setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("todos_phys.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("T3", "T1", "T2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$diam2<-as.factor(datos$diam2)
library(ggplot2)
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() ──
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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(ph.grano, type = "mean_sd")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 63 × 7
## curva diam2 gen variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 T3 0 CCN51 ph.grano 3 5.6 0.027
## 2 T3 1 CCN51 ph.grano 3 5.53 0.068
## 3 T3 2 CCN51 ph.grano 3 5.45 0.092
## 4 T3 3 CCN51 ph.grano 3 4.70 0.141
## 5 T3 4 CCN51 ph.grano 3 4.30 0.116
## 6 T3 5 CCN51 ph.grano 3 4.37 0.167
## 7 T3 6 CCN51 ph.grano 3 5.05 0.766
## 8 T3 0 ICS95 ph.grano 3 5.60 0.052
## 9 T3 1 ICS95 ph.grano 3 5.50 0.064
## 10 T3 2 ICS95 ph.grano 3 4.93 0.366
## 11 T3 3 ICS95 ph.grano 3 3.62 0.39
## 12 T3 4 ICS95 ph.grano 3 3.97 0.36
## 13 T3 5 ICS95 ph.grano 3 4.66 0.914
## 14 T3 6 ICS95 ph.grano 3 4.78 0.394
## 15 T3 0 TCS01 ph.grano 3 5.69 0.019
## 16 T3 1 TCS01 ph.grano 3 5.23 0.396
## 17 T3 2 TCS01 ph.grano 3 4.76 0.367
## 18 T3 3 TCS01 ph.grano 3 3.65 0.11
## 19 T3 4 TCS01 ph.grano 3 3.67 0.133
## 20 T3 5 TCS01 ph.grano 3 2.82 0.181
## 21 T3 6 TCS01 ph.grano 3 4.05 0.191
## 22 T1 0 CCN51 ph.grano 3 5.41 0.069
## 23 T1 1 CCN51 ph.grano 3 5.57 0.148
## 24 T1 2 CCN51 ph.grano 3 4.31 0.137
## 25 T1 3 CCN51 ph.grano 3 4.59 0.476
## 26 T1 4 CCN51 ph.grano 3 4.17 0.678
## 27 T1 5 CCN51 ph.grano 3 5.04 0.502
## 28 T1 6 CCN51 ph.grano 3 5.01 0.586
## 29 T1 0 ICS95 ph.grano 3 5.04 0.108
## 30 T1 1 ICS95 ph.grano 3 5.36 0.033
## 31 T1 2 ICS95 ph.grano 3 4.20 0.153
## 32 T1 3 ICS95 ph.grano 3 4.65 0.26
## 33 T1 4 ICS95 ph.grano 3 4.19 0.148
## 34 T1 5 ICS95 ph.grano 3 5.4 0.058
## 35 T1 6 ICS95 ph.grano 3 5.50 0.37
## 36 T1 0 TCS01 ph.grano 3 5.54 0.141
## 37 T1 1 TCS01 ph.grano 3 5.28 0.323
## 38 T1 2 TCS01 ph.grano 3 4.99 0.101
## 39 T1 3 TCS01 ph.grano 3 4.83 0.053
## 40 T1 4 TCS01 ph.grano 3 4.45 1.15
## 41 T1 5 TCS01 ph.grano 3 5.08 0.37
## 42 T1 6 TCS01 ph.grano 3 5.80 0.083
## 43 T2 0 CCN51 ph.grano 3 5.33 0.036
## 44 T2 1 CCN51 ph.grano 3 6.35 0.289
## 45 T2 2 CCN51 ph.grano 3 5.41 0.272
## 46 T2 3 CCN51 ph.grano 3 4.51 0.117
## 47 T2 4 CCN51 ph.grano 3 4.44 0.04
## 48 T2 5 CCN51 ph.grano 3 4.82 0.184
## 49 T2 6 CCN51 ph.grano 3 5.22 0.201
## 50 T2 0 ICS95 ph.grano 3 6.12 0.647
## 51 T2 1 ICS95 ph.grano 3 6.74 0.14
## 52 T2 2 ICS95 ph.grano 3 6.05 0.066
## 53 T2 3 ICS95 ph.grano 3 5.78 0.717
## 54 T2 4 ICS95 ph.grano 3 5.34 0.634
## 55 T2 5 ICS95 ph.grano 3 5.52 0.451
## 56 T2 6 ICS95 ph.grano 3 5.87 0.434
## 57 T2 0 TCS01 ph.grano 3 5.8 0.354
## 58 T2 1 TCS01 ph.grano 3 6.32 0.308
## 59 T2 2 TCS01 ph.grano 3 5.45 0.933
## 60 T2 3 TCS01 ph.grano 3 5.79 0.208
## 61 T2 4 TCS01 ph.grano 3 4.95 0.446
## 62 T2 5 TCS01 ph.grano 3 5.18 0.472
## 63 T2 6 TCS01 ph.grano 3 5.62 0.685
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "ph.grano",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(ph.grano)
## [1] curva diam2 gen time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm<-datos %>%
group_by(curva, gen, diam2) %>%
shapiro_test(ph.grano)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 63 × 6
## curva diam2 gen variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 T3 0 CCN51 ph.grano 0.839 0.210
## 2 T3 1 CCN51 ph.grano 0.993 0.845
## 3 T3 2 CCN51 ph.grano 0.994 0.850
## 4 T3 3 CCN51 ph.grano 0.998 0.922
## 5 T3 4 CCN51 ph.grano 0.904 0.397
## 6 T3 5 CCN51 ph.grano 0.805 0.126
## 7 T3 6 CCN51 ph.grano 0.774 0.0549
## 8 T3 0 ICS95 ph.grano 1.00 0.979
## 9 T3 1 ICS95 ph.grano 0.945 0.546
## 10 T3 2 ICS95 ph.grano 0.785 0.0783
## 11 T3 3 ICS95 ph.grano 0.887 0.345
## 12 T3 4 ICS95 ph.grano 0.770 0.0452
## 13 T3 5 ICS95 ph.grano 0.872 0.301
## 14 T3 6 ICS95 ph.grano 0.992 0.828
## 15 T3 0 TCS01 ph.grano 0.795 0.103
## 16 T3 1 TCS01 ph.grano 0.921 0.455
## 17 T3 2 TCS01 ph.grano 0.947 0.554
## 18 T3 3 TCS01 ph.grano 0.934 0.502
## 19 T3 4 TCS01 ph.grano 0.975 0.697
## 20 T3 5 TCS01 ph.grano 0.958 0.605
## 21 T3 6 TCS01 ph.grano 0.981 0.739
## 22 T1 0 CCN51 ph.grano 0.869 0.293
## 23 T1 1 CCN51 ph.grano 0.976 0.705
## 24 T1 2 CCN51 ph.grano 0.907 0.407
## 25 T1 3 CCN51 ph.grano 0.972 0.681
## 26 T1 4 CCN51 ph.grano 0.790 0.0916
## 27 T1 5 CCN51 ph.grano 0.977 0.711
## 28 T1 6 CCN51 ph.grano 0.995 0.860
## 29 T1 0 ICS95 ph.grano 0.998 0.908
## 30 T1 1 ICS95 ph.grano 0.991 0.814
## 31 T1 2 ICS95 ph.grano 0.999 0.942
## 32 T1 3 ICS95 ph.grano 0.916 0.438
## 33 T1 4 ICS95 ph.grano 0.891 0.356
## 34 T1 5 ICS95 ph.grano 0.882 0.331
## 35 T1 6 ICS95 ph.grano 0.917 0.443
## 36 T1 0 TCS01 ph.grano 0.971 0.673
## 37 T1 1 TCS01 ph.grano 0.944 0.545
## 38 T1 2 TCS01 ph.grano 0.940 0.529
## 39 T1 3 TCS01 ph.grano 0.904 0.398
## 40 T1 4 TCS01 ph.grano 0.894 0.367
## 41 T1 5 TCS01 ph.grano 0.922 0.461
## 42 T1 6 TCS01 ph.grano 0.927 0.476
## 43 T2 0 CCN51 ph.grano 0.808 0.134
## 44 T2 1 CCN51 ph.grano 0.851 0.242
## 45 T2 2 CCN51 ph.grano 0.915 0.436
## 46 T2 3 CCN51 ph.grano 0.990 0.811
## 47 T2 4 CCN51 ph.grano 0.866 0.286
## 48 T2 5 CCN51 ph.grano 0.811 0.140
## 49 T2 6 CCN51 ph.grano 0.952 0.578
## 50 T2 0 ICS95 ph.grano 0.795 0.103
## 51 T2 1 ICS95 ph.grano 1.00 0.961
## 52 T2 2 ICS95 ph.grano 0.966 0.648
## 53 T2 3 ICS95 ph.grano 0.946 0.553
## 54 T2 4 ICS95 ph.grano 1.00 0.960
## 55 T2 5 ICS95 ph.grano 0.922 0.460
## 56 T2 6 ICS95 ph.grano 1.00 0.985
## 57 T2 0 TCS01 ph.grano 0.978 0.719
## 58 T2 1 TCS01 ph.grano 0.937 0.515
## 59 T2 2 TCS01 ph.grano 0.891 0.357
## 60 T2 3 TCS01 ph.grano 0.989 0.798
## 61 T2 4 TCS01 ph.grano 0.939 0.523
## 62 T2 5 TCS01 ph.grano 0.796 0.105
## 63 T2 6 TCS01 ph.grano 0.856 0.256
##Create QQ plot for each cell of design:
ggqqplot(datos, "ph.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## the data.
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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.
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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
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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.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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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.
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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.
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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
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## The following aesthetics were dropped during statistical transformation: sample
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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
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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
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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
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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?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the 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(ph.grano ~ curva*gen)
lev %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 8 18 0.995 0.472
## 2 1 8 18 0.737 0.659
## 3 2 8 18 0.976 0.485
## 4 3 8 18 1.04 0.443
## 5 4 8 18 0.866 0.561
## 6 5 8 18 0.645 0.731
## 7 6 8 18 0.431 0.887
##Computation
res.aov <- anova_test(
data = datos, dv = ph.grano, wid = id,
within = diam2, between = c(curva, gen)
)
res.aov
## ANOVA Table (type II tests)
##
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 curva 2 18 33.079 9.37e-07 * 0.570
## 2 gen 2 18 1.740 2.04e-01 0.065
## 3 diam2 6 108 57.323 2.43e-31 * 0.671
## 4 curva:gen 4 18 7.321 1.00e-03 * 0.369
## 5 curva:diam2 12 108 10.094 5.16e-13 * 0.418
## 6 gen:diam2 12 108 2.226 1.50e-02 * 0.137
## 7 curva:gen:diam2 24 108 2.317 2.00e-03 * 0.248
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.135 0.052
## 2 curva:diam2 0.135 0.052
## 3 gen:diam2 0.135 0.052
## 4 curva:gen:diam2 0.135 0.052
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.631 3.79, 68.14 1.30e-20 * 0.82 4.92, 88.55
## 2 curva:diam2 0.631 7.57, 68.14 6.32e-09 * 0.82 9.84, 88.55
## 3 gen:diam2 0.631 7.57, 68.14 3.90e-02 * 0.82 9.84, 88.55
## 4 curva:gen:diam2 0.631 15.14, 68.14 1.00e-02 * 0.82 19.68, 88.55
## p[HF] p[HF]<.05
## 1 4.14e-26 *
## 2 5.05e-11 *
## 3 2.40e-02 *
## 4 4.00e-03 *
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2 18 33.079 9.37e-07 * 0.570
## 2 gen 2 18 1.740 2.04e-01 0.065
## 3 diam2 6 108 57.323 2.43e-31 * 0.671
## 4 curva:gen 4 18 7.321 1.00e-03 * 0.369
## 5 curva:diam2 12 108 10.094 5.16e-13 * 0.418
## 6 gen:diam2 12 108 2.226 1.50e-02 * 0.137
## 7 curva:gen:diam2 24 108 2.317 2.00e-03 * 0.248
#Table by error
res.aov.error <- aov(ph.grano ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = ph.grano ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 5.063228
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 25.626444 1.348209 11.342945 6.972298
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 0.6223744
## 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 39.43986 13.88929 3.06248 6.37718 12.38450
## Deg. of Freedom 6 12 12 24 108
##
## Residual standard error: 0.3386315
## 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 4.66 0.0784 18 4.50 4.83
## T1 4.97 0.0784 18 4.81 5.14
## T2 5.55 0.0784 18 5.39 5.72
##
## 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 -0.308 0.111 18 -2.779 0.0317
## T3 - T2 -0.888 0.111 18 -8.010 <.0001
## T1 - T2 -0.580 0.111 18 -5.231 0.0002
##
## 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 5.00 0.136 18 4.71 5.28
## ICS95 4.72 0.136 18 4.44 5.01
## TCS01 4.27 0.136 18 3.98 4.56
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 4.87 0.136 18 4.59 5.16
## ICS95 4.91 0.136 18 4.62 5.19
## TCS01 5.14 0.136 18 4.85 5.42
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 5.16 0.136 18 4.87 5.44
## ICS95 5.92 0.136 18 5.63 6.20
## TCS01 5.59 0.136 18 5.30 5.87
##
## Results are averaged over the levels of: diam2
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.274 0.192 18 1.427 0.3487
## CCN51 - TCS01 0.729 0.192 18 3.796 0.0036
## ICS95 - TCS01 0.455 0.192 18 2.369 0.0714
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -0.035 0.192 18 -0.182 0.9819
## CCN51 - TCS01 -0.267 0.192 18 -1.391 0.3663
## ICS95 - TCS01 -0.232 0.192 18 -1.209 0.4634
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -0.760 0.192 18 -3.959 0.0025
## CCN51 - TCS01 -0.431 0.192 18 -2.245 0.0904
## ICS95 - TCS01 0.329 0.192 18 1.714 0.2271
##
## 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 5.60 0.226 90.9 5.15 6.05
## 1 5.53 0.226 90.9 5.08 5.98
## 2 5.45 0.226 90.9 5.00 5.90
## 3 4.69 0.226 90.9 4.25 5.14
## 4 4.30 0.226 90.9 3.86 4.75
## 5 4.37 0.226 90.9 3.92 4.82
## 6 5.05 0.226 90.9 4.60 5.50
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.41 0.226 90.9 4.96 5.86
## 1 5.57 0.226 90.9 5.12 6.02
## 2 4.31 0.226 90.9 3.86 4.76
## 3 4.59 0.226 90.9 4.14 5.04
## 4 4.17 0.226 90.9 3.72 4.62
## 5 5.04 0.226 90.9 4.59 5.49
## 6 5.01 0.226 90.9 4.56 5.46
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.33 0.226 90.9 4.88 5.78
## 1 6.35 0.226 90.9 5.90 6.80
## 2 5.41 0.226 90.9 4.96 5.86
## 3 4.51 0.226 90.9 4.06 4.96
## 4 4.44 0.226 90.9 3.99 4.89
## 5 4.82 0.226 90.9 4.37 5.27
## 6 5.22 0.226 90.9 4.77 5.67
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.60 0.226 90.9 5.15 6.05
## 1 5.50 0.226 90.9 5.05 5.95
## 2 4.93 0.226 90.9 4.48 5.38
## 3 3.62 0.226 90.9 3.17 4.07
## 4 3.97 0.226 90.9 3.52 4.42
## 5 4.66 0.226 90.9 4.21 5.11
## 6 4.78 0.226 90.9 4.33 5.22
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.04 0.226 90.9 4.59 5.49
## 1 5.35 0.226 90.9 4.91 5.80
## 2 4.20 0.226 90.9 3.75 4.65
## 3 4.65 0.226 90.9 4.20 5.10
## 4 4.19 0.226 90.9 3.74 4.64
## 5 5.40 0.226 90.9 4.95 5.85
## 6 5.50 0.226 90.9 5.05 5.95
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 6.12 0.226 90.9 5.67 6.56
## 1 6.74 0.226 90.9 6.29 7.19
## 2 6.05 0.226 90.9 5.60 6.50
## 3 5.78 0.226 90.9 5.33 6.23
## 4 5.34 0.226 90.9 4.89 5.78
## 5 5.52 0.226 90.9 5.07 5.97
## 6 5.87 0.226 90.9 5.42 6.32
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.69 0.226 90.9 5.24 6.14
## 1 5.23 0.226 90.9 4.78 5.68
## 2 4.76 0.226 90.9 4.31 5.21
## 3 3.65 0.226 90.9 3.20 4.10
## 4 3.67 0.226 90.9 3.22 4.12
## 5 2.83 0.226 90.9 2.38 3.27
## 6 4.05 0.226 90.9 3.60 4.50
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.54 0.226 90.9 5.09 5.99
## 1 5.28 0.226 90.9 4.83 5.73
## 2 4.99 0.226 90.9 4.54 5.44
## 3 4.83 0.226 90.9 4.38 5.28
## 4 4.45 0.226 90.9 4.00 4.90
## 5 5.08 0.226 90.9 4.63 5.53
## 6 5.80 0.226 90.9 5.35 6.25
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.80 0.226 90.9 5.35 6.25
## 1 6.31 0.226 90.9 5.87 6.76
## 2 5.45 0.226 90.9 5.00 5.90
## 3 5.79 0.226 90.9 5.34 6.24
## 4 4.95 0.226 90.9 4.50 5.40
## 5 5.18 0.226 90.9 4.73 5.63
## 6 5.62 0.226 90.9 5.17 6.07
##
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.07233 0.276 108 0.262 1.0000
## 0 - 2 0.14700 0.276 108 0.532 0.9983
## 0 - 3 0.90500 0.276 108 3.273 0.0234
## 0 - 4 1.29500 0.276 108 4.684 0.0002
## 0 - 5 1.23367 0.276 108 4.462 0.0004
## 0 - 6 0.55267 0.276 108 1.999 0.4215
## 1 - 2 0.07467 0.276 108 0.270 1.0000
## 1 - 3 0.83267 0.276 108 3.012 0.0491
## 1 - 4 1.22267 0.276 108 4.422 0.0005
## 1 - 5 1.16133 0.276 108 4.200 0.0011
## 1 - 6 0.48033 0.276 108 1.737 0.5928
## 2 - 3 0.75800 0.276 108 2.741 0.0980
## 2 - 4 1.14800 0.276 108 4.152 0.0013
## 2 - 5 1.08667 0.276 108 3.930 0.0028
## 2 - 6 0.40567 0.276 108 1.467 0.7634
## 3 - 4 0.39000 0.276 108 1.411 0.7951
## 3 - 5 0.32867 0.276 108 1.189 0.8970
## 3 - 6 -0.35233 0.276 108 -1.274 0.8622
## 4 - 5 -0.06133 0.276 108 -0.222 1.0000
## 4 - 6 -0.74233 0.276 108 -2.685 0.1121
## 5 - 6 -0.68100 0.276 108 -2.463 0.1835
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.16067 0.276 108 -0.581 0.9972
## 0 - 2 1.10500 0.276 108 3.997 0.0022
## 0 - 3 0.82100 0.276 108 2.969 0.0550
## 0 - 4 1.24433 0.276 108 4.500 0.0003
## 0 - 5 0.37500 0.276 108 1.356 0.8234
## 0 - 6 0.40100 0.276 108 1.450 0.7731
## 1 - 2 1.26567 0.276 108 4.578 0.0002
## 1 - 3 0.98167 0.276 108 3.550 0.0100
## 1 - 4 1.40500 0.276 108 5.082 <.0001
## 1 - 5 0.53567 0.276 108 1.937 0.4605
## 1 - 6 0.56167 0.276 108 2.031 0.4013
## 2 - 3 -0.28400 0.276 108 -1.027 0.9466
## 2 - 4 0.13933 0.276 108 0.504 0.9988
## 2 - 5 -0.73000 0.276 108 -2.640 0.1244
## 2 - 6 -0.70400 0.276 108 -2.546 0.1536
## 3 - 4 0.42333 0.276 108 1.531 0.7256
## 3 - 5 -0.44600 0.276 108 -1.613 0.6743
## 3 - 6 -0.42000 0.276 108 -1.519 0.7329
## 4 - 5 -0.86933 0.276 108 -3.144 0.0340
## 4 - 6 -0.84333 0.276 108 -3.050 0.0442
## 5 - 6 0.02600 0.276 108 0.094 1.0000
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -1.01300 0.276 108 -3.664 0.0069
## 0 - 2 -0.07733 0.276 108 -0.280 1.0000
## 0 - 3 0.81867 0.276 108 2.961 0.0562
## 0 - 4 0.89100 0.276 108 3.223 0.0272
## 0 - 5 0.51367 0.276 108 1.858 0.5126
## 0 - 6 0.11000 0.276 108 0.398 0.9997
## 1 - 2 0.93567 0.276 108 3.384 0.0168
## 1 - 3 1.83167 0.276 108 6.625 <.0001
## 1 - 4 1.90400 0.276 108 6.886 <.0001
## 1 - 5 1.52667 0.276 108 5.522 <.0001
## 1 - 6 1.12300 0.276 108 4.062 0.0017
## 2 - 3 0.89600 0.276 108 3.241 0.0258
## 2 - 4 0.96833 0.276 108 3.502 0.0116
## 2 - 5 0.59100 0.276 108 2.137 0.3386
## 2 - 6 0.18733 0.276 108 0.678 0.9936
## 3 - 4 0.07233 0.276 108 0.262 1.0000
## 3 - 5 -0.30500 0.276 108 -1.103 0.9259
## 3 - 6 -0.70867 0.276 108 -2.563 0.1480
## 4 - 5 -0.37733 0.276 108 -1.365 0.8191
## 4 - 6 -0.78100 0.276 108 -2.825 0.0798
## 5 - 6 -0.40367 0.276 108 -1.460 0.7676
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.10200 0.276 108 0.369 0.9998
## 0 - 2 0.67033 0.276 108 2.424 0.1987
## 0 - 3 1.98167 0.276 108 7.167 <.0001
## 0 - 4 1.63033 0.276 108 5.897 <.0001
## 0 - 5 0.94300 0.276 108 3.411 0.0155
## 0 - 6 0.82933 0.276 108 2.999 0.0507
## 1 - 2 0.56833 0.276 108 2.056 0.3866
## 1 - 3 1.87967 0.276 108 6.798 <.0001
## 1 - 4 1.52833 0.276 108 5.528 <.0001
## 1 - 5 0.84100 0.276 108 3.042 0.0452
## 1 - 6 0.72733 0.276 108 2.631 0.1271
## 2 - 3 1.31133 0.276 108 4.743 0.0001
## 2 - 4 0.96000 0.276 108 3.472 0.0128
## 2 - 5 0.27267 0.276 108 0.986 0.9560
## 2 - 6 0.15900 0.276 108 0.575 0.9974
## 3 - 4 -0.35133 0.276 108 -1.271 0.8637
## 3 - 5 -1.03867 0.276 108 -3.757 0.0051
## 3 - 6 -1.15233 0.276 108 -4.168 0.0012
## 4 - 5 -0.68733 0.276 108 -2.486 0.1748
## 4 - 6 -0.80100 0.276 108 -2.897 0.0664
## 5 - 6 -0.11367 0.276 108 -0.411 0.9996
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.31367 0.276 108 -1.134 0.9160
## 0 - 2 0.83833 0.276 108 3.032 0.0464
## 0 - 3 0.38700 0.276 108 1.400 0.8009
## 0 - 4 0.84933 0.276 108 3.072 0.0416
## 0 - 5 -0.35900 0.276 108 -1.298 0.8513
## 0 - 6 -0.46300 0.276 108 -1.675 0.6343
## 1 - 2 1.15200 0.276 108 4.166 0.0012
## 1 - 3 0.70067 0.276 108 2.534 0.1577
## 1 - 4 1.16300 0.276 108 4.206 0.0010
## 1 - 5 -0.04533 0.276 108 -0.164 1.0000
## 1 - 6 -0.14933 0.276 108 -0.540 0.9982
## 2 - 3 -0.45133 0.276 108 -1.632 0.6618
## 2 - 4 0.01100 0.276 108 0.040 1.0000
## 2 - 5 -1.19733 0.276 108 -4.330 0.0006
## 2 - 6 -1.30133 0.276 108 -4.707 0.0001
## 3 - 4 0.46233 0.276 108 1.672 0.6359
## 3 - 5 -0.74600 0.276 108 -2.698 0.1087
## 3 - 6 -0.85000 0.276 108 -3.074 0.0414
## 4 - 5 -1.20833 0.276 108 -4.370 0.0006
## 4 - 6 -1.31233 0.276 108 -4.746 0.0001
## 5 - 6 -0.10400 0.276 108 -0.376 0.9998
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.62333 0.276 108 -2.254 0.2760
## 0 - 2 0.06600 0.276 108 0.239 1.0000
## 0 - 3 0.33400 0.276 108 1.208 0.8897
## 0 - 4 0.77967 0.276 108 2.820 0.0808
## 0 - 5 0.59533 0.276 108 2.153 0.3298
## 0 - 6 0.24200 0.276 108 0.875 0.9755
## 1 - 2 0.68933 0.276 108 2.493 0.1722
## 1 - 3 0.95733 0.276 108 3.462 0.0132
## 1 - 4 1.40300 0.276 108 5.074 <.0001
## 1 - 5 1.21867 0.276 108 4.408 0.0005
## 1 - 6 0.86533 0.276 108 3.130 0.0354
## 2 - 3 0.26800 0.276 108 0.969 0.9595
## 2 - 4 0.71367 0.276 108 2.581 0.1422
## 2 - 5 0.52933 0.276 108 1.914 0.4754
## 2 - 6 0.17600 0.276 108 0.637 0.9954
## 3 - 4 0.44567 0.276 108 1.612 0.6750
## 3 - 5 0.26133 0.276 108 0.945 0.9642
## 3 - 6 -0.09200 0.276 108 -0.333 0.9999
## 4 - 5 -0.18433 0.276 108 -0.667 0.9941
## 4 - 6 -0.53767 0.276 108 -1.945 0.4559
## 5 - 6 -0.35333 0.276 108 -1.278 0.8606
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.46000 0.276 108 1.664 0.6414
## 0 - 2 0.92733 0.276 108 3.354 0.0184
## 0 - 3 2.03767 0.276 108 7.370 <.0001
## 0 - 4 2.01867 0.276 108 7.301 <.0001
## 0 - 5 2.86633 0.276 108 10.367 <.0001
## 0 - 6 1.64133 0.276 108 5.936 <.0001
## 1 - 2 0.46733 0.276 108 1.690 0.6240
## 1 - 3 1.57767 0.276 108 5.706 <.0001
## 1 - 4 1.55867 0.276 108 5.637 <.0001
## 1 - 5 2.40633 0.276 108 8.703 <.0001
## 1 - 6 1.18133 0.276 108 4.273 0.0008
## 2 - 3 1.11033 0.276 108 4.016 0.0021
## 2 - 4 1.09133 0.276 108 3.947 0.0026
## 2 - 5 1.93900 0.276 108 7.013 <.0001
## 2 - 6 0.71400 0.276 108 2.582 0.1418
## 3 - 4 -0.01900 0.276 108 -0.069 1.0000
## 3 - 5 0.82867 0.276 108 2.997 0.0510
## 3 - 6 -0.39633 0.276 108 -1.433 0.7825
## 4 - 5 0.84767 0.276 108 3.066 0.0423
## 4 - 6 -0.37733 0.276 108 -1.365 0.8191
## 5 - 6 -1.22500 0.276 108 -4.431 0.0004
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.26067 0.276 108 0.943 0.9646
## 0 - 2 0.54933 0.276 108 1.987 0.4290
## 0 - 3 0.70767 0.276 108 2.559 0.1492
## 0 - 4 1.09267 0.276 108 3.952 0.0026
## 0 - 5 0.46367 0.276 108 1.677 0.6327
## 0 - 6 -0.25533 0.276 108 -0.923 0.9680
## 1 - 2 0.28867 0.276 108 1.044 0.9424
## 1 - 3 0.44700 0.276 108 1.617 0.6719
## 1 - 4 0.83200 0.276 108 3.009 0.0494
## 1 - 5 0.20300 0.276 108 0.734 0.9901
## 1 - 6 -0.51600 0.276 108 -1.866 0.5070
## 2 - 3 0.15833 0.276 108 0.573 0.9974
## 2 - 4 0.54333 0.276 108 1.965 0.4428
## 2 - 5 -0.08567 0.276 108 -0.310 0.9999
## 2 - 6 -0.80467 0.276 108 -2.910 0.0642
## 3 - 4 0.38500 0.276 108 1.392 0.8047
## 3 - 5 -0.24400 0.276 108 -0.882 0.9745
## 3 - 6 -0.96300 0.276 108 -3.483 0.0123
## 4 - 5 -0.62900 0.276 108 -2.275 0.2658
## 4 - 6 -1.34800 0.276 108 -4.875 0.0001
## 5 - 6 -0.71900 0.276 108 -2.600 0.1361
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.51467 0.276 108 -1.861 0.5102
## 0 - 2 0.34967 0.276 108 1.265 0.8664
## 0 - 3 0.00867 0.276 108 0.031 1.0000
## 0 - 4 0.85367 0.276 108 3.087 0.0399
## 0 - 5 0.61600 0.276 108 2.228 0.2895
## 0 - 6 0.18033 0.276 108 0.652 0.9948
## 1 - 2 0.86433 0.276 108 3.126 0.0358
## 1 - 3 0.52333 0.276 108 1.893 0.4896
## 1 - 4 1.36833 0.276 108 4.949 0.0001
## 1 - 5 1.13067 0.276 108 4.089 0.0016
## 1 - 6 0.69500 0.276 108 2.514 0.1648
## 2 - 3 -0.34100 0.276 108 -1.233 0.8796
## 2 - 4 0.50400 0.276 108 1.823 0.5358
## 2 - 5 0.26633 0.276 108 0.963 0.9607
## 2 - 6 -0.16933 0.276 108 -0.612 0.9963
## 3 - 4 0.84500 0.276 108 3.056 0.0435
## 3 - 5 0.60733 0.276 108 2.197 0.3061
## 3 - 6 0.17167 0.276 108 0.621 0.9960
## 4 - 5 -0.23767 0.276 108 -0.860 0.9776
## 4 - 6 -0.67333 0.276 108 -2.435 0.1943
## 5 - 6 -0.43567 0.276 108 -1.576 0.6980
##
## 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 5.60 0.226 90.9 5.15 6.05
## 1 CCN51 5.53 0.226 90.9 5.08 5.98
## 2 CCN51 5.45 0.226 90.9 5.00 5.90
## 3 CCN51 4.69 0.226 90.9 4.25 5.14
## 4 CCN51 4.30 0.226 90.9 3.86 4.75
## 5 CCN51 4.37 0.226 90.9 3.92 4.82
## 6 CCN51 5.05 0.226 90.9 4.60 5.50
## 0 ICS95 5.60 0.226 90.9 5.15 6.05
## 1 ICS95 5.50 0.226 90.9 5.05 5.95
## 2 ICS95 4.93 0.226 90.9 4.48 5.38
## 3 ICS95 3.62 0.226 90.9 3.17 4.07
## 4 ICS95 3.97 0.226 90.9 3.52 4.42
## 5 ICS95 4.66 0.226 90.9 4.21 5.11
## 6 ICS95 4.78 0.226 90.9 4.33 5.22
## 0 TCS01 5.69 0.226 90.9 5.24 6.14
## 1 TCS01 5.23 0.226 90.9 4.78 5.68
## 2 TCS01 4.76 0.226 90.9 4.31 5.21
## 3 TCS01 3.65 0.226 90.9 3.20 4.10
## 4 TCS01 3.67 0.226 90.9 3.22 4.12
## 5 TCS01 2.83 0.226 90.9 2.38 3.27
## 6 TCS01 4.05 0.226 90.9 3.60 4.50
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 5.41 0.226 90.9 4.96 5.86
## 1 CCN51 5.57 0.226 90.9 5.12 6.02
## 2 CCN51 4.31 0.226 90.9 3.86 4.76
## 3 CCN51 4.59 0.226 90.9 4.14 5.04
## 4 CCN51 4.17 0.226 90.9 3.72 4.62
## 5 CCN51 5.04 0.226 90.9 4.59 5.49
## 6 CCN51 5.01 0.226 90.9 4.56 5.46
## 0 ICS95 5.04 0.226 90.9 4.59 5.49
## 1 ICS95 5.35 0.226 90.9 4.91 5.80
## 2 ICS95 4.20 0.226 90.9 3.75 4.65
## 3 ICS95 4.65 0.226 90.9 4.20 5.10
## 4 ICS95 4.19 0.226 90.9 3.74 4.64
## 5 ICS95 5.40 0.226 90.9 4.95 5.85
## 6 ICS95 5.50 0.226 90.9 5.05 5.95
## 0 TCS01 5.54 0.226 90.9 5.09 5.99
## 1 TCS01 5.28 0.226 90.9 4.83 5.73
## 2 TCS01 4.99 0.226 90.9 4.54 5.44
## 3 TCS01 4.83 0.226 90.9 4.38 5.28
## 4 TCS01 4.45 0.226 90.9 4.00 4.90
## 5 TCS01 5.08 0.226 90.9 4.63 5.53
## 6 TCS01 5.80 0.226 90.9 5.35 6.25
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 5.33 0.226 90.9 4.88 5.78
## 1 CCN51 6.35 0.226 90.9 5.90 6.80
## 2 CCN51 5.41 0.226 90.9 4.96 5.86
## 3 CCN51 4.51 0.226 90.9 4.06 4.96
## 4 CCN51 4.44 0.226 90.9 3.99 4.89
## 5 CCN51 4.82 0.226 90.9 4.37 5.27
## 6 CCN51 5.22 0.226 90.9 4.77 5.67
## 0 ICS95 6.12 0.226 90.9 5.67 6.56
## 1 ICS95 6.74 0.226 90.9 6.29 7.19
## 2 ICS95 6.05 0.226 90.9 5.60 6.50
## 3 ICS95 5.78 0.226 90.9 5.33 6.23
## 4 ICS95 5.34 0.226 90.9 4.89 5.78
## 5 ICS95 5.52 0.226 90.9 5.07 5.97
## 6 ICS95 5.87 0.226 90.9 5.42 6.32
## 0 TCS01 5.80 0.226 90.9 5.35 6.25
## 1 TCS01 6.31 0.226 90.9 5.87 6.76
## 2 TCS01 5.45 0.226 90.9 5.00 5.90
## 3 TCS01 5.79 0.226 90.9 5.34 6.24
## 4 TCS01 4.95 0.226 90.9 4.50 5.40
## 5 TCS01 5.18 0.226 90.9 4.73 5.63
## 6 TCS01 5.62 0.226 90.9 5.17 6.07
##
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 0.07233 0.276 108.0 0.262 1.0000
## 0 CCN51 - 2 CCN51 0.14700 0.276 108.0 0.532 1.0000
## 0 CCN51 - 3 CCN51 0.90500 0.276 108.0 3.273 0.1435
## 0 CCN51 - 4 CCN51 1.29500 0.276 108.0 4.684 0.0015
## 0 CCN51 - 5 CCN51 1.23367 0.276 108.0 4.462 0.0034
## 0 CCN51 - 6 CCN51 0.55267 0.276 108.0 1.999 0.9091
## 0 CCN51 - 0 ICS95 -0.00467 0.320 90.9 -0.015 1.0000
## 0 CCN51 - 1 ICS95 0.09733 0.320 90.9 0.304 1.0000
## 0 CCN51 - 2 ICS95 0.66567 0.320 90.9 2.080 0.8738
## 0 CCN51 - 3 ICS95 1.97700 0.320 90.9 6.178 <.0001
## 0 CCN51 - 4 ICS95 1.62567 0.320 90.9 5.080 0.0004
## 0 CCN51 - 5 ICS95 0.93833 0.320 90.9 2.932 0.3094
## 0 CCN51 - 6 ICS95 0.82467 0.320 90.9 2.577 0.5534
## 0 CCN51 - 0 TCS01 -0.09167 0.320 90.9 -0.286 1.0000
## 0 CCN51 - 1 TCS01 0.36833 0.320 90.9 1.151 0.9999
## 0 CCN51 - 2 TCS01 0.83567 0.320 90.9 2.611 0.5279
## 0 CCN51 - 3 TCS01 1.94600 0.320 90.9 6.081 <.0001
## 0 CCN51 - 4 TCS01 1.92700 0.320 90.9 6.021 <.0001
## 0 CCN51 - 5 TCS01 2.77467 0.320 90.9 8.670 <.0001
## 0 CCN51 - 6 TCS01 1.54967 0.320 90.9 4.842 0.0009
## 1 CCN51 - 2 CCN51 0.07467 0.276 108.0 0.270 1.0000
## 1 CCN51 - 3 CCN51 0.83267 0.276 108.0 3.012 0.2607
## 1 CCN51 - 4 CCN51 1.22267 0.276 108.0 4.422 0.0039
## 1 CCN51 - 5 CCN51 1.16133 0.276 108.0 4.200 0.0087
## 1 CCN51 - 6 CCN51 0.48033 0.276 108.0 1.737 0.9754
## 1 CCN51 - 0 ICS95 -0.07700 0.320 90.9 -0.241 1.0000
## 1 CCN51 - 1 ICS95 0.02500 0.320 90.9 0.078 1.0000
## 1 CCN51 - 2 ICS95 0.59333 0.320 90.9 1.854 0.9521
## 1 CCN51 - 3 ICS95 1.90467 0.320 90.9 5.952 <.0001
## 1 CCN51 - 4 ICS95 1.55333 0.320 90.9 4.854 0.0009
## 1 CCN51 - 5 ICS95 0.86600 0.320 90.9 2.706 0.4589
## 1 CCN51 - 6 ICS95 0.75233 0.320 90.9 2.351 0.7167
## 1 CCN51 - 0 TCS01 -0.16400 0.320 90.9 -0.512 1.0000
## 1 CCN51 - 1 TCS01 0.29600 0.320 90.9 0.925 1.0000
## 1 CCN51 - 2 TCS01 0.76333 0.320 90.9 2.385 0.6930
## 1 CCN51 - 3 TCS01 1.87367 0.320 90.9 5.855 <.0001
## 1 CCN51 - 4 TCS01 1.85467 0.320 90.9 5.795 <.0001
## 1 CCN51 - 5 TCS01 2.70233 0.320 90.9 8.444 <.0001
## 1 CCN51 - 6 TCS01 1.47733 0.320 90.9 4.616 0.0022
## 2 CCN51 - 3 CCN51 0.75800 0.276 108.0 2.741 0.4317
## 2 CCN51 - 4 CCN51 1.14800 0.276 108.0 4.152 0.0103
## 2 CCN51 - 5 CCN51 1.08667 0.276 108.0 3.930 0.0216
## 2 CCN51 - 6 CCN51 0.40567 0.276 108.0 1.467 0.9964
## 2 CCN51 - 0 ICS95 -0.15167 0.320 90.9 -0.474 1.0000
## 2 CCN51 - 1 ICS95 -0.04967 0.320 90.9 -0.155 1.0000
## 2 CCN51 - 2 ICS95 0.51867 0.320 90.9 1.621 0.9879
## 2 CCN51 - 3 ICS95 1.83000 0.320 90.9 5.718 <.0001
## 2 CCN51 - 4 ICS95 1.47867 0.320 90.9 4.620 0.0022
## 2 CCN51 - 5 ICS95 0.79133 0.320 90.9 2.473 0.6303
## 2 CCN51 - 6 ICS95 0.67767 0.320 90.9 2.118 0.8559
## 2 CCN51 - 0 TCS01 -0.23867 0.320 90.9 -0.746 1.0000
## 2 CCN51 - 1 TCS01 0.22133 0.320 90.9 0.692 1.0000
## 2 CCN51 - 2 TCS01 0.68867 0.320 90.9 2.152 0.8383
## 2 CCN51 - 3 TCS01 1.79900 0.320 90.9 5.621 <.0001
## 2 CCN51 - 4 TCS01 1.78000 0.320 90.9 5.562 0.0001
## 2 CCN51 - 5 TCS01 2.62767 0.320 90.9 8.211 <.0001
## 2 CCN51 - 6 TCS01 1.40267 0.320 90.9 4.383 0.0051
## 3 CCN51 - 4 CCN51 0.39000 0.276 108.0 1.411 0.9978
## 3 CCN51 - 5 CCN51 0.32867 0.276 108.0 1.189 0.9998
## 3 CCN51 - 6 CCN51 -0.35233 0.276 108.0 -1.274 0.9995
## 3 CCN51 - 0 ICS95 -0.90967 0.320 90.9 -2.842 0.3653
## 3 CCN51 - 1 ICS95 -0.80767 0.320 90.9 -2.524 0.5927
## 3 CCN51 - 2 ICS95 -0.23933 0.320 90.9 -0.748 1.0000
## 3 CCN51 - 3 ICS95 1.07200 0.320 90.9 3.350 0.1219
## 3 CCN51 - 4 ICS95 0.72067 0.320 90.9 2.252 0.7811
## 3 CCN51 - 5 ICS95 0.03333 0.320 90.9 0.104 1.0000
## 3 CCN51 - 6 ICS95 -0.08033 0.320 90.9 -0.251 1.0000
## 3 CCN51 - 0 TCS01 -0.99667 0.320 90.9 -3.114 0.2124
## 3 CCN51 - 1 TCS01 -0.53667 0.320 90.9 -1.677 0.9825
## 3 CCN51 - 2 TCS01 -0.06933 0.320 90.9 -0.217 1.0000
## 3 CCN51 - 3 TCS01 1.04100 0.320 90.9 3.253 0.1546
## 3 CCN51 - 4 TCS01 1.02200 0.320 90.9 3.193 0.1777
## 3 CCN51 - 5 TCS01 1.86967 0.320 90.9 5.842 <.0001
## 3 CCN51 - 6 TCS01 0.64467 0.320 90.9 2.014 0.9017
## 4 CCN51 - 5 CCN51 -0.06133 0.276 108.0 -0.222 1.0000
## 4 CCN51 - 6 CCN51 -0.74233 0.276 108.0 -2.685 0.4725
## 4 CCN51 - 0 ICS95 -1.29967 0.320 90.9 -4.061 0.0153
## 4 CCN51 - 1 ICS95 -1.19767 0.320 90.9 -3.742 0.0414
## 4 CCN51 - 2 ICS95 -0.62933 0.320 90.9 -1.967 0.9194
## 4 CCN51 - 3 ICS95 0.68200 0.320 90.9 2.131 0.8491
## 4 CCN51 - 4 ICS95 0.33067 0.320 90.9 1.033 1.0000
## 4 CCN51 - 5 ICS95 -0.35667 0.320 90.9 -1.114 0.9999
## 4 CCN51 - 6 ICS95 -0.47033 0.320 90.9 -1.470 0.9962
## 4 CCN51 - 0 TCS01 -1.38667 0.320 90.9 -4.333 0.0061
## 4 CCN51 - 1 TCS01 -0.92667 0.320 90.9 -2.896 0.3316
## 4 CCN51 - 2 TCS01 -0.45933 0.320 90.9 -1.435 0.9972
## 4 CCN51 - 3 TCS01 0.65100 0.320 90.9 2.034 0.8937
## 4 CCN51 - 4 TCS01 0.63200 0.320 90.9 1.975 0.9165
## 4 CCN51 - 5 TCS01 1.47967 0.320 90.9 4.624 0.0021
## 4 CCN51 - 6 TCS01 0.25467 0.320 90.9 0.796 1.0000
## 5 CCN51 - 6 CCN51 -0.68100 0.276 108.0 -2.463 0.6374
## 5 CCN51 - 0 ICS95 -1.23833 0.320 90.9 -3.869 0.0281
## 5 CCN51 - 1 ICS95 -1.13633 0.320 90.9 -3.551 0.0717
## 5 CCN51 - 2 ICS95 -0.56800 0.320 90.9 -1.775 0.9685
## 5 CCN51 - 3 ICS95 0.74333 0.320 90.9 2.323 0.7357
## 5 CCN51 - 4 ICS95 0.39200 0.320 90.9 1.225 0.9997
## 5 CCN51 - 5 ICS95 -0.29533 0.320 90.9 -0.923 1.0000
## 5 CCN51 - 6 ICS95 -0.40900 0.320 90.9 -1.278 0.9994
## 5 CCN51 - 0 TCS01 -1.32533 0.320 90.9 -4.141 0.0117
## 5 CCN51 - 1 TCS01 -0.86533 0.320 90.9 -2.704 0.4604
## 5 CCN51 - 2 TCS01 -0.39800 0.320 90.9 -1.244 0.9996
## 5 CCN51 - 3 TCS01 0.71233 0.320 90.9 2.226 0.7968
## 5 CCN51 - 4 TCS01 0.69333 0.320 90.9 2.166 0.8305
## 5 CCN51 - 5 TCS01 1.54100 0.320 90.9 4.815 0.0010
## 5 CCN51 - 6 TCS01 0.31600 0.320 90.9 0.987 1.0000
## 6 CCN51 - 0 ICS95 -0.55733 0.320 90.9 -1.742 0.9740
## 6 CCN51 - 1 ICS95 -0.45533 0.320 90.9 -1.423 0.9975
## 6 CCN51 - 2 ICS95 0.11300 0.320 90.9 0.353 1.0000
## 6 CCN51 - 3 ICS95 1.42433 0.320 90.9 4.451 0.0040
## 6 CCN51 - 4 ICS95 1.07300 0.320 90.9 3.353 0.1209
## 6 CCN51 - 5 ICS95 0.38567 0.320 90.9 1.205 0.9997
## 6 CCN51 - 6 ICS95 0.27200 0.320 90.9 0.850 1.0000
## 6 CCN51 - 0 TCS01 -0.64433 0.320 90.9 -2.013 0.9021
## 6 CCN51 - 1 TCS01 -0.18433 0.320 90.9 -0.576 1.0000
## 6 CCN51 - 2 TCS01 0.28300 0.320 90.9 0.884 1.0000
## 6 CCN51 - 3 TCS01 1.39333 0.320 90.9 4.354 0.0056
## 6 CCN51 - 4 TCS01 1.37433 0.320 90.9 4.294 0.0069
## 6 CCN51 - 5 TCS01 2.22200 0.320 90.9 6.943 <.0001
## 6 CCN51 - 6 TCS01 0.99700 0.320 90.9 3.115 0.2119
## 0 ICS95 - 1 ICS95 0.10200 0.276 108.0 0.369 1.0000
## 0 ICS95 - 2 ICS95 0.67033 0.276 108.0 2.424 0.6655
## 0 ICS95 - 3 ICS95 1.98167 0.276 108.0 7.167 <.0001
## 0 ICS95 - 4 ICS95 1.63033 0.276 108.0 5.897 <.0001
## 0 ICS95 - 5 ICS95 0.94300 0.276 108.0 3.411 0.1009
## 0 ICS95 - 6 ICS95 0.82933 0.276 108.0 2.999 0.2673
## 0 ICS95 - 0 TCS01 -0.08700 0.320 90.9 -0.272 1.0000
## 0 ICS95 - 1 TCS01 0.37300 0.320 90.9 1.166 0.9998
## 0 ICS95 - 2 TCS01 0.84033 0.320 90.9 2.626 0.5172
## 0 ICS95 - 3 TCS01 1.95067 0.320 90.9 6.095 <.0001
## 0 ICS95 - 4 TCS01 1.93167 0.320 90.9 6.036 <.0001
## 0 ICS95 - 5 TCS01 2.77933 0.320 90.9 8.685 <.0001
## 0 ICS95 - 6 TCS01 1.55433 0.320 90.9 4.857 0.0009
## 1 ICS95 - 2 ICS95 0.56833 0.276 108.0 2.056 0.8862
## 1 ICS95 - 3 ICS95 1.87967 0.276 108.0 6.798 <.0001
## 1 ICS95 - 4 ICS95 1.52833 0.276 108.0 5.528 <.0001
## 1 ICS95 - 5 ICS95 0.84100 0.276 108.0 3.042 0.2447
## 1 ICS95 - 6 ICS95 0.72733 0.276 108.0 2.631 0.5124
## 1 ICS95 - 0 TCS01 -0.18900 0.320 90.9 -0.591 1.0000
## 1 ICS95 - 1 TCS01 0.27100 0.320 90.9 0.847 1.0000
## 1 ICS95 - 2 TCS01 0.73833 0.320 90.9 2.307 0.7460
## 1 ICS95 - 3 TCS01 1.84867 0.320 90.9 5.777 <.0001
## 1 ICS95 - 4 TCS01 1.82967 0.320 90.9 5.717 <.0001
## 1 ICS95 - 5 TCS01 2.67733 0.320 90.9 8.366 <.0001
## 1 ICS95 - 6 TCS01 1.45233 0.320 90.9 4.538 0.0029
## 2 ICS95 - 3 ICS95 1.31133 0.276 108.0 4.743 0.0012
## 2 ICS95 - 4 ICS95 0.96000 0.276 108.0 3.472 0.0855
## 2 ICS95 - 5 ICS95 0.27267 0.276 108.0 0.986 1.0000
## 2 ICS95 - 6 ICS95 0.15900 0.276 108.0 0.575 1.0000
## 2 ICS95 - 0 TCS01 -0.75733 0.320 90.9 -2.366 0.7060
## 2 ICS95 - 1 TCS01 -0.29733 0.320 90.9 -0.929 1.0000
## 2 ICS95 - 2 TCS01 0.17000 0.320 90.9 0.531 1.0000
## 2 ICS95 - 3 TCS01 1.28033 0.320 90.9 4.001 0.0186
## 2 ICS95 - 4 TCS01 1.26133 0.320 90.9 3.941 0.0225
## 2 ICS95 - 5 TCS01 2.10900 0.320 90.9 6.590 <.0001
## 2 ICS95 - 6 TCS01 0.88400 0.320 90.9 2.762 0.4193
## 3 ICS95 - 4 ICS95 -0.35133 0.276 108.0 -1.271 0.9995
## 3 ICS95 - 5 ICS95 -1.03867 0.276 108.0 -3.757 0.0374
## 3 ICS95 - 6 ICS95 -1.15233 0.276 108.0 -4.168 0.0097
## 3 ICS95 - 0 TCS01 -2.06867 0.320 90.9 -6.464 <.0001
## 3 ICS95 - 1 TCS01 -1.60867 0.320 90.9 -5.027 0.0005
## 3 ICS95 - 2 TCS01 -1.14133 0.320 90.9 -3.566 0.0686
## 3 ICS95 - 3 TCS01 -0.03100 0.320 90.9 -0.097 1.0000
## 3 ICS95 - 4 TCS01 -0.05000 0.320 90.9 -0.156 1.0000
## 3 ICS95 - 5 TCS01 0.79767 0.320 90.9 2.493 0.6158
## 3 ICS95 - 6 TCS01 -0.42733 0.320 90.9 -1.335 0.9989
## 4 ICS95 - 5 ICS95 -0.68733 0.276 108.0 -2.486 0.6205
## 4 ICS95 - 6 ICS95 -0.80100 0.276 108.0 -2.897 0.3276
## 4 ICS95 - 0 TCS01 -1.71733 0.320 90.9 -5.366 0.0001
## 4 ICS95 - 1 TCS01 -1.25733 0.320 90.9 -3.929 0.0234
## 4 ICS95 - 2 TCS01 -0.79000 0.320 90.9 -2.469 0.6333
## 4 ICS95 - 3 TCS01 0.32033 0.320 90.9 1.001 1.0000
## 4 ICS95 - 4 TCS01 0.30133 0.320 90.9 0.942 1.0000
## 4 ICS95 - 5 TCS01 1.14900 0.320 90.9 3.590 0.0642
## 4 ICS95 - 6 TCS01 -0.07600 0.320 90.9 -0.237 1.0000
## 5 ICS95 - 6 ICS95 -0.11367 0.276 108.0 -0.411 1.0000
## 5 ICS95 - 0 TCS01 -1.03000 0.320 90.9 -3.218 0.1677
## 5 ICS95 - 1 TCS01 -0.57000 0.320 90.9 -1.781 0.9674
## 5 ICS95 - 2 TCS01 -0.10267 0.320 90.9 -0.321 1.0000
## 5 ICS95 - 3 TCS01 1.00767 0.320 90.9 3.149 0.1968
## 5 ICS95 - 4 TCS01 0.98867 0.320 90.9 3.089 0.2243
## 5 ICS95 - 5 TCS01 1.83633 0.320 90.9 5.738 <.0001
## 5 ICS95 - 6 TCS01 0.61133 0.320 90.9 1.910 0.9372
## 6 ICS95 - 0 TCS01 -0.91633 0.320 90.9 -2.863 0.3519
## 6 ICS95 - 1 TCS01 -0.45633 0.320 90.9 -1.426 0.9974
## 6 ICS95 - 2 TCS01 0.01100 0.320 90.9 0.034 1.0000
## 6 ICS95 - 3 TCS01 1.12133 0.320 90.9 3.504 0.0815
## 6 ICS95 - 4 TCS01 1.10233 0.320 90.9 3.445 0.0955
## 6 ICS95 - 5 TCS01 1.95000 0.320 90.9 6.093 <.0001
## 6 ICS95 - 6 TCS01 0.72500 0.320 90.9 2.265 0.7727
## 0 TCS01 - 1 TCS01 0.46000 0.276 108.0 1.664 0.9844
## 0 TCS01 - 2 TCS01 0.92733 0.276 108.0 3.354 0.1170
## 0 TCS01 - 3 TCS01 2.03767 0.276 108.0 7.370 <.0001
## 0 TCS01 - 4 TCS01 2.01867 0.276 108.0 7.301 <.0001
## 0 TCS01 - 5 TCS01 2.86633 0.276 108.0 10.367 <.0001
## 0 TCS01 - 6 TCS01 1.64133 0.276 108.0 5.936 <.0001
## 1 TCS01 - 2 TCS01 0.46733 0.276 108.0 1.690 0.9815
## 1 TCS01 - 3 TCS01 1.57767 0.276 108.0 5.706 <.0001
## 1 TCS01 - 4 TCS01 1.55867 0.276 108.0 5.637 <.0001
## 1 TCS01 - 5 TCS01 2.40633 0.276 108.0 8.703 <.0001
## 1 TCS01 - 6 TCS01 1.18133 0.276 108.0 4.273 0.0067
## 2 TCS01 - 3 TCS01 1.11033 0.276 108.0 4.016 0.0163
## 2 TCS01 - 4 TCS01 1.09133 0.276 108.0 3.947 0.0205
## 2 TCS01 - 5 TCS01 1.93900 0.276 108.0 7.013 <.0001
## 2 TCS01 - 6 TCS01 0.71400 0.276 108.0 2.582 0.5484
## 3 TCS01 - 4 TCS01 -0.01900 0.276 108.0 -0.069 1.0000
## 3 TCS01 - 5 TCS01 0.82867 0.276 108.0 2.997 0.2687
## 3 TCS01 - 6 TCS01 -0.39633 0.276 108.0 -1.433 0.9974
## 4 TCS01 - 5 TCS01 0.84767 0.276 108.0 3.066 0.2323
## 4 TCS01 - 6 TCS01 -0.37733 0.276 108.0 -1.365 0.9986
## 5 TCS01 - 6 TCS01 -1.22500 0.276 108.0 -4.431 0.0038
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -0.16067 0.276 108.0 -0.581 1.0000
## 0 CCN51 - 2 CCN51 1.10500 0.276 108.0 3.997 0.0174
## 0 CCN51 - 3 CCN51 0.82100 0.276 108.0 2.969 0.2843
## 0 CCN51 - 4 CCN51 1.24433 0.276 108.0 4.500 0.0029
## 0 CCN51 - 5 CCN51 0.37500 0.276 108.0 1.356 0.9987
## 0 CCN51 - 6 CCN51 0.40100 0.276 108.0 1.450 0.9969
## 0 CCN51 - 0 ICS95 0.37167 0.320 90.9 1.161 0.9998
## 0 CCN51 - 1 ICS95 0.05800 0.320 90.9 0.181 1.0000
## 0 CCN51 - 2 ICS95 1.21000 0.320 90.9 3.781 0.0369
## 0 CCN51 - 3 ICS95 0.75867 0.320 90.9 2.371 0.7031
## 0 CCN51 - 4 ICS95 1.22100 0.320 90.9 3.815 0.0332
## 0 CCN51 - 5 ICS95 0.01267 0.320 90.9 0.040 1.0000
## 0 CCN51 - 6 ICS95 -0.09133 0.320 90.9 -0.285 1.0000
## 0 CCN51 - 0 TCS01 -0.12900 0.320 90.9 -0.403 1.0000
## 0 CCN51 - 1 TCS01 0.13167 0.320 90.9 0.411 1.0000
## 0 CCN51 - 2 TCS01 0.42033 0.320 90.9 1.313 0.9991
## 0 CCN51 - 3 TCS01 0.57867 0.320 90.9 1.808 0.9622
## 0 CCN51 - 4 TCS01 0.96367 0.320 90.9 3.011 0.2644
## 0 CCN51 - 5 TCS01 0.33467 0.320 90.9 1.046 1.0000
## 0 CCN51 - 6 TCS01 -0.38433 0.320 90.9 -1.201 0.9998
## 1 CCN51 - 2 CCN51 1.26567 0.276 108.0 4.578 0.0022
## 1 CCN51 - 3 CCN51 0.98167 0.276 108.0 3.550 0.0688
## 1 CCN51 - 4 CCN51 1.40500 0.276 108.0 5.082 0.0003
## 1 CCN51 - 5 CCN51 0.53567 0.276 108.0 1.937 0.9303
## 1 CCN51 - 6 CCN51 0.56167 0.276 108.0 2.031 0.8963
## 1 CCN51 - 0 ICS95 0.53233 0.320 90.9 1.663 0.9839
## 1 CCN51 - 1 ICS95 0.21867 0.320 90.9 0.683 1.0000
## 1 CCN51 - 2 ICS95 1.37067 0.320 90.9 4.283 0.0072
## 1 CCN51 - 3 ICS95 0.91933 0.320 90.9 2.873 0.3459
## 1 CCN51 - 4 ICS95 1.38167 0.320 90.9 4.317 0.0064
## 1 CCN51 - 5 ICS95 0.17333 0.320 90.9 0.542 1.0000
## 1 CCN51 - 6 ICS95 0.06933 0.320 90.9 0.217 1.0000
## 1 CCN51 - 0 TCS01 0.03167 0.320 90.9 0.099 1.0000
## 1 CCN51 - 1 TCS01 0.29233 0.320 90.9 0.913 1.0000
## 1 CCN51 - 2 TCS01 0.58100 0.320 90.9 1.815 0.9607
## 1 CCN51 - 3 TCS01 0.73933 0.320 90.9 2.310 0.7439
## 1 CCN51 - 4 TCS01 1.12433 0.320 90.9 3.513 0.0794
## 1 CCN51 - 5 TCS01 0.49533 0.320 90.9 1.548 0.9929
## 1 CCN51 - 6 TCS01 -0.22367 0.320 90.9 -0.699 1.0000
## 2 CCN51 - 3 CCN51 -0.28400 0.276 108.0 -1.027 1.0000
## 2 CCN51 - 4 CCN51 0.13933 0.276 108.0 0.504 1.0000
## 2 CCN51 - 5 CCN51 -0.73000 0.276 108.0 -2.640 0.5053
## 2 CCN51 - 6 CCN51 -0.70400 0.276 108.0 -2.546 0.5755
## 2 CCN51 - 0 ICS95 -0.73333 0.320 90.9 -2.291 0.7561
## 2 CCN51 - 1 ICS95 -1.04700 0.320 90.9 -3.272 0.1478
## 2 CCN51 - 2 ICS95 0.10500 0.320 90.9 0.328 1.0000
## 2 CCN51 - 3 ICS95 -0.34633 0.320 90.9 -1.082 0.9999
## 2 CCN51 - 4 ICS95 0.11600 0.320 90.9 0.362 1.0000
## 2 CCN51 - 5 ICS95 -1.09233 0.320 90.9 -3.413 0.1036
## 2 CCN51 - 6 ICS95 -1.19633 0.320 90.9 -3.738 0.0419
## 2 CCN51 - 0 TCS01 -1.23400 0.320 90.9 -3.856 0.0293
## 2 CCN51 - 1 TCS01 -0.97333 0.320 90.9 -3.041 0.2484
## 2 CCN51 - 2 TCS01 -0.68467 0.320 90.9 -2.139 0.8448
## 2 CCN51 - 3 TCS01 -0.52633 0.320 90.9 -1.645 0.9858
## 2 CCN51 - 4 TCS01 -0.14133 0.320 90.9 -0.442 1.0000
## 2 CCN51 - 5 TCS01 -0.77033 0.320 90.9 -2.407 0.6776
## 2 CCN51 - 6 TCS01 -1.48933 0.320 90.9 -4.654 0.0019
## 3 CCN51 - 4 CCN51 0.42333 0.276 108.0 1.531 0.9940
## 3 CCN51 - 5 CCN51 -0.44600 0.276 108.0 -1.613 0.9890
## 3 CCN51 - 6 CCN51 -0.42000 0.276 108.0 -1.519 0.9945
## 3 CCN51 - 0 ICS95 -0.44933 0.320 90.9 -1.404 0.9979
## 3 CCN51 - 1 ICS95 -0.76300 0.320 90.9 -2.384 0.6937
## 3 CCN51 - 2 ICS95 0.38900 0.320 90.9 1.216 0.9997
## 3 CCN51 - 3 ICS95 -0.06233 0.320 90.9 -0.195 1.0000
## 3 CCN51 - 4 ICS95 0.40000 0.320 90.9 1.250 0.9996
## 3 CCN51 - 5 ICS95 -0.80833 0.320 90.9 -2.526 0.5912
## 3 CCN51 - 6 ICS95 -0.91233 0.320 90.9 -2.851 0.3599
## 3 CCN51 - 0 TCS01 -0.95000 0.320 90.9 -2.969 0.2881
## 3 CCN51 - 1 TCS01 -0.68933 0.320 90.9 -2.154 0.8372
## 3 CCN51 - 2 TCS01 -0.40067 0.320 90.9 -1.252 0.9995
## 3 CCN51 - 3 TCS01 -0.24233 0.320 90.9 -0.757 1.0000
## 3 CCN51 - 4 TCS01 0.14267 0.320 90.9 0.446 1.0000
## 3 CCN51 - 5 TCS01 -0.48633 0.320 90.9 -1.520 0.9943
## 3 CCN51 - 6 TCS01 -1.20533 0.320 90.9 -3.766 0.0385
## 4 CCN51 - 5 CCN51 -0.86933 0.276 108.0 -3.144 0.1951
## 4 CCN51 - 6 CCN51 -0.84333 0.276 108.0 -3.050 0.2403
## 4 CCN51 - 0 ICS95 -0.87267 0.320 90.9 -2.727 0.4441
## 4 CCN51 - 1 ICS95 -1.18633 0.320 90.9 -3.707 0.0459
## 4 CCN51 - 2 ICS95 -0.03433 0.320 90.9 -0.107 1.0000
## 4 CCN51 - 3 ICS95 -0.48567 0.320 90.9 -1.518 0.9944
## 4 CCN51 - 4 ICS95 -0.02333 0.320 90.9 -0.073 1.0000
## 4 CCN51 - 5 ICS95 -1.23167 0.320 90.9 -3.849 0.0300
## 4 CCN51 - 6 ICS95 -1.33567 0.320 90.9 -4.174 0.0105
## 4 CCN51 - 0 TCS01 -1.37333 0.320 90.9 -4.291 0.0070
## 4 CCN51 - 1 TCS01 -1.11267 0.320 90.9 -3.477 0.0876
## 4 CCN51 - 2 TCS01 -0.82400 0.320 90.9 -2.575 0.5549
## 4 CCN51 - 3 TCS01 -0.66567 0.320 90.9 -2.080 0.8738
## 4 CCN51 - 4 TCS01 -0.28067 0.320 90.9 -0.877 1.0000
## 4 CCN51 - 5 TCS01 -0.90967 0.320 90.9 -2.842 0.3653
## 4 CCN51 - 6 TCS01 -1.62867 0.320 90.9 -5.089 0.0004
## 5 CCN51 - 6 CCN51 0.02600 0.276 108.0 0.094 1.0000
## 5 CCN51 - 0 ICS95 -0.00333 0.320 90.9 -0.010 1.0000
## 5 CCN51 - 1 ICS95 -0.31700 0.320 90.9 -0.991 1.0000
## 5 CCN51 - 2 ICS95 0.83500 0.320 90.9 2.609 0.5295
## 5 CCN51 - 3 ICS95 0.38367 0.320 90.9 1.199 0.9998
## 5 CCN51 - 4 ICS95 0.84600 0.320 90.9 2.644 0.5042
## 5 CCN51 - 5 ICS95 -0.36233 0.320 90.9 -1.132 0.9999
## 5 CCN51 - 6 ICS95 -0.46633 0.320 90.9 -1.457 0.9966
## 5 CCN51 - 0 TCS01 -0.50400 0.320 90.9 -1.575 0.9913
## 5 CCN51 - 1 TCS01 -0.24333 0.320 90.9 -0.760 1.0000
## 5 CCN51 - 2 TCS01 0.04533 0.320 90.9 0.142 1.0000
## 5 CCN51 - 3 TCS01 0.20367 0.320 90.9 0.636 1.0000
## 5 CCN51 - 4 TCS01 0.58867 0.320 90.9 1.839 0.9555
## 5 CCN51 - 5 TCS01 -0.04033 0.320 90.9 -0.126 1.0000
## 5 CCN51 - 6 TCS01 -0.75933 0.320 90.9 -2.373 0.7017
## 6 CCN51 - 0 ICS95 -0.02933 0.320 90.9 -0.092 1.0000
## 6 CCN51 - 1 ICS95 -0.34300 0.320 90.9 -1.072 1.0000
## 6 CCN51 - 2 ICS95 0.80900 0.320 90.9 2.528 0.5896
## 6 CCN51 - 3 ICS95 0.35767 0.320 90.9 1.118 0.9999
## 6 CCN51 - 4 ICS95 0.82000 0.320 90.9 2.562 0.5642
## 6 CCN51 - 5 ICS95 -0.38833 0.320 90.9 -1.213 0.9997
## 6 CCN51 - 6 ICS95 -0.49233 0.320 90.9 -1.538 0.9934
## 6 CCN51 - 0 TCS01 -0.53000 0.320 90.9 -1.656 0.9847
## 6 CCN51 - 1 TCS01 -0.26933 0.320 90.9 -0.842 1.0000
## 6 CCN51 - 2 TCS01 0.01933 0.320 90.9 0.060 1.0000
## 6 CCN51 - 3 TCS01 0.17767 0.320 90.9 0.555 1.0000
## 6 CCN51 - 4 TCS01 0.56267 0.320 90.9 1.758 0.9713
## 6 CCN51 - 5 TCS01 -0.06633 0.320 90.9 -0.207 1.0000
## 6 CCN51 - 6 TCS01 -0.78533 0.320 90.9 -2.454 0.6439
## 0 ICS95 - 1 ICS95 -0.31367 0.276 108.0 -1.134 0.9999
## 0 ICS95 - 2 ICS95 0.83833 0.276 108.0 3.032 0.2497
## 0 ICS95 - 3 ICS95 0.38700 0.276 108.0 1.400 0.9981
## 0 ICS95 - 4 ICS95 0.84933 0.276 108.0 3.072 0.2293
## 0 ICS95 - 5 ICS95 -0.35900 0.276 108.0 -1.298 0.9993
## 0 ICS95 - 6 ICS95 -0.46300 0.276 108.0 -1.675 0.9833
## 0 ICS95 - 0 TCS01 -0.50067 0.320 90.9 -1.564 0.9919
## 0 ICS95 - 1 TCS01 -0.24000 0.320 90.9 -0.750 1.0000
## 0 ICS95 - 2 TCS01 0.04867 0.320 90.9 0.152 1.0000
## 0 ICS95 - 3 TCS01 0.20700 0.320 90.9 0.647 1.0000
## 0 ICS95 - 4 TCS01 0.59200 0.320 90.9 1.850 0.9531
## 0 ICS95 - 5 TCS01 -0.03700 0.320 90.9 -0.116 1.0000
## 0 ICS95 - 6 TCS01 -0.75600 0.320 90.9 -2.362 0.7089
## 1 ICS95 - 2 ICS95 1.15200 0.276 108.0 4.166 0.0098
## 1 ICS95 - 3 ICS95 0.70067 0.276 108.0 2.534 0.5845
## 1 ICS95 - 4 ICS95 1.16300 0.276 108.0 4.206 0.0085
## 1 ICS95 - 5 ICS95 -0.04533 0.276 108.0 -0.164 1.0000
## 1 ICS95 - 6 ICS95 -0.14933 0.276 108.0 -0.540 1.0000
## 1 ICS95 - 0 TCS01 -0.18700 0.320 90.9 -0.584 1.0000
## 1 ICS95 - 1 TCS01 0.07367 0.320 90.9 0.230 1.0000
## 1 ICS95 - 2 TCS01 0.36233 0.320 90.9 1.132 0.9999
## 1 ICS95 - 3 TCS01 0.52067 0.320 90.9 1.627 0.9874
## 1 ICS95 - 4 TCS01 0.90567 0.320 90.9 2.830 0.3735
## 1 ICS95 - 5 TCS01 0.27667 0.320 90.9 0.865 1.0000
## 1 ICS95 - 6 TCS01 -0.44233 0.320 90.9 -1.382 0.9983
## 2 ICS95 - 3 ICS95 -0.45133 0.276 108.0 -1.632 0.9874
## 2 ICS95 - 4 ICS95 0.01100 0.276 108.0 0.040 1.0000
## 2 ICS95 - 5 ICS95 -1.19733 0.276 108.0 -4.330 0.0055
## 2 ICS95 - 6 ICS95 -1.30133 0.276 108.0 -4.707 0.0013
## 2 ICS95 - 0 TCS01 -1.33900 0.320 90.9 -4.184 0.0101
## 2 ICS95 - 1 TCS01 -1.07833 0.320 90.9 -3.370 0.1159
## 2 ICS95 - 2 TCS01 -0.78967 0.320 90.9 -2.468 0.6341
## 2 ICS95 - 3 TCS01 -0.63133 0.320 90.9 -1.973 0.9172
## 2 ICS95 - 4 TCS01 -0.24633 0.320 90.9 -0.770 1.0000
## 2 ICS95 - 5 TCS01 -0.87533 0.320 90.9 -2.735 0.4382
## 2 ICS95 - 6 TCS01 -1.59433 0.320 90.9 -4.982 0.0005
## 3 ICS95 - 4 ICS95 0.46233 0.276 108.0 1.672 0.9836
## 3 ICS95 - 5 ICS95 -0.74600 0.276 108.0 -2.698 0.4628
## 3 ICS95 - 6 ICS95 -0.85000 0.276 108.0 -3.074 0.2281
## 3 ICS95 - 0 TCS01 -0.88767 0.320 90.9 -2.774 0.4114
## 3 ICS95 - 1 TCS01 -0.62700 0.320 90.9 -1.959 0.9218
## 3 ICS95 - 2 TCS01 -0.33833 0.320 90.9 -1.057 1.0000
## 3 ICS95 - 3 TCS01 -0.18000 0.320 90.9 -0.562 1.0000
## 3 ICS95 - 4 TCS01 0.20500 0.320 90.9 0.641 1.0000
## 3 ICS95 - 5 TCS01 -0.42400 0.320 90.9 -1.325 0.9990
## 3 ICS95 - 6 TCS01 -1.14300 0.320 90.9 -3.572 0.0676
## 4 ICS95 - 5 ICS95 -1.20833 0.276 108.0 -4.370 0.0047
## 4 ICS95 - 6 ICS95 -1.31233 0.276 108.0 -4.746 0.0011
## 4 ICS95 - 0 TCS01 -1.35000 0.320 90.9 -4.218 0.0090
## 4 ICS95 - 1 TCS01 -1.08933 0.320 90.9 -3.404 0.1061
## 4 ICS95 - 2 TCS01 -0.80067 0.320 90.9 -2.502 0.6089
## 4 ICS95 - 3 TCS01 -0.64233 0.320 90.9 -2.007 0.9045
## 4 ICS95 - 4 TCS01 -0.25733 0.320 90.9 -0.804 1.0000
## 4 ICS95 - 5 TCS01 -0.88633 0.320 90.9 -2.770 0.4143
## 4 ICS95 - 6 TCS01 -1.60533 0.320 90.9 -5.016 0.0005
## 5 ICS95 - 6 ICS95 -0.10400 0.276 108.0 -0.376 1.0000
## 5 ICS95 - 0 TCS01 -0.14167 0.320 90.9 -0.443 1.0000
## 5 ICS95 - 1 TCS01 0.11900 0.320 90.9 0.372 1.0000
## 5 ICS95 - 2 TCS01 0.40767 0.320 90.9 1.274 0.9994
## 5 ICS95 - 3 TCS01 0.56600 0.320 90.9 1.769 0.9696
## 5 ICS95 - 4 TCS01 0.95100 0.320 90.9 2.972 0.2864
## 5 ICS95 - 5 TCS01 0.32200 0.320 90.9 1.006 1.0000
## 5 ICS95 - 6 TCS01 -0.39700 0.320 90.9 -1.241 0.9996
## 6 ICS95 - 0 TCS01 -0.03767 0.320 90.9 -0.118 1.0000
## 6 ICS95 - 1 TCS01 0.22300 0.320 90.9 0.697 1.0000
## 6 ICS95 - 2 TCS01 0.51167 0.320 90.9 1.599 0.9896
## 6 ICS95 - 3 TCS01 0.67000 0.320 90.9 2.094 0.8675
## 6 ICS95 - 4 TCS01 1.05500 0.320 90.9 3.297 0.1390
## 6 ICS95 - 5 TCS01 0.42600 0.320 90.9 1.331 0.9989
## 6 ICS95 - 6 TCS01 -0.29300 0.320 90.9 -0.916 1.0000
## 0 TCS01 - 1 TCS01 0.26067 0.276 108.0 0.943 1.0000
## 0 TCS01 - 2 TCS01 0.54933 0.276 108.0 1.987 0.9136
## 0 TCS01 - 3 TCS01 0.70767 0.276 108.0 2.559 0.5656
## 0 TCS01 - 4 TCS01 1.09267 0.276 108.0 3.952 0.0202
## 0 TCS01 - 5 TCS01 0.46367 0.276 108.0 1.677 0.9830
## 0 TCS01 - 6 TCS01 -0.25533 0.276 108.0 -0.923 1.0000
## 1 TCS01 - 2 TCS01 0.28867 0.276 108.0 1.044 1.0000
## 1 TCS01 - 3 TCS01 0.44700 0.276 108.0 1.617 0.9887
## 1 TCS01 - 4 TCS01 0.83200 0.276 108.0 3.009 0.2620
## 1 TCS01 - 5 TCS01 0.20300 0.276 108.0 0.734 1.0000
## 1 TCS01 - 6 TCS01 -0.51600 0.276 108.0 -1.866 0.9502
## 2 TCS01 - 3 TCS01 0.15833 0.276 108.0 0.573 1.0000
## 2 TCS01 - 4 TCS01 0.54333 0.276 108.0 1.965 0.9212
## 2 TCS01 - 5 TCS01 -0.08567 0.276 108.0 -0.310 1.0000
## 2 TCS01 - 6 TCS01 -0.80467 0.276 108.0 -2.910 0.3194
## 3 TCS01 - 4 TCS01 0.38500 0.276 108.0 1.392 0.9982
## 3 TCS01 - 5 TCS01 -0.24400 0.276 108.0 -0.882 1.0000
## 3 TCS01 - 6 TCS01 -0.96300 0.276 108.0 -3.483 0.0830
## 4 TCS01 - 5 TCS01 -0.62900 0.276 108.0 -2.275 0.7678
## 4 TCS01 - 6 TCS01 -1.34800 0.276 108.0 -4.875 0.0007
## 5 TCS01 - 6 TCS01 -0.71900 0.276 108.0 -2.600 0.5349
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -1.01300 0.276 108.0 -3.664 0.0495
## 0 CCN51 - 2 CCN51 -0.07733 0.276 108.0 -0.280 1.0000
## 0 CCN51 - 3 CCN51 0.81867 0.276 108.0 2.961 0.2892
## 0 CCN51 - 4 CCN51 0.89100 0.276 108.0 3.223 0.1624
## 0 CCN51 - 5 CCN51 0.51367 0.276 108.0 1.858 0.9523
## 0 CCN51 - 6 CCN51 0.11000 0.276 108.0 0.398 1.0000
## 0 CCN51 - 0 ICS95 -0.78200 0.320 90.9 -2.444 0.6515
## 0 CCN51 - 1 ICS95 -1.40533 0.320 90.9 -4.391 0.0049
## 0 CCN51 - 2 ICS95 -0.71600 0.320 90.9 -2.237 0.7899
## 0 CCN51 - 3 ICS95 -0.44800 0.320 90.9 -1.400 0.9979
## 0 CCN51 - 4 ICS95 -0.00233 0.320 90.9 -0.007 1.0000
## 0 CCN51 - 5 ICS95 -0.18667 0.320 90.9 -0.583 1.0000
## 0 CCN51 - 6 ICS95 -0.54000 0.320 90.9 -1.687 0.9813
## 0 CCN51 - 0 TCS01 -0.46700 0.320 90.9 -1.459 0.9965
## 0 CCN51 - 1 TCS01 -0.98167 0.320 90.9 -3.067 0.2351
## 0 CCN51 - 2 TCS01 -0.11733 0.320 90.9 -0.367 1.0000
## 0 CCN51 - 3 TCS01 -0.45833 0.320 90.9 -1.432 0.9972
## 0 CCN51 - 4 TCS01 0.38667 0.320 90.9 1.208 0.9997
## 0 CCN51 - 5 TCS01 0.14900 0.320 90.9 0.466 1.0000
## 0 CCN51 - 6 TCS01 -0.28667 0.320 90.9 -0.896 1.0000
## 1 CCN51 - 2 CCN51 0.93567 0.276 108.0 3.384 0.1082
## 1 CCN51 - 3 CCN51 1.83167 0.276 108.0 6.625 <.0001
## 1 CCN51 - 4 CCN51 1.90400 0.276 108.0 6.886 <.0001
## 1 CCN51 - 5 CCN51 1.52667 0.276 108.0 5.522 <.0001
## 1 CCN51 - 6 CCN51 1.12300 0.276 108.0 4.062 0.0140
## 1 CCN51 - 0 ICS95 0.23100 0.320 90.9 0.722 1.0000
## 1 CCN51 - 1 ICS95 -0.39233 0.320 90.9 -1.226 0.9997
## 1 CCN51 - 2 ICS95 0.29700 0.320 90.9 0.928 1.0000
## 1 CCN51 - 3 ICS95 0.56500 0.320 90.9 1.765 0.9701
## 1 CCN51 - 4 ICS95 1.01067 0.320 90.9 3.158 0.1927
## 1 CCN51 - 5 ICS95 0.82633 0.320 90.9 2.582 0.5495
## 1 CCN51 - 6 ICS95 0.47300 0.320 90.9 1.478 0.9959
## 1 CCN51 - 0 TCS01 0.54600 0.320 90.9 1.706 0.9789
## 1 CCN51 - 1 TCS01 0.03133 0.320 90.9 0.098 1.0000
## 1 CCN51 - 2 TCS01 0.89567 0.320 90.9 2.799 0.3944
## 1 CCN51 - 3 TCS01 0.55467 0.320 90.9 1.733 0.9752
## 1 CCN51 - 4 TCS01 1.39967 0.320 90.9 4.374 0.0053
## 1 CCN51 - 5 TCS01 1.16200 0.320 90.9 3.631 0.0572
## 1 CCN51 - 6 TCS01 0.72633 0.320 90.9 2.270 0.7700
## 2 CCN51 - 3 CCN51 0.89600 0.276 108.0 3.241 0.1554
## 2 CCN51 - 4 CCN51 0.96833 0.276 108.0 3.502 0.0787
## 2 CCN51 - 5 CCN51 0.59100 0.276 108.0 2.137 0.8473
## 2 CCN51 - 6 CCN51 0.18733 0.276 108.0 0.678 1.0000
## 2 CCN51 - 0 ICS95 -0.70467 0.320 90.9 -2.202 0.8108
## 2 CCN51 - 1 ICS95 -1.32800 0.320 90.9 -4.150 0.0114
## 2 CCN51 - 2 ICS95 -0.63867 0.320 90.9 -1.996 0.9089
## 2 CCN51 - 3 ICS95 -0.37067 0.320 90.9 -1.158 0.9999
## 2 CCN51 - 4 ICS95 0.07500 0.320 90.9 0.234 1.0000
## 2 CCN51 - 5 ICS95 -0.10933 0.320 90.9 -0.342 1.0000
## 2 CCN51 - 6 ICS95 -0.46267 0.320 90.9 -1.446 0.9969
## 2 CCN51 - 0 TCS01 -0.38967 0.320 90.9 -1.218 0.9997
## 2 CCN51 - 1 TCS01 -0.90433 0.320 90.9 -2.826 0.3763
## 2 CCN51 - 2 TCS01 -0.04000 0.320 90.9 -0.125 1.0000
## 2 CCN51 - 3 TCS01 -0.38100 0.320 90.9 -1.191 0.9998
## 2 CCN51 - 4 TCS01 0.46400 0.320 90.9 1.450 0.9968
## 2 CCN51 - 5 TCS01 0.22633 0.320 90.9 0.707 1.0000
## 2 CCN51 - 6 TCS01 -0.20933 0.320 90.9 -0.654 1.0000
## 3 CCN51 - 4 CCN51 0.07233 0.276 108.0 0.262 1.0000
## 3 CCN51 - 5 CCN51 -0.30500 0.276 108.0 -1.103 0.9999
## 3 CCN51 - 6 CCN51 -0.70867 0.276 108.0 -2.563 0.5629
## 3 CCN51 - 0 ICS95 -1.60067 0.320 90.9 -5.002 0.0005
## 3 CCN51 - 1 ICS95 -2.22400 0.320 90.9 -6.949 <.0001
## 3 CCN51 - 2 ICS95 -1.53467 0.320 90.9 -4.795 0.0011
## 3 CCN51 - 3 ICS95 -1.26667 0.320 90.9 -3.958 0.0213
## 3 CCN51 - 4 ICS95 -0.82100 0.320 90.9 -2.565 0.5619
## 3 CCN51 - 5 ICS95 -1.00533 0.320 90.9 -3.141 0.2000
## 3 CCN51 - 6 ICS95 -1.35867 0.320 90.9 -4.245 0.0082
## 3 CCN51 - 0 TCS01 -1.28567 0.320 90.9 -4.017 0.0176
## 3 CCN51 - 1 TCS01 -1.80033 0.320 90.9 -5.626 <.0001
## 3 CCN51 - 2 TCS01 -0.93600 0.320 90.9 -2.925 0.3138
## 3 CCN51 - 3 TCS01 -1.27700 0.320 90.9 -3.990 0.0192
## 3 CCN51 - 4 TCS01 -0.43200 0.320 90.9 -1.350 0.9987
## 3 CCN51 - 5 TCS01 -0.66967 0.320 90.9 -2.093 0.8680
## 3 CCN51 - 6 TCS01 -1.10533 0.320 90.9 -3.454 0.0931
## 4 CCN51 - 5 CCN51 -0.37733 0.276 108.0 -1.365 0.9986
## 4 CCN51 - 6 CCN51 -0.78100 0.276 108.0 -2.825 0.3744
## 4 CCN51 - 0 ICS95 -1.67300 0.320 90.9 -5.228 0.0002
## 4 CCN51 - 1 ICS95 -2.29633 0.320 90.9 -7.175 <.0001
## 4 CCN51 - 2 ICS95 -1.60700 0.320 90.9 -5.021 0.0005
## 4 CCN51 - 3 ICS95 -1.33900 0.320 90.9 -4.184 0.0101
## 4 CCN51 - 4 ICS95 -0.89333 0.320 90.9 -2.791 0.3993
## 4 CCN51 - 5 ICS95 -1.07767 0.320 90.9 -3.367 0.1165
## 4 CCN51 - 6 ICS95 -1.43100 0.320 90.9 -4.472 0.0037
## 4 CCN51 - 0 TCS01 -1.35800 0.320 90.9 -4.243 0.0083
## 4 CCN51 - 1 TCS01 -1.87267 0.320 90.9 -5.852 <.0001
## 4 CCN51 - 2 TCS01 -1.00833 0.320 90.9 -3.151 0.1959
## 4 CCN51 - 3 TCS01 -1.34933 0.320 90.9 -4.216 0.0091
## 4 CCN51 - 4 TCS01 -0.50433 0.320 90.9 -1.576 0.9912
## 4 CCN51 - 5 TCS01 -0.74200 0.320 90.9 -2.319 0.7384
## 4 CCN51 - 6 TCS01 -1.17767 0.320 90.9 -3.680 0.0497
## 5 CCN51 - 6 CCN51 -0.40367 0.276 108.0 -1.460 0.9967
## 5 CCN51 - 0 ICS95 -1.29567 0.320 90.9 -4.049 0.0159
## 5 CCN51 - 1 ICS95 -1.91900 0.320 90.9 -5.996 <.0001
## 5 CCN51 - 2 ICS95 -1.22967 0.320 90.9 -3.842 0.0306
## 5 CCN51 - 3 ICS95 -0.96167 0.320 90.9 -3.005 0.2678
## 5 CCN51 - 4 ICS95 -0.51600 0.320 90.9 -1.612 0.9886
## 5 CCN51 - 5 ICS95 -0.70033 0.320 90.9 -2.188 0.8185
## 5 CCN51 - 6 ICS95 -1.05367 0.320 90.9 -3.292 0.1405
## 5 CCN51 - 0 TCS01 -0.98067 0.320 90.9 -3.064 0.2367
## 5 CCN51 - 1 TCS01 -1.49533 0.320 90.9 -4.673 0.0018
## 5 CCN51 - 2 TCS01 -0.63100 0.320 90.9 -1.972 0.9176
## 5 CCN51 - 3 TCS01 -0.97200 0.320 90.9 -3.037 0.2506
## 5 CCN51 - 4 TCS01 -0.12700 0.320 90.9 -0.397 1.0000
## 5 CCN51 - 5 TCS01 -0.36467 0.320 90.9 -1.139 0.9999
## 5 CCN51 - 6 TCS01 -0.80033 0.320 90.9 -2.501 0.6096
## 6 CCN51 - 0 ICS95 -0.89200 0.320 90.9 -2.787 0.4021
## 6 CCN51 - 1 ICS95 -1.51533 0.320 90.9 -4.735 0.0014
## 6 CCN51 - 2 ICS95 -0.82600 0.320 90.9 -2.581 0.5503
## 6 CCN51 - 3 ICS95 -0.55800 0.320 90.9 -1.744 0.9737
## 6 CCN51 - 4 ICS95 -0.11233 0.320 90.9 -0.351 1.0000
## 6 CCN51 - 5 ICS95 -0.29667 0.320 90.9 -0.927 1.0000
## 6 CCN51 - 6 ICS95 -0.65000 0.320 90.9 -2.031 0.8950
## 6 CCN51 - 0 TCS01 -0.57700 0.320 90.9 -1.803 0.9633
## 6 CCN51 - 1 TCS01 -1.09167 0.320 90.9 -3.411 0.1041
## 6 CCN51 - 2 TCS01 -0.22733 0.320 90.9 -0.710 1.0000
## 6 CCN51 - 3 TCS01 -0.56833 0.320 90.9 -1.776 0.9683
## 6 CCN51 - 4 TCS01 0.27667 0.320 90.9 0.865 1.0000
## 6 CCN51 - 5 TCS01 0.03900 0.320 90.9 0.122 1.0000
## 6 CCN51 - 6 TCS01 -0.39667 0.320 90.9 -1.239 0.9996
## 0 ICS95 - 1 ICS95 -0.62333 0.276 108.0 -2.254 0.7807
## 0 ICS95 - 2 ICS95 0.06600 0.276 108.0 0.239 1.0000
## 0 ICS95 - 3 ICS95 0.33400 0.276 108.0 1.208 0.9997
## 0 ICS95 - 4 ICS95 0.77967 0.276 108.0 2.820 0.3776
## 0 ICS95 - 5 ICS95 0.59533 0.276 108.0 2.153 0.8391
## 0 ICS95 - 6 ICS95 0.24200 0.276 108.0 0.875 1.0000
## 0 ICS95 - 0 TCS01 0.31500 0.320 90.9 0.984 1.0000
## 0 ICS95 - 1 TCS01 -0.19967 0.320 90.9 -0.624 1.0000
## 0 ICS95 - 2 TCS01 0.66467 0.320 90.9 2.077 0.8752
## 0 ICS95 - 3 TCS01 0.32367 0.320 90.9 1.011 1.0000
## 0 ICS95 - 4 TCS01 1.16867 0.320 90.9 3.652 0.0539
## 0 ICS95 - 5 TCS01 0.93100 0.320 90.9 2.909 0.3232
## 0 ICS95 - 6 TCS01 0.49533 0.320 90.9 1.548 0.9929
## 1 ICS95 - 2 ICS95 0.68933 0.276 108.0 2.493 0.6151
## 1 ICS95 - 3 ICS95 0.95733 0.276 108.0 3.462 0.0878
## 1 ICS95 - 4 ICS95 1.40300 0.276 108.0 5.074 0.0003
## 1 ICS95 - 5 ICS95 1.21867 0.276 108.0 4.408 0.0041
## 1 ICS95 - 6 ICS95 0.86533 0.276 108.0 3.130 0.2016
## 1 ICS95 - 0 TCS01 0.93833 0.320 90.9 2.932 0.3094
## 1 ICS95 - 1 TCS01 0.42367 0.320 90.9 1.324 0.9990
## 1 ICS95 - 2 TCS01 1.28800 0.320 90.9 4.025 0.0172
## 1 ICS95 - 3 TCS01 0.94700 0.320 90.9 2.959 0.2935
## 1 ICS95 - 4 TCS01 1.79200 0.320 90.9 5.600 <.0001
## 1 ICS95 - 5 TCS01 1.55433 0.320 90.9 4.857 0.0009
## 1 ICS95 - 6 TCS01 1.11867 0.320 90.9 3.496 0.0833
## 2 ICS95 - 3 ICS95 0.26800 0.276 108.0 0.969 1.0000
## 2 ICS95 - 4 ICS95 0.71367 0.276 108.0 2.581 0.5493
## 2 ICS95 - 5 ICS95 0.52933 0.276 108.0 1.914 0.9372
## 2 ICS95 - 6 ICS95 0.17600 0.276 108.0 0.637 1.0000
## 2 ICS95 - 0 TCS01 0.24900 0.320 90.9 0.778 1.0000
## 2 ICS95 - 1 TCS01 -0.26567 0.320 90.9 -0.830 1.0000
## 2 ICS95 - 2 TCS01 0.59867 0.320 90.9 1.871 0.9480
## 2 ICS95 - 3 TCS01 0.25767 0.320 90.9 0.805 1.0000
## 2 ICS95 - 4 TCS01 1.10267 0.320 90.9 3.446 0.0952
## 2 ICS95 - 5 TCS01 0.86500 0.320 90.9 2.703 0.4611
## 2 ICS95 - 6 TCS01 0.42933 0.320 90.9 1.342 0.9988
## 3 ICS95 - 4 ICS95 0.44567 0.276 108.0 1.612 0.9891
## 3 ICS95 - 5 ICS95 0.26133 0.276 108.0 0.945 1.0000
## 3 ICS95 - 6 ICS95 -0.09200 0.276 108.0 -0.333 1.0000
## 3 ICS95 - 0 TCS01 -0.01900 0.320 90.9 -0.059 1.0000
## 3 ICS95 - 1 TCS01 -0.53367 0.320 90.9 -1.668 0.9835
## 3 ICS95 - 2 TCS01 0.33067 0.320 90.9 1.033 1.0000
## 3 ICS95 - 3 TCS01 -0.01033 0.320 90.9 -0.032 1.0000
## 3 ICS95 - 4 TCS01 0.83467 0.320 90.9 2.608 0.5302
## 3 ICS95 - 5 TCS01 0.59700 0.320 90.9 1.865 0.9493
## 3 ICS95 - 6 TCS01 0.16133 0.320 90.9 0.504 1.0000
## 4 ICS95 - 5 ICS95 -0.18433 0.276 108.0 -0.667 1.0000
## 4 ICS95 - 6 ICS95 -0.53767 0.276 108.0 -1.945 0.9280
## 4 ICS95 - 0 TCS01 -0.46467 0.320 90.9 -1.452 0.9967
## 4 ICS95 - 1 TCS01 -0.97933 0.320 90.9 -3.060 0.2388
## 4 ICS95 - 2 TCS01 -0.11500 0.320 90.9 -0.359 1.0000
## 4 ICS95 - 3 TCS01 -0.45600 0.320 90.9 -1.425 0.9974
## 4 ICS95 - 4 TCS01 0.38900 0.320 90.9 1.216 0.9997
## 4 ICS95 - 5 TCS01 0.15133 0.320 90.9 0.473 1.0000
## 4 ICS95 - 6 TCS01 -0.28433 0.320 90.9 -0.888 1.0000
## 5 ICS95 - 6 ICS95 -0.35333 0.276 108.0 -1.278 0.9994
## 5 ICS95 - 0 TCS01 -0.28033 0.320 90.9 -0.876 1.0000
## 5 ICS95 - 1 TCS01 -0.79500 0.320 90.9 -2.484 0.6219
## 5 ICS95 - 2 TCS01 0.06933 0.320 90.9 0.217 1.0000
## 5 ICS95 - 3 TCS01 -0.27167 0.320 90.9 -0.849 1.0000
## 5 ICS95 - 4 TCS01 0.57333 0.320 90.9 1.792 0.9655
## 5 ICS95 - 5 TCS01 0.33567 0.320 90.9 1.049 1.0000
## 5 ICS95 - 6 TCS01 -0.10000 0.320 90.9 -0.312 1.0000
## 6 ICS95 - 0 TCS01 0.07300 0.320 90.9 0.228 1.0000
## 6 ICS95 - 1 TCS01 -0.44167 0.320 90.9 -1.380 0.9983
## 6 ICS95 - 2 TCS01 0.42267 0.320 90.9 1.321 0.9990
## 6 ICS95 - 3 TCS01 0.08167 0.320 90.9 0.255 1.0000
## 6 ICS95 - 4 TCS01 0.92667 0.320 90.9 2.896 0.3316
## 6 ICS95 - 5 TCS01 0.68900 0.320 90.9 2.153 0.8378
## 6 ICS95 - 6 TCS01 0.25333 0.320 90.9 0.792 1.0000
## 0 TCS01 - 1 TCS01 -0.51467 0.276 108.0 -1.861 0.9514
## 0 TCS01 - 2 TCS01 0.34967 0.276 108.0 1.265 0.9995
## 0 TCS01 - 3 TCS01 0.00867 0.276 108.0 0.031 1.0000
## 0 TCS01 - 4 TCS01 0.85367 0.276 108.0 3.087 0.2215
## 0 TCS01 - 5 TCS01 0.61600 0.276 108.0 2.228 0.7968
## 0 TCS01 - 6 TCS01 0.18033 0.276 108.0 0.652 1.0000
## 1 TCS01 - 2 TCS01 0.86433 0.276 108.0 3.126 0.2033
## 1 TCS01 - 3 TCS01 0.52333 0.276 108.0 1.893 0.9433
## 1 TCS01 - 4 TCS01 1.36833 0.276 108.0 4.949 0.0005
## 1 TCS01 - 5 TCS01 1.13067 0.276 108.0 4.089 0.0128
## 1 TCS01 - 6 TCS01 0.69500 0.276 108.0 2.514 0.5999
## 2 TCS01 - 3 TCS01 -0.34100 0.276 108.0 -1.233 0.9997
## 2 TCS01 - 4 TCS01 0.50400 0.276 108.0 1.823 0.9602
## 2 TCS01 - 5 TCS01 0.26633 0.276 108.0 0.963 1.0000
## 2 TCS01 - 6 TCS01 -0.16933 0.276 108.0 -0.612 1.0000
## 3 TCS01 - 4 TCS01 0.84500 0.276 108.0 3.056 0.2372
## 3 TCS01 - 5 TCS01 0.60733 0.276 108.0 2.197 0.8152
## 3 TCS01 - 6 TCS01 0.17167 0.276 108.0 0.621 1.0000
## 4 TCS01 - 5 TCS01 -0.23767 0.276 108.0 -0.860 1.0000
## 4 TCS01 - 6 TCS01 -0.67333 0.276 108.0 -2.435 0.6577
## 5 TCS01 - 6 TCS01 -0.43567 0.276 108.0 -1.576 0.9916
##
## P value adjustment: tukey method for comparing a family of 21 estimates
emm_diam2 <- emmeans(res.aov.error, pairwise ~ diam2)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_diam2
## $emmeans
## diam2 emmean SE df lower.CL upper.CL
## 0 5.57 0.0754 90.9 5.42 5.72
## 1 5.76 0.0754 90.9 5.61 5.91
## 2 5.06 0.0754 90.9 4.91 5.21
## 3 4.68 0.0754 90.9 4.53 4.83
## 4 4.39 0.0754 90.9 4.24 4.54
## 5 4.77 0.0754 90.9 4.62 4.92
## 6 5.21 0.0754 90.9 5.06 5.36
##
## Results are averaged over the levels of: curva, gen
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.1923 0.0922 108 -2.086 0.3684
## 0 - 2 0.5084 0.0922 108 5.516 <.0001
## 0 - 3 0.8890 0.0922 108 9.646 <.0001
## 0 - 4 1.1839 0.0922 108 12.845 <.0001
## 0 - 5 0.8053 0.0922 108 8.738 <.0001
## 0 - 6 0.3598 0.0922 108 3.904 0.0031
## 1 - 2 0.7007 0.0922 108 7.602 <.0001
## 1 - 3 1.0813 0.0922 108 11.732 <.0001
## 1 - 4 1.3761 0.0922 108 14.931 <.0001
## 1 - 5 0.9976 0.0922 108 10.824 <.0001
## 1 - 6 0.5521 0.0922 108 5.990 <.0001
## 2 - 3 0.3806 0.0922 108 4.130 0.0014
## 2 - 4 0.6754 0.0922 108 7.329 <.0001
## 2 - 5 0.2969 0.0922 108 3.221 0.0272
## 2 - 6 -0.1486 0.0922 108 -1.612 0.6748
## 3 - 4 0.2948 0.0922 108 3.199 0.0291
## 3 - 5 -0.0837 0.0922 108 -0.909 0.9705
## 3 - 6 -0.5292 0.0922 108 -5.742 <.0001
## 4 - 5 -0.3786 0.0922 108 -4.107 0.0015
## 4 - 6 -0.8240 0.0922 108 -8.941 <.0001
## 5 - 6 -0.4455 0.0922 108 -4.834 0.0001
##
## Results are averaged over the levels of: curva, gen
## P value adjustment: tukey method for comparing a family of 7 estimates
emm_diam2_curva <- emmeans(res.aov.error, pairwise ~ diam2 | curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_diam2_curva
## $emmeans
## curva = T3:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.63 0.131 90.9 5.37 5.89
## 1 5.42 0.131 90.9 5.16 5.68
## 2 5.05 0.131 90.9 4.79 5.31
## 3 3.99 0.131 90.9 3.73 4.25
## 4 3.98 0.131 90.9 3.72 4.24
## 5 3.95 0.131 90.9 3.69 4.21
## 6 4.62 0.131 90.9 4.36 4.88
##
## curva = T1:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.33 0.131 90.9 5.07 5.59
## 1 5.40 0.131 90.9 5.14 5.66
## 2 4.50 0.131 90.9 4.24 4.76
## 3 4.69 0.131 90.9 4.43 4.95
## 4 4.27 0.131 90.9 4.01 4.53
## 5 5.17 0.131 90.9 4.91 5.43
## 6 5.44 0.131 90.9 5.18 5.70
##
## curva = T2:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.75 0.131 90.9 5.49 6.01
## 1 6.47 0.131 90.9 6.21 6.73
## 2 5.64 0.131 90.9 5.38 5.90
## 3 5.36 0.131 90.9 5.10 5.62
## 4 4.91 0.131 90.9 4.65 5.17
## 5 5.17 0.131 90.9 4.91 5.43
## 6 5.57 0.131 90.9 5.31 5.83
##
## Results are averaged over the levels of: gen
## 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 - 1 0.21144 0.16 108 1.325 0.8390
## 0 - 2 0.58156 0.16 108 3.643 0.0074
## 0 - 3 1.64144 0.16 108 10.283 <.0001
## 0 - 4 1.64800 0.16 108 10.324 <.0001
## 0 - 5 1.68100 0.16 108 10.530 <.0001
## 0 - 6 1.00778 0.16 108 6.313 <.0001
## 1 - 2 0.37011 0.16 108 2.319 0.2449
## 1 - 3 1.43000 0.16 108 8.958 <.0001
## 1 - 4 1.43656 0.16 108 8.999 <.0001
## 1 - 5 1.46956 0.16 108 9.206 <.0001
## 1 - 6 0.79633 0.16 108 4.989 <.0001
## 2 - 3 1.05989 0.16 108 6.640 <.0001
## 2 - 4 1.06644 0.16 108 6.681 <.0001
## 2 - 5 1.09944 0.16 108 6.887 <.0001
## 2 - 6 0.42622 0.16 108 2.670 0.1161
## 3 - 4 0.00656 0.16 108 0.041 1.0000
## 3 - 5 0.03956 0.16 108 0.248 1.0000
## 3 - 6 -0.63367 0.16 108 -3.970 0.0024
## 4 - 5 0.03300 0.16 108 0.207 1.0000
## 4 - 6 -0.64022 0.16 108 -4.011 0.0021
## 5 - 6 -0.67322 0.16 108 -4.217 0.0010
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.07122 0.16 108 -0.446 0.9994
## 0 - 2 0.83089 0.16 108 5.205 <.0001
## 0 - 3 0.63856 0.16 108 4.000 0.0022
## 0 - 4 1.06211 0.16 108 6.653 <.0001
## 0 - 5 0.15989 0.16 108 1.002 0.9526
## 0 - 6 -0.10578 0.16 108 -0.663 0.9943
## 1 - 2 0.90211 0.16 108 5.651 <.0001
## 1 - 3 0.70978 0.16 108 4.446 0.0004
## 1 - 4 1.13333 0.16 108 7.100 <.0001
## 1 - 5 0.23111 0.16 108 1.448 0.7745
## 1 - 6 -0.03456 0.16 108 -0.216 1.0000
## 2 - 3 -0.19233 0.16 108 -1.205 0.8909
## 2 - 4 0.23122 0.16 108 1.448 0.7741
## 2 - 5 -0.67100 0.16 108 -4.203 0.0010
## 2 - 6 -0.93667 0.16 108 -5.868 <.0001
## 3 - 4 0.42356 0.16 108 2.653 0.1207
## 3 - 5 -0.47867 0.16 108 -2.999 0.0508
## 3 - 6 -0.74433 0.16 108 -4.663 0.0002
## 4 - 5 -0.90222 0.16 108 -5.652 <.0001
## 4 - 6 -1.16789 0.16 108 -7.316 <.0001
## 5 - 6 -0.26567 0.16 108 -1.664 0.6411
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.71700 0.16 108 -4.492 0.0003
## 0 - 2 0.11278 0.16 108 0.706 0.9920
## 0 - 3 0.38711 0.16 108 2.425 0.1984
## 0 - 4 0.84144 0.16 108 5.271 <.0001
## 0 - 5 0.57500 0.16 108 3.602 0.0084
## 0 - 6 0.17744 0.16 108 1.112 0.9233
## 1 - 2 0.82978 0.16 108 5.198 <.0001
## 1 - 3 1.10411 0.16 108 6.917 <.0001
## 1 - 4 1.55844 0.16 108 9.763 <.0001
## 1 - 5 1.29200 0.16 108 8.094 <.0001
## 1 - 6 0.89444 0.16 108 5.603 <.0001
## 2 - 3 0.27433 0.16 108 1.719 0.6053
## 2 - 4 0.72867 0.16 108 4.565 0.0003
## 2 - 5 0.46222 0.16 108 2.896 0.0667
## 2 - 6 0.06467 0.16 108 0.405 0.9996
## 3 - 4 0.45433 0.16 108 2.846 0.0756
## 3 - 5 0.18789 0.16 108 1.177 0.9013
## 3 - 6 -0.20967 0.16 108 -1.313 0.8443
## 4 - 5 -0.26644 0.16 108 -1.669 0.6379
## 4 - 6 -0.66400 0.16 108 -4.160 0.0012
## 5 - 6 -0.39756 0.16 108 -2.490 0.1732
##
## Results are averaged over the levels of: gen
## P value adjustment: tukey method for comparing a family of 7 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(ph.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm1<-datos.curve1 %>%
group_by(gen, diam2) %>%
shapiro_test(ph.grano)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.grano 0.839 0.210
## 2 1 CCN51 ph.grano 0.993 0.845
## 3 2 CCN51 ph.grano 0.994 0.850
## 4 3 CCN51 ph.grano 0.998 0.922
## 5 4 CCN51 ph.grano 0.904 0.397
## 6 5 CCN51 ph.grano 0.805 0.126
## 7 6 CCN51 ph.grano 0.774 0.0549
## 8 0 ICS95 ph.grano 1.00 0.979
## 9 1 ICS95 ph.grano 0.945 0.546
## 10 2 ICS95 ph.grano 0.785 0.0783
## 11 3 ICS95 ph.grano 0.887 0.345
## 12 4 ICS95 ph.grano 0.770 0.0452
## 13 5 ICS95 ph.grano 0.872 0.301
## 14 6 ICS95 ph.grano 0.992 0.828
## 15 0 TCS01 ph.grano 0.795 0.103
## 16 1 TCS01 ph.grano 0.921 0.455
## 17 2 TCS01 ph.grano 0.947 0.554
## 18 3 TCS01 ph.grano 0.934 0.502
## 19 4 TCS01 ph.grano 0.975 0.697
## 20 5 TCS01 ph.grano 0.958 0.605
## 21 6 TCS01 ph.grano 0.981 0.739
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "ph.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev1<-datos.curve1 %>%
group_by(diam2) %>%
levene_test(ph.grano ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.775 0.502
## 2 1 2 6 1.49 0.298
## 3 2 2 6 0.444 0.661
## 4 3 2 6 0.735 0.518
## 5 4 2 6 0.370 0.705
## 6 5 2 6 1.06 0.405
## 7 6 2 6 0.384 0.697
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = ph.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov1)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 11.772 8.00e-03 * 0.536
## 2 diam2 2.49 14.93 47.894 1.36e-07 * 0.849
## 3 gen:diam2 4.98 14.93 4.756 9.00e-03 * 0.528
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 9.725 0.000503 * 0.805
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 10.363 0.000373 * 0.773
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 73.305 9.38e-09 * 0.959
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(ph.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve2 %>%
group_by(gen, diam2) %>%
shapiro_test(ph.grano)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.grano 0.869 0.293
## 2 1 CCN51 ph.grano 0.976 0.705
## 3 2 CCN51 ph.grano 0.907 0.407
## 4 3 CCN51 ph.grano 0.972 0.681
## 5 4 CCN51 ph.grano 0.790 0.0916
## 6 5 CCN51 ph.grano 0.977 0.711
## 7 6 CCN51 ph.grano 0.995 0.860
## 8 0 ICS95 ph.grano 0.998 0.908
## 9 1 ICS95 ph.grano 0.991 0.814
## 10 2 ICS95 ph.grano 0.999 0.942
## 11 3 ICS95 ph.grano 0.916 0.438
## 12 4 ICS95 ph.grano 0.891 0.356
## 13 5 ICS95 ph.grano 0.882 0.331
## 14 6 ICS95 ph.grano 0.917 0.443
## 15 0 TCS01 ph.grano 0.971 0.673
## 16 1 TCS01 ph.grano 0.944 0.545
## 17 2 TCS01 ph.grano 0.940 0.529
## 18 3 TCS01 ph.grano 0.904 0.398
## 19 4 TCS01 ph.grano 0.894 0.367
## 20 5 TCS01 ph.grano 0.922 0.461
## 21 6 TCS01 ph.grano 0.927 0.476
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "ph.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve2 %>%
group_by(diam2) %>%
levene_test(ph.grano ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.367 0.708
## 2 1 2 6 1.35 0.328
## 3 2 2 6 0.131 0.880
## 4 3 2 6 1.33 0.332
## 5 4 2 6 0.724 0.523
## 6 5 2 6 1.16 0.375
## 7 6 2 6 1.28 0.344
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = ph.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 3.429 0.102000 0.122
## 2 diam2 2.19 13.11 13.144 0.000607 * 0.658
## 3 gen:diam2 4.37 13.11 1.169 0.371000 0.255
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 4.166 0.017 * 0.661
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 37.148 4.55e-07 * 0.914
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 2.574 0.077 0.532
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(ph.grano)
## [1] diam2 gen curva time.let muestra
## [6] ph.testa acidez.testa ph.grano acidez.grano id
## [11] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
##Normality assumption
##Compute Shapiro-Wilk test for each combinations of factor levels:
norm2<-datos.curve3 %>%
group_by(gen, diam2) %>%
shapiro_test(ph.grano)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.grano 0.808 0.134
## 2 1 CCN51 ph.grano 0.851 0.242
## 3 2 CCN51 ph.grano 0.915 0.436
## 4 3 CCN51 ph.grano 0.990 0.811
## 5 4 CCN51 ph.grano 0.866 0.286
## 6 5 CCN51 ph.grano 0.811 0.140
## 7 6 CCN51 ph.grano 0.952 0.578
## 8 0 ICS95 ph.grano 0.795 0.103
## 9 1 ICS95 ph.grano 1.00 0.961
## 10 2 ICS95 ph.grano 0.966 0.648
## 11 3 ICS95 ph.grano 0.946 0.553
## 12 4 ICS95 ph.grano 1.00 0.960
## 13 5 ICS95 ph.grano 0.922 0.460
## 14 6 ICS95 ph.grano 1.00 0.985
## 15 0 TCS01 ph.grano 0.978 0.719
## 16 1 TCS01 ph.grano 0.937 0.515
## 17 2 TCS01 ph.grano 0.891 0.357
## 18 3 TCS01 ph.grano 0.989 0.798
## 19 4 TCS01 ph.grano 0.939 0.523
## 20 5 TCS01 ph.grano 0.796 0.105
## 21 6 TCS01 ph.grano 0.856 0.256
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "ph.grano", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve3 %>%
group_by(diam2) %>%
levene_test(ph.grano ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.717 0.526
## 2 1 2 6 0.243 0.792
## 3 2 2 6 1.24 0.355
## 4 3 2 6 1.47 0.302
## 5 4 2 6 1.57 0.283
## 6 5 2 6 0.274 0.770
## 7 6 2 6 0.458 0.653
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = ph.grano, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 3.861 8.40e-02 0.432
## 2 diam2 2.46 14.76 24.157 1.13e-05 * 0.623
## 3 gen:diam2 4.92 14.76 1.583 2.26e-01 0.178
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 56.341 4.28e-08 * 0.939
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 8.556 0.00091 * 0.517
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = ph.grano, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 6 12 3.37 0.035 * 0.473
## 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=ph.grano)) +
geom_point(aes(y=ph.grano)) +
scale_y_continuous(name = expression("Nib pH")) + # 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 = "ph.grano", groupvars = c("curva", "gen","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data_out/pH_mean_grano.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=ph.grano-ci, ymax=ph.grano+ci), width=.1) +
geom_line(aes(y=ph.grano)) +
geom_point(aes(y=ph.grano)) +
scale_y_continuous(name = expression("Nib pH")) + # 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
