0 ICS95 - 4 TCS01 -19.0667 0.755 121 -25.268 <.0001
## 0 ICS95 - 5 TCS01 -19.1000 0.755 121 -25.312 <.0001
## 0 ICS95 - 6 TCS01 -15.6667 0.755 121 -20.762 <.0001
## 1 ICS95 - 2 ICS95 0.9833 0.722 108 1.363 0.9986
## 1 ICS95 - 3 ICS95 -6.1167 0.722 108 -8.476 <.0001
## 1 ICS95 - 4 ICS95 -6.5667 0.722 108 -9.100 <.0001
## 1 ICS95 - 5 ICS95 -6.1000 0.722 108 -8.453 <.0001
## 1 ICS95 - 6 ICS95 -4.8333 0.722 108 -6.698 <.0001
## 1 ICS95 - 0 TCS01 11.7000 0.755 121 15.506 <.0001
## 1 ICS95 - 1 TCS01 0.3500 0.755 121 0.464 1.0000
## 1 ICS95 - 2 TCS01 -0.4500 0.755 121 -0.596 1.0000
## 1 ICS95 - 3 TCS01 -5.7667 0.755 121 -7.642 <.0001
## 1 ICS95 - 4 TCS01 -7.3167 0.755 121 -9.696 <.0001
## 1 ICS95 - 5 TCS01 -7.3500 0.755 121 -9.741 <.0001
## 1 ICS95 - 6 TCS01 -3.9167 0.755 121 -5.191 0.0002
## 2 ICS95 - 3 ICS95 -7.1000 0.722 108 -9.839 <.0001
## 2 ICS95 - 4 ICS95 -7.5500 0.722 108 -10.463 <.0001
## 2 ICS95 - 5 ICS95 -7.0833 0.722 108 -9.816 <.0001
## 2 ICS95 - 6 ICS95 -5.8167 0.722 108 -8.061 <.0001
## 2 ICS95 - 0 TCS01 10.7167 0.755 121 14.202 <.0001
## 2 ICS95 - 1 TCS01 -0.6333 0.755 121 -0.839 1.0000
## 2 ICS95 - 2 TCS01 -1.4333 0.755 121 -1.900 0.9422
## 2 ICS95 - 3 TCS01 -6.7500 0.755 121 -8.946 <.0001
## 2 ICS95 - 4 TCS01 -8.3000 0.755 121 -11.000 <.0001
## 2 ICS95 - 5 TCS01 -8.3333 0.755 121 -11.044 <.0001
## 2 ICS95 - 6 TCS01 -4.9000 0.755 121 -6.494 <.0001
## 3 ICS95 - 4 ICS95 -0.4500 0.722 108 -0.624 1.0000
## 3 ICS95 - 5 ICS95 0.0167 0.722 108 0.023 1.0000
## 3 ICS95 - 6 ICS95 1.2833 0.722 108 1.778 0.9687
## 3 ICS95 - 0 TCS01 17.8167 0.755 121 23.612 <.0001
## 3 ICS95 - 1 TCS01 6.4667 0.755 121 8.570 <.0001
## 3 ICS95 - 2 TCS01 5.6667 0.755 121 7.510 <.0001
## 3 ICS95 - 3 TCS01 0.3500 0.755 121 0.464 1.0000
## 3 ICS95 - 4 TCS01 -1.2000 0.755 121 -1.590 0.9908
## 3 ICS95 - 5 TCS01 -1.2333 0.755 121 -1.634 0.9875
## 3 ICS95 - 6 TCS01 2.2000 0.755 121 2.916 0.3144
## 4 ICS95 - 5 ICS95 0.4667 0.722 108 0.647 1.0000
## 4 ICS95 - 6 ICS95 1.7333 0.722 108 2.402 0.6816
## 4 ICS95 - 0 TCS01 18.2667 0.755 121 24.208 <.0001
## 4 ICS95 - 1 TCS01 6.9167 0.755 121 9.166 <.0001
## 4 ICS95 - 2 TCS01 6.1167 0.755 121 8.106 <.0001
## 4 ICS95 - 3 TCS01 0.8000 0.755 121 1.060 1.0000
## 4 ICS95 - 4 TCS01 -0.7500 0.755 121 -0.994 1.0000
## 4 ICS95 - 5 TCS01 -0.7833 0.755 121 -1.038 1.0000
## 4 ICS95 - 6 TCS01 2.6500 0.755 121 3.512 0.0749
## 5 ICS95 - 6 ICS95 1.2667 0.722 108 1.755 0.9726
## 5 ICS95 - 0 TCS01 17.8000 0.755 121 23.590 <.0001
## 5 ICS95 - 1 TCS01 6.4500 0.755 121 8.548 <.0001
## 5 ICS95 - 2 TCS01 5.6500 0.755 121 7.488 <.0001
## 5 ICS95 - 3 TCS01 0.3333 0.755 121 0.442 1.0000
## 5 ICS95 - 4 TCS01 -1.2167 0.755 121 -1.612 0.9893
## 5 ICS95 - 5 TCS01 -1.2500 0.755 121 -1.657 0.9854
## 5 ICS95 - 6 TCS01 2.1833 0.755 121 2.893 0.3281
## 6 ICS95 - 0 TCS01 16.5333 0.755 121 21.911 <.0001
## 6 ICS95 - 1 TCS01 5.1833 0.755 121 6.869 <.0001
## 6 ICS95 - 2 TCS01 4.3833 0.755 121 5.809 <.0001
## 6 ICS95 - 3 TCS01 -0.9333 0.755 121 -1.237 0.9997
## 6 ICS95 - 4 TCS01 -2.4833 0.755 121 -3.291 0.1352
## 6 ICS95 - 5 TCS01 -2.5167 0.755 121 -3.335 0.1208
## 6 ICS95 - 6 TCS01 0.9167 0.755 121 1.215 0.9997
## 0 TCS01 - 1 TCS01 -11.3500 0.722 108 -15.729 <.0001
## 0 TCS01 - 2 TCS01 -12.1500 0.722 108 -16.837 <.0001
## 0 TCS01 - 3 TCS01 -17.4667 0.722 108 -24.205 <.0001
## 0 TCS01 - 4 TCS01 -19.0167 0.722 108 -26.353 <.0001
## 0 TCS01 - 5 TCS01 -19.0500 0.722 108 -26.399 <.0001
## 0 TCS01 - 6 TCS01 -15.6167 0.722 108 -21.641 <.0001
## 1 TCS01 - 2 TCS01 -0.8000 0.722 108 -1.109 0.9999
## 1 TCS01 - 3 TCS01 -6.1167 0.722 108 -8.476 <.0001
## 1 TCS01 - 4 TCS01 -7.6667 0.722 108 -10.624 <.0001
## 1 TCS01 - 5 TCS01 -7.7000 0.722 108 -10.671 <.0001
## 1 TCS01 - 6 TCS01 -4.2667 0.722 108 -5.913 <.0001
## 2 TCS01 - 3 TCS01 -5.3167 0.722 108 -7.368 <.0001
## 2 TCS01 - 4 TCS01 -6.8667 0.722 108 -9.516 <.0001
## 2 TCS01 - 5 TCS01 -6.9000 0.722 108 -9.562 <.0001
## 2 TCS01 - 6 TCS01 -3.4667 0.722 108 -4.804 0.0009
## 3 TCS01 - 4 TCS01 -1.5500 0.722 108 -2.148 0.8418
## 3 TCS01 - 5 TCS01 -1.5833 0.722 108 -2.194 0.8166
## 3 TCS01 - 6 TCS01 1.8500 0.722 108 2.564 0.5624
## 4 TCS01 - 5 TCS01 -0.0333 0.722 108 -0.046 1.0000
## 4 TCS01 - 6 TCS01 3.4000 0.722 108 4.712 0.0013
## 5 TCS01 - 6 TCS01 3.4333 0.722 108 4.758 0.0011
##
## 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(temp)
## [1] diam2 gen curva tiem.let dia progamada
## [7] muestra temp id 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(temp)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 63 × 9
## curva tiem.let diam2 dia gen progamada muestra temp id
## <fct> <chr> <fct> <int> <fct> <int> <fct> <dbl> <fct>
## 1 T3 cero 0 0 CCN51 35 14 25 1
## 2 T3 cero 0 0 CCN51 35 25 25 2
## 3 T3 cero 0 0 CCN51 35 36 25 3
## 4 T3 cero 0 0 ICS95 35 14 25 4
## 5 T3 cero 0 0 ICS95 35 25 25 5
## 6 T3 cero 0 0 ICS95 35 36 25 6
## 7 T3 cero 0 0 TCS01 35 14 25 7
## 8 T3 cero 0 0 TCS01 35 25 25 8
## 9 T3 cero 0 0 TCS01 35 36 25 9
## 10 T3 uno 1 1 CCN51 35 14 36.6 1
## 11 T3 uno 1 1 CCN51 35 25 38.0 2
## 12 T3 uno 1 1 CCN51 35 36 37.3 3
## 13 T3 uno 1 1 ICS95 35 14 39.8 4
## 14 T3 uno 1 1 ICS95 35 25 40.8 5
## 15 T3 uno 1 1 ICS95 35 36 40.2 6
## 16 T3 uno 1 1 TCS01 35 14 38 7
## 17 T3 uno 1 1 TCS01 35 25 38.4 8
## 18 T3 uno 1 1 TCS01 35 36 38.6 9
## 19 T3 dos 2 2 CCN51 40 14 41 1
## 20 T3 dos 2 2 CCN51 40 25 40.9 2
## 21 T3 dos 2 2 CCN51 40 36 40.7 3
## 22 T3 dos 2 2 ICS95 40 14 41.2 4
## 23 T3 dos 2 2 ICS95 40 25 40.7 5
## 24 T3 dos 2 2 ICS95 40 36 41.6 6
## 25 T3 dos 2 2 TCS01 40 14 41.2 7
## 26 T3 dos 2 2 TCS01 40 25 42.4 8
## 27 T3 dos 2 2 TCS01 40 36 41.7 9
## 28 T3 tres 3 3 CCN51 44 14 46.7 1
## 29 T3 tres 3 3 CCN51 44 25 46.6 2
## 30 T3 tres 3 3 CCN51 44 36 45 3
## 31 T3 tres 3 3 ICS95 44 14 47.8 4
## 32 T3 tres 3 3 ICS95 44 25 47.6 5
## 33 T3 tres 3 3 ICS95 44 36 46.8 6
## 34 T3 tres 3 3 TCS01 44 14 46.8 7
## 35 T3 tres 3 3 TCS01 44 25 46.8 8
## 36 T3 tres 3 3 TCS01 44 36 46.3 9
## 37 T3 cuatro 4 4 CCN51 46 14 45.4 1
## 38 T3 cuatro 4 4 CCN51 46 25 45.2 2
## 39 T3 cuatro 4 4 CCN51 46 36 45.0 3
## 40 T3 cuatro 4 4 ICS95 46 14 47.0 4
## 41 T3 cuatro 4 4 ICS95 46 25 46.2 5
## 42 T3 cuatro 4 4 ICS95 46 36 46 6
## 43 T3 cuatro 4 4 TCS01 46 14 41.0 7
## 44 T3 cuatro 4 4 TCS01 46 25 43.8 8
## 45 T3 cuatro 4 4 TCS01 46 36 42.6 9
## 46 T3 cinco 5 5 CCN51 48 14 43.4 1
## 47 T3 cinco 5 5 CCN51 48 25 44.5 2
## 48 T3 cinco 5 5 CCN51 48 36 42.8 3
## 49 T3 cinco 5 5 ICS95 48 14 44.2 4
## 50 T3 cinco 5 5 ICS95 48 25 45.2 5
## 51 T3 cinco 5 5 ICS95 48 36 46.4 6
## 52 T3 cinco 5 5 TCS01 48 14 34.4 7
## 53 T3 cinco 5 5 TCS01 48 25 42.9 8
## 54 T3 cinco 5 5 TCS01 48 36 43.4 9
## 55 T3 Seis 6 6 CCN51 47 14 37.4 1
## 56 T3 Seis 6 6 CCN51 47 25 38.8 2
## 57 T3 Seis 6 6 CCN51 47 36 38.3 3
## 58 T3 Seis 6 6 ICS95 47 14 38.8 4
## 59 T3 Seis 6 6 ICS95 47 25 39.0 5
## 60 T3 Seis 6 6 ICS95 47 36 39.8 6
## 61 T3 Seis 6 6 TCS01 47 14 36.7 7
## 62 T3 Seis 6 6 TCS01 47 25 34.0 8
## 63 T3 Seis 6 6 TCS01 47 36 36.8 9
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "temp", 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?
##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(temp ~ 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 NaN NaN
## 2 1 2 6 0.399 0.688
## 3 2 2 6 0.956 0.436
## 4 3 2 6 0.365 0.709
## 5 4 2 6 1.73 0.256
## 6 5 2 6 0.708 0.529
## 7 6 2 6 0.344 0.722
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = temp, 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 10.125 1.20e-02 * 0.387
## 2 diam2 1.51 9.04 284.522 1.35e-08 * 0.975
## 3 gen:diam2 3.01 9.04 2.768 1.03e-01 0.429
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = temp, 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 12 520.276 8.77e-14 * 0.994
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = temp, 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 12 441.41 2.34e-13 * 0.995
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = temp, 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 33.993 7.48e-07 * 0.933
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(temp)
## [1] diam2 gen curva tiem.let dia progamada
## [7] muestra temp id 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(temp)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 temp 0.942 0.537
## 2 1 CCN51 temp 0.824 0.174
## 3 2 CCN51 temp 0.995 0.862
## 4 3 CCN51 temp 0.767 0.0372
## 5 4 CCN51 temp 0.936 0.510
## 6 5 CCN51 temp 0.75 0
## 7 6 CCN51 temp 0.974 0.688
## 8 0 ICS95 temp 0.893 0.363
## 9 1 ICS95 temp 0.987 0.780
## 10 2 ICS95 temp 0.996 0.878
## 11 3 ICS95 temp 0.900 0.384
## 12 4 ICS95 temp 0.871 0.298
## 13 5 ICS95 temp 0.778 0.0624
## 14 6 ICS95 temp 0.993 0.843
## 15 0 TCS01 temp 0.75 0
## 16 1 TCS01 temp 1 1.00
## 17 2 TCS01 temp 0.75 0
## 18 3 TCS01 temp 0.816 0.154
## 19 4 TCS01 temp 0.936 0.510
## 20 5 TCS01 temp 0.915 0.433
## 21 6 TCS01 temp 0.897 0.377
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "temp", 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.curve2 %>%
group_by(diam2) %>%
levene_test(temp ~ 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 0.146 0.867
## 2 1 2 6 0.972 0.431
## 3 2 2 6 0.0346 0.966
## 4 3 2 6 0.0118 0.988
## 5 4 2 6 0.210 0.816
## 6 5 2 6 0.158 0.857
## 7 6 2 6 0.756 0.509
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = temp, 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 1.671 2.65e-01 0.118
## 2 diam2 2.04 12.23 1361.141 4.06e-15 * 0.994
## 3 gen:diam2 4.08 12.23 3.048 5.80e-02 0.436
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = temp, 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 12 309.313 1.95e-12 * 0.993
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = temp, 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 12 738.094 1.09e-14 * 0.996
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = temp, 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 12 524.237 8.38e-14 * 0.994
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(temp)
## [1] diam2 gen curva tiem.let dia progamada
## [7] muestra temp id 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(temp)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 temp 0.980 0.726
## 2 1 CCN51 temp 0.824 0.174
## 3 2 CCN51 temp 0.991 0.817
## 4 3 CCN51 temp 0.904 0.398
## 5 4 CCN51 temp 0.964 0.637
## 6 5 CCN51 temp 0.976 0.702
## 7 6 CCN51 temp 0.835 0.202
## 8 0 ICS95 temp 1 1.00
## 9 1 ICS95 temp 1 1.00
## 10 2 ICS95 temp 0.980 0.726
## 11 3 ICS95 temp 0.858 0.263
## 12 4 ICS95 temp 0.881 0.328
## 13 5 ICS95 temp 0.75 0
## 14 6 ICS95 temp 0.781 0.0704
## 15 0 TCS01 temp 0.842 0.220
## 16 1 TCS01 temp 0.818 0.157
## 17 2 TCS01 temp 0.818 0.157
## 18 3 TCS01 temp 0.912 0.424
## 19 4 TCS01 temp 0.971 0.675
## 20 5 TCS01 temp 0.867 0.288
## 21 6 TCS01 temp 0.904 0.399
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "temp", 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(temp ~ 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 0.0460 0.955
## 2 1 2 6 0.256 0.782
## 3 2 2 6 0.370 0.706
## 4 3 2 6 0.0126 0.987
## 5 4 2 6 1.19 0.368
## 6 5 2 6 0.162 0.854
## 7 6 2 6 0.227 0.803
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = temp, 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 7.031 2.7e-02 * 0.416
## 2 diam2 2.09 12.56 1059.324 8.3e-15 * 0.992
## 3 gen:diam2 4.19 12.56 7.205 3.0e-03 * 0.626
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = temp, 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 12 238.845 9.05e-12 * 0.991
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = temp, 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 12 683.673 1.72e-14 * 0.996
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = temp, 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 12 359.847 7.91e-13 * 0.989
## 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=temp)) +
geom_point(aes(y=temp)) +
scale_y_continuous(name = expression("Temperature (°C)")) + # 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
size = 0.25)) +
theme(text = element_text(size = 12))
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pht
## Gráfica por genotipo
datos2<-summarySE (datos, measurevar = "temp", groupvars = c("curva", "gen","diam2"))
write.csv(datos2,"~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data_out/temp_mean.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(~curva) +
geom_errorbar(aes(ymin=temp-ci, ymax=temp+ci), width=.1) +
geom_smooth(aes(y=temp), method="loess") +
geom_point(aes(y=temp)) +
scale_y_continuous(name = expression("Cd (mg*Kg"^"-1)")) + # 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
size = 0.25)) +
theme(text = element_text(size = 15))
pht2
## `geom_smooth()` using formula = 'y ~ x'
