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.testa, 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.testa 3 3.19 0.013
## 2 T3 1 CCN51 ph.testa 3 3.13 0.051
## 3 T3 2 CCN51 ph.testa 3 4.02 0.169
## 4 T3 3 CCN51 ph.testa 3 4.07 0.216
## 5 T3 4 CCN51 ph.testa 3 3.89 0.232
## 6 T3 5 CCN51 ph.testa 3 5.21 0.027
## 7 T3 6 CCN51 ph.testa 3 6.66 0.135
## 8 T3 0 ICS95 ph.testa 3 3.23 0.006
## 9 T3 1 ICS95 ph.testa 3 3.26 0.335
## 10 T3 2 ICS95 ph.testa 3 3.49 0.146
## 11 T3 3 ICS95 ph.testa 3 3.03 0.293
## 12 T3 4 ICS95 ph.testa 3 3.74 0.192
## 13 T3 5 ICS95 ph.testa 3 5.01 0.839
## 14 T3 6 ICS95 ph.testa 3 6.62 0.11
## 15 T3 0 TCS01 ph.testa 3 3.62 0.064
## 16 T3 1 TCS01 ph.testa 3 3.32 0.108
## 17 T3 2 TCS01 ph.testa 3 3.68 0.024
## 18 T3 3 TCS01 ph.testa 3 3.51 0.03
## 19 T3 4 TCS01 ph.testa 3 3.64 0.072
## 20 T3 5 TCS01 ph.testa 3 3.13 0.127
## 21 T3 6 TCS01 ph.testa 3 4.43 0.16
## 22 T1 0 CCN51 ph.testa 3 2.96 0.091
## 23 T1 1 CCN51 ph.testa 3 2.77 0.092
## 24 T1 2 CCN51 ph.testa 3 2.56 0.021
## 25 T1 3 CCN51 ph.testa 3 3.29 0.103
## 26 T1 4 CCN51 ph.testa 3 4.63 1.05
## 27 T1 5 CCN51 ph.testa 3 5.58 0.655
## 28 T1 6 CCN51 ph.testa 3 5.54 0.134
## 29 T1 0 ICS95 ph.testa 3 2.85 0.096
## 30 T1 1 ICS95 ph.testa 3 2.37 0.102
## 31 T1 2 ICS95 ph.testa 3 1.89 0.079
## 32 T1 3 ICS95 ph.testa 3 2.43 0.094
## 33 T1 4 ICS95 ph.testa 3 2.54 0.124
## 34 T1 5 ICS95 ph.testa 3 4.22 0.373
## 35 T1 6 ICS95 ph.testa 3 5.04 0.632
## 36 T1 0 TCS01 ph.testa 3 3.47 0.056
## 37 T1 1 TCS01 ph.testa 3 2.65 0.352
## 38 T1 2 TCS01 ph.testa 3 3.39 0.202
## 39 T1 3 TCS01 ph.testa 3 4.16 0.318
## 40 T1 4 TCS01 ph.testa 3 5.52 0.301
## 41 T1 5 TCS01 ph.testa 3 6.24 0.561
## 42 T1 6 TCS01 ph.testa 3 6.80 0.353
## 43 T2 0 CCN51 ph.testa 3 2.4 0.162
## 44 T2 1 CCN51 ph.testa 3 3.13 0.041
## 45 T2 2 CCN51 ph.testa 3 3.10 0.032
## 46 T2 3 CCN51 ph.testa 3 3.54 0.113
## 47 T2 4 CCN51 ph.testa 3 3.72 0.048
## 48 T2 5 CCN51 ph.testa 3 4.07 0.213
## 49 T2 6 CCN51 ph.testa 3 5.30 0.48
## 50 T2 0 ICS95 ph.testa 3 3.43 0.544
## 51 T2 1 ICS95 ph.testa 3 2.91 0.063
## 52 T2 2 ICS95 ph.testa 3 2.95 0.052
## 53 T2 3 ICS95 ph.testa 3 3.22 0.067
## 54 T2 4 ICS95 ph.testa 3 3.36 0.086
## 55 T2 5 ICS95 ph.testa 3 3.69 0.025
## 56 T2 6 ICS95 ph.testa 3 4.66 0.18
## 57 T2 0 TCS01 ph.testa 3 3.31 0.289
## 58 T2 1 TCS01 ph.testa 3 3.12 0.202
## 59 T2 2 TCS01 ph.testa 3 3.63 0.242
## 60 T2 3 TCS01 ph.testa 3 4.21 0.363
## 61 T2 4 TCS01 ph.testa 3 4.50 0.624
## 62 T2 5 TCS01 ph.testa 3 5.73 1.11
## 63 T2 6 TCS01 ph.testa 3 6.54 1.06
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "ph.testa",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(ph.testa)
## [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.testa)
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.testa 0.995 0.868
## 2 T3 1 CCN51 ph.testa 0.960 0.615
## 3 T3 2 CCN51 ph.testa 0.963 0.633
## 4 T3 3 CCN51 ph.testa 0.843 0.222
## 5 T3 4 CCN51 ph.testa 1.00 0.962
## 6 T3 5 CCN51 ph.testa 1 1.00
## 7 T3 6 CCN51 ph.testa 0.999 0.947
## 8 T3 0 ICS95 ph.testa 0.991 0.817
## 9 T3 1 ICS95 ph.testa 0.843 0.223
## 10 T3 2 ICS95 ph.testa 0.996 0.872
## 11 T3 3 ICS95 ph.testa 0.944 0.546
## 12 T3 4 ICS95 ph.testa 0.822 0.169
## 13 T3 5 ICS95 ph.testa 0.914 0.431
## 14 T3 6 ICS95 ph.testa 0.916 0.439
## 15 T3 0 TCS01 ph.testa 0.872 0.301
## 16 T3 1 TCS01 ph.testa 0.960 0.613
## 17 T3 2 TCS01 ph.testa 0.958 0.607
## 18 T3 3 TCS01 ph.testa 0.992 0.832
## 19 T3 4 TCS01 ph.testa 0.988 0.786
## 20 T3 5 TCS01 ph.testa 0.982 0.742
## 21 T3 6 TCS01 ph.testa 0.983 0.748
## 22 T1 0 CCN51 ph.testa 0.866 0.283
## 23 T1 1 CCN51 ph.testa 0.950 0.570
## 24 T1 2 CCN51 ph.testa 0.908 0.413
## 25 T1 3 CCN51 ph.testa 0.983 0.749
## 26 T1 4 CCN51 ph.testa 0.942 0.537
## 27 T1 5 CCN51 ph.testa 0.976 0.701
## 28 T1 6 CCN51 ph.testa 0.812 0.142
## 29 T1 0 ICS95 ph.testa 0.910 0.419
## 30 T1 1 ICS95 ph.testa 0.920 0.453
## 31 T1 2 ICS95 ph.testa 0.947 0.554
## 32 T1 3 ICS95 ph.testa 0.975 0.694
## 33 T1 4 ICS95 ph.testa 0.970 0.669
## 34 T1 5 ICS95 ph.testa 0.788 0.0870
## 35 T1 6 ICS95 ph.testa 0.842 0.218
## 36 T1 0 TCS01 ph.testa 0.968 0.656
## 37 T1 1 TCS01 ph.testa 0.852 0.245
## 38 T1 2 TCS01 ph.testa 0.997 0.896
## 39 T1 3 TCS01 ph.testa 0.998 0.922
## 40 T1 4 TCS01 ph.testa 0.974 0.691
## 41 T1 5 TCS01 ph.testa 0.807 0.131
## 42 T1 6 TCS01 ph.testa 0.841 0.217
## 43 T2 0 CCN51 ph.testa 0.987 0.778
## 44 T2 1 CCN51 ph.testa 1.00 0.959
## 45 T2 2 CCN51 ph.testa 1.00 0.982
## 46 T2 3 CCN51 ph.testa 0.787 0.0843
## 47 T2 4 CCN51 ph.testa 0.939 0.524
## 48 T2 5 CCN51 ph.testa 0.801 0.116
## 49 T2 6 CCN51 ph.testa 0.754 0.00795
## 50 T2 0 ICS95 ph.testa 0.781 0.0703
## 51 T2 1 ICS95 ph.testa 0.990 0.808
## 52 T2 2 ICS95 ph.testa 0.957 0.602
## 53 T2 3 ICS95 ph.testa 0.909 0.416
## 54 T2 4 ICS95 ph.testa 0.775 0.0553
## 55 T2 5 ICS95 ph.testa 0.800 0.114
## 56 T2 6 ICS95 ph.testa 0.925 0.471
## 57 T2 0 TCS01 ph.testa 0.935 0.508
## 58 T2 1 TCS01 ph.testa 0.980 0.726
## 59 T2 2 TCS01 ph.testa 1.00 0.975
## 60 T2 3 TCS01 ph.testa 0.993 0.837
## 61 T2 4 TCS01 ph.testa 0.825 0.175
## 62 T2 5 TCS01 ph.testa 0.894 0.366
## 63 T2 6 TCS01 ph.testa 0.997 0.897
##Create QQ plot for each cell of design:
ggqqplot(datos, "ph.testa", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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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
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## 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
## the data.
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## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the 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.testa ~ 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 0.838 0.582
## 2 1 8 18 0.687 0.698
## 3 2 8 18 1.46 0.240
## 4 3 8 18 0.994 0.473
## 5 4 8 18 1.28 0.316
## 6 5 8 18 0.901 0.536
## 7 6 8 18 1.11 0.403
##Computation
res.aov <- anova_test(
data = datos, dv = ph.testa, 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 1.872 1.83e-01 0.058
## 2 gen 2 18 29.962 1.87e-06 * 0.496
## 3 diam2 6 108 270.784 1.11e-62 * 0.914
## 4 curva:gen 4 18 25.822 3.03e-07 * 0.629
## 5 curva:diam2 12 108 11.529 1.38e-14 * 0.475
## 6 gen:diam2 12 108 5.605 2.15e-07 * 0.305
## 7 curva:gen:diam2 24 108 9.760 1.36e-17 * 0.605
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.06 0.002 *
## 2 curva:diam2 0.06 0.002 *
## 3 gen:diam2 0.06 0.002 *
## 4 curva:gen:diam2 0.06 0.002 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.541 3.24, 58.39 5.75e-35 * 0.674 4.04, 72.75
## 2 curva:diam2 0.541 6.49, 58.39 8.79e-09 * 0.674 8.08, 72.75
## 3 gen:diam2 0.541 6.49, 58.39 8.26e-05 * 0.674 8.08, 72.75
## 4 curva:gen:diam2 0.541 12.98, 58.39 2.23e-10 * 0.674 16.17, 72.75
## p[HF] p[HF]<.05
## 1 5.41e-43 *
## 2 1.81e-10 *
## 3 1.45e-05 *
## 4 1.79e-12 *
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2.00 18.00 1.872 1.83e-01 0.058
## 2 gen 2.00 18.00 29.962 1.87e-06 * 0.496
## 3 diam2 3.24 58.39 270.784 5.75e-35 * 0.914
## 4 curva:gen 4.00 18.00 25.822 3.03e-07 * 0.629
## 5 curva:diam2 6.49 58.39 11.529 8.79e-09 * 0.475
## 6 gen:diam2 6.49 58.39 5.605 8.26e-05 * 0.305
## 7 curva:gen:diam2 12.98 58.39 9.760 2.23e-10 * 0.605
#Table by error
res.aov.error <- aov(ph.testa ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = ph.testa ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 3.892746
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 0.967823 15.493617 26.705626 4.654032
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 0.5084853
## 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 167.28868 14.24536 6.92520 24.11885 11.12027
## Deg. of Freedom 6 12 12 24 108
##
## Residual standard error: 0.3208823
## 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 3.99 0.0641 18 3.86 4.13
## T1 3.85 0.0641 18 3.72 3.99
## T2 3.83 0.0641 18 3.70 3.97
##
## 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 0.1425 0.0906 18 1.573 0.2825
## T3 - T2 0.1597 0.0906 18 1.762 0.2104
## T1 - T2 0.0172 0.0906 18 0.189 0.9804
##
## 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 4.31 0.111 18 4.08 4.54
## ICS95 4.06 0.111 18 3.82 4.29
## TCS01 3.62 0.111 18 3.38 3.85
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 3.90 0.111 18 3.67 4.14
## ICS95 3.05 0.111 18 2.81 3.28
## TCS01 4.60 0.111 18 4.37 4.84
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 3.61 0.111 18 3.37 3.84
## ICS95 3.46 0.111 18 3.23 3.69
## TCS01 4.43 0.111 18 4.20 4.67
##
## 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 0.253 0.157 18 1.612 0.2662
## CCN51 - TCS01 0.692 0.157 18 4.407 0.0009
## ICS95 - TCS01 0.439 0.157 18 2.795 0.0306
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.857 0.157 18 5.459 0.0001
## CCN51 - TCS01 -0.700 0.157 18 -4.463 0.0008
## ICS95 - TCS01 -1.557 0.157 18 -9.922 <.0001
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.147 0.157 18 0.935 0.6259
## CCN51 - TCS01 -0.825 0.157 18 -5.256 0.0002
## ICS95 - TCS01 -0.971 0.157 18 -6.191 <.0001
##
## 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 3.19 0.204 106 2.79 3.60
## 1 3.13 0.204 106 2.73 3.54
## 2 4.02 0.204 106 3.61 4.42
## 3 4.06 0.204 106 3.66 4.47
## 4 3.89 0.204 106 3.48 4.29
## 5 5.21 0.204 106 4.80 5.61
## 6 6.65 0.204 106 6.25 7.06
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 2.96 0.204 106 2.55 3.36
## 1 2.77 0.204 106 2.36 3.17
## 2 2.56 0.204 106 2.15 2.96
## 3 3.29 0.204 106 2.89 3.70
## 4 4.63 0.204 106 4.22 5.03
## 5 5.58 0.204 106 5.18 5.99
## 6 5.54 0.204 106 5.13 5.94
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 2.40 0.204 106 1.99 2.80
## 1 3.13 0.204 106 2.72 3.53
## 2 3.10 0.204 106 2.69 3.50
## 3 3.54 0.204 106 3.14 3.95
## 4 3.72 0.204 106 3.32 4.13
## 5 4.07 0.204 106 3.67 4.48
## 6 5.30 0.204 106 4.89 5.70
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.23 0.204 106 2.83 3.64
## 1 3.27 0.204 106 2.86 3.67
## 2 3.49 0.204 106 3.09 3.90
## 3 3.03 0.204 106 2.63 3.44
## 4 3.74 0.204 106 3.33 4.14
## 5 5.01 0.204 106 4.60 5.41
## 6 6.62 0.204 106 6.21 7.02
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 2.85 0.204 106 2.45 3.26
## 1 2.37 0.204 106 1.96 2.77
## 2 1.89 0.204 106 1.48 2.29
## 3 2.43 0.204 106 2.02 2.83
## 4 2.54 0.204 106 2.13 2.94
## 5 4.22 0.204 106 3.81 4.62
## 6 5.04 0.204 106 4.63 5.44
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.43 0.204 106 3.02 3.83
## 1 2.91 0.204 106 2.50 3.31
## 2 2.95 0.204 106 2.55 3.36
## 3 3.22 0.204 106 2.82 3.63
## 4 3.36 0.204 106 2.95 3.76
## 5 3.69 0.204 106 3.29 4.10
## 6 4.67 0.204 106 4.26 5.07
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.62 0.204 106 3.22 4.03
## 1 3.32 0.204 106 2.91 3.72
## 2 3.68 0.204 106 3.27 4.08
## 3 3.51 0.204 106 3.10 3.92
## 4 3.64 0.204 106 3.23 4.04
## 5 3.13 0.204 106 2.72 3.53
## 6 4.43 0.204 106 4.02 4.83
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.47 0.204 106 3.06 3.87
## 1 2.65 0.204 106 2.24 3.05
## 2 3.39 0.204 106 2.98 3.79
## 3 4.16 0.204 106 3.76 4.57
## 4 5.52 0.204 106 5.12 5.93
## 5 6.24 0.204 106 5.83 6.64
## 6 6.80 0.204 106 6.40 7.21
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.31 0.204 106 2.90 3.71
## 1 3.12 0.204 106 2.71 3.52
## 2 3.63 0.204 106 3.22 4.03
## 3 4.21 0.204 106 3.80 4.61
## 4 4.50 0.204 106 4.09 4.90
## 5 5.73 0.204 106 5.33 6.14
## 6 6.54 0.204 106 6.13 6.94
##
## 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 0.0613 0.262 108 0.234 1.0000
## 0 - 2 -0.8277 0.262 108 -3.159 0.0326
## 0 - 3 -0.8727 0.262 108 -3.331 0.0197
## 0 - 4 -0.6977 0.262 108 -2.663 0.1180
## 0 - 5 -2.0150 0.262 108 -7.691 <.0001
## 0 - 6 -3.4627 0.262 108 -13.216 <.0001
## 1 - 2 -0.8890 0.262 108 -3.393 0.0163
## 1 - 3 -0.9340 0.262 108 -3.565 0.0095
## 1 - 4 -0.7590 0.262 108 -2.897 0.0664
## 1 - 5 -2.0763 0.262 108 -7.925 <.0001
## 1 - 6 -3.5240 0.262 108 -13.450 <.0001
## 2 - 3 -0.0450 0.262 108 -0.172 1.0000
## 2 - 4 0.1300 0.262 108 0.496 0.9989
## 2 - 5 -1.1873 0.262 108 -4.532 0.0003
## 2 - 6 -2.6350 0.262 108 -10.057 <.0001
## 3 - 4 0.1750 0.262 108 0.668 0.9941
## 3 - 5 -1.1423 0.262 108 -4.360 0.0006
## 3 - 6 -2.5900 0.262 108 -9.886 <.0001
## 4 - 5 -1.3173 0.262 108 -5.028 <.0001
## 4 - 6 -2.7650 0.262 108 -10.553 <.0001
## 5 - 6 -1.4477 0.262 108 -5.525 <.0001
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.1863 0.262 108 0.711 0.9917
## 0 - 2 0.3990 0.262 108 1.523 0.7305
## 0 - 3 -0.3360 0.262 108 -1.282 0.8585
## 0 - 4 -1.6720 0.262 108 -6.382 <.0001
## 0 - 5 -2.6273 0.262 108 -10.028 <.0001
## 0 - 6 -2.5820 0.262 108 -9.855 <.0001
## 1 - 2 0.2127 0.262 108 0.812 0.9833
## 1 - 3 -0.5223 0.262 108 -1.994 0.4247
## 1 - 4 -1.8583 0.262 108 -7.093 <.0001
## 1 - 5 -2.8137 0.262 108 -10.739 <.0001
## 1 - 6 -2.7683 0.262 108 -10.566 <.0001
## 2 - 3 -0.7350 0.262 108 -2.805 0.0838
## 2 - 4 -2.0710 0.262 108 -7.905 <.0001
## 2 - 5 -3.0263 0.262 108 -11.551 <.0001
## 2 - 6 -2.9810 0.262 108 -11.378 <.0001
## 3 - 4 -1.3360 0.262 108 -5.099 <.0001
## 3 - 5 -2.2913 0.262 108 -8.746 <.0001
## 3 - 6 -2.2460 0.262 108 -8.573 <.0001
## 4 - 5 -0.9553 0.262 108 -3.646 0.0073
## 4 - 6 -0.9100 0.262 108 -3.473 0.0127
## 5 - 6 0.0453 0.262 108 0.173 1.0000
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.7263 0.262 108 -2.772 0.0909
## 0 - 2 -0.6987 0.262 108 -2.667 0.1170
## 0 - 3 -1.1410 0.262 108 -4.355 0.0006
## 0 - 4 -1.3230 0.262 108 -5.050 <.0001
## 0 - 5 -1.6723 0.262 108 -6.383 <.0001
## 0 - 6 -2.8957 0.262 108 -11.052 <.0001
## 1 - 2 0.0277 0.262 108 0.106 1.0000
## 1 - 3 -0.4147 0.262 108 -1.583 0.6936
## 1 - 4 -0.5967 0.262 108 -2.277 0.2646
## 1 - 5 -0.9460 0.262 108 -3.611 0.0082
## 1 - 6 -2.1693 0.262 108 -8.280 <.0001
## 2 - 3 -0.4423 0.262 108 -1.688 0.6253
## 2 - 4 -0.6243 0.262 108 -2.383 0.2160
## 2 - 5 -0.9737 0.262 108 -3.716 0.0058
## 2 - 6 -2.1970 0.262 108 -8.386 <.0001
## 3 - 4 -0.1820 0.262 108 -0.695 0.9927
## 3 - 5 -0.5313 0.262 108 -2.028 0.4034
## 3 - 6 -1.7547 0.262 108 -6.697 <.0001
## 4 - 5 -0.3493 0.262 108 -1.333 0.8348
## 4 - 6 -1.5727 0.262 108 -6.003 <.0001
## 5 - 6 -1.2233 0.262 108 -4.669 0.0002
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.0307 0.262 108 -0.117 1.0000
## 0 - 2 -0.2560 0.262 108 -0.977 0.9579
## 0 - 3 0.2010 0.262 108 0.767 0.9876
## 0 - 4 -0.5010 0.262 108 -1.912 0.4769
## 0 - 5 -1.7750 0.262 108 -6.775 <.0001
## 0 - 6 -3.3853 0.262 108 -12.921 <.0001
## 1 - 2 -0.2253 0.262 108 -0.860 0.9776
## 1 - 3 0.2317 0.262 108 0.884 0.9742
## 1 - 4 -0.4703 0.262 108 -1.795 0.5542
## 1 - 5 -1.7443 0.262 108 -6.658 <.0001
## 1 - 6 -3.3547 0.262 108 -12.804 <.0001
## 2 - 3 0.4570 0.262 108 1.744 0.5881
## 2 - 4 -0.2450 0.262 108 -0.935 0.9660
## 2 - 5 -1.5190 0.262 108 -5.798 <.0001
## 2 - 6 -3.1293 0.262 108 -11.944 <.0001
## 3 - 4 -0.7020 0.262 108 -2.679 0.1136
## 3 - 5 -1.9760 0.262 108 -7.542 <.0001
## 3 - 6 -3.5863 0.262 108 -13.688 <.0001
## 4 - 5 -1.2740 0.262 108 -4.863 0.0001
## 4 - 6 -2.8843 0.262 108 -11.009 <.0001
## 5 - 6 -1.6103 0.262 108 -6.146 <.0001
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.4837 0.262 108 1.846 0.5204
## 0 - 2 0.9633 0.262 108 3.677 0.0066
## 0 - 3 0.4240 0.262 108 1.618 0.6709
## 0 - 4 0.3147 0.262 108 1.201 0.8924
## 0 - 5 -1.3663 0.262 108 -5.215 <.0001
## 0 - 6 -2.1850 0.262 108 -8.340 <.0001
## 1 - 2 0.4797 0.262 108 1.831 0.5305
## 1 - 3 -0.0597 0.262 108 -0.228 1.0000
## 1 - 4 -0.1690 0.262 108 -0.645 0.9951
## 1 - 5 -1.8500 0.262 108 -7.061 <.0001
## 1 - 6 -2.6687 0.262 108 -10.186 <.0001
## 2 - 3 -0.5393 0.262 108 -2.059 0.3848
## 2 - 4 -0.6487 0.262 108 -2.476 0.1786
## 2 - 5 -2.3297 0.262 108 -8.892 <.0001
## 2 - 6 -3.1483 0.262 108 -12.017 <.0001
## 3 - 4 -0.1093 0.262 108 -0.417 0.9996
## 3 - 5 -1.7903 0.262 108 -6.833 <.0001
## 3 - 6 -2.6090 0.262 108 -9.958 <.0001
## 4 - 5 -1.6810 0.262 108 -6.416 <.0001
## 4 - 6 -2.4997 0.262 108 -9.541 <.0001
## 5 - 6 -0.8187 0.262 108 -3.125 0.0359
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.5190 0.262 108 1.981 0.4327
## 0 - 2 0.4780 0.262 108 1.824 0.5347
## 0 - 3 0.2040 0.262 108 0.779 0.9866
## 0 - 4 0.0690 0.262 108 0.263 1.0000
## 0 - 5 -0.2627 0.262 108 -1.003 0.9524
## 0 - 6 -1.2367 0.262 108 -4.720 0.0001
## 1 - 2 -0.0410 0.262 108 -0.156 1.0000
## 1 - 3 -0.3150 0.262 108 -1.202 0.8919
## 1 - 4 -0.4500 0.262 108 -1.718 0.6059
## 1 - 5 -0.7817 0.262 108 -2.983 0.0529
## 1 - 6 -1.7557 0.262 108 -6.701 <.0001
## 2 - 3 -0.2740 0.262 108 -1.046 0.9420
## 2 - 4 -0.4090 0.262 108 -1.561 0.7071
## 2 - 5 -0.7407 0.262 108 -2.827 0.0794
## 2 - 6 -1.7147 0.262 108 -6.545 <.0001
## 3 - 4 -0.1350 0.262 108 -0.515 0.9986
## 3 - 5 -0.4667 0.262 108 -1.781 0.5635
## 3 - 6 -1.4407 0.262 108 -5.499 <.0001
## 4 - 5 -0.3317 0.262 108 -1.266 0.8658
## 4 - 6 -1.3057 0.262 108 -4.983 <.0001
## 5 - 6 -0.9740 0.262 108 -3.718 0.0058
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.3023 0.262 108 1.154 0.9095
## 0 - 2 -0.0553 0.262 108 -0.211 1.0000
## 0 - 3 0.1113 0.262 108 0.425 0.9995
## 0 - 4 -0.0163 0.262 108 -0.062 1.0000
## 0 - 5 0.4957 0.262 108 1.892 0.4902
## 0 - 6 -0.8053 0.262 108 -3.074 0.0414
## 1 - 2 -0.3577 0.262 108 -1.365 0.8189
## 1 - 3 -0.1910 0.262 108 -0.729 0.9905
## 1 - 4 -0.3187 0.262 108 -1.216 0.8864
## 1 - 5 0.1933 0.262 108 0.738 0.9899
## 1 - 6 -1.1077 0.262 108 -4.228 0.0010
## 2 - 3 0.1667 0.262 108 0.636 0.9954
## 2 - 4 0.0390 0.262 108 0.149 1.0000
## 2 - 5 0.5510 0.262 108 2.103 0.3584
## 2 - 6 -0.7500 0.262 108 -2.863 0.0725
## 3 - 4 -0.1277 0.262 108 -0.487 0.9990
## 3 - 5 0.3843 0.262 108 1.467 0.7636
## 3 - 6 -0.9167 0.262 108 -3.499 0.0117
## 4 - 5 0.5120 0.262 108 1.954 0.4497
## 4 - 6 -0.7890 0.262 108 -3.011 0.0491
## 5 - 6 -1.3010 0.262 108 -4.966 0.0001
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.8190 0.262 108 3.126 0.0358
## 0 - 2 0.0800 0.262 108 0.305 0.9999
## 0 - 3 -0.6953 0.262 108 -2.654 0.1205
## 0 - 4 -2.0563 0.262 108 -7.849 <.0001
## 0 - 5 -2.7737 0.262 108 -10.587 <.0001
## 0 - 6 -3.3377 0.262 108 -12.739 <.0001
## 1 - 2 -0.7390 0.262 108 -2.821 0.0807
## 1 - 3 -1.5143 0.262 108 -5.780 <.0001
## 1 - 4 -2.8753 0.262 108 -10.975 <.0001
## 1 - 5 -3.5927 0.262 108 -13.713 <.0001
## 1 - 6 -4.1567 0.262 108 -15.865 <.0001
## 2 - 3 -0.7753 0.262 108 -2.959 0.0564
## 2 - 4 -2.1363 0.262 108 -8.154 <.0001
## 2 - 5 -2.8537 0.262 108 -10.892 <.0001
## 2 - 6 -3.4177 0.262 108 -13.045 <.0001
## 3 - 4 -1.3610 0.262 108 -5.195 <.0001
## 3 - 5 -2.0783 0.262 108 -7.933 <.0001
## 3 - 6 -2.6423 0.262 108 -10.085 <.0001
## 4 - 5 -0.7173 0.262 108 -2.738 0.0988
## 4 - 6 -1.2813 0.262 108 -4.891 0.0001
## 5 - 6 -0.5640 0.262 108 -2.153 0.3301
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.1877 0.262 108 0.716 0.9913
## 0 - 2 -0.3207 0.262 108 -1.224 0.8834
## 0 - 3 -0.8997 0.262 108 -3.434 0.0144
## 0 - 4 -1.1907 0.262 108 -4.545 0.0003
## 0 - 5 -2.4267 0.262 108 -9.262 <.0001
## 0 - 6 -3.2287 0.262 108 -12.323 <.0001
## 1 - 2 -0.5083 0.262 108 -1.940 0.4587
## 1 - 3 -1.0873 0.262 108 -4.150 0.0013
## 1 - 4 -1.3783 0.262 108 -5.261 <.0001
## 1 - 5 -2.6143 0.262 108 -9.978 <.0001
## 1 - 6 -3.4163 0.262 108 -13.039 <.0001
## 2 - 3 -0.5790 0.262 108 -2.210 0.2990
## 2 - 4 -0.8700 0.262 108 -3.321 0.0203
## 2 - 5 -2.1060 0.262 108 -8.038 <.0001
## 2 - 6 -2.9080 0.262 108 -11.099 <.0001
## 3 - 4 -0.2910 0.262 108 -1.111 0.9236
## 3 - 5 -1.5270 0.262 108 -5.828 <.0001
## 3 - 6 -2.3290 0.262 108 -8.889 <.0001
## 4 - 5 -1.2360 0.262 108 -4.718 0.0001
## 4 - 6 -2.0380 0.262 108 -7.779 <.0001
## 5 - 6 -0.8020 0.262 108 -3.061 0.0429
##
## 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 3.19 0.204 106 2.79 3.60
## 1 CCN51 3.13 0.204 106 2.73 3.54
## 2 CCN51 4.02 0.204 106 3.61 4.42
## 3 CCN51 4.06 0.204 106 3.66 4.47
## 4 CCN51 3.89 0.204 106 3.48 4.29
## 5 CCN51 5.21 0.204 106 4.80 5.61
## 6 CCN51 6.65 0.204 106 6.25 7.06
## 0 ICS95 3.23 0.204 106 2.83 3.64
## 1 ICS95 3.27 0.204 106 2.86 3.67
## 2 ICS95 3.49 0.204 106 3.09 3.90
## 3 ICS95 3.03 0.204 106 2.63 3.44
## 4 ICS95 3.74 0.204 106 3.33 4.14
## 5 ICS95 5.01 0.204 106 4.60 5.41
## 6 ICS95 6.62 0.204 106 6.21 7.02
## 0 TCS01 3.62 0.204 106 3.22 4.03
## 1 TCS01 3.32 0.204 106 2.91 3.72
## 2 TCS01 3.68 0.204 106 3.27 4.08
## 3 TCS01 3.51 0.204 106 3.10 3.92
## 4 TCS01 3.64 0.204 106 3.23 4.04
## 5 TCS01 3.13 0.204 106 2.72 3.53
## 6 TCS01 4.43 0.204 106 4.02 4.83
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 2.96 0.204 106 2.55 3.36
## 1 CCN51 2.77 0.204 106 2.36 3.17
## 2 CCN51 2.56 0.204 106 2.15 2.96
## 3 CCN51 3.29 0.204 106 2.89 3.70
## 4 CCN51 4.63 0.204 106 4.22 5.03
## 5 CCN51 5.58 0.204 106 5.18 5.99
## 6 CCN51 5.54 0.204 106 5.13 5.94
## 0 ICS95 2.85 0.204 106 2.45 3.26
## 1 ICS95 2.37 0.204 106 1.96 2.77
## 2 ICS95 1.89 0.204 106 1.48 2.29
## 3 ICS95 2.43 0.204 106 2.02 2.83
## 4 ICS95 2.54 0.204 106 2.13 2.94
## 5 ICS95 4.22 0.204 106 3.81 4.62
## 6 ICS95 5.04 0.204 106 4.63 5.44
## 0 TCS01 3.47 0.204 106 3.06 3.87
## 1 TCS01 2.65 0.204 106 2.24 3.05
## 2 TCS01 3.39 0.204 106 2.98 3.79
## 3 TCS01 4.16 0.204 106 3.76 4.57
## 4 TCS01 5.52 0.204 106 5.12 5.93
## 5 TCS01 6.24 0.204 106 5.83 6.64
## 6 TCS01 6.80 0.204 106 6.40 7.21
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 2.40 0.204 106 1.99 2.80
## 1 CCN51 3.13 0.204 106 2.72 3.53
## 2 CCN51 3.10 0.204 106 2.69 3.50
## 3 CCN51 3.54 0.204 106 3.14 3.95
## 4 CCN51 3.72 0.204 106 3.32 4.13
## 5 CCN51 4.07 0.204 106 3.67 4.48
## 6 CCN51 5.30 0.204 106 4.89 5.70
## 0 ICS95 3.43 0.204 106 3.02 3.83
## 1 ICS95 2.91 0.204 106 2.50 3.31
## 2 ICS95 2.95 0.204 106 2.55 3.36
## 3 ICS95 3.22 0.204 106 2.82 3.63
## 4 ICS95 3.36 0.204 106 2.95 3.76
## 5 ICS95 3.69 0.204 106 3.29 4.10
## 6 ICS95 4.67 0.204 106 4.26 5.07
## 0 TCS01 3.31 0.204 106 2.90 3.71
## 1 TCS01 3.12 0.204 106 2.71 3.52
## 2 TCS01 3.63 0.204 106 3.22 4.03
## 3 TCS01 4.21 0.204 106 3.80 4.61
## 4 TCS01 4.50 0.204 106 4.09 4.90
## 5 TCS01 5.73 0.204 106 5.33 6.14
## 6 TCS01 6.54 0.204 106 6.13 6.94
##
## 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 0.06133 0.262 108 0.234 1.0000
## 0 CCN51 - 2 CCN51 -0.82767 0.262 108 -3.159 0.1885
## 0 CCN51 - 3 CCN51 -0.87267 0.262 108 -3.331 0.1242
## 0 CCN51 - 4 CCN51 -0.69767 0.262 108 -2.663 0.4886
## 0 CCN51 - 5 CCN51 -2.01500 0.262 108 -7.691 <.0001
## 0 CCN51 - 6 CCN51 -3.46267 0.262 108 -13.216 <.0001
## 0 CCN51 - 0 ICS95 -0.04233 0.289 106 -0.147 1.0000
## 0 CCN51 - 1 ICS95 -0.07300 0.289 106 -0.253 1.0000
## 0 CCN51 - 2 ICS95 -0.29833 0.289 106 -1.033 1.0000
## 0 CCN51 - 3 ICS95 0.15867 0.289 106 0.549 1.0000
## 0 CCN51 - 4 ICS95 -0.54333 0.289 106 -1.881 0.9464
## 0 CCN51 - 5 ICS95 -1.81733 0.289 106 -6.291 <.0001
## 0 CCN51 - 6 ICS95 -3.42767 0.289 106 -11.865 <.0001
## 0 CCN51 - 0 TCS01 -0.42933 0.289 106 -1.486 0.9958
## 0 CCN51 - 1 TCS01 -0.12700 0.289 106 -0.440 1.0000
## 0 CCN51 - 2 TCS01 -0.48467 0.289 106 -1.678 0.9829
## 0 CCN51 - 3 TCS01 -0.31800 0.289 106 -1.101 0.9999
## 0 CCN51 - 4 TCS01 -0.44567 0.289 106 -1.543 0.9934
## 0 CCN51 - 5 TCS01 0.06633 0.289 106 0.230 1.0000
## 0 CCN51 - 6 TCS01 -1.23467 0.289 106 -4.274 0.0068
## 1 CCN51 - 2 CCN51 -0.88900 0.262 108 -3.393 0.1057
## 1 CCN51 - 3 CCN51 -0.93400 0.262 108 -3.565 0.0660
## 1 CCN51 - 4 CCN51 -0.75900 0.262 108 -2.897 0.3277
## 1 CCN51 - 5 CCN51 -2.07633 0.262 108 -7.925 <.0001
## 1 CCN51 - 6 CCN51 -3.52400 0.262 108 -13.450 <.0001
## 1 CCN51 - 0 ICS95 -0.10367 0.289 106 -0.359 1.0000
## 1 CCN51 - 1 ICS95 -0.13433 0.289 106 -0.465 1.0000
## 1 CCN51 - 2 ICS95 -0.35967 0.289 106 -1.245 0.9996
## 1 CCN51 - 3 ICS95 0.09733 0.289 106 0.337 1.0000
## 1 CCN51 - 4 ICS95 -0.60467 0.289 106 -2.093 0.8691
## 1 CCN51 - 5 ICS95 -1.87867 0.289 106 -6.503 <.0001
## 1 CCN51 - 6 ICS95 -3.48900 0.289 106 -12.077 <.0001
## 1 CCN51 - 0 TCS01 -0.49067 0.289 106 -1.698 0.9805
## 1 CCN51 - 1 TCS01 -0.18833 0.289 106 -0.652 1.0000
## 1 CCN51 - 2 TCS01 -0.54600 0.289 106 -1.890 0.9440
## 1 CCN51 - 3 TCS01 -0.37933 0.289 106 -1.313 0.9992
## 1 CCN51 - 4 TCS01 -0.50700 0.289 106 -1.755 0.9726
## 1 CCN51 - 5 TCS01 0.00500 0.289 106 0.017 1.0000
## 1 CCN51 - 6 TCS01 -1.29600 0.289 106 -4.486 0.0031
## 2 CCN51 - 3 CCN51 -0.04500 0.262 108 -0.172 1.0000
## 2 CCN51 - 4 CCN51 0.13000 0.262 108 0.496 1.0000
## 2 CCN51 - 5 CCN51 -1.18733 0.262 108 -4.532 0.0026
## 2 CCN51 - 6 CCN51 -2.63500 0.262 108 -10.057 <.0001
## 2 CCN51 - 0 ICS95 0.78533 0.289 106 2.718 0.4484
## 2 CCN51 - 1 ICS95 0.75467 0.289 106 2.612 0.5262
## 2 CCN51 - 2 ICS95 0.52933 0.289 106 1.832 0.9580
## 2 CCN51 - 3 ICS95 0.98633 0.289 106 3.414 0.1003
## 2 CCN51 - 4 ICS95 0.28433 0.289 106 0.984 1.0000
## 2 CCN51 - 5 ICS95 -0.98967 0.289 106 -3.426 0.0973
## 2 CCN51 - 6 ICS95 -2.60000 0.289 106 -9.000 <.0001
## 2 CCN51 - 0 TCS01 0.39833 0.289 106 1.379 0.9984
## 2 CCN51 - 1 TCS01 0.70067 0.289 106 2.425 0.6649
## 2 CCN51 - 2 TCS01 0.34300 0.289 106 1.187 0.9998
## 2 CCN51 - 3 TCS01 0.50967 0.289 106 1.764 0.9711
## 2 CCN51 - 4 TCS01 0.38200 0.289 106 1.322 0.9991
## 2 CCN51 - 5 TCS01 0.89400 0.289 106 3.095 0.2185
## 2 CCN51 - 6 TCS01 -0.40700 0.289 106 -1.409 0.9979
## 3 CCN51 - 4 CCN51 0.17500 0.262 108 0.668 1.0000
## 3 CCN51 - 5 CCN51 -1.14233 0.262 108 -4.360 0.0049
## 3 CCN51 - 6 CCN51 -2.59000 0.262 108 -9.886 <.0001
## 3 CCN51 - 0 ICS95 0.83033 0.289 106 2.874 0.3424
## 3 CCN51 - 1 ICS95 0.79967 0.289 106 2.768 0.4133
## 3 CCN51 - 2 ICS95 0.57433 0.289 106 1.988 0.9130
## 3 CCN51 - 3 ICS95 1.03133 0.289 106 3.570 0.0654
## 3 CCN51 - 4 ICS95 0.32933 0.289 106 1.140 0.9999
## 3 CCN51 - 5 ICS95 -0.94467 0.289 106 -3.270 0.1451
## 3 CCN51 - 6 ICS95 -2.55500 0.289 106 -8.844 <.0001
## 3 CCN51 - 0 TCS01 0.44333 0.289 106 1.535 0.9938
## 3 CCN51 - 1 TCS01 0.74567 0.289 106 2.581 0.5495
## 3 CCN51 - 2 TCS01 0.38800 0.289 106 1.343 0.9989
## 3 CCN51 - 3 TCS01 0.55467 0.289 106 1.920 0.9355
## 3 CCN51 - 4 TCS01 0.42700 0.289 106 1.478 0.9961
## 3 CCN51 - 5 TCS01 0.93900 0.289 106 3.250 0.1522
## 3 CCN51 - 6 TCS01 -0.36200 0.289 106 -1.253 0.9996
## 4 CCN51 - 5 CCN51 -1.31733 0.262 108 -5.028 0.0004
## 4 CCN51 - 6 CCN51 -2.76500 0.262 108 -10.553 <.0001
## 4 CCN51 - 0 ICS95 0.65533 0.289 106 2.268 0.7718
## 4 CCN51 - 1 ICS95 0.62467 0.289 106 2.162 0.8341
## 4 CCN51 - 2 ICS95 0.39933 0.289 106 1.382 0.9983
## 4 CCN51 - 3 ICS95 0.85633 0.289 106 2.964 0.2877
## 4 CCN51 - 4 ICS95 0.15433 0.289 106 0.534 1.0000
## 4 CCN51 - 5 ICS95 -1.11967 0.289 106 -3.876 0.0260
## 4 CCN51 - 6 ICS95 -2.73000 0.289 106 -9.450 <.0001
## 4 CCN51 - 0 TCS01 0.26833 0.289 106 0.929 1.0000
## 4 CCN51 - 1 TCS01 0.57067 0.289 106 1.975 0.9175
## 4 CCN51 - 2 TCS01 0.21300 0.289 106 0.737 1.0000
## 4 CCN51 - 3 TCS01 0.37967 0.289 106 1.314 0.9992
## 4 CCN51 - 4 TCS01 0.25200 0.289 106 0.872 1.0000
## 4 CCN51 - 5 TCS01 0.76400 0.289 106 2.645 0.5022
## 4 CCN51 - 6 TCS01 -0.53700 0.289 106 -1.859 0.9519
## 5 CCN51 - 6 CCN51 -1.44767 0.262 108 -5.525 <.0001
## 5 CCN51 - 0 ICS95 1.97267 0.289 106 6.828 <.0001
## 5 CCN51 - 1 ICS95 1.94200 0.289 106 6.722 <.0001
## 5 CCN51 - 2 ICS95 1.71667 0.289 106 5.942 <.0001
## 5 CCN51 - 3 ICS95 2.17367 0.289 106 7.524 <.0001
## 5 CCN51 - 4 ICS95 1.47167 0.289 106 5.094 0.0003
## 5 CCN51 - 5 ICS95 0.19767 0.289 106 0.684 1.0000
## 5 CCN51 - 6 ICS95 -1.41267 0.289 106 -4.890 0.0007
## 5 CCN51 - 0 TCS01 1.58567 0.289 106 5.489 0.0001
## 5 CCN51 - 1 TCS01 1.88800 0.289 106 6.535 <.0001
## 5 CCN51 - 2 TCS01 1.53033 0.289 106 5.297 0.0001
## 5 CCN51 - 3 TCS01 1.69700 0.289 106 5.874 <.0001
## 5 CCN51 - 4 TCS01 1.56933 0.289 106 5.432 0.0001
## 5 CCN51 - 5 TCS01 2.08133 0.289 106 7.204 <.0001
## 5 CCN51 - 6 TCS01 0.78033 0.289 106 2.701 0.4609
## 6 CCN51 - 0 ICS95 3.42033 0.289 106 11.839 <.0001
## 6 CCN51 - 1 ICS95 3.38967 0.289 106 11.733 <.0001
## 6 CCN51 - 2 ICS95 3.16433 0.289 106 10.953 <.0001
## 6 CCN51 - 3 ICS95 3.62133 0.289 106 12.535 <.0001
## 6 CCN51 - 4 ICS95 2.91933 0.289 106 10.105 <.0001
## 6 CCN51 - 5 ICS95 1.64533 0.289 106 5.695 <.0001
## 6 CCN51 - 6 ICS95 0.03500 0.289 106 0.121 1.0000
## 6 CCN51 - 0 TCS01 3.03333 0.289 106 10.500 <.0001
## 6 CCN51 - 1 TCS01 3.33567 0.289 106 11.546 <.0001
## 6 CCN51 - 2 TCS01 2.97800 0.289 106 10.308 <.0001
## 6 CCN51 - 3 TCS01 3.14467 0.289 106 10.885 <.0001
## 6 CCN51 - 4 TCS01 3.01700 0.289 106 10.443 <.0001
## 6 CCN51 - 5 TCS01 3.52900 0.289 106 12.215 <.0001
## 6 CCN51 - 6 TCS01 2.22800 0.289 106 7.712 <.0001
## 0 ICS95 - 1 ICS95 -0.03067 0.262 108 -0.117 1.0000
## 0 ICS95 - 2 ICS95 -0.25600 0.262 108 -0.977 1.0000
## 0 ICS95 - 3 ICS95 0.20100 0.262 108 0.767 1.0000
## 0 ICS95 - 4 ICS95 -0.50100 0.262 108 -1.912 0.9379
## 0 ICS95 - 5 ICS95 -1.77500 0.262 108 -6.775 <.0001
## 0 ICS95 - 6 ICS95 -3.38533 0.262 108 -12.921 <.0001
## 0 ICS95 - 0 TCS01 -0.38700 0.289 106 -1.340 0.9989
## 0 ICS95 - 1 TCS01 -0.08467 0.289 106 -0.293 1.0000
## 0 ICS95 - 2 TCS01 -0.44233 0.289 106 -1.531 0.9940
## 0 ICS95 - 3 TCS01 -0.27567 0.289 106 -0.954 1.0000
## 0 ICS95 - 4 TCS01 -0.40333 0.289 106 -1.396 0.9981
## 0 ICS95 - 5 TCS01 0.10867 0.289 106 0.376 1.0000
## 0 ICS95 - 6 TCS01 -1.19233 0.289 106 -4.127 0.0113
## 1 ICS95 - 2 ICS95 -0.22533 0.262 108 -0.860 1.0000
## 1 ICS95 - 3 ICS95 0.23167 0.262 108 0.884 1.0000
## 1 ICS95 - 4 ICS95 -0.47033 0.262 108 -1.795 0.9657
## 1 ICS95 - 5 ICS95 -1.74433 0.262 108 -6.658 <.0001
## 1 ICS95 - 6 ICS95 -3.35467 0.262 108 -12.804 <.0001
## 1 ICS95 - 0 TCS01 -0.35633 0.289 106 -1.233 0.9997
## 1 ICS95 - 1 TCS01 -0.05400 0.289 106 -0.187 1.0000
## 1 ICS95 - 2 TCS01 -0.41167 0.289 106 -1.425 0.9975
## 1 ICS95 - 3 TCS01 -0.24500 0.289 106 -0.848 1.0000
## 1 ICS95 - 4 TCS01 -0.37267 0.289 106 -1.290 0.9993
## 1 ICS95 - 5 TCS01 0.13933 0.289 106 0.482 1.0000
## 1 ICS95 - 6 TCS01 -1.16167 0.289 106 -4.021 0.0162
## 2 ICS95 - 3 ICS95 0.45700 0.262 108 1.744 0.9743
## 2 ICS95 - 4 ICS95 -0.24500 0.262 108 -0.935 1.0000
## 2 ICS95 - 5 ICS95 -1.51900 0.262 108 -5.798 <.0001
## 2 ICS95 - 6 ICS95 -3.12933 0.262 108 -11.944 <.0001
## 2 ICS95 - 0 TCS01 -0.13100 0.289 106 -0.453 1.0000
## 2 ICS95 - 1 TCS01 0.17133 0.289 106 0.593 1.0000
## 2 ICS95 - 2 TCS01 -0.18633 0.289 106 -0.645 1.0000
## 2 ICS95 - 3 TCS01 -0.01967 0.289 106 -0.068 1.0000
## 2 ICS95 - 4 TCS01 -0.14733 0.289 106 -0.510 1.0000
## 2 ICS95 - 5 TCS01 0.36467 0.289 106 1.262 0.9995
## 2 ICS95 - 6 TCS01 -0.93633 0.289 106 -3.241 0.1556
## 3 ICS95 - 4 ICS95 -0.70200 0.262 108 -2.679 0.4764
## 3 ICS95 - 5 ICS95 -1.97600 0.262 108 -7.542 <.0001
## 3 ICS95 - 6 ICS95 -3.58633 0.262 108 -13.688 <.0001
## 3 ICS95 - 0 TCS01 -0.58800 0.289 106 -2.035 0.8946
## 3 ICS95 - 1 TCS01 -0.28567 0.289 106 -0.989 1.0000
## 3 ICS95 - 2 TCS01 -0.64333 0.289 106 -2.227 0.7974
## 3 ICS95 - 3 TCS01 -0.47667 0.289 106 -1.650 0.9857
## 3 ICS95 - 4 TCS01 -0.60433 0.289 106 -2.092 0.8696
## 3 ICS95 - 5 TCS01 -0.09233 0.289 106 -0.320 1.0000
## 3 ICS95 - 6 TCS01 -1.39333 0.289 106 -4.823 0.0009
## 4 ICS95 - 5 ICS95 -1.27400 0.262 108 -4.863 0.0007
## 4 ICS95 - 6 ICS95 -2.88433 0.262 108 -11.009 <.0001
## 4 ICS95 - 0 TCS01 0.11400 0.289 106 0.395 1.0000
## 4 ICS95 - 1 TCS01 0.41633 0.289 106 1.441 0.9971
## 4 ICS95 - 2 TCS01 0.05867 0.289 106 0.203 1.0000
## 4 ICS95 - 3 TCS01 0.22533 0.289 106 0.780 1.0000
## 4 ICS95 - 4 TCS01 0.09767 0.289 106 0.338 1.0000
## 4 ICS95 - 5 TCS01 0.60967 0.289 106 2.110 0.8608
## 4 ICS95 - 6 TCS01 -0.69133 0.289 106 -2.393 0.6880
## 5 ICS95 - 6 ICS95 -1.61033 0.262 108 -6.146 <.0001
## 5 ICS95 - 0 TCS01 1.38800 0.289 106 4.804 0.0009
## 5 ICS95 - 1 TCS01 1.69033 0.289 106 5.851 <.0001
## 5 ICS95 - 2 TCS01 1.33267 0.289 106 4.613 0.0019
## 5 ICS95 - 3 TCS01 1.49933 0.289 106 5.190 0.0002
## 5 ICS95 - 4 TCS01 1.37167 0.289 106 4.748 0.0012
## 5 ICS95 - 5 TCS01 1.88367 0.289 106 6.520 <.0001
## 5 ICS95 - 6 TCS01 0.58267 0.289 106 2.017 0.9020
## 6 ICS95 - 0 TCS01 2.99833 0.289 106 10.379 <.0001
## 6 ICS95 - 1 TCS01 3.30067 0.289 106 11.425 <.0001
## 6 ICS95 - 2 TCS01 2.94300 0.289 106 10.187 <.0001
## 6 ICS95 - 3 TCS01 3.10967 0.289 106 10.764 <.0001
## 6 ICS95 - 4 TCS01 2.98200 0.289 106 10.322 <.0001
## 6 ICS95 - 5 TCS01 3.49400 0.289 106 12.094 <.0001
## 6 ICS95 - 6 TCS01 2.19300 0.289 106 7.591 <.0001
## 0 TCS01 - 1 TCS01 0.30233 0.262 108 1.154 0.9999
## 0 TCS01 - 2 TCS01 -0.05533 0.262 108 -0.211 1.0000
## 0 TCS01 - 3 TCS01 0.11133 0.262 108 0.425 1.0000
## 0 TCS01 - 4 TCS01 -0.01633 0.262 108 -0.062 1.0000
## 0 TCS01 - 5 TCS01 0.49567 0.262 108 1.892 0.9436
## 0 TCS01 - 6 TCS01 -0.80533 0.262 108 -3.074 0.2283
## 1 TCS01 - 2 TCS01 -0.35767 0.262 108 -1.365 0.9986
## 1 TCS01 - 3 TCS01 -0.19100 0.262 108 -0.729 1.0000
## 1 TCS01 - 4 TCS01 -0.31867 0.262 108 -1.216 0.9997
## 1 TCS01 - 5 TCS01 0.19333 0.262 108 0.738 1.0000
## 1 TCS01 - 6 TCS01 -1.10767 0.262 108 -4.228 0.0079
## 2 TCS01 - 3 TCS01 0.16667 0.262 108 0.636 1.0000
## 2 TCS01 - 4 TCS01 0.03900 0.262 108 0.149 1.0000
## 2 TCS01 - 5 TCS01 0.55100 0.262 108 2.103 0.8644
## 2 TCS01 - 6 TCS01 -0.75000 0.262 108 -2.863 0.3495
## 3 TCS01 - 4 TCS01 -0.12767 0.262 108 -0.487 1.0000
## 3 TCS01 - 5 TCS01 0.38433 0.262 108 1.467 0.9965
## 3 TCS01 - 6 TCS01 -0.91667 0.262 108 -3.499 0.0795
## 4 TCS01 - 5 TCS01 0.51200 0.262 108 1.954 0.9249
## 4 TCS01 - 6 TCS01 -0.78900 0.262 108 -3.011 0.2608
## 5 TCS01 - 6 TCS01 -1.30100 0.262 108 -4.966 0.0005
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 0.18633 0.262 108 0.711 1.0000
## 0 CCN51 - 2 CCN51 0.39900 0.262 108 1.523 0.9944
## 0 CCN51 - 3 CCN51 -0.33600 0.262 108 -1.282 0.9994
## 0 CCN51 - 4 CCN51 -1.67200 0.262 108 -6.382 <.0001
## 0 CCN51 - 5 CCN51 -2.62733 0.262 108 -10.028 <.0001
## 0 CCN51 - 6 CCN51 -2.58200 0.262 108 -9.855 <.0001
## 0 CCN51 - 0 ICS95 0.10433 0.289 106 0.361 1.0000
## 0 CCN51 - 1 ICS95 0.58800 0.289 106 2.035 0.8946
## 0 CCN51 - 2 ICS95 1.06767 0.289 106 3.696 0.0453
## 0 CCN51 - 3 ICS95 0.52833 0.289 106 1.829 0.9588
## 0 CCN51 - 4 ICS95 0.41900 0.289 106 1.450 0.9969
## 0 CCN51 - 5 ICS95 -1.26200 0.289 106 -4.368 0.0048
## 0 CCN51 - 6 ICS95 -2.08067 0.289 106 -7.202 <.0001
## 0 CCN51 - 0 TCS01 -0.51000 0.289 106 -1.765 0.9709
## 0 CCN51 - 1 TCS01 0.30900 0.289 106 1.070 1.0000
## 0 CCN51 - 2 TCS01 -0.43000 0.289 106 -1.488 0.9957
## 0 CCN51 - 3 TCS01 -1.20533 0.289 106 -4.172 0.0097
## 0 CCN51 - 4 TCS01 -2.56633 0.289 106 -8.883 <.0001
## 0 CCN51 - 5 TCS01 -3.28367 0.289 106 -11.366 <.0001
## 0 CCN51 - 6 TCS01 -3.84767 0.289 106 -13.318 <.0001
## 1 CCN51 - 2 CCN51 0.21267 0.262 108 0.812 1.0000
## 1 CCN51 - 3 CCN51 -0.52233 0.262 108 -1.994 0.9110
## 1 CCN51 - 4 CCN51 -1.85833 0.262 108 -7.093 <.0001
## 1 CCN51 - 5 CCN51 -2.81367 0.262 108 -10.739 <.0001
## 1 CCN51 - 6 CCN51 -2.76833 0.262 108 -10.566 <.0001
## 1 CCN51 - 0 ICS95 -0.08200 0.289 106 -0.284 1.0000
## 1 CCN51 - 1 ICS95 0.40167 0.289 106 1.390 0.9982
## 1 CCN51 - 2 ICS95 0.88133 0.289 106 3.051 0.2404
## 1 CCN51 - 3 ICS95 0.34200 0.289 106 1.184 0.9998
## 1 CCN51 - 4 ICS95 0.23267 0.289 106 0.805 1.0000
## 1 CCN51 - 5 ICS95 -1.44833 0.289 106 -5.013 0.0004
## 1 CCN51 - 6 ICS95 -2.26700 0.289 106 -7.847 <.0001
## 1 CCN51 - 0 TCS01 -0.69633 0.289 106 -2.410 0.6757
## 1 CCN51 - 1 TCS01 0.12267 0.289 106 0.425 1.0000
## 1 CCN51 - 2 TCS01 -0.61633 0.289 106 -2.133 0.8492
## 1 CCN51 - 3 TCS01 -1.39167 0.289 106 -4.817 0.0009
## 1 CCN51 - 4 TCS01 -2.75267 0.289 106 -9.528 <.0001
## 1 CCN51 - 5 TCS01 -3.47000 0.289 106 -12.011 <.0001
## 1 CCN51 - 6 TCS01 -4.03400 0.289 106 -13.963 <.0001
## 2 CCN51 - 3 CCN51 -0.73500 0.262 108 -2.805 0.3874
## 2 CCN51 - 4 CCN51 -2.07100 0.262 108 -7.905 <.0001
## 2 CCN51 - 5 CCN51 -3.02633 0.262 108 -11.551 <.0001
## 2 CCN51 - 6 CCN51 -2.98100 0.262 108 -11.378 <.0001
## 2 CCN51 - 0 ICS95 -0.29467 0.289 106 -1.020 1.0000
## 2 CCN51 - 1 ICS95 0.18900 0.289 106 0.654 1.0000
## 2 CCN51 - 2 ICS95 0.66867 0.289 106 2.315 0.7419
## 2 CCN51 - 3 ICS95 0.12933 0.289 106 0.448 1.0000
## 2 CCN51 - 4 ICS95 0.02000 0.289 106 0.069 1.0000
## 2 CCN51 - 5 ICS95 -1.66100 0.289 106 -5.749 <.0001
## 2 CCN51 - 6 ICS95 -2.47967 0.289 106 -8.583 <.0001
## 2 CCN51 - 0 TCS01 -0.90900 0.289 106 -3.146 0.1945
## 2 CCN51 - 1 TCS01 -0.09000 0.289 106 -0.312 1.0000
## 2 CCN51 - 2 TCS01 -0.82900 0.289 106 -2.870 0.3453
## 2 CCN51 - 3 TCS01 -1.60433 0.289 106 -5.553 <.0001
## 2 CCN51 - 4 TCS01 -2.96533 0.289 106 -10.264 <.0001
## 2 CCN51 - 5 TCS01 -3.68267 0.289 106 -12.747 <.0001
## 2 CCN51 - 6 TCS01 -4.24667 0.289 106 -14.700 <.0001
## 3 CCN51 - 4 CCN51 -1.33600 0.262 108 -5.099 0.0003
## 3 CCN51 - 5 CCN51 -2.29133 0.262 108 -8.746 <.0001
## 3 CCN51 - 6 CCN51 -2.24600 0.262 108 -8.573 <.0001
## 3 CCN51 - 0 ICS95 0.44033 0.289 106 1.524 0.9943
## 3 CCN51 - 1 ICS95 0.92400 0.289 106 3.198 0.1724
## 3 CCN51 - 2 ICS95 1.40367 0.289 106 4.859 0.0007
## 3 CCN51 - 3 ICS95 0.86433 0.289 106 2.992 0.2719
## 3 CCN51 - 4 ICS95 0.75500 0.289 106 2.613 0.5254
## 3 CCN51 - 5 ICS95 -0.92600 0.289 106 -3.205 0.1696
## 3 CCN51 - 6 ICS95 -1.74467 0.289 106 -6.039 <.0001
## 3 CCN51 - 0 TCS01 -0.17400 0.289 106 -0.602 1.0000
## 3 CCN51 - 1 TCS01 0.64500 0.289 106 2.233 0.7939
## 3 CCN51 - 2 TCS01 -0.09400 0.289 106 -0.325 1.0000
## 3 CCN51 - 3 TCS01 -0.86933 0.289 106 -3.009 0.2624
## 3 CCN51 - 4 TCS01 -2.23033 0.289 106 -7.720 <.0001
## 3 CCN51 - 5 TCS01 -2.94767 0.289 106 -10.203 <.0001
## 3 CCN51 - 6 TCS01 -3.51167 0.289 106 -12.155 <.0001
## 4 CCN51 - 5 CCN51 -0.95533 0.262 108 -3.646 0.0521
## 4 CCN51 - 6 CCN51 -0.91000 0.262 108 -3.473 0.0852
## 4 CCN51 - 0 ICS95 1.77633 0.289 106 6.149 <.0001
## 4 CCN51 - 1 ICS95 2.26000 0.289 106 7.823 <.0001
## 4 CCN51 - 2 ICS95 2.73967 0.289 106 9.483 <.0001
## 4 CCN51 - 3 ICS95 2.20033 0.289 106 7.616 <.0001
## 4 CCN51 - 4 ICS95 2.09100 0.289 106 7.238 <.0001
## 4 CCN51 - 5 ICS95 0.41000 0.289 106 1.419 0.9977
## 4 CCN51 - 6 ICS95 -0.40867 0.289 106 -1.415 0.9978
## 4 CCN51 - 0 TCS01 1.16200 0.289 106 4.022 0.0161
## 4 CCN51 - 1 TCS01 1.98100 0.289 106 6.857 <.0001
## 4 CCN51 - 2 TCS01 1.24200 0.289 106 4.299 0.0062
## 4 CCN51 - 3 TCS01 0.46667 0.289 106 1.615 0.9887
## 4 CCN51 - 4 TCS01 -0.89433 0.289 106 -3.096 0.2180
## 4 CCN51 - 5 TCS01 -1.61167 0.289 106 -5.579 <.0001
## 4 CCN51 - 6 TCS01 -2.17567 0.289 106 -7.531 <.0001
## 5 CCN51 - 6 CCN51 0.04533 0.262 108 0.173 1.0000
## 5 CCN51 - 0 ICS95 2.73167 0.289 106 9.455 <.0001
## 5 CCN51 - 1 ICS95 3.21533 0.289 106 11.130 <.0001
## 5 CCN51 - 2 ICS95 3.69500 0.289 106 12.790 <.0001
## 5 CCN51 - 3 ICS95 3.15567 0.289 106 10.923 <.0001
## 5 CCN51 - 4 ICS95 3.04633 0.289 106 10.545 <.0001
## 5 CCN51 - 5 ICS95 1.36533 0.289 106 4.726 0.0013
## 5 CCN51 - 6 ICS95 0.54667 0.289 106 1.892 0.9434
## 5 CCN51 - 0 TCS01 2.11733 0.289 106 7.329 <.0001
## 5 CCN51 - 1 TCS01 2.93633 0.289 106 10.164 <.0001
## 5 CCN51 - 2 TCS01 2.19733 0.289 106 7.606 <.0001
## 5 CCN51 - 3 TCS01 1.42200 0.289 106 4.922 0.0006
## 5 CCN51 - 4 TCS01 0.06100 0.289 106 0.211 1.0000
## 5 CCN51 - 5 TCS01 -0.65633 0.289 106 -2.272 0.7696
## 5 CCN51 - 6 TCS01 -1.22033 0.289 106 -4.224 0.0081
## 6 CCN51 - 0 ICS95 2.68633 0.289 106 9.299 <.0001
## 6 CCN51 - 1 ICS95 3.17000 0.289 106 10.973 <.0001
## 6 CCN51 - 2 ICS95 3.64967 0.289 106 12.633 <.0001
## 6 CCN51 - 3 ICS95 3.11033 0.289 106 10.766 <.0001
## 6 CCN51 - 4 ICS95 3.00100 0.289 106 10.388 <.0001
## 6 CCN51 - 5 ICS95 1.32000 0.289 106 4.569 0.0023
## 6 CCN51 - 6 ICS95 0.50133 0.289 106 1.735 0.9756
## 6 CCN51 - 0 TCS01 2.07200 0.289 106 7.172 <.0001
## 6 CCN51 - 1 TCS01 2.89100 0.289 106 10.007 <.0001
## 6 CCN51 - 2 TCS01 2.15200 0.289 106 7.449 <.0001
## 6 CCN51 - 3 TCS01 1.37667 0.289 106 4.765 0.0011
## 6 CCN51 - 4 TCS01 0.01567 0.289 106 0.054 1.0000
## 6 CCN51 - 5 TCS01 -0.70167 0.289 106 -2.429 0.6624
## 6 CCN51 - 6 TCS01 -1.26567 0.289 106 -4.381 0.0046
## 0 ICS95 - 1 ICS95 0.48367 0.262 108 1.846 0.9551
## 0 ICS95 - 2 ICS95 0.96333 0.262 108 3.677 0.0476
## 0 ICS95 - 3 ICS95 0.42400 0.262 108 1.618 0.9886
## 0 ICS95 - 4 ICS95 0.31467 0.262 108 1.201 0.9998
## 0 ICS95 - 5 ICS95 -1.36633 0.262 108 -5.215 0.0002
## 0 ICS95 - 6 ICS95 -2.18500 0.262 108 -8.340 <.0001
## 0 ICS95 - 0 TCS01 -0.61433 0.289 106 -2.126 0.8528
## 0 ICS95 - 1 TCS01 0.20467 0.289 106 0.708 1.0000
## 0 ICS95 - 2 TCS01 -0.53433 0.289 106 -1.850 0.9541
## 0 ICS95 - 3 TCS01 -1.30967 0.289 106 -4.533 0.0026
## 0 ICS95 - 4 TCS01 -2.67067 0.289 106 -9.244 <.0001
## 0 ICS95 - 5 TCS01 -3.38800 0.289 106 -11.727 <.0001
## 0 ICS95 - 6 TCS01 -3.95200 0.289 106 -13.680 <.0001
## 1 ICS95 - 2 ICS95 0.47967 0.262 108 1.831 0.9585
## 1 ICS95 - 3 ICS95 -0.05967 0.262 108 -0.228 1.0000
## 1 ICS95 - 4 ICS95 -0.16900 0.262 108 -0.645 1.0000
## 1 ICS95 - 5 ICS95 -1.85000 0.262 108 -7.061 <.0001
## 1 ICS95 - 6 ICS95 -2.66867 0.262 108 -10.186 <.0001
## 1 ICS95 - 0 TCS01 -1.09800 0.289 106 -3.801 0.0329
## 1 ICS95 - 1 TCS01 -0.27900 0.289 106 -0.966 1.0000
## 1 ICS95 - 2 TCS01 -1.01800 0.289 106 -3.524 0.0744
## 1 ICS95 - 3 TCS01 -1.79333 0.289 106 -6.208 <.0001
## 1 ICS95 - 4 TCS01 -3.15433 0.289 106 -10.919 <.0001
## 1 ICS95 - 5 TCS01 -3.87167 0.289 106 -13.402 <.0001
## 1 ICS95 - 6 TCS01 -4.43567 0.289 106 -15.354 <.0001
## 2 ICS95 - 3 ICS95 -0.53933 0.262 108 -2.059 0.8849
## 2 ICS95 - 4 ICS95 -0.64867 0.262 108 -2.476 0.6279
## 2 ICS95 - 5 ICS95 -2.32967 0.262 108 -8.892 <.0001
## 2 ICS95 - 6 ICS95 -3.14833 0.262 108 -12.017 <.0001
## 2 ICS95 - 0 TCS01 -1.57767 0.289 106 -5.461 0.0001
## 2 ICS95 - 1 TCS01 -0.75867 0.289 106 -2.626 0.5159
## 2 ICS95 - 2 TCS01 -1.49767 0.289 106 -5.184 0.0002
## 2 ICS95 - 3 TCS01 -2.27300 0.289 106 -7.868 <.0001
## 2 ICS95 - 4 TCS01 -3.63400 0.289 106 -12.579 <.0001
## 2 ICS95 - 5 TCS01 -4.35133 0.289 106 -15.062 <.0001
## 2 ICS95 - 6 TCS01 -4.91533 0.289 106 -17.014 <.0001
## 3 ICS95 - 4 ICS95 -0.10933 0.262 108 -0.417 1.0000
## 3 ICS95 - 5 ICS95 -1.79033 0.262 108 -6.833 <.0001
## 3 ICS95 - 6 ICS95 -2.60900 0.262 108 -9.958 <.0001
## 3 ICS95 - 0 TCS01 -1.03833 0.289 106 -3.594 0.0610
## 3 ICS95 - 1 TCS01 -0.21933 0.289 106 -0.759 1.0000
## 3 ICS95 - 2 TCS01 -0.95833 0.289 106 -3.317 0.1289
## 3 ICS95 - 3 TCS01 -1.73367 0.289 106 -6.001 <.0001
## 3 ICS95 - 4 TCS01 -3.09467 0.289 106 -10.712 <.0001
## 3 ICS95 - 5 TCS01 -3.81200 0.289 106 -13.195 <.0001
## 3 ICS95 - 6 TCS01 -4.37600 0.289 106 -15.147 <.0001
## 4 ICS95 - 5 ICS95 -1.68100 0.262 108 -6.416 <.0001
## 4 ICS95 - 6 ICS95 -2.49967 0.262 108 -9.541 <.0001
## 4 ICS95 - 0 TCS01 -0.92900 0.289 106 -3.216 0.1654
## 4 ICS95 - 1 TCS01 -0.11000 0.289 106 -0.381 1.0000
## 4 ICS95 - 2 TCS01 -0.84900 0.289 106 -2.939 0.3025
## 4 ICS95 - 3 TCS01 -1.62433 0.289 106 -5.623 <.0001
## 4 ICS95 - 4 TCS01 -2.98533 0.289 106 -10.334 <.0001
## 4 ICS95 - 5 TCS01 -3.70267 0.289 106 -12.817 <.0001
## 4 ICS95 - 6 TCS01 -4.26667 0.289 106 -14.769 <.0001
## 5 ICS95 - 6 ICS95 -0.81867 0.262 108 -3.125 0.2039
## 5 ICS95 - 0 TCS01 0.75200 0.289 106 2.603 0.5331
## 5 ICS95 - 1 TCS01 1.57100 0.289 106 5.438 0.0001
## 5 ICS95 - 2 TCS01 0.83200 0.289 106 2.880 0.3387
## 5 ICS95 - 3 TCS01 0.05667 0.289 106 0.196 1.0000
## 5 ICS95 - 4 TCS01 -1.30433 0.289 106 -4.515 0.0028
## 5 ICS95 - 5 TCS01 -2.02167 0.289 106 -6.998 <.0001
## 5 ICS95 - 6 TCS01 -2.58567 0.289 106 -8.950 <.0001
## 6 ICS95 - 0 TCS01 1.57067 0.289 106 5.437 0.0001
## 6 ICS95 - 1 TCS01 2.38967 0.289 106 8.272 <.0001
## 6 ICS95 - 2 TCS01 1.65067 0.289 106 5.714 <.0001
## 6 ICS95 - 3 TCS01 0.87533 0.289 106 3.030 0.2512
## 6 ICS95 - 4 TCS01 -0.48567 0.289 106 -1.681 0.9825
## 6 ICS95 - 5 TCS01 -1.20300 0.289 106 -4.164 0.0100
## 6 ICS95 - 6 TCS01 -1.76700 0.289 106 -6.116 <.0001
## 0 TCS01 - 1 TCS01 0.81900 0.262 108 3.126 0.2033
## 0 TCS01 - 2 TCS01 0.08000 0.262 108 0.305 1.0000
## 0 TCS01 - 3 TCS01 -0.69533 0.262 108 -2.654 0.4951
## 0 TCS01 - 4 TCS01 -2.05633 0.262 108 -7.849 <.0001
## 0 TCS01 - 5 TCS01 -2.77367 0.262 108 -10.587 <.0001
## 0 TCS01 - 6 TCS01 -3.33767 0.262 108 -12.739 <.0001
## 1 TCS01 - 2 TCS01 -0.73900 0.262 108 -2.821 0.3771
## 1 TCS01 - 3 TCS01 -1.51433 0.262 108 -5.780 <.0001
## 1 TCS01 - 4 TCS01 -2.87533 0.262 108 -10.975 <.0001
## 1 TCS01 - 5 TCS01 -3.59267 0.262 108 -13.713 <.0001
## 1 TCS01 - 6 TCS01 -4.15667 0.262 108 -15.865 <.0001
## 2 TCS01 - 3 TCS01 -0.77533 0.262 108 -2.959 0.2901
## 2 TCS01 - 4 TCS01 -2.13633 0.262 108 -8.154 <.0001
## 2 TCS01 - 5 TCS01 -2.85367 0.262 108 -10.892 <.0001
## 2 TCS01 - 6 TCS01 -3.41767 0.262 108 -13.045 <.0001
## 3 TCS01 - 4 TCS01 -1.36100 0.262 108 -5.195 0.0002
## 3 TCS01 - 5 TCS01 -2.07833 0.262 108 -7.933 <.0001
## 3 TCS01 - 6 TCS01 -2.64233 0.262 108 -10.085 <.0001
## 4 TCS01 - 5 TCS01 -0.71733 0.262 108 -2.738 0.4342
## 4 TCS01 - 6 TCS01 -1.28133 0.262 108 -4.891 0.0006
## 5 TCS01 - 6 TCS01 -0.56400 0.262 108 -2.153 0.8394
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -0.72633 0.262 108 -2.772 0.4101
## 0 CCN51 - 2 CCN51 -0.69867 0.262 108 -2.667 0.4858
## 0 CCN51 - 3 CCN51 -1.14100 0.262 108 -4.355 0.0050
## 0 CCN51 - 4 CCN51 -1.32300 0.262 108 -5.050 0.0003
## 0 CCN51 - 5 CCN51 -1.67233 0.262 108 -6.383 <.0001
## 0 CCN51 - 6 CCN51 -2.89567 0.262 108 -11.052 <.0001
## 0 CCN51 - 0 ICS95 -1.02867 0.289 106 -3.561 0.0671
## 0 CCN51 - 1 ICS95 -0.50967 0.289 106 -1.764 0.9711
## 0 CCN51 - 2 ICS95 -0.55067 0.289 106 -1.906 0.9395
## 0 CCN51 - 3 ICS95 -0.82467 0.289 106 -2.855 0.3550
## 0 CCN51 - 4 ICS95 -0.95967 0.289 106 -3.322 0.1274
## 0 CCN51 - 5 ICS95 -1.29133 0.289 106 -4.470 0.0033
## 0 CCN51 - 6 ICS95 -2.26533 0.289 106 -7.841 <.0001
## 0 CCN51 - 0 TCS01 -0.90733 0.289 106 -3.141 0.1970
## 0 CCN51 - 1 TCS01 -0.71967 0.289 106 -2.491 0.6167
## 0 CCN51 - 2 TCS01 -1.22800 0.289 106 -4.251 0.0074
## 0 CCN51 - 3 TCS01 -1.80700 0.289 106 -6.255 <.0001
## 0 CCN51 - 4 TCS01 -2.09800 0.289 106 -7.262 <.0001
## 0 CCN51 - 5 TCS01 -3.33400 0.289 106 -11.540 <.0001
## 0 CCN51 - 6 TCS01 -4.13600 0.289 106 -14.316 <.0001
## 1 CCN51 - 2 CCN51 0.02767 0.262 108 0.106 1.0000
## 1 CCN51 - 3 CCN51 -0.41467 0.262 108 -1.583 0.9911
## 1 CCN51 - 4 CCN51 -0.59667 0.262 108 -2.277 0.7663
## 1 CCN51 - 5 CCN51 -0.94600 0.262 108 -3.611 0.0579
## 1 CCN51 - 6 CCN51 -2.16933 0.262 108 -8.280 <.0001
## 1 CCN51 - 0 ICS95 -0.30233 0.289 106 -1.047 1.0000
## 1 CCN51 - 1 ICS95 0.21667 0.289 106 0.750 1.0000
## 1 CCN51 - 2 ICS95 0.17567 0.289 106 0.608 1.0000
## 1 CCN51 - 3 ICS95 -0.09833 0.289 106 -0.340 1.0000
## 1 CCN51 - 4 ICS95 -0.23333 0.289 106 -0.808 1.0000
## 1 CCN51 - 5 ICS95 -0.56500 0.289 106 -1.956 0.9242
## 1 CCN51 - 6 ICS95 -1.53900 0.289 106 -5.327 0.0001
## 1 CCN51 - 0 TCS01 -0.18100 0.289 106 -0.627 1.0000
## 1 CCN51 - 1 TCS01 0.00667 0.289 106 0.023 1.0000
## 1 CCN51 - 2 TCS01 -0.50167 0.289 106 -1.736 0.9754
## 1 CCN51 - 3 TCS01 -1.08067 0.289 106 -3.741 0.0395
## 1 CCN51 - 4 TCS01 -1.37167 0.289 106 -4.748 0.0012
## 1 CCN51 - 5 TCS01 -2.60767 0.289 106 -9.026 <.0001
## 1 CCN51 - 6 TCS01 -3.40967 0.289 106 -11.802 <.0001
## 2 CCN51 - 3 CCN51 -0.44233 0.262 108 -1.688 0.9818
## 2 CCN51 - 4 CCN51 -0.62433 0.262 108 -2.383 0.6951
## 2 CCN51 - 5 CCN51 -0.97367 0.262 108 -3.716 0.0423
## 2 CCN51 - 6 CCN51 -2.19700 0.262 108 -8.386 <.0001
## 2 CCN51 - 0 ICS95 -0.33000 0.289 106 -1.142 0.9999
## 2 CCN51 - 1 ICS95 0.18900 0.289 106 0.654 1.0000
## 2 CCN51 - 2 ICS95 0.14800 0.289 106 0.512 1.0000
## 2 CCN51 - 3 ICS95 -0.12600 0.289 106 -0.436 1.0000
## 2 CCN51 - 4 ICS95 -0.26100 0.289 106 -0.903 1.0000
## 2 CCN51 - 5 ICS95 -0.59267 0.289 106 -2.051 0.8878
## 2 CCN51 - 6 ICS95 -1.56667 0.289 106 -5.423 0.0001
## 2 CCN51 - 0 TCS01 -0.20867 0.289 106 -0.722 1.0000
## 2 CCN51 - 1 TCS01 -0.02100 0.289 106 -0.073 1.0000
## 2 CCN51 - 2 TCS01 -0.52933 0.289 106 -1.832 0.9580
## 2 CCN51 - 3 TCS01 -1.10833 0.289 106 -3.836 0.0294
## 2 CCN51 - 4 TCS01 -1.39933 0.289 106 -4.844 0.0008
## 2 CCN51 - 5 TCS01 -2.63533 0.289 106 -9.122 <.0001
## 2 CCN51 - 6 TCS01 -3.43733 0.289 106 -11.898 <.0001
## 3 CCN51 - 4 CCN51 -0.18200 0.262 108 -0.695 1.0000
## 3 CCN51 - 5 CCN51 -0.53133 0.262 108 -2.028 0.8977
## 3 CCN51 - 6 CCN51 -1.75467 0.262 108 -6.697 <.0001
## 3 CCN51 - 0 ICS95 0.11233 0.289 106 0.389 1.0000
## 3 CCN51 - 1 ICS95 0.63133 0.289 106 2.185 0.8214
## 3 CCN51 - 2 ICS95 0.59033 0.289 106 2.043 0.8912
## 3 CCN51 - 3 ICS95 0.31633 0.289 106 1.095 0.9999
## 3 CCN51 - 4 ICS95 0.18133 0.289 106 0.628 1.0000
## 3 CCN51 - 5 ICS95 -0.15033 0.289 106 -0.520 1.0000
## 3 CCN51 - 6 ICS95 -1.12433 0.289 106 -3.892 0.0247
## 3 CCN51 - 0 TCS01 0.23367 0.289 106 0.809 1.0000
## 3 CCN51 - 1 TCS01 0.42133 0.289 106 1.458 0.9967
## 3 CCN51 - 2 TCS01 -0.08700 0.289 106 -0.301 1.0000
## 3 CCN51 - 3 TCS01 -0.66600 0.289 106 -2.305 0.7480
## 3 CCN51 - 4 TCS01 -0.95700 0.289 106 -3.313 0.1304
## 3 CCN51 - 5 TCS01 -2.19300 0.289 106 -7.591 <.0001
## 3 CCN51 - 6 TCS01 -2.99500 0.289 106 -10.367 <.0001
## 4 CCN51 - 5 CCN51 -0.34933 0.262 108 -1.333 0.9990
## 4 CCN51 - 6 CCN51 -1.57267 0.262 108 -6.003 <.0001
## 4 CCN51 - 0 ICS95 0.29433 0.289 106 1.019 1.0000
## 4 CCN51 - 1 ICS95 0.81333 0.289 106 2.815 0.3809
## 4 CCN51 - 2 ICS95 0.77233 0.289 106 2.673 0.4810
## 4 CCN51 - 3 ICS95 0.49833 0.289 106 1.725 0.9770
## 4 CCN51 - 4 ICS95 0.36333 0.289 106 1.258 0.9995
## 4 CCN51 - 5 ICS95 0.03167 0.289 106 0.110 1.0000
## 4 CCN51 - 6 ICS95 -0.94233 0.289 106 -3.262 0.1480
## 4 CCN51 - 0 TCS01 0.41567 0.289 106 1.439 0.9972
## 4 CCN51 - 1 TCS01 0.60333 0.289 106 2.088 0.8712
## 4 CCN51 - 2 TCS01 0.09500 0.289 106 0.329 1.0000
## 4 CCN51 - 3 TCS01 -0.48400 0.289 106 -1.675 0.9832
## 4 CCN51 - 4 TCS01 -0.77500 0.289 106 -2.683 0.4743
## 4 CCN51 - 5 TCS01 -2.01100 0.289 106 -6.961 <.0001
## 4 CCN51 - 6 TCS01 -2.81300 0.289 106 -9.737 <.0001
## 5 CCN51 - 6 CCN51 -1.22333 0.262 108 -4.669 0.0015
## 5 CCN51 - 0 ICS95 0.64367 0.289 106 2.228 0.7967
## 5 CCN51 - 1 ICS95 1.16267 0.289 106 4.024 0.0160
## 5 CCN51 - 2 ICS95 1.12167 0.289 106 3.883 0.0254
## 5 CCN51 - 3 ICS95 0.84767 0.289 106 2.934 0.3053
## 5 CCN51 - 4 ICS95 0.71267 0.289 106 2.467 0.6346
## 5 CCN51 - 5 ICS95 0.38100 0.289 106 1.319 0.9991
## 5 CCN51 - 6 ICS95 -0.59300 0.289 106 -2.053 0.8873
## 5 CCN51 - 0 TCS01 0.76500 0.289 106 2.648 0.4997
## 5 CCN51 - 1 TCS01 0.95267 0.289 106 3.298 0.1354
## 5 CCN51 - 2 TCS01 0.44433 0.289 106 1.538 0.9936
## 5 CCN51 - 3 TCS01 -0.13467 0.289 106 -0.466 1.0000
## 5 CCN51 - 4 TCS01 -0.42567 0.289 106 -1.473 0.9962
## 5 CCN51 - 5 TCS01 -1.66167 0.289 106 -5.752 <.0001
## 5 CCN51 - 6 TCS01 -2.46367 0.289 106 -8.528 <.0001
## 6 CCN51 - 0 ICS95 1.86700 0.289 106 6.462 <.0001
## 6 CCN51 - 1 ICS95 2.38600 0.289 106 8.259 <.0001
## 6 CCN51 - 2 ICS95 2.34500 0.289 106 8.117 <.0001
## 6 CCN51 - 3 ICS95 2.07100 0.289 106 7.169 <.0001
## 6 CCN51 - 4 ICS95 1.93600 0.289 106 6.701 <.0001
## 6 CCN51 - 5 ICS95 1.60433 0.289 106 5.553 <.0001
## 6 CCN51 - 6 ICS95 0.63033 0.289 106 2.182 0.8233
## 6 CCN51 - 0 TCS01 1.98833 0.289 106 6.882 <.0001
## 6 CCN51 - 1 TCS01 2.17600 0.289 106 7.532 <.0001
## 6 CCN51 - 2 TCS01 1.66767 0.289 106 5.773 <.0001
## 6 CCN51 - 3 TCS01 1.08867 0.289 106 3.768 0.0363
## 6 CCN51 - 4 TCS01 0.79767 0.289 106 2.761 0.4181
## 6 CCN51 - 5 TCS01 -0.43833 0.289 106 -1.517 0.9946
## 6 CCN51 - 6 TCS01 -1.24033 0.289 106 -4.293 0.0063
## 0 ICS95 - 1 ICS95 0.51900 0.262 108 1.981 0.9157
## 0 ICS95 - 2 ICS95 0.47800 0.262 108 1.824 0.9598
## 0 ICS95 - 3 ICS95 0.20400 0.262 108 0.779 1.0000
## 0 ICS95 - 4 ICS95 0.06900 0.262 108 0.263 1.0000
## 0 ICS95 - 5 ICS95 -0.26267 0.262 108 -1.003 1.0000
## 0 ICS95 - 6 ICS95 -1.23667 0.262 108 -4.720 0.0013
## 0 ICS95 - 0 TCS01 0.12133 0.289 106 0.420 1.0000
## 0 ICS95 - 1 TCS01 0.30900 0.289 106 1.070 1.0000
## 0 ICS95 - 2 TCS01 -0.19933 0.289 106 -0.690 1.0000
## 0 ICS95 - 3 TCS01 -0.77833 0.289 106 -2.694 0.4659
## 0 ICS95 - 4 TCS01 -1.06933 0.289 106 -3.701 0.0445
## 0 ICS95 - 5 TCS01 -2.30533 0.289 106 -7.980 <.0001
## 0 ICS95 - 6 TCS01 -3.10733 0.289 106 -10.756 <.0001
## 1 ICS95 - 2 ICS95 -0.04100 0.262 108 -0.156 1.0000
## 1 ICS95 - 3 ICS95 -0.31500 0.262 108 -1.202 0.9998
## 1 ICS95 - 4 ICS95 -0.45000 0.262 108 -1.718 0.9781
## 1 ICS95 - 5 ICS95 -0.78167 0.262 108 -2.983 0.2763
## 1 ICS95 - 6 ICS95 -1.75567 0.262 108 -6.701 <.0001
## 1 ICS95 - 0 TCS01 -0.39767 0.289 106 -1.376 0.9984
## 1 ICS95 - 1 TCS01 -0.21000 0.289 106 -0.727 1.0000
## 1 ICS95 - 2 TCS01 -0.71833 0.289 106 -2.486 0.6201
## 1 ICS95 - 3 TCS01 -1.29733 0.289 106 -4.491 0.0031
## 1 ICS95 - 4 TCS01 -1.58833 0.289 106 -5.498 0.0001
## 1 ICS95 - 5 TCS01 -2.82433 0.289 106 -9.776 <.0001
## 1 ICS95 - 6 TCS01 -3.62633 0.289 106 -12.552 <.0001
## 2 ICS95 - 3 ICS95 -0.27400 0.262 108 -1.046 1.0000
## 2 ICS95 - 4 ICS95 -0.40900 0.262 108 -1.561 0.9924
## 2 ICS95 - 5 ICS95 -0.74067 0.262 108 -2.827 0.3728
## 2 ICS95 - 6 ICS95 -1.71467 0.262 108 -6.545 <.0001
## 2 ICS95 - 0 TCS01 -0.35667 0.289 106 -1.235 0.9997
## 2 ICS95 - 1 TCS01 -0.16900 0.289 106 -0.585 1.0000
## 2 ICS95 - 2 TCS01 -0.67733 0.289 106 -2.345 0.7217
## 2 ICS95 - 3 TCS01 -1.25633 0.289 106 -4.349 0.0052
## 2 ICS95 - 4 TCS01 -1.54733 0.289 106 -5.356 0.0001
## 2 ICS95 - 5 TCS01 -2.78333 0.289 106 -9.634 <.0001
## 2 ICS95 - 6 TCS01 -3.58533 0.289 106 -12.410 <.0001
## 3 ICS95 - 4 ICS95 -0.13500 0.262 108 -0.515 1.0000
## 3 ICS95 - 5 ICS95 -0.46667 0.262 108 -1.781 0.9683
## 3 ICS95 - 6 ICS95 -1.44067 0.262 108 -5.499 0.0001
## 3 ICS95 - 0 TCS01 -0.08267 0.289 106 -0.286 1.0000
## 3 ICS95 - 1 TCS01 0.10500 0.289 106 0.363 1.0000
## 3 ICS95 - 2 TCS01 -0.40333 0.289 106 -1.396 0.9981
## 3 ICS95 - 3 TCS01 -0.98233 0.289 106 -3.400 0.1040
## 3 ICS95 - 4 TCS01 -1.27333 0.289 106 -4.408 0.0042
## 3 ICS95 - 5 TCS01 -2.50933 0.289 106 -8.686 <.0001
## 3 ICS95 - 6 TCS01 -3.31133 0.289 106 -11.462 <.0001
## 4 ICS95 - 5 ICS95 -0.33167 0.262 108 -1.266 0.9995
## 4 ICS95 - 6 ICS95 -1.30567 0.262 108 -4.983 0.0004
## 4 ICS95 - 0 TCS01 0.05233 0.289 106 0.181 1.0000
## 4 ICS95 - 1 TCS01 0.24000 0.289 106 0.831 1.0000
## 4 ICS95 - 2 TCS01 -0.26833 0.289 106 -0.929 1.0000
## 4 ICS95 - 3 TCS01 -0.84733 0.289 106 -2.933 0.3060
## 4 ICS95 - 4 TCS01 -1.13833 0.289 106 -3.940 0.0211
## 4 ICS95 - 5 TCS01 -2.37433 0.289 106 -8.219 <.0001
## 4 ICS95 - 6 TCS01 -3.17633 0.289 106 -10.995 <.0001
## 5 ICS95 - 6 ICS95 -0.97400 0.262 108 -3.718 0.0422
## 5 ICS95 - 0 TCS01 0.38400 0.289 106 1.329 0.9990
## 5 ICS95 - 1 TCS01 0.57167 0.289 106 1.979 0.9163
## 5 ICS95 - 2 TCS01 0.06333 0.289 106 0.219 1.0000
## 5 ICS95 - 3 TCS01 -0.51567 0.289 106 -1.785 0.9675
## 5 ICS95 - 4 TCS01 -0.80667 0.289 106 -2.792 0.3966
## 5 ICS95 - 5 TCS01 -2.04267 0.289 106 -7.071 <.0001
## 5 ICS95 - 6 TCS01 -2.84467 0.289 106 -9.847 <.0001
## 6 ICS95 - 0 TCS01 1.35800 0.289 106 4.701 0.0014
## 6 ICS95 - 1 TCS01 1.54567 0.289 106 5.350 0.0001
## 6 ICS95 - 2 TCS01 1.03733 0.289 106 3.591 0.0616
## 6 ICS95 - 3 TCS01 0.45833 0.289 106 1.586 0.9908
## 6 ICS95 - 4 TCS01 0.16733 0.289 106 0.579 1.0000
## 6 ICS95 - 5 TCS01 -1.06867 0.289 106 -3.699 0.0448
## 6 ICS95 - 6 TCS01 -1.87067 0.289 106 -6.475 <.0001
## 0 TCS01 - 1 TCS01 0.18767 0.262 108 0.716 1.0000
## 0 TCS01 - 2 TCS01 -0.32067 0.262 108 -1.224 0.9997
## 0 TCS01 - 3 TCS01 -0.89967 0.262 108 -3.434 0.0948
## 0 TCS01 - 4 TCS01 -1.19067 0.262 108 -4.545 0.0025
## 0 TCS01 - 5 TCS01 -2.42667 0.262 108 -9.262 <.0001
## 0 TCS01 - 6 TCS01 -3.22867 0.262 108 -12.323 <.0001
## 1 TCS01 - 2 TCS01 -0.50833 0.262 108 -1.940 0.9294
## 1 TCS01 - 3 TCS01 -1.08733 0.262 108 -4.150 0.0104
## 1 TCS01 - 4 TCS01 -1.37833 0.262 108 -5.261 0.0001
## 1 TCS01 - 5 TCS01 -2.61433 0.262 108 -9.978 <.0001
## 1 TCS01 - 6 TCS01 -3.41633 0.262 108 -13.039 <.0001
## 2 TCS01 - 3 TCS01 -0.57900 0.262 108 -2.210 0.8075
## 2 TCS01 - 4 TCS01 -0.87000 0.262 108 -3.321 0.1274
## 2 TCS01 - 5 TCS01 -2.10600 0.262 108 -8.038 <.0001
## 2 TCS01 - 6 TCS01 -2.90800 0.262 108 -11.099 <.0001
## 3 TCS01 - 4 TCS01 -0.29100 0.262 108 -1.111 0.9999
## 3 TCS01 - 5 TCS01 -1.52700 0.262 108 -5.828 <.0001
## 3 TCS01 - 6 TCS01 -2.32900 0.262 108 -8.889 <.0001
## 4 TCS01 - 5 TCS01 -1.23600 0.262 108 -4.718 0.0013
## 4 TCS01 - 6 TCS01 -2.03800 0.262 108 -7.779 <.0001
## 5 TCS01 - 6 TCS01 -0.80200 0.262 108 -3.061 0.2347
##
## 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 3.16 0.0681 106 3.03 3.30
## 1 2.96 0.0681 106 2.83 3.10
## 2 3.19 0.0681 106 3.05 3.32
## 3 3.50 0.0681 106 3.36 3.63
## 4 3.95 0.0681 106 3.81 4.08
## 5 4.76 0.0681 106 4.63 4.90
## 6 5.73 0.0681 106 5.60 5.87
##
## 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 0.2003 0.0873 108 2.293 0.2569
## 0 - 2 -0.0264 0.0873 108 -0.303 0.9999
## 0 - 3 -0.3338 0.0873 108 -3.822 0.0040
## 0 - 4 -0.7859 0.0873 108 -8.999 <.0001
## 0 - 5 -1.6026 0.0873 108 -18.350 <.0001
## 0 - 6 -2.5688 0.0873 108 -29.414 <.0001
## 1 - 2 -0.2267 0.0873 108 -2.596 0.1376
## 1 - 3 -0.5341 0.0873 108 -6.115 <.0001
## 1 - 4 -0.9862 0.0873 108 -11.292 <.0001
## 1 - 5 -1.8029 0.0873 108 -20.643 <.0001
## 1 - 6 -2.7690 0.0873 108 -31.707 <.0001
## 2 - 3 -0.3074 0.0873 108 -3.520 0.0110
## 2 - 4 -0.7595 0.0873 108 -8.696 <.0001
## 2 - 5 -1.5761 0.0873 108 -18.048 <.0001
## 2 - 6 -2.5423 0.0873 108 -29.111 <.0001
## 3 - 4 -0.4521 0.0873 108 -5.177 <.0001
## 3 - 5 -1.2688 0.0873 108 -14.528 <.0001
## 3 - 6 -2.2350 0.0873 108 -25.591 <.0001
## 4 - 5 -0.8167 0.0873 108 -9.351 <.0001
## 4 - 6 -1.7829 0.0873 108 -20.414 <.0001
## 5 - 6 -0.9662 0.0873 108 -11.063 <.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 3.35 0.118 106 3.12 3.58
## 1 3.24 0.118 106 3.00 3.47
## 2 3.73 0.118 106 3.50 3.96
## 3 3.54 0.118 106 3.30 3.77
## 4 3.75 0.118 106 3.52 3.99
## 5 4.45 0.118 106 4.21 4.68
## 6 5.90 0.118 106 5.67 6.13
##
## curva = T1:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.09 0.118 106 2.86 3.32
## 1 2.59 0.118 106 2.36 2.83
## 2 2.61 0.118 106 2.38 2.84
## 3 3.29 0.118 106 3.06 3.53
## 4 4.23 0.118 106 3.99 4.46
## 5 5.35 0.118 106 5.11 5.58
## 6 5.79 0.118 106 5.56 6.03
##
## curva = T2:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.04 0.118 106 2.81 3.28
## 1 3.05 0.118 106 2.82 3.29
## 2 3.23 0.118 106 2.99 3.46
## 3 3.66 0.118 106 3.42 3.89
## 4 3.86 0.118 106 3.63 4.09
## 5 4.50 0.118 106 4.27 4.73
## 6 5.50 0.118 106 5.26 5.73
##
## 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 0.11100 0.151 108 0.734 0.9902
## 0 - 2 -0.37967 0.151 108 -2.510 0.1661
## 0 - 3 -0.18678 0.151 108 -1.235 0.8790
## 0 - 4 -0.40500 0.151 108 -2.677 0.1141
## 0 - 5 -1.09811 0.151 108 -7.260 <.0001
## 0 - 6 -2.55111 0.151 108 -16.865 <.0001
## 1 - 2 -0.49067 0.151 108 -3.244 0.0255
## 1 - 3 -0.29778 0.151 108 -1.969 0.4406
## 1 - 4 -0.51600 0.151 108 -3.411 0.0154
## 1 - 5 -1.20911 0.151 108 -7.993 <.0001
## 1 - 6 -2.66211 0.151 108 -17.599 <.0001
## 2 - 3 0.19289 0.151 108 1.275 0.8618
## 2 - 4 -0.02533 0.151 108 -0.167 1.0000
## 2 - 5 -0.71844 0.151 108 -4.750 0.0001
## 2 - 6 -2.17144 0.151 108 -14.355 <.0001
## 3 - 4 -0.21822 0.151 108 -1.443 0.7774
## 3 - 5 -0.91133 0.151 108 -6.025 <.0001
## 3 - 6 -2.36433 0.151 108 -15.630 <.0001
## 4 - 5 -0.69311 0.151 108 -4.582 0.0002
## 4 - 6 -2.14611 0.151 108 -14.188 <.0001
## 5 - 6 -1.45300 0.151 108 -9.606 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 - 1 0.49633 0.151 108 3.281 0.0229
## 0 - 2 0.48078 0.151 108 3.178 0.0308
## 0 - 3 -0.20244 0.151 108 -1.338 0.8323
## 0 - 4 -1.13789 0.151 108 -7.522 <.0001
## 0 - 5 -2.25578 0.151 108 -14.913 <.0001
## 0 - 6 -2.70156 0.151 108 -17.860 <.0001
## 1 - 2 -0.01556 0.151 108 -0.103 1.0000
## 1 - 3 -0.69878 0.151 108 -4.620 0.0002
## 1 - 4 -1.63422 0.151 108 -10.804 <.0001
## 1 - 5 -2.75211 0.151 108 -18.194 <.0001
## 1 - 6 -3.19789 0.151 108 -21.141 <.0001
## 2 - 3 -0.68322 0.151 108 -4.517 0.0003
## 2 - 4 -1.61867 0.151 108 -10.701 <.0001
## 2 - 5 -2.73656 0.151 108 -18.091 <.0001
## 2 - 6 -3.18233 0.151 108 -21.038 <.0001
## 3 - 4 -0.93544 0.151 108 -6.184 <.0001
## 3 - 5 -2.05333 0.151 108 -13.574 <.0001
## 3 - 6 -2.49911 0.151 108 -16.521 <.0001
## 4 - 5 -1.11789 0.151 108 -7.390 <.0001
## 4 - 6 -1.56367 0.151 108 -10.337 <.0001
## 5 - 6 -0.44578 0.151 108 -2.947 0.0583
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.00656 0.151 108 -0.043 1.0000
## 0 - 2 -0.18044 0.151 108 -1.193 0.8954
## 0 - 3 -0.61222 0.151 108 -4.047 0.0018
## 0 - 4 -0.81489 0.151 108 -5.387 <.0001
## 0 - 5 -1.45389 0.151 108 -9.612 <.0001
## 0 - 6 -2.45367 0.151 108 -16.221 <.0001
## 1 - 2 -0.17389 0.151 108 -1.150 0.9110
## 1 - 3 -0.60567 0.151 108 -4.004 0.0021
## 1 - 4 -0.80833 0.151 108 -5.344 <.0001
## 1 - 5 -1.44733 0.151 108 -9.568 <.0001
## 1 - 6 -2.44711 0.151 108 -16.178 <.0001
## 2 - 3 -0.43178 0.151 108 -2.854 0.0741
## 2 - 4 -0.63444 0.151 108 -4.194 0.0011
## 2 - 5 -1.27344 0.151 108 -8.419 <.0001
## 2 - 6 -2.27322 0.151 108 -15.028 <.0001
## 3 - 4 -0.20267 0.151 108 -1.340 0.8316
## 3 - 5 -0.84167 0.151 108 -5.564 <.0001
## 3 - 6 -1.84144 0.151 108 -12.174 <.0001
## 4 - 5 -0.63900 0.151 108 -4.224 0.0010
## 4 - 6 -1.63878 0.151 108 -10.834 <.0001
## 5 - 6 -0.99978 0.151 108 -6.609 <.0001
##
## 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.testa)
## [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.testa)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.testa 0.995 0.868
## 2 1 CCN51 ph.testa 0.960 0.615
## 3 2 CCN51 ph.testa 0.963 0.633
## 4 3 CCN51 ph.testa 0.843 0.222
## 5 4 CCN51 ph.testa 1.00 0.962
## 6 5 CCN51 ph.testa 1 1.00
## 7 6 CCN51 ph.testa 0.999 0.947
## 8 0 ICS95 ph.testa 0.991 0.817
## 9 1 ICS95 ph.testa 0.843 0.223
## 10 2 ICS95 ph.testa 0.996 0.872
## 11 3 ICS95 ph.testa 0.944 0.546
## 12 4 ICS95 ph.testa 0.822 0.169
## 13 5 ICS95 ph.testa 0.914 0.431
## 14 6 ICS95 ph.testa 0.916 0.439
## 15 0 TCS01 ph.testa 0.872 0.301
## 16 1 TCS01 ph.testa 0.960 0.613
## 17 2 TCS01 ph.testa 0.958 0.607
## 18 3 TCS01 ph.testa 0.992 0.832
## 19 4 TCS01 ph.testa 0.988 0.786
## 20 5 TCS01 ph.testa 0.982 0.742
## 21 6 TCS01 ph.testa 0.983 0.748
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "ph.testa", 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.testa ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 7 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 1.21 0.361
## 2 1 2 6 0.815 0.486
## 3 2 2 6 1.19 0.366
## 4 3 2 6 0.879 0.463
## 5 4 2 6 0.527 0.615
## 6 5 2 6 1.77 0.249
## 7 6 2 6 0.128 0.882
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = ph.testa, 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 36.142 4.50e-04 * 0.684
## 2 diam2 1.71 10.25 143.156 4.95e-08 * 0.951
## 3 gen:diam2 3.42 10.25 23.210 5.42e-05 * 0.864
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = ph.testa, 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 12 296.028 2.53e-12 * 0.989
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = ph.testa, 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 36.782 4.81e-07 * 0.94
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = ph.testa, 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 68.236 1.42e-08 * 0.959
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(ph.testa)
## [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.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.testa 0.866 0.283
## 2 1 CCN51 ph.testa 0.950 0.570
## 3 2 CCN51 ph.testa 0.908 0.413
## 4 3 CCN51 ph.testa 0.983 0.749
## 5 4 CCN51 ph.testa 0.942 0.537
## 6 5 CCN51 ph.testa 0.976 0.701
## 7 6 CCN51 ph.testa 0.812 0.142
## 8 0 ICS95 ph.testa 0.910 0.419
## 9 1 ICS95 ph.testa 0.920 0.453
## 10 2 ICS95 ph.testa 0.947 0.554
## 11 3 ICS95 ph.testa 0.975 0.694
## 12 4 ICS95 ph.testa 0.970 0.669
## 13 5 ICS95 ph.testa 0.788 0.0870
## 14 6 ICS95 ph.testa 0.842 0.218
## 15 0 TCS01 ph.testa 0.968 0.656
## 16 1 TCS01 ph.testa 0.852 0.245
## 17 2 TCS01 ph.testa 0.997 0.896
## 18 3 TCS01 ph.testa 0.998 0.922
## 19 4 TCS01 ph.testa 0.974 0.691
## 20 5 TCS01 ph.testa 0.807 0.131
## 21 6 TCS01 ph.testa 0.841 0.217
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "ph.testa", 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.testa ~ 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 0.121 0.888
## 2 1 2 6 0.713 0.527
## 3 2 2 6 2.02 0.213
## 4 3 2 6 1.55 0.288
## 5 4 2 6 1.56 0.284
## 6 5 2 6 0.177 0.842
## 7 6 2 6 0.513 0.623
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = ph.testa, 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 6 51.776 1.64e-04 * 0.811
## 2 diam2 6 36 123.018 1.80e-22 * 0.939
## 3 gen:diam2 12 36 5.849 1.71e-05 * 0.594
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = ph.testa, 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 26.027 3.26e-06 * 0.908
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = ph.testa, 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 58.546 3.43e-08 * 0.952
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = ph.testa, 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 72.066 1.04e-08 * 0.966
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(ph.testa)
## [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.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 ph.testa 0.987 0.778
## 2 1 CCN51 ph.testa 1.00 0.959
## 3 2 CCN51 ph.testa 1.00 0.982
## 4 3 CCN51 ph.testa 0.787 0.0843
## 5 4 CCN51 ph.testa 0.939 0.524
## 6 5 CCN51 ph.testa 0.801 0.116
## 7 6 CCN51 ph.testa 0.754 0.00795
## 8 0 ICS95 ph.testa 0.781 0.0703
## 9 1 ICS95 ph.testa 0.990 0.808
## 10 2 ICS95 ph.testa 0.957 0.602
## 11 3 ICS95 ph.testa 0.909 0.416
## 12 4 ICS95 ph.testa 0.775 0.0553
## 13 5 ICS95 ph.testa 0.800 0.114
## 14 6 ICS95 ph.testa 0.925 0.471
## 15 0 TCS01 ph.testa 0.935 0.508
## 16 1 TCS01 ph.testa 0.980 0.726
## 17 2 TCS01 ph.testa 1.00 0.975
## 18 3 TCS01 ph.testa 0.993 0.837
## 19 4 TCS01 ph.testa 0.825 0.175
## 20 5 TCS01 ph.testa 0.894 0.366
## 21 6 TCS01 ph.testa 0.997 0.897
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "ph.testa", 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.testa ~ 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 0.324 0.735
## 2 1 2 6 1.67 0.265
## 3 2 2 6 2.57 0.156
## 4 3 2 6 1.74 0.254
## 5 4 2 6 1.07 0.401
## 6 5 2 6 1.52 0.293
## 7 6 2 6 1.33 0.333
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = ph.testa, 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 12.577 7.00e-03 * 0.607
## 2 diam2 1.76 10.55 55.558 3.48e-06 * 0.854
## 3 gen:diam2 3.52 10.55 4.634 2.30e-02 * 0.494
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = ph.testa, 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 56.215 4.34e-08 * 0.959
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = ph.testa, 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 24.688 4.34e-06 * 0.902
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = ph.testa, 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 16.091 4.19e-05 * 0.828
## 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.testa)) +
geom_point(aes(y=ph.testa)) +
scale_y_continuous(name = expression("Testa 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.testa", 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_testa.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=ph.testa-ci, ymax=ph.testa+ci), width=.1) +
geom_line(aes(y=ph.testa)) +
geom_point(aes(y=ph.testa)) +
scale_y_continuous(name = expression("Testa 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
