setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("todos_phys.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("T3", "T1", "T2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$diam2<-as.factor(datos$diam2)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.2
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.1 ✔ forcats 1.0.0
## Warning: package 'tibble' was built under R version 4.1.2
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'purrr' was built under R version 4.1.2
## Warning: package 'stringr' was built under R version 4.1.2
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library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
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library(rstatix)
##
## Attaching package: 'rstatix'
## The following objects are masked from 'package:plyr':
##
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## The following object is masked from 'package:stats':
##
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library(emmeans)
##Summary statistics
summ<-datos %>%
group_by(curva, gen, diam2) %>%
get_summary_stats(acidez.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 acidez.testa 3 0.797 0.13
## 2 T3 1 CCN51 acidez.testa 3 0.957 0.148
## 3 T3 2 CCN51 acidez.testa 3 0.22 0.02
## 4 T3 3 CCN51 acidez.testa 3 0.287 0.059
## 5 T3 4 CCN51 acidez.testa 3 0.4 0.066
## 6 T3 5 CCN51 acidez.testa 3 0.363 0.032
## 7 T3 6 CCN51 acidez.testa 3 0.117 0.006
## 8 T3 0 ICS95 acidez.testa 3 0.95 0.111
## 9 T3 1 ICS95 acidez.testa 3 1.06 0.144
## 10 T3 2 ICS95 acidez.testa 3 0.37 0.108
## 11 T3 3 ICS95 acidez.testa 3 0.993 0.621
## 12 T3 4 ICS95 acidez.testa 3 0.873 0.34
## 13 T3 5 ICS95 acidez.testa 3 0.443 0.188
## 14 T3 6 ICS95 acidez.testa 3 0.17 0.04
## 15 T3 0 TCS01 acidez.testa 3 0.727 0.064
## 16 T3 1 TCS01 acidez.testa 3 0.893 0.181
## 17 T3 2 TCS01 acidez.testa 3 0.777 0.121
## 18 T3 3 TCS01 acidez.testa 3 2.13 0.174
## 19 T3 4 TCS01 acidez.testa 3 2.13 0.206
## 20 T3 5 TCS01 acidez.testa 3 1.90 0.133
## 21 T3 6 TCS01 acidez.testa 3 1.03 0.1
## 22 T1 0 CCN51 acidez.testa 3 0.637 0.049
## 23 T1 1 CCN51 acidez.testa 3 1.15 0.285
## 24 T1 2 CCN51 acidez.testa 3 1.01 0.078
## 25 T1 3 CCN51 acidez.testa 3 1.47 0.397
## 26 T1 4 CCN51 acidez.testa 3 0.807 0.44
## 27 T1 5 CCN51 acidez.testa 3 0.537 0.218
## 28 T1 6 CCN51 acidez.testa 3 0.577 0.345
## 29 T1 0 ICS95 acidez.testa 3 0.697 0.081
## 30 T1 1 ICS95 acidez.testa 3 1.18 0.075
## 31 T1 2 ICS95 acidez.testa 3 1.26 0.342
## 32 T1 3 ICS95 acidez.testa 3 1.15 0.04
## 33 T1 4 ICS95 acidez.testa 3 1.24 0.036
## 34 T1 5 ICS95 acidez.testa 3 0.647 0.199
## 35 T1 6 ICS95 acidez.testa 3 0.4 0.321
## 36 T1 0 TCS01 acidez.testa 3 0.587 0.015
## 37 T1 1 TCS01 acidez.testa 3 0.94 0.12
## 38 T1 2 TCS01 acidez.testa 3 0.7 0.201
## 39 T1 3 TCS01 acidez.testa 3 0.493 0.091
## 40 T1 4 TCS01 acidez.testa 3 0.41 0.05
## 41 T1 5 TCS01 acidez.testa 3 0.4 0.303
## 42 T1 6 TCS01 acidez.testa 3 0.253 0.167
## 43 T2 0 CCN51 acidez.testa 3 0.881 0.034
## 44 T2 1 CCN51 acidez.testa 3 1.21 0.112
## 45 T2 2 CCN51 acidez.testa 3 2.19 0.205
## 46 T2 3 CCN51 acidez.testa 3 2.96 0.438
## 47 T2 4 CCN51 acidez.testa 3 2.04 0.097
## 48 T2 5 CCN51 acidez.testa 3 1.46 0.366
## 49 T2 6 CCN51 acidez.testa 3 0.744 0.091
## 50 T2 0 ICS95 acidez.testa 3 0.709 0.077
## 51 T2 1 ICS95 acidez.testa 3 1.28 0.024
## 52 T2 2 ICS95 acidez.testa 3 1.43 0.236
## 53 T2 3 ICS95 acidez.testa 3 1.83 0.149
## 54 T2 4 ICS95 acidez.testa 3 1.55 0.48
## 55 T2 5 ICS95 acidez.testa 3 1.26 0.032
## 56 T2 6 ICS95 acidez.testa 3 0.693 0.183
## 57 T2 0 TCS01 acidez.testa 3 0.729 0.075
## 58 T2 1 TCS01 acidez.testa 3 1.12 0.253
## 59 T2 2 TCS01 acidez.testa 3 0.871 0.282
## 60 T2 3 TCS01 acidez.testa 3 0.881 0.297
## 61 T2 4 TCS01 acidez.testa 3 1.02 0.651
## 62 T2 5 TCS01 acidez.testa 3 0.473 0.287
## 63 T2 6 TCS01 acidez.testa 3 0.364 0.212
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "acidez.testa",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(acidez.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(acidez.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 acidez.testa 0.998 0.915
## 2 T3 1 CCN51 acidez.testa 0.954 0.588
## 3 T3 2 CCN51 acidez.testa 1 1.00
## 4 T3 3 CCN51 acidez.testa 0.881 0.328
## 5 T3 4 CCN51 acidez.testa 0.983 0.747
## 6 T3 5 CCN51 acidez.testa 0.871 0.298
## 7 T3 6 CCN51 acidez.testa 0.75 0
## 8 T3 0 ICS95 acidez.testa 0.976 0.702
## 9 T3 1 ICS95 acidez.testa 0.942 0.537
## 10 T3 2 ICS95 acidez.testa 0.942 0.537
## 11 T3 3 ICS95 acidez.testa 0.948 0.561
## 12 T3 4 ICS95 acidez.testa 0.885 0.339
## 13 T3 5 ICS95 acidez.testa 0.971 0.675
## 14 T3 6 ICS95 acidez.testa 1 1.00
## 15 T3 0 TCS01 acidez.testa 0.75 0
## 16 T3 1 TCS01 acidez.testa 0.878 0.317
## 17 T3 2 TCS01 acidez.testa 0.991 0.817
## 18 T3 3 TCS01 acidez.testa 0.900 0.387
## 19 T3 4 TCS01 acidez.testa 0.847 0.232
## 20 T3 5 TCS01 acidez.testa 0.75 0
## 21 T3 6 TCS01 acidez.testa 0.900 0.384
## 22 T1 0 CCN51 acidez.testa 0.832 0.194
## 23 T1 1 CCN51 acidez.testa 0.999 0.942
## 24 T1 2 CCN51 acidez.testa 0.932 0.497
## 25 T1 3 CCN51 acidez.testa 0.942 0.537
## 26 T1 4 CCN51 acidez.testa 0.956 0.596
## 27 T1 5 CCN51 acidez.testa 0.930 0.488
## 28 T1 6 CCN51 acidez.testa 0.941 0.532
## 29 T1 0 ICS95 acidez.testa 0.980 0.726
## 30 T1 1 ICS95 acidez.testa 0.75 0
## 31 T1 2 ICS95 acidez.testa 0.962 0.626
## 32 T1 3 ICS95 acidez.testa 0.980 0.726
## 33 T1 4 ICS95 acidez.testa 0.942 0.537
## 34 T1 5 ICS95 acidez.testa 0.915 0.437
## 35 T1 6 ICS95 acidez.testa 0.814 0.149
## 36 T1 0 TCS01 acidez.testa 0.964 0.637
## 37 T1 1 TCS01 acidez.testa 1 1.00
## 38 T1 2 TCS01 acidez.testa 0.993 0.835
## 39 T1 3 TCS01 acidez.testa 0.984 0.756
## 40 T1 4 TCS01 acidez.testa 1 1.00
## 41 T1 5 TCS01 acidez.testa 0.792 0.0944
## 42 T1 6 TCS01 acidez.testa 0.923 0.463
## 43 T2 0 CCN51 acidez.testa 0.979 0.722
## 44 T2 1 CCN51 acidez.testa 0.823 0.170
## 45 T2 2 CCN51 acidez.testa 0.884 0.337
## 46 T2 3 CCN51 acidez.testa 0.831 0.190
## 47 T2 4 CCN51 acidez.testa 0.759 0.0197
## 48 T2 5 CCN51 acidez.testa 0.904 0.397
## 49 T2 6 CCN51 acidez.testa 0.918 0.446
## 50 T2 0 ICS95 acidez.testa 0.974 0.689
## 51 T2 1 ICS95 acidez.testa 0.999 0.954
## 52 T2 2 ICS95 acidez.testa 0.870 0.296
## 53 T2 3 ICS95 acidez.testa 0.978 0.717
## 54 T2 4 ICS95 acidez.testa 0.999 0.928
## 55 T2 5 ICS95 acidez.testa 0.928 0.480
## 56 T2 6 ICS95 acidez.testa 0.908 0.411
## 57 T2 0 TCS01 acidez.testa 0.988 0.786
## 58 T2 1 TCS01 acidez.testa 0.965 0.641
## 59 T2 2 TCS01 acidez.testa 0.920 0.452
## 60 T2 3 TCS01 acidez.testa 0.997 0.898
## 61 T2 4 TCS01 acidez.testa 0.987 0.780
## 62 T2 5 TCS01 acidez.testa 0.841 0.217
## 63 T2 6 TCS01 acidez.testa 0.924 0.468
##Create QQ plot for each cell of design:
ggqqplot(datos, "acidez.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.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
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## The following aesthetics were dropped during statistical transformation: sample
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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.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## 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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## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## the data.
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## 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 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.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev<-datos %>%
group_by(diam2) %>%
levene_test(acidez.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 0.656 0.722
## 2 1 8 18 0.697 0.690
## 3 2 8 18 0.636 0.738
## 4 3 8 18 0.934 0.513
## 5 4 8 18 1.36 0.278
## 6 5 8 18 0.460 0.868
## 7 6 8 18 0.684 0.700
##Computation
res.aov <- anova_test(
data = datos, dv = acidez.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 41.490 1.82e-07 * 0.514
## 2 gen 2 18 1.717 2.08e-01 0.042
## 3 diam2 6 108 46.860 7.07e-28 * 0.667
## 4 curva:gen 4 18 55.141 7.39e-10 * 0.738
## 5 curva:diam2 12 108 10.199 3.93e-13 * 0.466
## 6 gen:diam2 12 108 2.685 3.00e-03 * 0.187
## 7 curva:gen:diam2 24 108 10.203 3.02e-18 * 0.636
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.099 0.015 *
## 2 curva:diam2 0.099 0.015 *
## 3 gen:diam2 0.099 0.015 *
## 4 curva:gen:diam2 0.099 0.015 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.608 3.65, 65.68 7.84e-18 * 0.782 4.69, 84.44
## 2 curva:diam2 0.608 7.3, 65.68 9.59e-09 * 0.782 9.38, 84.44
## 3 gen:diam2 0.608 7.3, 65.68 1.50e-02 * 0.782 9.38, 84.44
## 4 curva:gen:diam2 0.608 14.59, 65.68 7.66e-12 * 0.782 18.76, 84.44
## p[HF] p[HF]<.05
## 1 2.72e-22 *
## 2 1.07e-10 *
## 3 8.00e-03 *
## 4 1.09e-14 *
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2.00 18.00 41.490 1.82e-07 * 0.514
## 2 gen 2.00 18.00 1.717 2.08e-01 0.042
## 3 diam2 3.65 65.68 46.860 7.84e-18 * 0.667
## 4 curva:gen 4.00 18.00 55.141 7.39e-10 * 0.738
## 5 curva:diam2 7.30 65.68 10.199 9.59e-09 * 0.466
## 6 gen:diam2 7.30 65.68 2.685 1.50e-02 * 0.187
## 7 curva:gen:diam2 14.59 65.68 10.203 7.66e-12 * 0.636
#Table by error
res.aov.error <- aov(acidez.testa ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = acidez.testa ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 0.9496931
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 7.162679 0.296367 19.038559 1.553727
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 0.2937995
## 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 13.589033 5.914994 1.557364 11.834711 5.219858
## Deg. of Freedom 6 12 12 24 108
##
## Residual standard error: 0.2198454
## Estimated effects may be unbalanced
## Emmeans
emmip(res.aov.error, gen ~ diam2 | curva)
## Note: re-fitting model with sum-to-zero contrasts
emm_curva <- emmeans(res.aov.error, pairwise ~ curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_curva
## $emmeans
## curva emmean SE df lower.CL upper.CL
## T3 0.837 0.037 18 0.76 0.915
## T1 0.788 0.037 18 0.71 0.866
## T2 1.224 0.037 18 1.15 1.301
##
## 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 0.0494 0.0523 18 0.943 0.6210
## T3 - T2 -0.3861 0.0523 18 -7.375 <.0001
## T1 - T2 -0.4354 0.0523 18 -8.318 <.0001
##
## Results are averaged over the levels of: diam2, gen
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_curva <- emmeans(res.aov.error, pairwise ~ gen | curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_gen_curva
## $emmeans
## curva = T3:
## gen emmean SE df lower.CL upper.CL
## CCN51 0.449 0.0641 18 0.314 0.583
## ICS95 0.694 0.0641 18 0.560 0.829
## TCS01 1.370 0.0641 18 1.235 1.504
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 0.884 0.0641 18 0.750 1.019
## ICS95 0.940 0.0641 18 0.805 1.074
## TCS01 0.540 0.0641 18 0.406 0.675
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 1.641 0.0641 18 1.506 1.776
## ICS95 1.250 0.0641 18 1.116 1.385
## TCS01 0.779 0.0641 18 0.645 0.914
##
## 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 -0.2457 0.0907 18 -2.710 0.0364
## CCN51 - TCS01 -0.9210 0.0907 18 -10.157 <.0001
## ICS95 - TCS01 -0.6752 0.0907 18 -7.447 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -0.0552 0.0907 18 -0.609 0.8170
## CCN51 - TCS01 0.3438 0.0907 18 3.792 0.0036
## ICS95 - TCS01 0.3990 0.0907 18 4.401 0.0010
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.3908 0.0907 18 4.310 0.0012
## CCN51 - TCS01 0.8618 0.0907 18 9.505 <.0001
## ICS95 - TCS01 0.4710 0.0907 18 5.194 0.0002
##
## Results are averaged over the levels of: diam2
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_diam2 <- emmeans(res.aov.error, pairwise ~ diam2 | curva*gen)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2
## $emmeans
## curva = T3, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.797 0.134 119 0.5316 1.062
## 1 0.957 0.134 119 0.6916 1.222
## 2 0.220 0.134 119 -0.0451 0.485
## 3 0.287 0.134 119 0.0216 0.552
## 4 0.400 0.134 119 0.1349 0.665
## 5 0.363 0.134 119 0.0983 0.628
## 6 0.117 0.134 119 -0.1484 0.382
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.637 0.134 119 0.3716 0.902
## 1 1.150 0.134 119 0.8849 1.415
## 2 1.013 0.134 119 0.7483 1.278
## 3 1.470 0.134 119 1.2049 1.735
## 4 0.807 0.134 119 0.5416 1.072
## 5 0.537 0.134 119 0.2716 0.802
## 6 0.577 0.134 119 0.3116 0.842
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.881 0.134 119 0.6163 1.146
## 1 1.213 0.134 119 0.9476 1.478
## 2 2.186 0.134 119 1.9213 2.451
## 3 2.964 0.134 119 2.6989 3.229
## 4 2.043 0.134 119 1.7779 2.308
## 5 1.456 0.134 119 1.1909 1.721
## 6 0.744 0.134 119 0.4789 1.009
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.950 0.134 119 0.6849 1.215
## 1 1.060 0.134 119 0.7949 1.325
## 2 0.370 0.134 119 0.1049 0.635
## 3 0.993 0.134 119 0.7283 1.258
## 4 0.873 0.134 119 0.6083 1.138
## 5 0.443 0.134 119 0.1783 0.708
## 6 0.170 0.134 119 -0.0951 0.435
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.697 0.134 119 0.4316 0.962
## 1 1.183 0.134 119 0.9183 1.448
## 2 1.263 0.134 119 0.9983 1.528
## 3 1.147 0.134 119 0.8816 1.412
## 4 1.240 0.134 119 0.9749 1.505
## 5 0.647 0.134 119 0.3816 0.912
## 6 0.400 0.134 119 0.1349 0.665
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.709 0.134 119 0.4443 0.974
## 1 1.282 0.134 119 1.0166 1.547
## 2 1.430 0.134 119 1.1653 1.695
## 3 1.828 0.134 119 1.5633 2.093
## 4 1.549 0.134 119 1.2839 1.814
## 5 1.260 0.134 119 0.9949 1.525
## 6 0.693 0.134 119 0.4279 0.958
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.727 0.134 119 0.4616 0.992
## 1 0.893 0.134 119 0.6283 1.158
## 2 0.777 0.134 119 0.5116 1.042
## 3 2.133 0.134 119 1.8683 2.398
## 4 2.127 0.134 119 1.8616 2.392
## 5 1.897 0.134 119 1.6316 2.162
## 6 1.033 0.134 119 0.7683 1.298
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.587 0.134 119 0.3216 0.852
## 1 0.940 0.134 119 0.6749 1.205
## 2 0.700 0.134 119 0.4349 0.965
## 3 0.493 0.134 119 0.2283 0.758
## 4 0.410 0.134 119 0.1449 0.675
## 5 0.400 0.134 119 0.1349 0.665
## 6 0.253 0.134 119 -0.0117 0.518
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.729 0.134 119 0.4636 0.994
## 1 1.122 0.134 119 0.8566 1.387
## 2 0.871 0.134 119 0.6059 1.136
## 3 0.881 0.134 119 0.6156 1.146
## 4 1.016 0.134 119 0.7513 1.281
## 5 0.473 0.134 119 0.2079 0.738
## 6 0.364 0.134 119 0.0986 0.629
##
## 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 -0.16000 0.18 108 -0.891 0.9732
## 0 - 2 0.57667 0.18 108 3.213 0.0279
## 0 - 3 0.51000 0.18 108 2.841 0.0766
## 0 - 4 0.39667 0.18 108 2.210 0.2990
## 0 - 5 0.43333 0.18 108 2.414 0.2029
## 0 - 6 0.68000 0.18 108 3.788 0.0045
## 1 - 2 0.73667 0.18 108 4.104 0.0015
## 1 - 3 0.67000 0.18 108 3.733 0.0055
## 1 - 4 0.55667 0.18 108 3.101 0.0384
## 1 - 5 0.59333 0.18 108 3.305 0.0213
## 1 - 6 0.84000 0.18 108 4.680 0.0002
## 2 - 3 -0.06667 0.18 108 -0.371 0.9998
## 2 - 4 -0.18000 0.18 108 -1.003 0.9524
## 2 - 5 -0.14333 0.18 108 -0.799 0.9847
## 2 - 6 0.10333 0.18 108 0.576 0.9974
## 3 - 4 -0.11333 0.18 108 -0.631 0.9956
## 3 - 5 -0.07667 0.18 108 -0.427 0.9995
## 3 - 6 0.17000 0.18 108 0.947 0.9638
## 4 - 5 0.03667 0.18 108 0.204 1.0000
## 4 - 6 0.28333 0.18 108 1.578 0.6962
## 5 - 6 0.24667 0.18 108 1.374 0.8143
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.51333 0.18 108 -2.860 0.0731
## 0 - 2 -0.37667 0.18 108 -2.098 0.3611
## 0 - 3 -0.83333 0.18 108 -4.642 0.0002
## 0 - 4 -0.17000 0.18 108 -0.947 0.9638
## 0 - 5 0.10000 0.18 108 0.557 0.9978
## 0 - 6 0.06000 0.18 108 0.334 0.9999
## 1 - 2 0.13667 0.18 108 0.761 0.9880
## 1 - 3 -0.32000 0.18 108 -1.783 0.5625
## 1 - 4 0.34333 0.18 108 1.913 0.4765
## 1 - 5 0.61333 0.18 108 3.417 0.0152
## 1 - 6 0.57333 0.18 108 3.194 0.0295
## 2 - 3 -0.45667 0.18 108 -2.544 0.1543
## 2 - 4 0.20667 0.18 108 1.151 0.9104
## 2 - 5 0.47667 0.18 108 2.655 0.1201
## 2 - 6 0.43667 0.18 108 2.433 0.1954
## 3 - 4 0.66333 0.18 108 3.695 0.0062
## 3 - 5 0.93333 0.18 108 5.200 <.0001
## 3 - 6 0.89333 0.18 108 4.977 <.0001
## 4 - 5 0.27000 0.18 108 1.504 0.7418
## 4 - 6 0.23000 0.18 108 1.281 0.8590
## 5 - 6 -0.04000 0.18 108 -0.223 1.0000
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.33133 0.18 108 -1.846 0.5205
## 0 - 2 -1.30500 0.18 108 -7.270 <.0001
## 0 - 3 -2.08267 0.18 108 -11.602 <.0001
## 0 - 4 -1.16167 0.18 108 -6.472 <.0001
## 0 - 5 -0.57467 0.18 108 -3.201 0.0289
## 0 - 6 0.13733 0.18 108 0.765 0.9877
## 1 - 2 -0.97367 0.18 108 -5.424 <.0001
## 1 - 3 -1.75133 0.18 108 -9.757 <.0001
## 1 - 4 -0.83033 0.18 108 -4.626 0.0002
## 1 - 5 -0.24333 0.18 108 -1.356 0.8237
## 1 - 6 0.46867 0.18 108 2.611 0.1330
## 2 - 3 -0.77767 0.18 108 -4.332 0.0006
## 2 - 4 0.14333 0.18 108 0.799 0.9847
## 2 - 5 0.73033 0.18 108 4.069 0.0017
## 2 - 6 1.44233 0.18 108 8.035 <.0001
## 3 - 4 0.92100 0.18 108 5.131 <.0001
## 3 - 5 1.50800 0.18 108 8.401 <.0001
## 3 - 6 2.22000 0.18 108 12.367 <.0001
## 4 - 5 0.58700 0.18 108 3.270 0.0236
## 4 - 6 1.29900 0.18 108 7.237 <.0001
## 5 - 6 0.71200 0.18 108 3.967 0.0025
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.11000 0.18 108 -0.613 0.9963
## 0 - 2 0.58000 0.18 108 3.231 0.0265
## 0 - 3 -0.04333 0.18 108 -0.241 1.0000
## 0 - 4 0.07667 0.18 108 0.427 0.9995
## 0 - 5 0.50667 0.18 108 2.823 0.0803
## 0 - 6 0.78000 0.18 108 4.345 0.0006
## 1 - 2 0.69000 0.18 108 3.844 0.0038
## 1 - 3 0.06667 0.18 108 0.371 0.9998
## 1 - 4 0.18667 0.18 108 1.040 0.9435
## 1 - 5 0.61667 0.18 108 3.435 0.0143
## 1 - 6 0.89000 0.18 108 4.958 0.0001
## 2 - 3 -0.62333 0.18 108 -3.473 0.0128
## 2 - 4 -0.50333 0.18 108 -2.804 0.0841
## 2 - 5 -0.07333 0.18 108 -0.409 0.9996
## 2 - 6 0.20000 0.18 108 1.114 0.9225
## 3 - 4 0.12000 0.18 108 0.669 0.9940
## 3 - 5 0.55000 0.18 108 3.064 0.0425
## 3 - 6 0.82333 0.18 108 4.587 0.0002
## 4 - 5 0.43000 0.18 108 2.396 0.2106
## 4 - 6 0.70333 0.18 108 3.918 0.0029
## 5 - 6 0.27333 0.18 108 1.523 0.7306
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.48667 0.18 108 -2.711 0.1053
## 0 - 2 -0.56667 0.18 108 -3.157 0.0328
## 0 - 3 -0.45000 0.18 108 -2.507 0.1672
## 0 - 4 -0.54333 0.18 108 -3.027 0.0471
## 0 - 5 0.05000 0.18 108 0.279 1.0000
## 0 - 6 0.29667 0.18 108 1.653 0.6486
## 1 - 2 -0.08000 0.18 108 -0.446 0.9994
## 1 - 3 0.03667 0.18 108 0.204 1.0000
## 1 - 4 -0.05667 0.18 108 -0.316 0.9999
## 1 - 5 0.53667 0.18 108 2.990 0.0520
## 1 - 6 0.78333 0.18 108 4.364 0.0006
## 2 - 3 0.11667 0.18 108 0.650 0.9949
## 2 - 4 0.02333 0.18 108 0.130 1.0000
## 2 - 5 0.61667 0.18 108 3.435 0.0143
## 2 - 6 0.86333 0.18 108 4.810 0.0001
## 3 - 4 -0.09333 0.18 108 -0.520 0.9985
## 3 - 5 0.50000 0.18 108 2.785 0.0880
## 3 - 6 0.74667 0.18 108 4.160 0.0012
## 4 - 5 0.59333 0.18 108 3.305 0.0213
## 4 - 6 0.84000 0.18 108 4.680 0.0002
## 5 - 6 0.24667 0.18 108 1.374 0.8143
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.57233 0.18 108 -3.188 0.0300
## 0 - 2 -0.72100 0.18 108 -4.017 0.0021
## 0 - 3 -1.11900 0.18 108 -6.234 <.0001
## 0 - 4 -0.83967 0.18 108 -4.678 0.0002
## 0 - 5 -0.55067 0.18 108 -3.068 0.0421
## 0 - 6 0.01633 0.18 108 0.091 1.0000
## 1 - 2 -0.14867 0.18 108 -0.828 0.9815
## 1 - 3 -0.54667 0.18 108 -3.045 0.0448
## 1 - 4 -0.26733 0.18 108 -1.489 0.7506
## 1 - 5 0.02167 0.18 108 0.121 1.0000
## 1 - 6 0.58867 0.18 108 3.279 0.0230
## 2 - 3 -0.39800 0.18 108 -2.217 0.2951
## 2 - 4 -0.11867 0.18 108 -0.661 0.9944
## 2 - 5 0.17033 0.18 108 0.949 0.9635
## 2 - 6 0.73733 0.18 108 4.108 0.0015
## 3 - 4 0.27933 0.18 108 1.556 0.7102
## 3 - 5 0.56833 0.18 108 3.166 0.0319
## 3 - 6 1.13533 0.18 108 6.325 <.0001
## 4 - 5 0.28900 0.18 108 1.610 0.6762
## 4 - 6 0.85600 0.18 108 4.769 0.0001
## 5 - 6 0.56700 0.18 108 3.159 0.0326
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.16667 0.18 108 -0.928 0.9672
## 0 - 2 -0.05000 0.18 108 -0.279 1.0000
## 0 - 3 -1.40667 0.18 108 -7.836 <.0001
## 0 - 4 -1.40000 0.18 108 -7.799 <.0001
## 0 - 5 -1.17000 0.18 108 -6.518 <.0001
## 0 - 6 -0.30667 0.18 108 -1.708 0.6120
## 1 - 2 0.11667 0.18 108 0.650 0.9949
## 1 - 3 -1.24000 0.18 108 -6.908 <.0001
## 1 - 4 -1.23333 0.18 108 -6.871 <.0001
## 1 - 5 -1.00333 0.18 108 -5.590 <.0001
## 1 - 6 -0.14000 0.18 108 -0.780 0.9864
## 2 - 3 -1.35667 0.18 108 -7.558 <.0001
## 2 - 4 -1.35000 0.18 108 -7.521 <.0001
## 2 - 5 -1.12000 0.18 108 -6.239 <.0001
## 2 - 6 -0.25667 0.18 108 -1.430 0.7845
## 3 - 4 0.00667 0.18 108 0.037 1.0000
## 3 - 5 0.23667 0.18 108 1.318 0.8419
## 3 - 6 1.10000 0.18 108 6.128 <.0001
## 4 - 5 0.23000 0.18 108 1.281 0.8590
## 4 - 6 1.09333 0.18 108 6.091 <.0001
## 5 - 6 0.86333 0.18 108 4.810 0.0001
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.35333 0.18 108 -1.968 0.4407
## 0 - 2 -0.11333 0.18 108 -0.631 0.9956
## 0 - 3 0.09333 0.18 108 0.520 0.9985
## 0 - 4 0.17667 0.18 108 0.984 0.9564
## 0 - 5 0.18667 0.18 108 1.040 0.9435
## 0 - 6 0.33333 0.18 108 1.857 0.5131
## 1 - 2 0.24000 0.18 108 1.337 0.8330
## 1 - 3 0.44667 0.18 108 2.488 0.1739
## 1 - 4 0.53000 0.18 108 2.953 0.0574
## 1 - 5 0.54000 0.18 108 3.008 0.0495
## 1 - 6 0.68667 0.18 108 3.825 0.0040
## 2 - 3 0.20667 0.18 108 1.151 0.9104
## 2 - 4 0.29000 0.18 108 1.616 0.6726
## 2 - 5 0.30000 0.18 108 1.671 0.6365
## 2 - 6 0.44667 0.18 108 2.488 0.1739
## 3 - 4 0.08333 0.18 108 0.464 0.9992
## 3 - 5 0.09333 0.18 108 0.520 0.9985
## 3 - 6 0.24000 0.18 108 1.337 0.8330
## 4 - 5 0.01000 0.18 108 0.056 1.0000
## 4 - 6 0.15667 0.18 108 0.873 0.9759
## 5 - 6 0.14667 0.18 108 0.817 0.9827
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 1 -0.39300 0.18 108 -2.189 0.3099
## 0 - 2 -0.14233 0.18 108 -0.793 0.9852
## 0 - 3 -0.15200 0.18 108 -0.847 0.9793
## 0 - 4 -0.28767 0.18 108 -1.603 0.6810
## 0 - 5 0.25567 0.18 108 1.424 0.7876
## 0 - 6 0.36500 0.18 108 2.033 0.4001
## 1 - 2 0.25067 0.18 108 1.396 0.8026
## 1 - 3 0.24100 0.18 108 1.343 0.8302
## 1 - 4 0.10533 0.18 108 0.587 0.9971
## 1 - 5 0.64867 0.18 108 3.614 0.0081
## 1 - 6 0.75800 0.18 108 4.223 0.0010
## 2 - 3 -0.00967 0.18 108 -0.054 1.0000
## 2 - 4 -0.14533 0.18 108 -0.810 0.9835
## 2 - 5 0.39800 0.18 108 2.217 0.2951
## 2 - 6 0.50733 0.18 108 2.826 0.0795
## 3 - 4 -0.13567 0.18 108 -0.756 0.9885
## 3 - 5 0.40767 0.18 108 2.271 0.2677
## 3 - 6 0.51700 0.18 108 2.880 0.0694
## 4 - 5 0.54333 0.18 108 3.027 0.0471
## 4 - 6 0.65267 0.18 108 3.636 0.0076
## 5 - 6 0.10933 0.18 108 0.609 0.9964
##
## P value adjustment: tukey method for comparing a family of 7 estimates
emm_gen_diam2_trend <- emmeans(res.aov.error, pairwise ~ diam2*gen | curva)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2_trend
## $emmeans
## curva = T3:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.797 0.134 119 0.5316 1.062
## 1 CCN51 0.957 0.134 119 0.6916 1.222
## 2 CCN51 0.220 0.134 119 -0.0451 0.485
## 3 CCN51 0.287 0.134 119 0.0216 0.552
## 4 CCN51 0.400 0.134 119 0.1349 0.665
## 5 CCN51 0.363 0.134 119 0.0983 0.628
## 6 CCN51 0.117 0.134 119 -0.1484 0.382
## 0 ICS95 0.950 0.134 119 0.6849 1.215
## 1 ICS95 1.060 0.134 119 0.7949 1.325
## 2 ICS95 0.370 0.134 119 0.1049 0.635
## 3 ICS95 0.993 0.134 119 0.7283 1.258
## 4 ICS95 0.873 0.134 119 0.6083 1.138
## 5 ICS95 0.443 0.134 119 0.1783 0.708
## 6 ICS95 0.170 0.134 119 -0.0951 0.435
## 0 TCS01 0.727 0.134 119 0.4616 0.992
## 1 TCS01 0.893 0.134 119 0.6283 1.158
## 2 TCS01 0.777 0.134 119 0.5116 1.042
## 3 TCS01 2.133 0.134 119 1.8683 2.398
## 4 TCS01 2.127 0.134 119 1.8616 2.392
## 5 TCS01 1.897 0.134 119 1.6316 2.162
## 6 TCS01 1.033 0.134 119 0.7683 1.298
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.637 0.134 119 0.3716 0.902
## 1 CCN51 1.150 0.134 119 0.8849 1.415
## 2 CCN51 1.013 0.134 119 0.7483 1.278
## 3 CCN51 1.470 0.134 119 1.2049 1.735
## 4 CCN51 0.807 0.134 119 0.5416 1.072
## 5 CCN51 0.537 0.134 119 0.2716 0.802
## 6 CCN51 0.577 0.134 119 0.3116 0.842
## 0 ICS95 0.697 0.134 119 0.4316 0.962
## 1 ICS95 1.183 0.134 119 0.9183 1.448
## 2 ICS95 1.263 0.134 119 0.9983 1.528
## 3 ICS95 1.147 0.134 119 0.8816 1.412
## 4 ICS95 1.240 0.134 119 0.9749 1.505
## 5 ICS95 0.647 0.134 119 0.3816 0.912
## 6 ICS95 0.400 0.134 119 0.1349 0.665
## 0 TCS01 0.587 0.134 119 0.3216 0.852
## 1 TCS01 0.940 0.134 119 0.6749 1.205
## 2 TCS01 0.700 0.134 119 0.4349 0.965
## 3 TCS01 0.493 0.134 119 0.2283 0.758
## 4 TCS01 0.410 0.134 119 0.1449 0.675
## 5 TCS01 0.400 0.134 119 0.1349 0.665
## 6 TCS01 0.253 0.134 119 -0.0117 0.518
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.881 0.134 119 0.6163 1.146
## 1 CCN51 1.213 0.134 119 0.9476 1.478
## 2 CCN51 2.186 0.134 119 1.9213 2.451
## 3 CCN51 2.964 0.134 119 2.6989 3.229
## 4 CCN51 2.043 0.134 119 1.7779 2.308
## 5 CCN51 1.456 0.134 119 1.1909 1.721
## 6 CCN51 0.744 0.134 119 0.4789 1.009
## 0 ICS95 0.709 0.134 119 0.4443 0.974
## 1 ICS95 1.282 0.134 119 1.0166 1.547
## 2 ICS95 1.430 0.134 119 1.1653 1.695
## 3 ICS95 1.828 0.134 119 1.5633 2.093
## 4 ICS95 1.549 0.134 119 1.2839 1.814
## 5 ICS95 1.260 0.134 119 0.9949 1.525
## 6 ICS95 0.693 0.134 119 0.4279 0.958
## 0 TCS01 0.729 0.134 119 0.4636 0.994
## 1 TCS01 1.122 0.134 119 0.8566 1.387
## 2 TCS01 0.871 0.134 119 0.6059 1.136
## 3 TCS01 0.881 0.134 119 0.6156 1.146
## 4 TCS01 1.016 0.134 119 0.7513 1.281
## 5 TCS01 0.473 0.134 119 0.2079 0.738
## 6 TCS01 0.364 0.134 119 0.0986 0.629
##
## 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 -0.160000 0.180 108 -0.891 1.0000
## 0 CCN51 - 2 CCN51 0.576667 0.180 108 3.213 0.1663
## 0 CCN51 - 3 CCN51 0.510000 0.180 108 2.841 0.3634
## 0 CCN51 - 4 CCN51 0.396667 0.180 108 2.210 0.8075
## 0 CCN51 - 5 CCN51 0.433333 0.180 108 2.414 0.6730
## 0 CCN51 - 6 CCN51 0.680000 0.180 108 3.788 0.0340
## 0 CCN51 - 0 ICS95 -0.153333 0.189 119 -0.810 1.0000
## 0 CCN51 - 1 ICS95 -0.263333 0.189 119 -1.391 0.9983
## 0 CCN51 - 2 ICS95 0.426667 0.189 119 2.254 0.7817
## 0 CCN51 - 3 ICS95 -0.196667 0.189 119 -1.039 1.0000
## 0 CCN51 - 4 ICS95 -0.076667 0.189 119 -0.405 1.0000
## 0 CCN51 - 5 ICS95 0.353333 0.189 119 1.866 0.9507
## 0 CCN51 - 6 ICS95 0.626667 0.189 119 3.310 0.1291
## 0 CCN51 - 0 TCS01 0.070000 0.189 119 0.370 1.0000
## 0 CCN51 - 1 TCS01 -0.096667 0.189 119 -0.511 1.0000
## 0 CCN51 - 2 TCS01 0.020000 0.189 119 0.106 1.0000
## 0 CCN51 - 3 TCS01 -1.336667 0.189 119 -7.061 <.0001
## 0 CCN51 - 4 TCS01 -1.330000 0.189 119 -7.025 <.0001
## 0 CCN51 - 5 TCS01 -1.100000 0.189 119 -5.811 <.0001
## 0 CCN51 - 6 TCS01 -0.236667 0.189 119 -1.250 0.9996
## 1 CCN51 - 2 CCN51 0.736667 0.180 108 4.104 0.0121
## 1 CCN51 - 3 CCN51 0.670000 0.180 108 3.733 0.0403
## 1 CCN51 - 4 CCN51 0.556667 0.180 108 3.101 0.2149
## 1 CCN51 - 5 CCN51 0.593333 0.180 108 3.305 0.1324
## 1 CCN51 - 6 CCN51 0.840000 0.180 108 4.680 0.0015
## 1 CCN51 - 0 ICS95 0.006667 0.189 119 0.035 1.0000
## 1 CCN51 - 1 ICS95 -0.103333 0.189 119 -0.546 1.0000
## 1 CCN51 - 2 ICS95 0.586667 0.189 119 3.099 0.2142
## 1 CCN51 - 3 ICS95 -0.036667 0.189 119 -0.194 1.0000
## 1 CCN51 - 4 ICS95 0.083333 0.189 119 0.440 1.0000
## 1 CCN51 - 5 ICS95 0.513333 0.189 119 2.712 0.4522
## 1 CCN51 - 6 ICS95 0.786667 0.189 119 4.155 0.0097
## 1 CCN51 - 0 TCS01 0.230000 0.189 119 1.215 0.9997
## 1 CCN51 - 1 TCS01 0.063333 0.189 119 0.335 1.0000
## 1 CCN51 - 2 TCS01 0.180000 0.189 119 0.951 1.0000
## 1 CCN51 - 3 TCS01 -1.176667 0.189 119 -6.215 <.0001
## 1 CCN51 - 4 TCS01 -1.170000 0.189 119 -6.180 <.0001
## 1 CCN51 - 5 TCS01 -0.940000 0.189 119 -4.965 0.0004
## 1 CCN51 - 6 TCS01 -0.076667 0.189 119 -0.405 1.0000
## 2 CCN51 - 3 CCN51 -0.066667 0.180 108 -0.371 1.0000
## 2 CCN51 - 4 CCN51 -0.180000 0.180 108 -1.003 1.0000
## 2 CCN51 - 5 CCN51 -0.143333 0.180 108 -0.799 1.0000
## 2 CCN51 - 6 CCN51 0.103333 0.180 108 0.576 1.0000
## 2 CCN51 - 0 ICS95 -0.730000 0.189 119 -3.856 0.0266
## 2 CCN51 - 1 ICS95 -0.840000 0.189 119 -4.437 0.0035
## 2 CCN51 - 2 ICS95 -0.150000 0.189 119 -0.792 1.0000
## 2 CCN51 - 3 ICS95 -0.773333 0.189 119 -4.085 0.0124
## 2 CCN51 - 4 ICS95 -0.653333 0.189 119 -3.451 0.0889
## 2 CCN51 - 5 ICS95 -0.223333 0.189 119 -1.180 0.9998
## 2 CCN51 - 6 ICS95 0.050000 0.189 119 0.264 1.0000
## 2 CCN51 - 0 TCS01 -0.506667 0.189 119 -2.676 0.4779
## 2 CCN51 - 1 TCS01 -0.673333 0.189 119 -3.557 0.0661
## 2 CCN51 - 2 TCS01 -0.556667 0.189 119 -2.940 0.2996
## 2 CCN51 - 3 TCS01 -1.913333 0.189 119 -10.107 <.0001
## 2 CCN51 - 4 TCS01 -1.906667 0.189 119 -10.072 <.0001
## 2 CCN51 - 5 TCS01 -1.676667 0.189 119 -8.857 <.0001
## 2 CCN51 - 6 TCS01 -0.813333 0.189 119 -4.296 0.0059
## 3 CCN51 - 4 CCN51 -0.113333 0.180 108 -0.631 1.0000
## 3 CCN51 - 5 CCN51 -0.076667 0.180 108 -0.427 1.0000
## 3 CCN51 - 6 CCN51 0.170000 0.180 108 0.947 1.0000
## 3 CCN51 - 0 ICS95 -0.663333 0.189 119 -3.504 0.0768
## 3 CCN51 - 1 ICS95 -0.773333 0.189 119 -4.085 0.0124
## 3 CCN51 - 2 ICS95 -0.083333 0.189 119 -0.440 1.0000
## 3 CCN51 - 3 ICS95 -0.706667 0.189 119 -3.733 0.0392
## 3 CCN51 - 4 ICS95 -0.586667 0.189 119 -3.099 0.2142
## 3 CCN51 - 5 ICS95 -0.156667 0.189 119 -0.828 1.0000
## 3 CCN51 - 6 ICS95 0.116667 0.189 119 0.616 1.0000
## 3 CCN51 - 0 TCS01 -0.440000 0.189 119 -2.324 0.7360
## 3 CCN51 - 1 TCS01 -0.606667 0.189 119 -3.205 0.1676
## 3 CCN51 - 2 TCS01 -0.490000 0.189 119 -2.588 0.5435
## 3 CCN51 - 3 TCS01 -1.846667 0.189 119 -9.755 <.0001
## 3 CCN51 - 4 TCS01 -1.840000 0.189 119 -9.719 <.0001
## 3 CCN51 - 5 TCS01 -1.610000 0.189 119 -8.504 <.0001
## 3 CCN51 - 6 TCS01 -0.746667 0.189 119 -3.944 0.0199
## 4 CCN51 - 5 CCN51 0.036667 0.180 108 0.204 1.0000
## 4 CCN51 - 6 CCN51 0.283333 0.180 108 1.578 0.9914
## 4 CCN51 - 0 ICS95 -0.550000 0.189 119 -2.905 0.3210
## 4 CCN51 - 1 ICS95 -0.660000 0.189 119 -3.486 0.0807
## 4 CCN51 - 2 ICS95 0.030000 0.189 119 0.158 1.0000
## 4 CCN51 - 3 ICS95 -0.593333 0.189 119 -3.134 0.1978
## 4 CCN51 - 4 ICS95 -0.473333 0.189 119 -2.500 0.6097
## 4 CCN51 - 5 ICS95 -0.043333 0.189 119 -0.229 1.0000
## 4 CCN51 - 6 ICS95 0.230000 0.189 119 1.215 0.9997
## 4 CCN51 - 0 TCS01 -0.326667 0.189 119 -1.726 0.9774
## 4 CCN51 - 1 TCS01 -0.493333 0.189 119 -2.606 0.5303
## 4 CCN51 - 2 TCS01 -0.376667 0.189 119 -1.990 0.9132
## 4 CCN51 - 3 TCS01 -1.733333 0.189 119 -9.156 <.0001
## 4 CCN51 - 4 TCS01 -1.726667 0.189 119 -9.121 <.0001
## 4 CCN51 - 5 TCS01 -1.496667 0.189 119 -7.906 <.0001
## 4 CCN51 - 6 TCS01 -0.633333 0.189 119 -3.345 0.1179
## 5 CCN51 - 6 CCN51 0.246667 0.180 108 1.374 0.9985
## 5 CCN51 - 0 ICS95 -0.586667 0.189 119 -3.099 0.2142
## 5 CCN51 - 1 ICS95 -0.696667 0.189 119 -3.680 0.0460
## 5 CCN51 - 2 ICS95 -0.006667 0.189 119 -0.035 1.0000
## 5 CCN51 - 3 ICS95 -0.630000 0.189 119 -3.328 0.1234
## 5 CCN51 - 4 ICS95 -0.510000 0.189 119 -2.694 0.4650
## 5 CCN51 - 5 ICS95 -0.080000 0.189 119 -0.423 1.0000
## 5 CCN51 - 6 ICS95 0.193333 0.189 119 1.021 1.0000
## 5 CCN51 - 0 TCS01 -0.363333 0.189 119 -1.919 0.9364
## 5 CCN51 - 1 TCS01 -0.530000 0.189 119 -2.800 0.3900
## 5 CCN51 - 2 TCS01 -0.413333 0.189 119 -2.183 0.8233
## 5 CCN51 - 3 TCS01 -1.770000 0.189 119 -9.350 <.0001
## 5 CCN51 - 4 TCS01 -1.763333 0.189 119 -9.314 <.0001
## 5 CCN51 - 5 TCS01 -1.533333 0.189 119 -8.100 <.0001
## 5 CCN51 - 6 TCS01 -0.670000 0.189 119 -3.539 0.0695
## 6 CCN51 - 0 ICS95 -0.833333 0.189 119 -4.402 0.0040
## 6 CCN51 - 1 ICS95 -0.943333 0.189 119 -4.983 0.0004
## 6 CCN51 - 2 ICS95 -0.253333 0.189 119 -1.338 0.9990
## 6 CCN51 - 3 ICS95 -0.876667 0.189 119 -4.631 0.0017
## 6 CCN51 - 4 ICS95 -0.756667 0.189 119 -3.997 0.0167
## 6 CCN51 - 5 ICS95 -0.326667 0.189 119 -1.726 0.9774
## 6 CCN51 - 6 ICS95 -0.053333 0.189 119 -0.282 1.0000
## 6 CCN51 - 0 TCS01 -0.610000 0.189 119 -3.222 0.1607
## 6 CCN51 - 1 TCS01 -0.776667 0.189 119 -4.103 0.0117
## 6 CCN51 - 2 TCS01 -0.660000 0.189 119 -3.486 0.0807
## 6 CCN51 - 3 TCS01 -2.016667 0.189 119 -10.653 <.0001
## 6 CCN51 - 4 TCS01 -2.010000 0.189 119 -10.617 <.0001
## 6 CCN51 - 5 TCS01 -1.780000 0.189 119 -9.402 <.0001
## 6 CCN51 - 6 TCS01 -0.916667 0.189 119 -4.842 0.0007
## 0 ICS95 - 1 ICS95 -0.110000 0.180 108 -0.613 1.0000
## 0 ICS95 - 2 ICS95 0.580000 0.180 108 3.231 0.1590
## 0 ICS95 - 3 ICS95 -0.043333 0.180 108 -0.241 1.0000
## 0 ICS95 - 4 ICS95 0.076667 0.180 108 0.427 1.0000
## 0 ICS95 - 5 ICS95 0.506667 0.180 108 2.823 0.3757
## 0 ICS95 - 6 ICS95 0.780000 0.180 108 4.345 0.0052
## 0 ICS95 - 0 TCS01 0.223333 0.189 119 1.180 0.9998
## 0 ICS95 - 1 TCS01 0.056667 0.189 119 0.299 1.0000
## 0 ICS95 - 2 TCS01 0.173333 0.189 119 0.916 1.0000
## 0 ICS95 - 3 TCS01 -1.183333 0.189 119 -6.251 <.0001
## 0 ICS95 - 4 TCS01 -1.176667 0.189 119 -6.215 <.0001
## 0 ICS95 - 5 TCS01 -0.946667 0.189 119 -5.001 0.0004
## 0 ICS95 - 6 TCS01 -0.083333 0.189 119 -0.440 1.0000
## 1 ICS95 - 2 ICS95 0.690000 0.180 108 3.844 0.0285
## 1 ICS95 - 3 ICS95 0.066667 0.180 108 0.371 1.0000
## 1 ICS95 - 4 ICS95 0.186667 0.180 108 1.040 1.0000
## 1 ICS95 - 5 ICS95 0.616667 0.180 108 3.435 0.0944
## 1 ICS95 - 6 ICS95 0.890000 0.180 108 4.958 0.0005
## 1 ICS95 - 0 TCS01 0.333333 0.189 119 1.761 0.9721
## 1 ICS95 - 1 TCS01 0.166667 0.189 119 0.880 1.0000
## 1 ICS95 - 2 TCS01 0.283333 0.189 119 1.497 0.9956
## 1 ICS95 - 3 TCS01 -1.073333 0.189 119 -5.670 <.0001
## 1 ICS95 - 4 TCS01 -1.066667 0.189 119 -5.634 <.0001
## 1 ICS95 - 5 TCS01 -0.836667 0.189 119 -4.420 0.0037
## 1 ICS95 - 6 TCS01 0.026667 0.189 119 0.141 1.0000
## 2 ICS95 - 3 ICS95 -0.623333 0.180 108 -3.473 0.0854
## 2 ICS95 - 4 ICS95 -0.503333 0.180 108 -2.804 0.3883
## 2 ICS95 - 5 ICS95 -0.073333 0.180 108 -0.409 1.0000
## 2 ICS95 - 6 ICS95 0.200000 0.180 108 1.114 0.9999
## 2 ICS95 - 0 TCS01 -0.356667 0.189 119 -1.884 0.9463
## 2 ICS95 - 1 TCS01 -0.523333 0.189 119 -2.764 0.4145
## 2 ICS95 - 2 TCS01 -0.406667 0.189 119 -2.148 0.8425
## 2 ICS95 - 3 TCS01 -1.763333 0.189 119 -9.314 <.0001
## 2 ICS95 - 4 TCS01 -1.756667 0.189 119 -9.279 <.0001
## 2 ICS95 - 5 TCS01 -1.526667 0.189 119 -8.064 <.0001
## 2 ICS95 - 6 TCS01 -0.663333 0.189 119 -3.504 0.0768
## 3 ICS95 - 4 ICS95 0.120000 0.180 108 0.669 1.0000
## 3 ICS95 - 5 ICS95 0.550000 0.180 108 3.064 0.2332
## 3 ICS95 - 6 ICS95 0.823333 0.180 108 4.587 0.0021
## 3 ICS95 - 0 TCS01 0.266667 0.189 119 1.409 0.9979
## 3 ICS95 - 1 TCS01 0.100000 0.189 119 0.528 1.0000
## 3 ICS95 - 2 TCS01 0.216667 0.189 119 1.144 0.9999
## 3 ICS95 - 3 TCS01 -1.140000 0.189 119 -6.022 <.0001
## 3 ICS95 - 4 TCS01 -1.133333 0.189 119 -5.987 <.0001
## 3 ICS95 - 5 TCS01 -0.903333 0.189 119 -4.772 0.0010
## 3 ICS95 - 6 TCS01 -0.040000 0.189 119 -0.211 1.0000
## 4 ICS95 - 5 ICS95 0.430000 0.180 108 2.396 0.6863
## 4 ICS95 - 6 ICS95 0.703333 0.180 108 3.918 0.0225
## 4 ICS95 - 0 TCS01 0.146667 0.189 119 0.775 1.0000
## 4 ICS95 - 1 TCS01 -0.020000 0.189 119 -0.106 1.0000
## 4 ICS95 - 2 TCS01 0.096667 0.189 119 0.511 1.0000
## 4 ICS95 - 3 TCS01 -1.260000 0.189 119 -6.656 <.0001
## 4 ICS95 - 4 TCS01 -1.253333 0.189 119 -6.620 <.0001
## 4 ICS95 - 5 TCS01 -1.023333 0.189 119 -5.406 0.0001
## 4 ICS95 - 6 TCS01 -0.160000 0.189 119 -0.845 1.0000
## 5 ICS95 - 6 ICS95 0.273333 0.180 108 1.523 0.9944
## 5 ICS95 - 0 TCS01 -0.283333 0.189 119 -1.497 0.9956
## 5 ICS95 - 1 TCS01 -0.450000 0.189 119 -2.377 0.6996
## 5 ICS95 - 2 TCS01 -0.333333 0.189 119 -1.761 0.9721
## 5 ICS95 - 3 TCS01 -1.690000 0.189 119 -8.927 <.0001
## 5 ICS95 - 4 TCS01 -1.683333 0.189 119 -8.892 <.0001
## 5 ICS95 - 5 TCS01 -1.453333 0.189 119 -7.677 <.0001
## 5 ICS95 - 6 TCS01 -0.590000 0.189 119 -3.117 0.2059
## 6 ICS95 - 0 TCS01 -0.556667 0.189 119 -2.940 0.2996
## 6 ICS95 - 1 TCS01 -0.723333 0.189 119 -3.821 0.0297
## 6 ICS95 - 2 TCS01 -0.606667 0.189 119 -3.205 0.1676
## 6 ICS95 - 3 TCS01 -1.963333 0.189 119 -10.371 <.0001
## 6 ICS95 - 4 TCS01 -1.956667 0.189 119 -10.336 <.0001
## 6 ICS95 - 5 TCS01 -1.726667 0.189 119 -9.121 <.0001
## 6 ICS95 - 6 TCS01 -0.863333 0.189 119 -4.560 0.0022
## 0 TCS01 - 1 TCS01 -0.166667 0.180 108 -0.928 1.0000
## 0 TCS01 - 2 TCS01 -0.050000 0.180 108 -0.279 1.0000
## 0 TCS01 - 3 TCS01 -1.406667 0.180 108 -7.836 <.0001
## 0 TCS01 - 4 TCS01 -1.400000 0.180 108 -7.799 <.0001
## 0 TCS01 - 5 TCS01 -1.170000 0.180 108 -6.518 <.0001
## 0 TCS01 - 6 TCS01 -0.306667 0.180 108 -1.708 0.9793
## 1 TCS01 - 2 TCS01 0.116667 0.180 108 0.650 1.0000
## 1 TCS01 - 3 TCS01 -1.240000 0.180 108 -6.908 <.0001
## 1 TCS01 - 4 TCS01 -1.233333 0.180 108 -6.871 <.0001
## 1 TCS01 - 5 TCS01 -1.003333 0.180 108 -5.590 <.0001
## 1 TCS01 - 6 TCS01 -0.140000 0.180 108 -0.780 1.0000
## 2 TCS01 - 3 TCS01 -1.356667 0.180 108 -7.558 <.0001
## 2 TCS01 - 4 TCS01 -1.350000 0.180 108 -7.521 <.0001
## 2 TCS01 - 5 TCS01 -1.120000 0.180 108 -6.239 <.0001
## 2 TCS01 - 6 TCS01 -0.256667 0.180 108 -1.430 0.9974
## 3 TCS01 - 4 TCS01 0.006667 0.180 108 0.037 1.0000
## 3 TCS01 - 5 TCS01 0.236667 0.180 108 1.318 0.9991
## 3 TCS01 - 6 TCS01 1.100000 0.180 108 6.128 <.0001
## 4 TCS01 - 5 TCS01 0.230000 0.180 108 1.281 0.9994
## 4 TCS01 - 6 TCS01 1.093333 0.180 108 6.091 <.0001
## 5 TCS01 - 6 TCS01 0.863333 0.180 108 4.810 0.0009
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -0.513333 0.180 108 -2.860 0.3513
## 0 CCN51 - 2 CCN51 -0.376667 0.180 108 -2.098 0.8667
## 0 CCN51 - 3 CCN51 -0.833333 0.180 108 -4.642 0.0017
## 0 CCN51 - 4 CCN51 -0.170000 0.180 108 -0.947 1.0000
## 0 CCN51 - 5 CCN51 0.100000 0.180 108 0.557 1.0000
## 0 CCN51 - 6 CCN51 0.060000 0.180 108 0.334 1.0000
## 0 CCN51 - 0 ICS95 -0.060000 0.189 119 -0.317 1.0000
## 0 CCN51 - 1 ICS95 -0.546667 0.189 119 -2.888 0.3320
## 0 CCN51 - 2 ICS95 -0.626667 0.189 119 -3.310 0.1291
## 0 CCN51 - 3 ICS95 -0.510000 0.189 119 -2.694 0.4650
## 0 CCN51 - 4 ICS95 -0.603333 0.189 119 -3.187 0.1748
## 0 CCN51 - 5 ICS95 -0.010000 0.189 119 -0.053 1.0000
## 0 CCN51 - 6 ICS95 0.236667 0.189 119 1.250 0.9996
## 0 CCN51 - 0 TCS01 0.050000 0.189 119 0.264 1.0000
## 0 CCN51 - 1 TCS01 -0.303333 0.189 119 -1.602 0.9900
## 0 CCN51 - 2 TCS01 -0.063333 0.189 119 -0.335 1.0000
## 0 CCN51 - 3 TCS01 0.143333 0.189 119 0.757 1.0000
## 0 CCN51 - 4 TCS01 0.226667 0.189 119 1.197 0.9998
## 0 CCN51 - 5 TCS01 0.236667 0.189 119 1.250 0.9996
## 0 CCN51 - 6 TCS01 0.383333 0.189 119 2.025 0.8997
## 1 CCN51 - 2 CCN51 0.136667 0.180 108 0.761 1.0000
## 1 CCN51 - 3 CCN51 -0.320000 0.180 108 -1.783 0.9680
## 1 CCN51 - 4 CCN51 0.343333 0.180 108 1.913 0.9377
## 1 CCN51 - 5 CCN51 0.613333 0.180 108 3.417 0.0992
## 1 CCN51 - 6 CCN51 0.573333 0.180 108 3.194 0.1738
## 1 CCN51 - 0 ICS95 0.453333 0.189 119 2.395 0.6871
## 1 CCN51 - 1 ICS95 -0.033333 0.189 119 -0.176 1.0000
## 1 CCN51 - 2 ICS95 -0.113333 0.189 119 -0.599 1.0000
## 1 CCN51 - 3 ICS95 0.003333 0.189 119 0.018 1.0000
## 1 CCN51 - 4 ICS95 -0.090000 0.189 119 -0.475 1.0000
## 1 CCN51 - 5 ICS95 0.503333 0.189 119 2.659 0.4909
## 1 CCN51 - 6 ICS95 0.750000 0.189 119 3.962 0.0188
## 1 CCN51 - 0 TCS01 0.563333 0.189 119 2.976 0.2790
## 1 CCN51 - 1 TCS01 0.210000 0.189 119 1.109 0.9999
## 1 CCN51 - 2 TCS01 0.450000 0.189 119 2.377 0.6996
## 1 CCN51 - 3 TCS01 0.656667 0.189 119 3.469 0.0847
## 1 CCN51 - 4 TCS01 0.740000 0.189 119 3.909 0.0224
## 1 CCN51 - 5 TCS01 0.750000 0.189 119 3.962 0.0188
## 1 CCN51 - 6 TCS01 0.896667 0.189 119 4.736 0.0011
## 2 CCN51 - 3 CCN51 -0.456667 0.180 108 -2.544 0.5771
## 2 CCN51 - 4 CCN51 0.206667 0.180 108 1.151 0.9999
## 2 CCN51 - 5 CCN51 0.476667 0.180 108 2.655 0.4940
## 2 CCN51 - 6 CCN51 0.436667 0.180 108 2.433 0.6596
## 2 CCN51 - 0 ICS95 0.316667 0.189 119 1.673 0.9838
## 2 CCN51 - 1 ICS95 -0.170000 0.189 119 -0.898 1.0000
## 2 CCN51 - 2 ICS95 -0.250000 0.189 119 -1.321 0.9991
## 2 CCN51 - 3 ICS95 -0.133333 0.189 119 -0.704 1.0000
## 2 CCN51 - 4 ICS95 -0.226667 0.189 119 -1.197 0.9998
## 2 CCN51 - 5 ICS95 0.366667 0.189 119 1.937 0.9311
## 2 CCN51 - 6 ICS95 0.613333 0.189 119 3.240 0.1539
## 2 CCN51 - 0 TCS01 0.426667 0.189 119 2.254 0.7817
## 2 CCN51 - 1 TCS01 0.073333 0.189 119 0.387 1.0000
## 2 CCN51 - 2 TCS01 0.313333 0.189 119 1.655 0.9855
## 2 CCN51 - 3 TCS01 0.520000 0.189 119 2.747 0.4269
## 2 CCN51 - 4 TCS01 0.603333 0.189 119 3.187 0.1748
## 2 CCN51 - 5 TCS01 0.613333 0.189 119 3.240 0.1539
## 2 CCN51 - 6 TCS01 0.760000 0.189 119 4.015 0.0158
## 3 CCN51 - 4 CCN51 0.663333 0.180 108 3.695 0.0451
## 3 CCN51 - 5 CCN51 0.933333 0.180 108 5.200 0.0002
## 3 CCN51 - 6 CCN51 0.893333 0.180 108 4.977 0.0005
## 3 CCN51 - 0 ICS95 0.773333 0.189 119 4.085 0.0124
## 3 CCN51 - 1 ICS95 0.286667 0.189 119 1.514 0.9949
## 3 CCN51 - 2 ICS95 0.206667 0.189 119 1.092 0.9999
## 3 CCN51 - 3 ICS95 0.323333 0.189 119 1.708 0.9797
## 3 CCN51 - 4 ICS95 0.230000 0.189 119 1.215 0.9997
## 3 CCN51 - 5 ICS95 0.823333 0.189 119 4.349 0.0048
## 3 CCN51 - 6 ICS95 1.070000 0.189 119 5.652 <.0001
## 3 CCN51 - 0 TCS01 0.883333 0.189 119 4.666 0.0014
## 3 CCN51 - 1 TCS01 0.530000 0.189 119 2.800 0.3900
## 3 CCN51 - 2 TCS01 0.770000 0.189 119 4.067 0.0132
## 3 CCN51 - 3 TCS01 0.976667 0.189 119 5.159 0.0002
## 3 CCN51 - 4 TCS01 1.060000 0.189 119 5.599 <.0001
## 3 CCN51 - 5 TCS01 1.070000 0.189 119 5.652 <.0001
## 3 CCN51 - 6 TCS01 1.216667 0.189 119 6.427 <.0001
## 4 CCN51 - 5 CCN51 0.270000 0.180 108 1.504 0.9952
## 4 CCN51 - 6 CCN51 0.230000 0.180 108 1.281 0.9994
## 4 CCN51 - 0 ICS95 0.110000 0.189 119 0.581 1.0000
## 4 CCN51 - 1 ICS95 -0.376667 0.189 119 -1.990 0.9132
## 4 CCN51 - 2 ICS95 -0.456667 0.189 119 -2.412 0.6745
## 4 CCN51 - 3 ICS95 -0.340000 0.189 119 -1.796 0.9660
## 4 CCN51 - 4 ICS95 -0.433333 0.189 119 -2.289 0.7593
## 4 CCN51 - 5 ICS95 0.160000 0.189 119 0.845 1.0000
## 4 CCN51 - 6 ICS95 0.406667 0.189 119 2.148 0.8425
## 4 CCN51 - 0 TCS01 0.220000 0.189 119 1.162 0.9999
## 4 CCN51 - 1 TCS01 -0.133333 0.189 119 -0.704 1.0000
## 4 CCN51 - 2 TCS01 0.106667 0.189 119 0.563 1.0000
## 4 CCN51 - 3 TCS01 0.313333 0.189 119 1.655 0.9855
## 4 CCN51 - 4 TCS01 0.396667 0.189 119 2.095 0.8689
## 4 CCN51 - 5 TCS01 0.406667 0.189 119 2.148 0.8425
## 4 CCN51 - 6 TCS01 0.553333 0.189 119 2.923 0.3102
## 5 CCN51 - 6 CCN51 -0.040000 0.180 108 -0.223 1.0000
## 5 CCN51 - 0 ICS95 -0.160000 0.189 119 -0.845 1.0000
## 5 CCN51 - 1 ICS95 -0.646667 0.189 119 -3.416 0.0979
## 5 CCN51 - 2 ICS95 -0.726667 0.189 119 -3.838 0.0281
## 5 CCN51 - 3 ICS95 -0.610000 0.189 119 -3.222 0.1607
## 5 CCN51 - 4 ICS95 -0.703333 0.189 119 -3.715 0.0413
## 5 CCN51 - 5 ICS95 -0.110000 0.189 119 -0.581 1.0000
## 5 CCN51 - 6 ICS95 0.136667 0.189 119 0.722 1.0000
## 5 CCN51 - 0 TCS01 -0.050000 0.189 119 -0.264 1.0000
## 5 CCN51 - 1 TCS01 -0.403333 0.189 119 -2.131 0.8516
## 5 CCN51 - 2 TCS01 -0.163333 0.189 119 -0.863 1.0000
## 5 CCN51 - 3 TCS01 0.043333 0.189 119 0.229 1.0000
## 5 CCN51 - 4 TCS01 0.126667 0.189 119 0.669 1.0000
## 5 CCN51 - 5 TCS01 0.136667 0.189 119 0.722 1.0000
## 5 CCN51 - 6 TCS01 0.283333 0.189 119 1.497 0.9956
## 6 CCN51 - 0 ICS95 -0.120000 0.189 119 -0.634 1.0000
## 6 CCN51 - 1 ICS95 -0.606667 0.189 119 -3.205 0.1676
## 6 CCN51 - 2 ICS95 -0.686667 0.189 119 -3.627 0.0539
## 6 CCN51 - 3 ICS95 -0.570000 0.189 119 -3.011 0.2594
## 6 CCN51 - 4 ICS95 -0.663333 0.189 119 -3.504 0.0768
## 6 CCN51 - 5 ICS95 -0.070000 0.189 119 -0.370 1.0000
## 6 CCN51 - 6 ICS95 0.176667 0.189 119 0.933 1.0000
## 6 CCN51 - 0 TCS01 -0.010000 0.189 119 -0.053 1.0000
## 6 CCN51 - 1 TCS01 -0.363333 0.189 119 -1.919 0.9364
## 6 CCN51 - 2 TCS01 -0.123333 0.189 119 -0.651 1.0000
## 6 CCN51 - 3 TCS01 0.083333 0.189 119 0.440 1.0000
## 6 CCN51 - 4 TCS01 0.166667 0.189 119 0.880 1.0000
## 6 CCN51 - 5 TCS01 0.176667 0.189 119 0.933 1.0000
## 6 CCN51 - 6 TCS01 0.323333 0.189 119 1.708 0.9797
## 0 ICS95 - 1 ICS95 -0.486667 0.180 108 -2.711 0.4534
## 0 ICS95 - 2 ICS95 -0.566667 0.180 108 -3.157 0.1895
## 0 ICS95 - 3 ICS95 -0.450000 0.180 108 -2.507 0.6049
## 0 ICS95 - 4 ICS95 -0.543333 0.180 108 -3.027 0.2525
## 0 ICS95 - 5 ICS95 0.050000 0.180 108 0.279 1.0000
## 0 ICS95 - 6 ICS95 0.296667 0.180 108 1.653 0.9855
## 0 ICS95 - 0 TCS01 0.110000 0.189 119 0.581 1.0000
## 0 ICS95 - 1 TCS01 -0.243333 0.189 119 -1.285 0.9994
## 0 ICS95 - 2 TCS01 -0.003333 0.189 119 -0.018 1.0000
## 0 ICS95 - 3 TCS01 0.203333 0.189 119 1.074 1.0000
## 0 ICS95 - 4 TCS01 0.286667 0.189 119 1.514 0.9949
## 0 ICS95 - 5 TCS01 0.296667 0.189 119 1.567 0.9923
## 0 ICS95 - 6 TCS01 0.443333 0.189 119 2.342 0.7241
## 1 ICS95 - 2 ICS95 -0.080000 0.180 108 -0.446 1.0000
## 1 ICS95 - 3 ICS95 0.036667 0.180 108 0.204 1.0000
## 1 ICS95 - 4 ICS95 -0.056667 0.180 108 -0.316 1.0000
## 1 ICS95 - 5 ICS95 0.536667 0.180 108 2.990 0.2727
## 1 ICS95 - 6 ICS95 0.783333 0.180 108 4.364 0.0049
## 1 ICS95 - 0 TCS01 0.596667 0.189 119 3.152 0.1899
## 1 ICS95 - 1 TCS01 0.243333 0.189 119 1.285 0.9994
## 1 ICS95 - 2 TCS01 0.483333 0.189 119 2.553 0.5700
## 1 ICS95 - 3 TCS01 0.690000 0.189 119 3.645 0.0511
## 1 ICS95 - 4 TCS01 0.773333 0.189 119 4.085 0.0124
## 1 ICS95 - 5 TCS01 0.783333 0.189 119 4.138 0.0103
## 1 ICS95 - 6 TCS01 0.930000 0.189 119 4.913 0.0005
## 2 ICS95 - 3 ICS95 0.116667 0.180 108 0.650 1.0000
## 2 ICS95 - 4 ICS95 0.023333 0.180 108 0.130 1.0000
## 2 ICS95 - 5 ICS95 0.616667 0.180 108 3.435 0.0944
## 2 ICS95 - 6 ICS95 0.863333 0.180 108 4.810 0.0009
## 2 ICS95 - 0 TCS01 0.676667 0.189 119 3.574 0.0629
## 2 ICS95 - 1 TCS01 0.323333 0.189 119 1.708 0.9797
## 2 ICS95 - 2 TCS01 0.563333 0.189 119 2.976 0.2790
## 2 ICS95 - 3 TCS01 0.770000 0.189 119 4.067 0.0132
## 2 ICS95 - 4 TCS01 0.853333 0.189 119 4.508 0.0027
## 2 ICS95 - 5 TCS01 0.863333 0.189 119 4.560 0.0022
## 2 ICS95 - 6 TCS01 1.010000 0.189 119 5.335 0.0001
## 3 ICS95 - 4 ICS95 -0.093333 0.180 108 -0.520 1.0000
## 3 ICS95 - 5 ICS95 0.500000 0.180 108 2.785 0.4010
## 3 ICS95 - 6 ICS95 0.746667 0.180 108 4.160 0.0100
## 3 ICS95 - 0 TCS01 0.560000 0.189 119 2.958 0.2892
## 3 ICS95 - 1 TCS01 0.206667 0.189 119 1.092 0.9999
## 3 ICS95 - 2 TCS01 0.446667 0.189 119 2.359 0.7119
## 3 ICS95 - 3 TCS01 0.653333 0.189 119 3.451 0.0889
## 3 ICS95 - 4 TCS01 0.736667 0.189 119 3.891 0.0237
## 3 ICS95 - 5 TCS01 0.746667 0.189 119 3.944 0.0199
## 3 ICS95 - 6 TCS01 0.893333 0.189 119 4.719 0.0012
## 4 ICS95 - 5 ICS95 0.593333 0.180 108 3.305 0.1324
## 4 ICS95 - 6 ICS95 0.840000 0.180 108 4.680 0.0015
## 4 ICS95 - 0 TCS01 0.653333 0.189 119 3.451 0.0889
## 4 ICS95 - 1 TCS01 0.300000 0.189 119 1.585 0.9912
## 4 ICS95 - 2 TCS01 0.540000 0.189 119 2.852 0.3547
## 4 ICS95 - 3 TCS01 0.746667 0.189 119 3.944 0.0199
## 4 ICS95 - 4 TCS01 0.830000 0.189 119 4.384 0.0042
## 4 ICS95 - 5 TCS01 0.840000 0.189 119 4.437 0.0035
## 4 ICS95 - 6 TCS01 0.986667 0.189 119 5.212 0.0002
## 5 ICS95 - 6 ICS95 0.246667 0.180 108 1.374 0.9985
## 5 ICS95 - 0 TCS01 0.060000 0.189 119 0.317 1.0000
## 5 ICS95 - 1 TCS01 -0.293333 0.189 119 -1.549 0.9932
## 5 ICS95 - 2 TCS01 -0.053333 0.189 119 -0.282 1.0000
## 5 ICS95 - 3 TCS01 0.153333 0.189 119 0.810 1.0000
## 5 ICS95 - 4 TCS01 0.236667 0.189 119 1.250 0.9996
## 5 ICS95 - 5 TCS01 0.246667 0.189 119 1.303 0.9993
## 5 ICS95 - 6 TCS01 0.393333 0.189 119 2.078 0.8771
## 6 ICS95 - 0 TCS01 -0.186667 0.189 119 -0.986 1.0000
## 6 ICS95 - 1 TCS01 -0.540000 0.189 119 -2.852 0.3547
## 6 ICS95 - 2 TCS01 -0.300000 0.189 119 -1.585 0.9912
## 6 ICS95 - 3 TCS01 -0.093333 0.189 119 -0.493 1.0000
## 6 ICS95 - 4 TCS01 -0.010000 0.189 119 -0.053 1.0000
## 6 ICS95 - 5 TCS01 0.000000 0.189 119 0.000 1.0000
## 6 ICS95 - 6 TCS01 0.146667 0.189 119 0.775 1.0000
## 0 TCS01 - 1 TCS01 -0.353333 0.180 108 -1.968 0.9201
## 0 TCS01 - 2 TCS01 -0.113333 0.180 108 -0.631 1.0000
## 0 TCS01 - 3 TCS01 0.093333 0.180 108 0.520 1.0000
## 0 TCS01 - 4 TCS01 0.176667 0.180 108 0.984 1.0000
## 0 TCS01 - 5 TCS01 0.186667 0.180 108 1.040 1.0000
## 0 TCS01 - 6 TCS01 0.333333 0.180 108 1.857 0.9525
## 1 TCS01 - 2 TCS01 0.240000 0.180 108 1.337 0.9989
## 1 TCS01 - 3 TCS01 0.446667 0.180 108 2.488 0.6187
## 1 TCS01 - 4 TCS01 0.530000 0.180 108 2.953 0.2940
## 1 TCS01 - 5 TCS01 0.540000 0.180 108 3.008 0.2625
## 1 TCS01 - 6 TCS01 0.686667 0.180 108 3.825 0.0302
## 2 TCS01 - 3 TCS01 0.206667 0.180 108 1.151 0.9999
## 2 TCS01 - 4 TCS01 0.290000 0.180 108 1.616 0.9888
## 2 TCS01 - 5 TCS01 0.300000 0.180 108 1.671 0.9837
## 2 TCS01 - 6 TCS01 0.446667 0.180 108 2.488 0.6187
## 3 TCS01 - 4 TCS01 0.083333 0.180 108 0.464 1.0000
## 3 TCS01 - 5 TCS01 0.093333 0.180 108 0.520 1.0000
## 3 TCS01 - 6 TCS01 0.240000 0.180 108 1.337 0.9989
## 4 TCS01 - 5 TCS01 0.010000 0.180 108 0.056 1.0000
## 4 TCS01 - 6 TCS01 0.156667 0.180 108 0.873 1.0000
## 5 TCS01 - 6 TCS01 0.146667 0.180 108 0.817 1.0000
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 1 CCN51 -0.331333 0.180 108 -1.846 0.9551
## 0 CCN51 - 2 CCN51 -1.305000 0.180 108 -7.270 <.0001
## 0 CCN51 - 3 CCN51 -2.082667 0.180 108 -11.602 <.0001
## 0 CCN51 - 4 CCN51 -1.161667 0.180 108 -6.472 <.0001
## 0 CCN51 - 5 CCN51 -0.574667 0.180 108 -3.201 0.1707
## 0 CCN51 - 6 CCN51 0.137333 0.180 108 0.765 1.0000
## 0 CCN51 - 0 ICS95 0.172000 0.189 119 0.909 1.0000
## 0 CCN51 - 1 ICS95 -0.400333 0.189 119 -2.115 0.8595
## 0 CCN51 - 2 ICS95 -0.549000 0.189 119 -2.900 0.3243
## 0 CCN51 - 3 ICS95 -0.947000 0.189 119 -5.002 0.0004
## 0 CCN51 - 4 ICS95 -0.667667 0.189 119 -3.527 0.0720
## 0 CCN51 - 5 ICS95 -0.378667 0.189 119 -2.000 0.9093
## 0 CCN51 - 6 ICS95 0.188333 0.189 119 0.995 1.0000
## 0 CCN51 - 0 TCS01 0.152667 0.189 119 0.806 1.0000
## 0 CCN51 - 1 TCS01 -0.240333 0.189 119 -1.270 0.9995
## 0 CCN51 - 2 TCS01 0.010333 0.189 119 0.055 1.0000
## 0 CCN51 - 3 TCS01 0.000667 0.189 119 0.004 1.0000
## 0 CCN51 - 4 TCS01 -0.135000 0.189 119 -0.713 1.0000
## 0 CCN51 - 5 TCS01 0.408333 0.189 119 2.157 0.8378
## 0 CCN51 - 6 TCS01 0.517667 0.189 119 2.734 0.4357
## 1 CCN51 - 2 CCN51 -0.973667 0.180 108 -5.424 0.0001
## 1 CCN51 - 3 CCN51 -1.751333 0.180 108 -9.757 <.0001
## 1 CCN51 - 4 CCN51 -0.830333 0.180 108 -4.626 0.0018
## 1 CCN51 - 5 CCN51 -0.243333 0.180 108 -1.356 0.9987
## 1 CCN51 - 6 CCN51 0.468667 0.180 108 2.611 0.5271
## 1 CCN51 - 0 ICS95 0.503333 0.189 119 2.659 0.4909
## 1 CCN51 - 1 ICS95 -0.069000 0.189 119 -0.364 1.0000
## 1 CCN51 - 2 ICS95 -0.217667 0.189 119 -1.150 0.9999
## 1 CCN51 - 3 ICS95 -0.615667 0.189 119 -3.252 0.1493
## 1 CCN51 - 4 ICS95 -0.336333 0.189 119 -1.777 0.9695
## 1 CCN51 - 5 ICS95 -0.047333 0.189 119 -0.250 1.0000
## 1 CCN51 - 6 ICS95 0.519667 0.189 119 2.745 0.4281
## 1 CCN51 - 0 TCS01 0.484000 0.189 119 2.557 0.5674
## 1 CCN51 - 1 TCS01 0.091000 0.189 119 0.481 1.0000
## 1 CCN51 - 2 TCS01 0.341667 0.189 119 1.805 0.9643
## 1 CCN51 - 3 TCS01 0.332000 0.189 119 1.754 0.9732
## 1 CCN51 - 4 TCS01 0.196333 0.189 119 1.037 1.0000
## 1 CCN51 - 5 TCS01 0.739667 0.189 119 3.907 0.0225
## 1 CCN51 - 6 TCS01 0.849000 0.189 119 4.485 0.0029
## 2 CCN51 - 3 CCN51 -0.777667 0.180 108 -4.332 0.0054
## 2 CCN51 - 4 CCN51 0.143333 0.180 108 0.799 1.0000
## 2 CCN51 - 5 CCN51 0.730333 0.180 108 4.069 0.0137
## 2 CCN51 - 6 CCN51 1.442333 0.180 108 8.035 <.0001
## 2 CCN51 - 0 ICS95 1.477000 0.189 119 7.802 <.0001
## 2 CCN51 - 1 ICS95 0.904667 0.189 119 4.779 0.0009
## 2 CCN51 - 2 ICS95 0.756000 0.189 119 3.993 0.0169
## 2 CCN51 - 3 ICS95 0.358000 0.189 119 1.891 0.9444
## 2 CCN51 - 4 ICS95 0.637333 0.189 119 3.367 0.1116
## 2 CCN51 - 5 ICS95 0.926333 0.189 119 4.893 0.0006
## 2 CCN51 - 6 ICS95 1.493333 0.189 119 7.888 <.0001
## 2 CCN51 - 0 TCS01 1.457667 0.189 119 7.700 <.0001
## 2 CCN51 - 1 TCS01 1.064667 0.189 119 5.624 <.0001
## 2 CCN51 - 2 TCS01 1.315333 0.189 119 6.948 <.0001
## 2 CCN51 - 3 TCS01 1.305667 0.189 119 6.897 <.0001
## 2 CCN51 - 4 TCS01 1.170000 0.189 119 6.180 <.0001
## 2 CCN51 - 5 TCS01 1.713333 0.189 119 9.050 <.0001
## 2 CCN51 - 6 TCS01 1.822667 0.189 119 9.628 <.0001
## 3 CCN51 - 4 CCN51 0.921000 0.180 108 5.131 0.0002
## 3 CCN51 - 5 CCN51 1.508000 0.180 108 8.401 <.0001
## 3 CCN51 - 6 CCN51 2.220000 0.180 108 12.367 <.0001
## 3 CCN51 - 0 ICS95 2.254667 0.189 119 11.910 <.0001
## 3 CCN51 - 1 ICS95 1.682333 0.189 119 8.887 <.0001
## 3 CCN51 - 2 ICS95 1.533667 0.189 119 8.101 <.0001
## 3 CCN51 - 3 ICS95 1.135667 0.189 119 5.999 <.0001
## 3 CCN51 - 4 ICS95 1.415000 0.189 119 7.474 <.0001
## 3 CCN51 - 5 ICS95 1.704000 0.189 119 9.001 <.0001
## 3 CCN51 - 6 ICS95 2.271000 0.189 119 11.996 <.0001
## 3 CCN51 - 0 TCS01 2.235333 0.189 119 11.808 <.0001
## 3 CCN51 - 1 TCS01 1.842333 0.189 119 9.732 <.0001
## 3 CCN51 - 2 TCS01 2.093000 0.189 119 11.056 <.0001
## 3 CCN51 - 3 TCS01 2.083333 0.189 119 11.005 <.0001
## 3 CCN51 - 4 TCS01 1.947667 0.189 119 10.288 <.0001
## 3 CCN51 - 5 TCS01 2.491000 0.189 119 13.158 <.0001
## 3 CCN51 - 6 TCS01 2.600333 0.189 119 13.736 <.0001
## 4 CCN51 - 5 CCN51 0.587000 0.180 108 3.270 0.1446
## 4 CCN51 - 6 CCN51 1.299000 0.180 108 7.237 <.0001
## 4 CCN51 - 0 ICS95 1.333667 0.189 119 7.045 <.0001
## 4 CCN51 - 1 ICS95 0.761333 0.189 119 4.022 0.0154
## 4 CCN51 - 2 ICS95 0.612667 0.189 119 3.236 0.1552
## 4 CCN51 - 3 ICS95 0.214667 0.189 119 1.134 0.9999
## 4 CCN51 - 4 ICS95 0.494000 0.189 119 2.609 0.5277
## 4 CCN51 - 5 ICS95 0.783000 0.189 119 4.136 0.0104
## 4 CCN51 - 6 ICS95 1.350000 0.189 119 7.131 <.0001
## 4 CCN51 - 0 TCS01 1.314333 0.189 119 6.943 <.0001
## 4 CCN51 - 1 TCS01 0.921333 0.189 119 4.867 0.0006
## 4 CCN51 - 2 TCS01 1.172000 0.189 119 6.191 <.0001
## 4 CCN51 - 3 TCS01 1.162333 0.189 119 6.140 <.0001
## 4 CCN51 - 4 TCS01 1.026667 0.189 119 5.423 0.0001
## 4 CCN51 - 5 TCS01 1.570000 0.189 119 8.293 <.0001
## 4 CCN51 - 6 TCS01 1.679333 0.189 119 8.871 <.0001
## 5 CCN51 - 6 CCN51 0.712000 0.180 108 3.967 0.0192
## 5 CCN51 - 0 ICS95 0.746667 0.189 119 3.944 0.0199
## 5 CCN51 - 1 ICS95 0.174333 0.189 119 0.921 1.0000
## 5 CCN51 - 2 ICS95 0.025667 0.189 119 0.136 1.0000
## 5 CCN51 - 3 ICS95 -0.372333 0.189 119 -1.967 0.9213
## 5 CCN51 - 4 ICS95 -0.093000 0.189 119 -0.491 1.0000
## 5 CCN51 - 5 ICS95 0.196000 0.189 119 1.035 1.0000
## 5 CCN51 - 6 ICS95 0.763000 0.189 119 4.030 0.0149
## 5 CCN51 - 0 TCS01 0.727333 0.189 119 3.842 0.0278
## 5 CCN51 - 1 TCS01 0.334333 0.189 119 1.766 0.9713
## 5 CCN51 - 2 TCS01 0.585000 0.189 119 3.090 0.2185
## 5 CCN51 - 3 TCS01 0.575333 0.189 119 3.039 0.2443
## 5 CCN51 - 4 TCS01 0.439667 0.189 119 2.322 0.7372
## 5 CCN51 - 5 TCS01 0.983000 0.189 119 5.192 0.0002
## 5 CCN51 - 6 TCS01 1.092333 0.189 119 5.770 <.0001
## 6 CCN51 - 0 ICS95 0.034667 0.189 119 0.183 1.0000
## 6 CCN51 - 1 ICS95 -0.537667 0.189 119 -2.840 0.3628
## 6 CCN51 - 2 ICS95 -0.686333 0.189 119 -3.625 0.0541
## 6 CCN51 - 3 ICS95 -1.084333 0.189 119 -5.728 <.0001
## 6 CCN51 - 4 ICS95 -0.805000 0.189 119 -4.252 0.0069
## 6 CCN51 - 5 ICS95 -0.516000 0.189 119 -2.726 0.4420
## 6 CCN51 - 6 ICS95 0.051000 0.189 119 0.269 1.0000
## 6 CCN51 - 0 TCS01 0.015333 0.189 119 0.081 1.0000
## 6 CCN51 - 1 TCS01 -0.377667 0.189 119 -1.995 0.9112
## 6 CCN51 - 2 TCS01 -0.127000 0.189 119 -0.671 1.0000
## 6 CCN51 - 3 TCS01 -0.136667 0.189 119 -0.722 1.0000
## 6 CCN51 - 4 TCS01 -0.272333 0.189 119 -1.439 0.9973
## 6 CCN51 - 5 TCS01 0.271000 0.189 119 1.431 0.9975
## 6 CCN51 - 6 TCS01 0.380333 0.189 119 2.009 0.9059
## 0 ICS95 - 1 ICS95 -0.572333 0.180 108 -3.188 0.1760
## 0 ICS95 - 2 ICS95 -0.721000 0.180 108 -4.017 0.0163
## 0 ICS95 - 3 ICS95 -1.119000 0.180 108 -6.234 <.0001
## 0 ICS95 - 4 ICS95 -0.839667 0.180 108 -4.678 0.0015
## 0 ICS95 - 5 ICS95 -0.550667 0.180 108 -3.068 0.2313
## 0 ICS95 - 6 ICS95 0.016333 0.180 108 0.091 1.0000
## 0 ICS95 - 0 TCS01 -0.019333 0.189 119 -0.102 1.0000
## 0 ICS95 - 1 TCS01 -0.412333 0.189 119 -2.178 0.8263
## 0 ICS95 - 2 TCS01 -0.161667 0.189 119 -0.854 1.0000
## 0 ICS95 - 3 TCS01 -0.171333 0.189 119 -0.905 1.0000
## 0 ICS95 - 4 TCS01 -0.307000 0.189 119 -1.622 0.9885
## 0 ICS95 - 5 TCS01 0.236333 0.189 119 1.248 0.9996
## 0 ICS95 - 6 TCS01 0.345667 0.189 119 1.826 0.9600
## 1 ICS95 - 2 ICS95 -0.148667 0.180 108 -0.828 1.0000
## 1 ICS95 - 3 ICS95 -0.546667 0.180 108 -3.045 0.2427
## 1 ICS95 - 4 ICS95 -0.267333 0.180 108 -1.489 0.9957
## 1 ICS95 - 5 ICS95 0.021667 0.180 108 0.121 1.0000
## 1 ICS95 - 6 ICS95 0.588667 0.180 108 3.279 0.1413
## 1 ICS95 - 0 TCS01 0.553000 0.189 119 2.921 0.3113
## 1 ICS95 - 1 TCS01 0.160000 0.189 119 0.845 1.0000
## 1 ICS95 - 2 TCS01 0.410667 0.189 119 2.169 0.8311
## 1 ICS95 - 3 TCS01 0.401000 0.189 119 2.118 0.8578
## 1 ICS95 - 4 TCS01 0.265333 0.189 119 1.402 0.9981
## 1 ICS95 - 5 TCS01 0.808667 0.189 119 4.272 0.0064
## 1 ICS95 - 6 TCS01 0.918000 0.189 119 4.849 0.0007
## 2 ICS95 - 3 ICS95 -0.398000 0.180 108 -2.217 0.8032
## 2 ICS95 - 4 ICS95 -0.118667 0.180 108 -0.661 1.0000
## 2 ICS95 - 5 ICS95 0.170333 0.180 108 0.949 1.0000
## 2 ICS95 - 6 ICS95 0.737333 0.180 108 4.108 0.0120
## 2 ICS95 - 0 TCS01 0.701667 0.189 119 3.706 0.0425
## 2 ICS95 - 1 TCS01 0.308667 0.189 119 1.630 0.9878
## 2 ICS95 - 2 TCS01 0.559333 0.189 119 2.955 0.2912
## 2 ICS95 - 3 TCS01 0.549667 0.189 119 2.903 0.3221
## 2 ICS95 - 4 TCS01 0.414000 0.189 119 2.187 0.8214
## 2 ICS95 - 5 TCS01 0.957333 0.189 119 5.057 0.0003
## 2 ICS95 - 6 TCS01 1.066667 0.189 119 5.634 <.0001
## 3 ICS95 - 4 ICS95 0.279333 0.180 108 1.556 0.9927
## 3 ICS95 - 5 ICS95 0.568333 0.180 108 3.166 0.1855
## 3 ICS95 - 6 ICS95 1.135333 0.180 108 6.325 <.0001
## 3 ICS95 - 0 TCS01 1.099667 0.189 119 5.809 <.0001
## 3 ICS95 - 1 TCS01 0.706667 0.189 119 3.733 0.0392
## 3 ICS95 - 2 TCS01 0.957333 0.189 119 5.057 0.0003
## 3 ICS95 - 3 TCS01 0.947667 0.189 119 5.006 0.0004
## 3 ICS95 - 4 TCS01 0.812000 0.189 119 4.289 0.0060
## 3 ICS95 - 5 TCS01 1.355333 0.189 119 7.159 <.0001
## 3 ICS95 - 6 TCS01 1.464667 0.189 119 7.737 <.0001
## 4 ICS95 - 5 ICS95 0.289000 0.180 108 1.610 0.9892
## 4 ICS95 - 6 ICS95 0.856000 0.180 108 4.769 0.0010
## 4 ICS95 - 0 TCS01 0.820333 0.189 119 4.333 0.0051
## 4 ICS95 - 1 TCS01 0.427333 0.189 119 2.257 0.7795
## 4 ICS95 - 2 TCS01 0.678000 0.189 119 3.581 0.0616
## 4 ICS95 - 3 TCS01 0.668333 0.189 119 3.530 0.0713
## 4 ICS95 - 4 TCS01 0.532667 0.189 119 2.814 0.3805
## 4 ICS95 - 5 TCS01 1.076000 0.189 119 5.684 <.0001
## 4 ICS95 - 6 TCS01 1.185333 0.189 119 6.261 <.0001
## 5 ICS95 - 6 ICS95 0.567000 0.180 108 3.159 0.1887
## 5 ICS95 - 0 TCS01 0.531333 0.189 119 2.807 0.3852
## 5 ICS95 - 1 TCS01 0.138333 0.189 119 0.731 1.0000
## 5 ICS95 - 2 TCS01 0.389000 0.189 119 2.055 0.8872
## 5 ICS95 - 3 TCS01 0.379333 0.189 119 2.004 0.9079
## 5 ICS95 - 4 TCS01 0.243667 0.189 119 1.287 0.9994
## 5 ICS95 - 5 TCS01 0.787000 0.189 119 4.157 0.0096
## 5 ICS95 - 6 TCS01 0.896333 0.189 119 4.735 0.0011
## 6 ICS95 - 0 TCS01 -0.035667 0.189 119 -0.188 1.0000
## 6 ICS95 - 1 TCS01 -0.428667 0.189 119 -2.264 0.7751
## 6 ICS95 - 2 TCS01 -0.178000 0.189 119 -0.940 1.0000
## 6 ICS95 - 3 TCS01 -0.187667 0.189 119 -0.991 1.0000
## 6 ICS95 - 4 TCS01 -0.323333 0.189 119 -1.708 0.9797
## 6 ICS95 - 5 TCS01 0.220000 0.189 119 1.162 0.9999
## 6 ICS95 - 6 TCS01 0.329333 0.189 119 1.740 0.9754
## 0 TCS01 - 1 TCS01 -0.393000 0.180 108 -2.189 0.8193
## 0 TCS01 - 2 TCS01 -0.142333 0.180 108 -0.793 1.0000
## 0 TCS01 - 3 TCS01 -0.152000 0.180 108 -0.847 1.0000
## 0 TCS01 - 4 TCS01 -0.287667 0.180 108 -1.603 0.9898
## 0 TCS01 - 5 TCS01 0.255667 0.180 108 1.424 0.9976
## 0 TCS01 - 6 TCS01 0.365000 0.180 108 2.033 0.8955
## 1 TCS01 - 2 TCS01 0.250667 0.180 108 1.396 0.9981
## 1 TCS01 - 3 TCS01 0.241000 0.180 108 1.343 0.9989
## 1 TCS01 - 4 TCS01 0.105333 0.180 108 0.587 1.0000
## 1 TCS01 - 5 TCS01 0.648667 0.180 108 3.614 0.0574
## 1 TCS01 - 6 TCS01 0.758000 0.180 108 4.223 0.0080
## 2 TCS01 - 3 TCS01 -0.009667 0.180 108 -0.054 1.0000
## 2 TCS01 - 4 TCS01 -0.145333 0.180 108 -0.810 1.0000
## 2 TCS01 - 5 TCS01 0.398000 0.180 108 2.217 0.8032
## 2 TCS01 - 6 TCS01 0.507333 0.180 108 2.826 0.3733
## 3 TCS01 - 4 TCS01 -0.135667 0.180 108 -0.756 1.0000
## 3 TCS01 - 5 TCS01 0.407667 0.180 108 2.271 0.7703
## 3 TCS01 - 6 TCS01 0.517000 0.180 108 2.880 0.3382
## 4 TCS01 - 5 TCS01 0.543333 0.180 108 3.027 0.2525
## 4 TCS01 - 6 TCS01 0.652667 0.180 108 3.636 0.0537
## 5 TCS01 - 6 TCS01 0.109333 0.180 108 0.609 1.0000
##
## P value adjustment: tukey method for comparing a family of 21 estimates
##Splitting dataframe by temperature ramp
## Protocol 3 (T3)
datos.curve1<-filter(datos, curva=="T3")
##Check assumptions
##Outliers
datos.curve1 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.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(acidez.testa)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 acidez.testa 0.998 0.915
## 2 1 CCN51 acidez.testa 0.954 0.588
## 3 2 CCN51 acidez.testa 1 1.00
## 4 3 CCN51 acidez.testa 0.881 0.328
## 5 4 CCN51 acidez.testa 0.983 0.747
## 6 5 CCN51 acidez.testa 0.871 0.298
## 7 6 CCN51 acidez.testa 0.75 0
## 8 0 ICS95 acidez.testa 0.976 0.702
## 9 1 ICS95 acidez.testa 0.942 0.537
## 10 2 ICS95 acidez.testa 0.942 0.537
## 11 3 ICS95 acidez.testa 0.948 0.561
## 12 4 ICS95 acidez.testa 0.885 0.339
## 13 5 ICS95 acidez.testa 0.971 0.675
## 14 6 ICS95 acidez.testa 1 1.00
## 15 0 TCS01 acidez.testa 0.75 0
## 16 1 TCS01 acidez.testa 0.878 0.317
## 17 2 TCS01 acidez.testa 0.991 0.817
## 18 3 TCS01 acidez.testa 0.900 0.387
## 19 4 TCS01 acidez.testa 0.847 0.232
## 20 5 TCS01 acidez.testa 0.75 0
## 21 6 TCS01 acidez.testa 0.900 0.384
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "acidez.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(acidez.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 0.410 0.681
## 2 1 2 6 0.0247 0.976
## 3 2 2 6 1.05 0.405
## 4 3 2 6 1.67 0.264
## 5 4 2 6 0.606 0.576
## 6 5 2 6 0.736 0.518
## 7 6 2 6 1.25 0.352
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = acidez.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 6 67.963 7.56e-05 * 0.859
## 2 diam2 6 36 23.722 3.85e-11 * 0.743
## 3 gen:diam2 12 36 18.618 6.06e-12 * 0.819
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = acidez.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 37.382 4.39e-07 * 0.947
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = acidez.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 5.163 0.008 * 0.663
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = acidez.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 91.996 2.5e-09 * 0.962
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.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(acidez.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 acidez.testa 0.832 0.194
## 2 1 CCN51 acidez.testa 0.999 0.942
## 3 2 CCN51 acidez.testa 0.932 0.497
## 4 3 CCN51 acidez.testa 0.942 0.537
## 5 4 CCN51 acidez.testa 0.956 0.596
## 6 5 CCN51 acidez.testa 0.930 0.488
## 7 6 CCN51 acidez.testa 0.941 0.532
## 8 0 ICS95 acidez.testa 0.980 0.726
## 9 1 ICS95 acidez.testa 0.75 0
## 10 2 ICS95 acidez.testa 0.962 0.626
## 11 3 ICS95 acidez.testa 0.980 0.726
## 12 4 ICS95 acidez.testa 0.942 0.537
## 13 5 ICS95 acidez.testa 0.915 0.437
## 14 6 ICS95 acidez.testa 0.814 0.149
## 15 0 TCS01 acidez.testa 0.964 0.637
## 16 1 TCS01 acidez.testa 1 1.00
## 17 2 TCS01 acidez.testa 0.993 0.835
## 18 3 TCS01 acidez.testa 0.984 0.756
## 19 4 TCS01 acidez.testa 1 1.00
## 20 5 TCS01 acidez.testa 0.792 0.0944
## 21 6 TCS01 acidez.testa 0.923 0.463
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "acidez.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(acidez.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 0.934 0.444
## 2 1 2 6 1.39 0.319
## 3 2 2 6 0.995 0.423
## 4 3 2 6 1.73 0.255
## 5 4 2 6 2.26 0.186
## 6 5 2 6 0.0539 0.948
## 7 6 2 6 0.208 0.818
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = acidez.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 12.264 8.00e-03 * 0.479
## 2 diam2 6 36 14.002 3.77e-08 * 0.644
## 3 gen:diam2 12 36 2.549 1.50e-02 * 0.397
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = acidez.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 4.953 0.009 * 0.638
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = acidez.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 8.636 0.000871 * 0.8
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = acidez.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 6.598 0.003 * 0.714
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(acidez.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(acidez.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 21 × 5
## diam2 gen variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 0 CCN51 acidez.testa 0.979 0.722
## 2 1 CCN51 acidez.testa 0.823 0.170
## 3 2 CCN51 acidez.testa 0.884 0.337
## 4 3 CCN51 acidez.testa 0.831 0.190
## 5 4 CCN51 acidez.testa 0.759 0.0197
## 6 5 CCN51 acidez.testa 0.904 0.397
## 7 6 CCN51 acidez.testa 0.918 0.446
## 8 0 ICS95 acidez.testa 0.974 0.689
## 9 1 ICS95 acidez.testa 0.999 0.954
## 10 2 ICS95 acidez.testa 0.870 0.296
## 11 3 ICS95 acidez.testa 0.978 0.717
## 12 4 ICS95 acidez.testa 0.999 0.928
## 13 5 ICS95 acidez.testa 0.928 0.480
## 14 6 ICS95 acidez.testa 0.908 0.411
## 15 0 TCS01 acidez.testa 0.988 0.786
## 16 1 TCS01 acidez.testa 0.965 0.641
## 17 2 TCS01 acidez.testa 0.920 0.452
## 18 3 TCS01 acidez.testa 0.997 0.898
## 19 4 TCS01 acidez.testa 0.987 0.780
## 20 5 TCS01 acidez.testa 0.841 0.217
## 21 6 TCS01 acidez.testa 0.924 0.468
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "acidez.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
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## the data.
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## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##Homogneity of variance assumption
##Compute the Levene’s test at each level of the within-subjects factor, here time variable:
lev2<-datos.curve3 %>%
group_by(diam2) %>%
levene_test(acidez.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 0.472 0.645
## 2 1 2 6 1.38 0.321
## 3 2 2 6 0.0562 0.946
## 4 3 2 6 0.340 0.725
## 5 4 2 6 1.37 0.323
## 6 5 2 6 0.758 0.509
## 7 6 2 6 0.308 0.746
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = acidez.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 35.993 4.55e-04 * 0.718
## 2 diam2 6 36 27.554 4.58e-12 * 0.783
## 3 gen:diam2 12 36 6.044 1.21e-05 * 0.613
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = acidez.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 30.175 1.45e-06 * 0.935
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = acidez.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 17.165 3e-05 * 0.82
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = acidez.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 2.141 0.123 0.467
## Gráficas por réplica y genotipo
datos$diam2<-as.numeric(as.character(datos$diam2))
##Gráfica por réplica compuesta
pht<- ggplot(datos, aes(x = diam2)) +
facet_grid(curva~gen*muestra) +
geom_line(aes(y=acidez.testa)) +
geom_point(aes(y=acidez.testa)) +
scale_y_continuous(name = expression("Testa acidity")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
linewidth = 0.25)) +
theme(text = element_text(size = 12))
pht
## Gráfica por genotipo
datos2<-summarySE (datos, measurevar = "acidez.testa", groupvars = c("curva", "gen","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data_out/acidity_mean_testa.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=acidez.testa-ci, ymax=acidez.testa+ci), width=.1) +
geom_line(aes(y=acidez.testa)) +
geom_point(aes(y=acidez.testa)) +
scale_y_continuous(name = expression("Testa acidity")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
linewidth = 0.25)) +
theme(text = element_text(size = 15))
pht2
