setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("granofinmod.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("T3", "T1", "T2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$diam2<-as.factor(datos$diam2)
library(ggplot2)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.1 ✔ forcats 1.0.0
## Warning: package 'tibble' was built under R version 4.1.2
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library(ggpubr)
##
## Attaching package: 'ggpubr'
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library(rstatix)
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## Attaching package: 'rstatix'
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## The following object is masked from 'package:stats':
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## filter
library(emmeans)
##Summary statistics
summ<-datos %>%
group_by(curva, gen, diam2) %>%
get_summary_stats(cd.grano.c, type = "mean_sd")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 7
## curva gen diam2 variable n mean sd
## <fct> <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 T3 CCN51 0 cd.grano.c 3 8.76 0.807
## 2 T3 CCN51 2 cd.grano.c 3 8.23 0.315
## 3 T3 CCN51 5 cd.grano.c 3 7.78 0.312
## 4 T3 CCN51 6 cd.grano.c 3 7.39 0.137
## 5 T3 ICS95 0 cd.grano.c 3 11.8 0.664
## 6 T3 ICS95 2 cd.grano.c 3 11.5 0.69
## 7 T3 ICS95 5 cd.grano.c 3 10.9 0.43
## 8 T3 ICS95 6 cd.grano.c 3 10.2 0.624
## 9 T3 TCS01 0 cd.grano.c 3 9.67 1.76
## 10 T3 TCS01 2 cd.grano.c 3 8.30 0.355
## 11 T3 TCS01 5 cd.grano.c 3 7.78 0.919
## 12 T3 TCS01 6 cd.grano.c 3 7.05 0.342
## 13 T1 CCN51 0 cd.grano.c 3 9.53 0.448
## 14 T1 CCN51 2 cd.grano.c 3 8.37 0.615
## 15 T1 CCN51 5 cd.grano.c 3 7.88 0.551
## 16 T1 CCN51 6 cd.grano.c 3 7.69 0.489
## 17 T1 ICS95 0 cd.grano.c 3 10.0 0.77
## 18 T1 ICS95 2 cd.grano.c 3 9.63 0.824
## 19 T1 ICS95 5 cd.grano.c 3 9.30 0.831
## 20 T1 ICS95 6 cd.grano.c 3 8.75 1.11
## 21 T1 TCS01 0 cd.grano.c 3 7.71 1.30
## 22 T1 TCS01 2 cd.grano.c 3 6.75 0.419
## 23 T1 TCS01 5 cd.grano.c 3 6.55 0.337
## 24 T1 TCS01 6 cd.grano.c 3 6.41 0.396
## 25 T2 CCN51 0 cd.grano.c 3 7.27 0.486
## 26 T2 CCN51 2 cd.grano.c 3 6.98 0.512
## 27 T2 CCN51 5 cd.grano.c 3 6.47 0.139
## 28 T2 CCN51 6 cd.grano.c 3 5.70 0.729
## 29 T2 ICS95 0 cd.grano.c 3 12.2 0.301
## 30 T2 ICS95 2 cd.grano.c 3 11.5 0.228
## 31 T2 ICS95 5 cd.grano.c 3 11.4 0.156
## 32 T2 ICS95 6 cd.grano.c 3 10.8 0.495
## 33 T2 TCS01 0 cd.grano.c 3 8.98 0.476
## 34 T2 TCS01 2 cd.grano.c 3 8.75 0.442
## 35 T2 TCS01 5 cd.grano.c 3 8.21 0.16
## 36 T2 TCS01 6 cd.grano.c 3 6.69 0.393
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "cd.grano.c",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] curva gen diam2 muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d 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(cd.grano.c)
norm %>% as_tibble() %>% print(n=Inf)
## # A tibble: 36 × 6
## curva gen diam2 variable statistic p
## <fct> <fct> <fct> <chr> <dbl> <dbl>
## 1 T3 CCN51 0 cd.grano.c 0.934 0.504
## 2 T3 CCN51 2 cd.grano.c 0.913 0.428
## 3 T3 CCN51 5 cd.grano.c 0.781 0.0705
## 4 T3 CCN51 6 cd.grano.c 0.904 0.400
## 5 T3 ICS95 0 cd.grano.c 0.994 0.847
## 6 T3 ICS95 2 cd.grano.c 0.971 0.672
## 7 T3 ICS95 5 cd.grano.c 0.774 0.0533
## 8 T3 ICS95 6 cd.grano.c 0.919 0.449
## 9 T3 TCS01 0 cd.grano.c 0.991 0.820
## 10 T3 TCS01 2 cd.grano.c 0.756 0.0134
## 11 T3 TCS01 5 cd.grano.c 0.824 0.173
## 12 T3 TCS01 6 cd.grano.c 0.994 0.857
## 13 T1 CCN51 0 cd.grano.c 0.999 0.940
## 14 T1 CCN51 2 cd.grano.c 0.857 0.258
## 15 T1 CCN51 5 cd.grano.c 0.893 0.363
## 16 T1 CCN51 6 cd.grano.c 0.993 0.836
## 17 T1 ICS95 0 cd.grano.c 0.978 0.718
## 18 T1 ICS95 2 cd.grano.c 0.828 0.182
## 19 T1 ICS95 5 cd.grano.c 0.844 0.226
## 20 T1 ICS95 6 cd.grano.c 0.785 0.0783
## 21 T1 TCS01 0 cd.grano.c 0.785 0.0788
## 22 T1 TCS01 2 cd.grano.c 0.754 0.00911
## 23 T1 TCS01 5 cd.grano.c 0.968 0.658
## 24 T1 TCS01 6 cd.grano.c 1.00 0.979
## 25 T2 CCN51 0 cd.grano.c 0.989 0.799
## 26 T2 CCN51 2 cd.grano.c 0.972 0.678
## 27 T2 CCN51 5 cd.grano.c 0.946 0.551
## 28 T2 CCN51 6 cd.grano.c 0.912 0.424
## 29 T2 ICS95 0 cd.grano.c 0.950 0.567
## 30 T2 ICS95 2 cd.grano.c 0.986 0.770
## 31 T2 ICS95 5 cd.grano.c 0.973 0.683
## 32 T2 ICS95 6 cd.grano.c 0.809 0.135
## 33 T2 TCS01 0 cd.grano.c 0.813 0.147
## 34 T2 TCS01 2 cd.grano.c 0.932 0.496
## 35 T2 TCS01 5 cd.grano.c 0.888 0.349
## 36 T2 TCS01 6 cd.grano.c 1.00 0.986
##Create QQ plot for each cell of design:
ggqqplot(datos, "cd.grano.c", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
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## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## Warning: The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## the data.
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## the 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 data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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##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(cd.grano.c ~ curva*gen)
lev %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 8 18 0.659 0.720
## 2 2 8 18 0.245 0.976
## 3 5 8 18 0.476 0.857
## 4 6 8 18 0.338 0.940
##Computation
res.aov <- anova_test(
data = datos, dv = cd.grano.c, 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 6.595 7.00e-03 * 0.326
## 2 gen 2 18 95.539 2.60e-10 * 0.875
## 3 diam2 3 54 71.579 8.13e-19 * 0.576
## 4 curva:gen 4 18 11.174 9.83e-05 * 0.621
## 5 curva:diam2 6 54 1.686 1.42e-01 0.060
## 6 gen:diam2 6 54 1.176 3.33e-01 0.043
## 7 curva:gen:diam2 12 54 1.286 2.54e-01 0.089
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.295 0.001 *
## 2 curva:diam2 0.295 0.001 *
## 3 gen:diam2 0.295 0.001 *
## 4 curva:gen:diam2 0.295 0.001 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.559 1.68, 30.21 1.76e-11 * 0.61 1.83, 32.96
## 2 curva:diam2 0.559 3.36, 30.21 1.87e-01 0.61 3.66, 32.96
## 3 gen:diam2 0.559 3.36, 30.21 3.38e-01 0.61 3.66, 32.96
## 4 curva:gen:diam2 0.559 6.71, 30.21 2.91e-01 0.61 7.33, 32.96
## p[HF] p[HF]<.05
## 1 2.47e-12 *
## 2 1.81e-01
## 3 3.38e-01
## 4 2.87e-01
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2.00 18.00 6.595 7.00e-03 * 0.326
## 2 gen 2.00 18.00 95.539 2.60e-10 * 0.875
## 3 diam2 1.68 30.21 71.579 1.76e-11 * 0.576
## 4 curva:gen 4.00 18.00 11.174 9.83e-05 * 0.621
## 5 curva:diam2 3.36 30.21 1.686 1.87e-01 0.060
## 6 gen:diam2 3.36 30.21 1.176 3.38e-01 0.043
## 7 curva:gen:diam2 6.71 30.21 1.286 2.91e-01 0.089
#Table by error
res.aov.error <- aov(cd.grano.c ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = cd.grano.c ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 8.690667
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 14.51771 210.30386 49.19511 19.81119
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 1.049105
## 24 out of 32 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 40.82593 1.92349 1.34164 2.93389 10.26652
## Deg. of Freedom 3 6 6 12 54
##
## Residual standard error: 0.4360284
## Estimated effects may be unbalanced
summary(res.aov.error)
##
## Error: id
## Df Sum Sq Mean Sq F value Pr(>F)
## curva 2 14.52 7.26 6.595 0.0071 **
## gen 2 210.30 105.15 95.539 2.60e-10 ***
## curva:gen 4 49.20 12.30 11.174 9.83e-05 ***
## Residuals 18 19.81 1.10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: id:diam2
## Df Sum Sq Mean Sq F value Pr(>F)
## diam2 3 40.83 13.609 71.579 <2e-16 ***
## diam2:curva 6 1.92 0.321 1.686 0.142
## diam2:gen 6 1.34 0.224 1.176 0.333
## diam2:curva:gen 12 2.93 0.244 1.286 0.254
## Residuals 54 10.27 0.190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 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 9.11 0.175 18 8.74 9.48
## T1 8.22 0.175 18 7.85 8.58
## T2 8.75 0.175 18 8.38 9.12
##
## 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 0.893 0.247 18 3.610 0.0054
## T3 - T2 0.361 0.247 18 1.458 0.3336
## T1 - T2 -0.532 0.247 18 -2.151 0.1075
##
## 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 8.04 0.303 18 7.40 8.68
## ICS95 11.09 0.303 18 10.45 11.72
## TCS01 8.20 0.303 18 7.56 8.84
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 8.37 0.303 18 7.73 9.01
## ICS95 9.42 0.303 18 8.79 10.06
## TCS01 6.85 0.303 18 6.22 7.49
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 6.61 0.303 18 5.97 7.24
## ICS95 11.48 0.303 18 10.84 12.12
## TCS01 8.16 0.303 18 7.52 8.79
##
## 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 -3.048 0.428 18 -7.117 <.0001
## CCN51 - TCS01 -0.161 0.428 18 -0.375 0.9258
## ICS95 - TCS01 2.888 0.428 18 6.742 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -1.055 0.428 18 -2.464 0.0595
## CCN51 - TCS01 1.514 0.428 18 3.535 0.0064
## ICS95 - TCS01 2.570 0.428 18 5.999 <.0001
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -4.875 0.428 18 -11.382 <.0001
## CCN51 - TCS01 -1.552 0.428 18 -3.624 0.0052
## ICS95 - TCS01 3.323 0.428 18 7.758 <.0001
##
## Results are averaged over the levels of: diam2
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_diam2 <- emmeans(res.aov.error, pairwise ~ diam2 | curva*gen)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2
## $emmeans
## curva = T3, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 8.76 0.373 38.1 8.01 9.52
## 2 8.23 0.373 38.1 7.47 8.98
## 5 7.78 0.373 38.1 7.02 8.53
## 6 7.39 0.373 38.1 6.63 8.15
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 9.53 0.373 38.1 8.78 10.29
## 2 8.37 0.373 38.1 7.61 9.12
## 5 7.88 0.373 38.1 7.13 8.64
## 6 7.69 0.373 38.1 6.93 8.45
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 7.27 0.373 38.1 6.51 8.02
## 2 6.98 0.373 38.1 6.22 7.73
## 5 6.47 0.373 38.1 5.72 7.23
## 6 5.70 0.373 38.1 4.95 6.46
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 11.75 0.373 38.1 10.99 12.51
## 2 11.48 0.373 38.1 10.72 12.23
## 5 10.94 0.373 38.1 10.18 11.69
## 6 10.18 0.373 38.1 9.43 10.94
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 10.02 0.373 38.1 9.26 10.77
## 2 9.63 0.373 38.1 8.87 10.38
## 5 9.31 0.373 38.1 8.55 10.06
## 6 8.75 0.373 38.1 7.99 9.50
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 12.16 0.373 38.1 11.41 12.92
## 2 11.55 0.373 38.1 10.79 12.30
## 5 11.43 0.373 38.1 10.67 12.19
## 6 10.78 0.373 38.1 10.03 11.54
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 9.67 0.373 38.1 8.92 10.43
## 2 8.30 0.373 38.1 7.54 9.05
## 5 7.78 0.373 38.1 7.02 8.53
## 6 7.05 0.373 38.1 6.29 7.80
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 7.71 0.373 38.1 6.95 8.46
## 2 6.75 0.373 38.1 5.99 7.51
## 5 6.55 0.373 38.1 5.79 7.30
## 6 6.41 0.373 38.1 5.66 7.17
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 8.98 0.373 38.1 8.23 9.74
## 2 8.75 0.373 38.1 7.99 9.50
## 5 8.21 0.373 38.1 7.46 8.97
## 6 6.69 0.373 38.1 5.93 7.44
##
## 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 - 2 0.535 0.356 54 1.502 0.4435
## 0 - 5 0.986 0.356 54 2.769 0.0375
## 0 - 6 1.372 0.356 54 3.854 0.0017
## 2 - 5 0.451 0.356 54 1.267 0.5877
## 2 - 6 0.837 0.356 54 2.352 0.0991
## 5 - 6 0.386 0.356 54 1.085 0.7000
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 1.166 0.356 54 3.276 0.0097
## 0 - 5 1.649 0.356 54 4.632 0.0001
## 0 - 6 1.843 0.356 54 5.177 <.0001
## 2 - 5 0.483 0.356 54 1.356 0.5322
## 2 - 6 0.677 0.356 54 1.901 0.2399
## 5 - 6 0.194 0.356 54 0.545 0.9475
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.288 0.356 54 0.810 0.8495
## 0 - 5 0.793 0.356 54 2.226 0.1291
## 0 - 6 1.565 0.356 54 4.396 0.0003
## 2 - 5 0.504 0.356 54 1.417 0.4947
## 2 - 6 1.277 0.356 54 3.586 0.0039
## 5 - 6 0.772 0.356 54 2.169 0.1449
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.271 0.356 54 0.760 0.8719
## 0 - 5 0.815 0.356 54 2.288 0.1135
## 0 - 6 1.566 0.356 54 4.398 0.0003
## 2 - 5 0.544 0.356 54 1.528 0.4282
## 2 - 6 1.295 0.356 54 3.637 0.0034
## 5 - 6 0.751 0.356 54 2.109 0.1631
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.391 0.356 54 1.098 0.6921
## 0 - 5 0.712 0.356 54 1.999 0.2011
## 0 - 6 1.269 0.356 54 3.564 0.0042
## 2 - 5 0.321 0.356 54 0.901 0.8045
## 2 - 6 0.878 0.356 54 2.466 0.0769
## 5 - 6 0.557 0.356 54 1.565 0.4067
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.617 0.356 54 1.734 0.3165
## 0 - 5 0.733 0.356 54 2.060 0.1795
## 0 - 6 1.380 0.356 54 3.877 0.0016
## 2 - 5 0.116 0.356 54 0.326 0.9879
## 2 - 6 0.763 0.356 54 2.143 0.1527
## 5 - 6 0.647 0.356 54 1.817 0.2765
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 1.375 0.356 54 3.862 0.0017
## 0 - 5 1.894 0.356 54 5.320 <.0001
## 0 - 6 2.627 0.356 54 7.378 <.0001
## 2 - 5 0.519 0.356 54 1.458 0.4697
## 2 - 6 1.252 0.356 54 3.516 0.0048
## 5 - 6 0.733 0.356 54 2.058 0.1801
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.959 0.356 54 2.693 0.0452
## 0 - 5 1.162 0.356 54 3.264 0.0100
## 0 - 6 1.296 0.356 54 3.639 0.0033
## 2 - 5 0.203 0.356 54 0.571 0.9403
## 2 - 6 0.337 0.356 54 0.947 0.7799
## 5 - 6 0.134 0.356 54 0.375 0.9818
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.236 0.356 54 0.664 0.9102
## 0 - 5 0.771 0.356 54 2.166 0.1460
## 0 - 6 2.296 0.356 54 6.448 <.0001
## 2 - 5 0.535 0.356 54 1.502 0.4435
## 2 - 6 2.059 0.356 54 5.784 <.0001
## 5 - 6 1.525 0.356 54 4.283 0.0004
##
## P value adjustment: tukey method for comparing a family of 4 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 8.76 0.373 38.1 8.01 9.52
## 2 CCN51 8.23 0.373 38.1 7.47 8.98
## 5 CCN51 7.78 0.373 38.1 7.02 8.53
## 6 CCN51 7.39 0.373 38.1 6.63 8.15
## 0 ICS95 11.75 0.373 38.1 10.99 12.51
## 2 ICS95 11.48 0.373 38.1 10.72 12.23
## 5 ICS95 10.94 0.373 38.1 10.18 11.69
## 6 ICS95 10.18 0.373 38.1 9.43 10.94
## 0 TCS01 9.67 0.373 38.1 8.92 10.43
## 2 TCS01 8.30 0.373 38.1 7.54 9.05
## 5 TCS01 7.78 0.373 38.1 7.02 8.53
## 6 TCS01 7.05 0.373 38.1 6.29 7.80
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 9.53 0.373 38.1 8.78 10.29
## 2 CCN51 8.37 0.373 38.1 7.61 9.12
## 5 CCN51 7.88 0.373 38.1 7.13 8.64
## 6 CCN51 7.69 0.373 38.1 6.93 8.45
## 0 ICS95 10.02 0.373 38.1 9.26 10.77
## 2 ICS95 9.63 0.373 38.1 8.87 10.38
## 5 ICS95 9.31 0.373 38.1 8.55 10.06
## 6 ICS95 8.75 0.373 38.1 7.99 9.50
## 0 TCS01 7.71 0.373 38.1 6.95 8.46
## 2 TCS01 6.75 0.373 38.1 5.99 7.51
## 5 TCS01 6.55 0.373 38.1 5.79 7.30
## 6 TCS01 6.41 0.373 38.1 5.66 7.17
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 7.27 0.373 38.1 6.51 8.02
## 2 CCN51 6.98 0.373 38.1 6.22 7.73
## 5 CCN51 6.47 0.373 38.1 5.72 7.23
## 6 CCN51 5.70 0.373 38.1 4.95 6.46
## 0 ICS95 12.16 0.373 38.1 11.41 12.92
## 2 ICS95 11.55 0.373 38.1 10.79 12.30
## 5 ICS95 11.43 0.373 38.1 10.67 12.19
## 6 ICS95 10.78 0.373 38.1 10.03 11.54
## 0 TCS01 8.98 0.373 38.1 8.23 9.74
## 2 TCS01 8.75 0.373 38.1 7.99 9.50
## 5 TCS01 8.21 0.373 38.1 7.46 8.97
## 6 TCS01 6.69 0.373 38.1 5.93 7.44
##
## 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 - 2 CCN51 0.5347 0.356 54.0 1.502 0.9337
## 0 CCN51 - 5 CCN51 0.9857 0.356 54.0 2.769 0.2227
## 0 CCN51 - 6 CCN51 1.3720 0.356 54.0 3.854 0.0148
## 0 CCN51 - 0 ICS95 -2.9877 0.528 38.1 -5.661 0.0001
## 0 CCN51 - 2 ICS95 -2.7170 0.528 38.1 -5.148 0.0005
## 0 CCN51 - 5 ICS95 -2.1730 0.528 38.1 -4.118 0.0093
## 0 CCN51 - 6 ICS95 -1.4220 0.528 38.1 -2.695 0.2674
## 0 CCN51 - 0 TCS01 -0.9113 0.528 38.1 -1.727 0.8447
## 0 CCN51 - 2 TCS01 0.4637 0.528 38.1 0.879 0.9990
## 0 CCN51 - 5 TCS01 0.9827 0.528 38.1 1.862 0.7739
## 0 CCN51 - 6 TCS01 1.7153 0.528 38.1 3.250 0.0857
## 2 CCN51 - 5 CCN51 0.4510 0.356 54.0 1.267 0.9799
## 2 CCN51 - 6 CCN51 0.8373 0.356 54.0 2.352 0.4547
## 2 CCN51 - 0 ICS95 -3.5223 0.528 38.1 -6.675 <.0001
## 2 CCN51 - 2 ICS95 -3.2517 0.528 38.1 -6.162 <.0001
## 2 CCN51 - 5 ICS95 -2.7077 0.528 38.1 -5.131 0.0005
## 2 CCN51 - 6 ICS95 -1.9567 0.528 38.1 -3.708 0.0281
## 2 CCN51 - 0 TCS01 -1.4460 0.528 38.1 -2.740 0.2463
## 2 CCN51 - 2 TCS01 -0.0710 0.528 38.1 -0.135 1.0000
## 2 CCN51 - 5 TCS01 0.4480 0.528 38.1 0.849 0.9993
## 2 CCN51 - 6 TCS01 1.1807 0.528 38.1 2.237 0.5348
## 5 CCN51 - 6 CCN51 0.3863 0.356 54.0 1.085 0.9941
## 5 CCN51 - 0 ICS95 -3.9733 0.528 38.1 -7.529 <.0001
## 5 CCN51 - 2 ICS95 -3.7027 0.528 38.1 -7.016 <.0001
## 5 CCN51 - 5 ICS95 -3.1587 0.528 38.1 -5.985 <.0001
## 5 CCN51 - 6 ICS95 -2.4077 0.528 38.1 -4.562 0.0026
## 5 CCN51 - 0 TCS01 -1.8970 0.528 38.1 -3.595 0.0374
## 5 CCN51 - 2 TCS01 -0.5220 0.528 38.1 -0.989 0.9971
## 5 CCN51 - 5 TCS01 -0.0030 0.528 38.1 -0.006 1.0000
## 5 CCN51 - 6 TCS01 0.7297 0.528 38.1 1.383 0.9600
## 6 CCN51 - 0 ICS95 -4.3597 0.528 38.1 -8.261 <.0001
## 6 CCN51 - 2 ICS95 -4.0890 0.528 38.1 -7.748 <.0001
## 6 CCN51 - 5 ICS95 -3.5450 0.528 38.1 -6.717 <.0001
## 6 CCN51 - 6 ICS95 -2.7940 0.528 38.1 -5.294 0.0003
## 6 CCN51 - 0 TCS01 -2.2833 0.528 38.1 -4.327 0.0052
## 6 CCN51 - 2 TCS01 -0.9083 0.528 38.1 -1.721 0.8474
## 6 CCN51 - 5 TCS01 -0.3893 0.528 38.1 -0.738 0.9998
## 6 CCN51 - 6 TCS01 0.3433 0.528 38.1 0.651 0.9999
## 0 ICS95 - 2 ICS95 0.2707 0.356 54.0 0.760 0.9998
## 0 ICS95 - 5 ICS95 0.8147 0.356 54.0 2.288 0.4969
## 0 ICS95 - 6 ICS95 1.5657 0.356 54.0 4.398 0.0028
## 0 ICS95 - 0 TCS01 2.0763 0.528 38.1 3.934 0.0154
## 0 ICS95 - 2 TCS01 3.4513 0.528 38.1 6.540 <.0001
## 0 ICS95 - 5 TCS01 3.9703 0.528 38.1 7.523 <.0001
## 0 ICS95 - 6 TCS01 4.7030 0.528 38.1 8.912 <.0001
## 2 ICS95 - 5 ICS95 0.5440 0.356 54.0 1.528 0.9259
## 2 ICS95 - 6 ICS95 1.2950 0.356 54.0 3.637 0.0274
## 2 ICS95 - 0 TCS01 1.8057 0.528 38.1 3.422 0.0573
## 2 ICS95 - 2 TCS01 3.1807 0.528 38.1 6.027 <.0001
## 2 ICS95 - 5 TCS01 3.6997 0.528 38.1 7.011 <.0001
## 2 ICS95 - 6 TCS01 4.4323 0.528 38.1 8.399 <.0001
## 5 ICS95 - 6 ICS95 0.7510 0.356 54.0 2.109 0.6185
## 5 ICS95 - 0 TCS01 1.2617 0.528 38.1 2.391 0.4359
## 5 ICS95 - 2 TCS01 2.6367 0.528 38.1 4.996 0.0007
## 5 ICS95 - 5 TCS01 3.1557 0.528 38.1 5.980 <.0001
## 5 ICS95 - 6 TCS01 3.8883 0.528 38.1 7.368 <.0001
## 6 ICS95 - 0 TCS01 0.5107 0.528 38.1 0.968 0.9976
## 6 ICS95 - 2 TCS01 1.8857 0.528 38.1 3.573 0.0395
## 6 ICS95 - 5 TCS01 2.4047 0.528 38.1 4.557 0.0027
## 6 ICS95 - 6 TCS01 3.1373 0.528 38.1 5.945 <.0001
## 0 TCS01 - 2 TCS01 1.3750 0.356 54.0 3.862 0.0144
## 0 TCS01 - 5 TCS01 1.8940 0.356 54.0 5.320 0.0001
## 0 TCS01 - 6 TCS01 2.6267 0.356 54.0 7.378 <.0001
## 2 TCS01 - 5 TCS01 0.5190 0.356 54.0 1.458 0.9454
## 2 TCS01 - 6 TCS01 1.2517 0.356 54.0 3.516 0.0383
## 5 TCS01 - 6 TCS01 0.7327 0.356 54.0 2.058 0.6531
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 1.1663 0.356 54.0 3.276 0.0713
## 0 CCN51 - 5 CCN51 1.6490 0.356 54.0 4.632 0.0013
## 0 CCN51 - 6 CCN51 1.8430 0.356 54.0 5.177 0.0002
## 0 CCN51 - 0 ICS95 -0.4837 0.528 38.1 -0.917 0.9985
## 0 CCN51 - 2 ICS95 -0.0927 0.528 38.1 -0.176 1.0000
## 0 CCN51 - 5 ICS95 0.2280 0.528 38.1 0.432 1.0000
## 0 CCN51 - 6 ICS95 0.7853 0.528 38.1 1.488 0.9349
## 0 CCN51 - 0 TCS01 1.8247 0.528 38.1 3.458 0.0525
## 0 CCN51 - 2 TCS01 2.7833 0.528 38.1 5.274 0.0003
## 0 CCN51 - 5 TCS01 2.9867 0.528 38.1 5.659 0.0001
## 0 CCN51 - 6 TCS01 3.1203 0.528 38.1 5.913 <.0001
## 2 CCN51 - 5 CCN51 0.4827 0.356 54.0 1.356 0.9669
## 2 CCN51 - 6 CCN51 0.6767 0.356 54.0 1.901 0.7535
## 2 CCN51 - 0 ICS95 -1.6500 0.528 38.1 -3.127 0.1130
## 2 CCN51 - 2 ICS95 -1.2590 0.528 38.1 -2.386 0.4391
## 2 CCN51 - 5 ICS95 -0.9383 0.528 38.1 -1.778 0.8194
## 2 CCN51 - 6 ICS95 -0.3810 0.528 38.1 -0.722 0.9998
## 2 CCN51 - 0 TCS01 0.6583 0.528 38.1 1.247 0.9809
## 2 CCN51 - 2 TCS01 1.6170 0.528 38.1 3.064 0.1294
## 2 CCN51 - 5 TCS01 1.8203 0.528 38.1 3.449 0.0536
## 2 CCN51 - 6 TCS01 1.9540 0.528 38.1 3.703 0.0284
## 5 CCN51 - 6 CCN51 0.1940 0.356 54.0 0.545 1.0000
## 5 CCN51 - 0 ICS95 -2.1327 0.528 38.1 -4.041 0.0115
## 5 CCN51 - 2 ICS95 -1.7417 0.528 38.1 -3.300 0.0764
## 5 CCN51 - 5 ICS95 -1.4210 0.528 38.1 -2.693 0.2683
## 5 CCN51 - 6 ICS95 -0.8637 0.528 38.1 -1.637 0.8844
## 5 CCN51 - 0 TCS01 0.1757 0.528 38.1 0.333 1.0000
## 5 CCN51 - 2 TCS01 1.1343 0.528 38.1 2.149 0.5930
## 5 CCN51 - 5 TCS01 1.3377 0.528 38.1 2.535 0.3504
## 5 CCN51 - 6 TCS01 1.4713 0.528 38.1 2.788 0.2253
## 6 CCN51 - 0 ICS95 -2.3267 0.528 38.1 -4.409 0.0041
## 6 CCN51 - 2 ICS95 -1.9357 0.528 38.1 -3.668 0.0311
## 6 CCN51 - 5 ICS95 -1.6150 0.528 38.1 -3.060 0.1304
## 6 CCN51 - 6 ICS95 -1.0577 0.528 38.1 -2.004 0.6879
## 6 CCN51 - 0 TCS01 -0.0183 0.528 38.1 -0.035 1.0000
## 6 CCN51 - 2 TCS01 0.9403 0.528 38.1 1.782 0.8175
## 6 CCN51 - 5 TCS01 1.1437 0.528 38.1 2.167 0.5813
## 6 CCN51 - 6 TCS01 1.2773 0.528 38.1 2.420 0.4176
## 0 ICS95 - 2 ICS95 0.3910 0.356 54.0 1.098 0.9935
## 0 ICS95 - 5 ICS95 0.7117 0.356 54.0 1.999 0.6920
## 0 ICS95 - 6 ICS95 1.2690 0.356 54.0 3.564 0.0335
## 0 ICS95 - 0 TCS01 2.3083 0.528 38.1 4.374 0.0045
## 0 ICS95 - 2 TCS01 3.2670 0.528 38.1 6.191 <.0001
## 0 ICS95 - 5 TCS01 3.4703 0.528 38.1 6.576 <.0001
## 0 ICS95 - 6 TCS01 3.6040 0.528 38.1 6.829 <.0001
## 2 ICS95 - 5 ICS95 0.3207 0.356 54.0 0.901 0.9989
## 2 ICS95 - 6 ICS95 0.8780 0.356 54.0 2.466 0.3823
## 2 ICS95 - 0 TCS01 1.9173 0.528 38.1 3.633 0.0340
## 2 ICS95 - 2 TCS01 2.8760 0.528 38.1 5.450 0.0002
## 2 ICS95 - 5 TCS01 3.0793 0.528 38.1 5.835 0.0001
## 2 ICS95 - 6 TCS01 3.2130 0.528 38.1 6.088 <.0001
## 5 ICS95 - 6 ICS95 0.5573 0.356 54.0 1.565 0.9138
## 5 ICS95 - 0 TCS01 1.5967 0.528 38.1 3.026 0.1404
## 5 ICS95 - 2 TCS01 2.5553 0.528 38.1 4.842 0.0012
## 5 ICS95 - 5 TCS01 2.7587 0.528 38.1 5.227 0.0004
## 5 ICS95 - 6 TCS01 2.8923 0.528 38.1 5.481 0.0002
## 6 ICS95 - 0 TCS01 1.0393 0.528 38.1 1.969 0.7097
## 6 ICS95 - 2 TCS01 1.9980 0.528 38.1 3.786 0.0229
## 6 ICS95 - 5 TCS01 2.2013 0.528 38.1 4.171 0.0081
## 6 ICS95 - 6 TCS01 2.3350 0.528 38.1 4.425 0.0039
## 0 TCS01 - 2 TCS01 0.9587 0.356 54.0 2.693 0.2577
## 0 TCS01 - 5 TCS01 1.1620 0.356 54.0 3.264 0.0735
## 0 TCS01 - 6 TCS01 1.2957 0.356 54.0 3.639 0.0273
## 2 TCS01 - 5 TCS01 0.2033 0.356 54.0 0.571 1.0000
## 2 TCS01 - 6 TCS01 0.3370 0.356 54.0 0.947 0.9982
## 5 TCS01 - 6 TCS01 0.1337 0.356 54.0 0.375 1.0000
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 0.2883 0.356 54.0 0.810 0.9996
## 0 CCN51 - 5 CCN51 0.7927 0.356 54.0 2.226 0.5387
## 0 CCN51 - 6 CCN51 1.5650 0.356 54.0 4.396 0.0028
## 0 CCN51 - 0 ICS95 -4.8960 0.528 38.1 -9.277 <.0001
## 0 CCN51 - 2 ICS95 -4.2787 0.528 38.1 -8.108 <.0001
## 0 CCN51 - 5 ICS95 -4.1627 0.528 38.1 -7.888 <.0001
## 0 CCN51 - 6 ICS95 -3.5157 0.528 38.1 -6.662 <.0001
## 0 CCN51 - 0 TCS01 -1.7163 0.528 38.1 -3.252 0.0853
## 0 CCN51 - 2 TCS01 -1.4800 0.528 38.1 -2.804 0.2185
## 0 CCN51 - 5 TCS01 -0.9453 0.528 38.1 -1.791 0.8125
## 0 CCN51 - 6 TCS01 0.5793 0.528 38.1 1.098 0.9930
## 2 CCN51 - 5 CCN51 0.5043 0.356 54.0 1.417 0.9550
## 2 CCN51 - 6 CCN51 1.2767 0.356 54.0 3.586 0.0316
## 2 CCN51 - 0 ICS95 -5.1843 0.528 38.1 -9.824 <.0001
## 2 CCN51 - 2 ICS95 -4.5670 0.528 38.1 -8.654 <.0001
## 2 CCN51 - 5 ICS95 -4.4510 0.528 38.1 -8.434 <.0001
## 2 CCN51 - 6 ICS95 -3.8040 0.528 38.1 -7.208 <.0001
## 2 CCN51 - 0 TCS01 -2.0047 0.528 38.1 -3.799 0.0221
## 2 CCN51 - 2 TCS01 -1.7683 0.528 38.1 -3.351 0.0678
## 2 CCN51 - 5 TCS01 -1.2337 0.528 38.1 -2.338 0.4694
## 2 CCN51 - 6 TCS01 0.2910 0.528 38.1 0.551 1.0000
## 5 CCN51 - 6 CCN51 0.7723 0.356 54.0 2.169 0.5777
## 5 CCN51 - 0 ICS95 -5.6887 0.528 38.1 -10.780 <.0001
## 5 CCN51 - 2 ICS95 -5.0713 0.528 38.1 -9.610 <.0001
## 5 CCN51 - 5 ICS95 -4.9553 0.528 38.1 -9.390 <.0001
## 5 CCN51 - 6 ICS95 -4.3083 0.528 38.1 -8.164 <.0001
## 5 CCN51 - 0 TCS01 -2.5090 0.528 38.1 -4.754 0.0015
## 5 CCN51 - 2 TCS01 -2.2727 0.528 38.1 -4.307 0.0055
## 5 CCN51 - 5 TCS01 -1.7380 0.528 38.1 -3.293 0.0776
## 5 CCN51 - 6 TCS01 -0.2133 0.528 38.1 -0.404 1.0000
## 6 CCN51 - 0 ICS95 -6.4610 0.528 38.1 -12.243 <.0001
## 6 CCN51 - 2 ICS95 -5.8437 0.528 38.1 -11.073 <.0001
## 6 CCN51 - 5 ICS95 -5.7277 0.528 38.1 -10.853 <.0001
## 6 CCN51 - 6 ICS95 -5.0807 0.528 38.1 -9.627 <.0001
## 6 CCN51 - 0 TCS01 -3.2813 0.528 38.1 -6.218 <.0001
## 6 CCN51 - 2 TCS01 -3.0450 0.528 38.1 -5.770 0.0001
## 6 CCN51 - 5 TCS01 -2.5103 0.528 38.1 -4.757 0.0015
## 6 CCN51 - 6 TCS01 -0.9857 0.528 38.1 -1.868 0.7707
## 0 ICS95 - 2 ICS95 0.6173 0.356 54.0 1.734 0.8444
## 0 ICS95 - 5 ICS95 0.7333 0.356 54.0 2.060 0.6519
## 0 ICS95 - 6 ICS95 1.3803 0.356 54.0 3.877 0.0138
## 0 ICS95 - 0 TCS01 3.1797 0.528 38.1 6.025 <.0001
## 0 ICS95 - 2 TCS01 3.4160 0.528 38.1 6.473 <.0001
## 0 ICS95 - 5 TCS01 3.9507 0.528 38.1 7.486 <.0001
## 0 ICS95 - 6 TCS01 5.4753 0.528 38.1 10.375 <.0001
## 2 ICS95 - 5 ICS95 0.1160 0.356 54.0 0.326 1.0000
## 2 ICS95 - 6 ICS95 0.7630 0.356 54.0 2.143 0.5956
## 2 ICS95 - 0 TCS01 2.5623 0.528 38.1 4.855 0.0011
## 2 ICS95 - 2 TCS01 2.7987 0.528 38.1 5.303 0.0003
## 2 ICS95 - 5 TCS01 3.3333 0.528 38.1 6.316 <.0001
## 2 ICS95 - 6 TCS01 4.8580 0.528 38.1 9.205 <.0001
## 5 ICS95 - 6 ICS95 0.6470 0.356 54.0 1.817 0.8014
## 5 ICS95 - 0 TCS01 2.4463 0.528 38.1 4.636 0.0021
## 5 ICS95 - 2 TCS01 2.6827 0.528 38.1 5.083 0.0006
## 5 ICS95 - 5 TCS01 3.2173 0.528 38.1 6.097 <.0001
## 5 ICS95 - 6 TCS01 4.7420 0.528 38.1 8.986 <.0001
## 6 ICS95 - 0 TCS01 1.7993 0.528 38.1 3.410 0.0590
## 6 ICS95 - 2 TCS01 2.0357 0.528 38.1 3.857 0.0190
## 6 ICS95 - 5 TCS01 2.5703 0.528 38.1 4.871 0.0011
## 6 ICS95 - 6 TCS01 4.0950 0.528 38.1 7.760 <.0001
## 0 TCS01 - 2 TCS01 0.2363 0.356 54.0 0.664 0.9999
## 0 TCS01 - 5 TCS01 0.7710 0.356 54.0 2.166 0.5802
## 0 TCS01 - 6 TCS01 2.2957 0.356 54.0 6.448 <.0001
## 2 TCS01 - 5 TCS01 0.5347 0.356 54.0 1.502 0.9337
## 2 TCS01 - 6 TCS01 2.0593 0.356 54.0 5.784 <.0001
## 5 TCS01 - 6 TCS01 1.5247 0.356 54.0 4.283 0.0040
##
## P value adjustment: tukey method for comparing a family of 12 estimates
emm_diam2 <- emmeans(res.aov.error, pairwise ~ diam2)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_diam2
## $emmeans
## diam2 emmean SE df lower.CL upper.CL
## 0 9.54 0.124 38.1 9.29 9.79
## 2 8.89 0.124 38.1 8.64 9.14
## 5 8.48 0.124 38.1 8.23 8.73
## 6 7.85 0.124 38.1 7.60 8.10
##
## Results are averaged over the levels of: curva, gen
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.649 0.119 54 5.466 <.0001
## 0 - 5 1.057 0.119 54 8.908 <.0001
## 0 - 6 1.690 0.119 54 14.244 <.0001
## 2 - 5 0.408 0.119 54 3.441 0.0060
## 2 - 6 1.042 0.119 54 8.777 <.0001
## 5 - 6 0.633 0.119 54 5.336 <.0001
##
## Results are averaged over the levels of: curva, gen
## P value adjustment: tukey method for comparing a family of 4 estimates
emm_diam2_curva <- emmeans(res.aov.error, pairwise ~ diam2 | curva)
## Note: re-fitting model with sum-to-zero contrasts
## NOTE: Results may be misleading due to involvement in interactions
emm_diam2_curva
## $emmeans
## curva = T3:
## diam2 emmean SE df lower.CL upper.CL
## 0 10.06 0.215 38.1 9.63 10.50
## 2 9.33 0.215 38.1 8.90 9.77
## 5 8.83 0.215 38.1 8.39 9.27
## 6 8.21 0.215 38.1 7.77 8.64
##
## curva = T1:
## diam2 emmean SE df lower.CL upper.CL
## 0 9.09 0.215 38.1 8.65 9.52
## 2 8.25 0.215 38.1 7.81 8.68
## 5 7.91 0.215 38.1 7.48 8.35
## 6 7.62 0.215 38.1 7.18 8.05
##
## curva = T2:
## diam2 emmean SE df lower.CL upper.CL
## 0 9.47 0.215 38.1 9.03 9.91
## 2 9.09 0.215 38.1 8.65 9.53
## 5 8.71 0.215 38.1 8.27 9.14
## 6 7.72 0.215 38.1 7.29 8.16
##
## Results are averaged over the levels of: gen
## Warning: EMMs are biased unless design is perfectly balanced
## Confidence level used: 0.95
##
## $contrasts
## curva = T3:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.727 0.206 54 3.536 0.0046
## 0 - 5 1.231 0.206 54 5.991 <.0001
## 0 - 6 1.855 0.206 54 9.024 <.0001
## 2 - 5 0.505 0.206 54 2.455 0.0789
## 2 - 6 1.128 0.206 54 5.488 <.0001
## 5 - 6 0.623 0.206 54 3.033 0.0189
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.839 0.206 54 4.080 0.0008
## 0 - 5 1.174 0.206 54 5.713 <.0001
## 0 - 6 1.469 0.206 54 7.148 <.0001
## 2 - 5 0.336 0.206 54 1.633 0.3694
## 2 - 6 0.631 0.206 54 3.068 0.0172
## 5 - 6 0.295 0.206 54 1.435 0.4834
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 - 2 0.381 0.206 54 1.852 0.2609
## 0 - 5 0.766 0.206 54 3.725 0.0026
## 0 - 6 1.747 0.206 54 8.499 <.0001
## 2 - 5 0.385 0.206 54 1.873 0.2517
## 2 - 6 1.366 0.206 54 6.647 <.0001
## 5 - 6 0.981 0.206 54 4.774 0.0001
##
## Results are averaged over the levels of: gen
## P value adjustment: tukey method for comparing a family of 4 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(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d 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(cd.grano.c)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.934 0.504
## 2 CCN51 2 cd.grano.c 0.913 0.428
## 3 CCN51 5 cd.grano.c 0.781 0.0705
## 4 CCN51 6 cd.grano.c 0.904 0.400
## 5 ICS95 0 cd.grano.c 0.994 0.847
## 6 ICS95 2 cd.grano.c 0.971 0.672
## 7 ICS95 5 cd.grano.c 0.774 0.0533
## 8 ICS95 6 cd.grano.c 0.919 0.449
## 9 TCS01 0 cd.grano.c 0.991 0.820
## 10 TCS01 2 cd.grano.c 0.756 0.0134
## 11 TCS01 5 cd.grano.c 0.824 0.173
## 12 TCS01 6 cd.grano.c 0.994 0.857
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "cd.grano.c", 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?
##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(cd.grano.c ~ gen)
lev1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.897 0.456
## 2 2 2 6 0.499 0.630
## 3 5 2 6 0.376 0.701
## 4 6 2 6 0.826 0.482
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = cd.grano.c, 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 27.867 9.18e-04 * 0.844
## 2 diam2 3 18 18.299 1.06e-05 * 0.560
## 3 gen:diam2 6 18 1.017 4.45e-01 0.124
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = cd.grano.c, 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 3 6 6.034 0.03 * 0.645
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = cd.grano.c, 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 3 6 6.312 0.028 * 0.59
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = cd.grano.c, 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 3 6 7.238 0.02 * 0.57
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d 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(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.999 0.940
## 2 CCN51 2 cd.grano.c 0.857 0.258
## 3 CCN51 5 cd.grano.c 0.893 0.363
## 4 CCN51 6 cd.grano.c 0.993 0.836
## 5 ICS95 0 cd.grano.c 0.978 0.718
## 6 ICS95 2 cd.grano.c 0.828 0.182
## 7 ICS95 5 cd.grano.c 0.844 0.226
## 8 ICS95 6 cd.grano.c 0.785 0.0783
## 9 TCS01 0 cd.grano.c 0.785 0.0788
## 10 TCS01 2 cd.grano.c 0.754 0.00911
## 11 TCS01 5 cd.grano.c 0.968 0.658
## 12 TCS01 6 cd.grano.c 1.00 0.979
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "cd.grano.c", 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?
##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(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.271 0.772
## 2 2 2 6 0.151 0.863
## 3 5 2 6 0.249 0.787
## 4 6 2 6 0.319 0.739
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = cd.grano.c, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.0 12.534 0.007 * 0.756
## 2 diam2 1.13 6.8 19.749 0.003 * 0.458
## 3 gen:diam2 2.27 6.8 0.861 0.477 0.069
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, 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 3 6 19.176 0.002 * 0.733
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, 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 3 6 18.459 0.002 * 0.288
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, 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 3 6 2.592 0.148 0.421
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.grano.c)
## [1] gen diam2 curva muestra id dia
## [7] cd.grano curva.1 protocolo gen.1 muestra.1 dia.1
## [13] diam Testa Grano cd.grano.1 cd.grano.c cd.grano.a
## [19] cd.grano.d 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(cd.grano.c)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.grano.c 0.989 0.799
## 2 CCN51 2 cd.grano.c 0.972 0.678
## 3 CCN51 5 cd.grano.c 0.946 0.551
## 4 CCN51 6 cd.grano.c 0.912 0.424
## 5 ICS95 0 cd.grano.c 0.950 0.567
## 6 ICS95 2 cd.grano.c 0.986 0.770
## 7 ICS95 5 cd.grano.c 0.973 0.683
## 8 ICS95 6 cd.grano.c 0.809 0.135
## 9 TCS01 0 cd.grano.c 0.813 0.147
## 10 TCS01 2 cd.grano.c 0.932 0.496
## 11 TCS01 5 cd.grano.c 0.888 0.349
## 12 TCS01 6 cd.grano.c 1.00 0.986
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "cd.grano.c", 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?
##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(cd.grano.c ~ gen)
lev2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 4 × 5
## diam2 df1 df2 statistic p
## <fct> <int> <int> <dbl> <dbl>
## 1 0 2 6 0.122 0.887
## 2 2 2 6 0.363 0.710
## 3 5 2 6 0.0112 0.989
## 4 6 2 6 0.187 0.834
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = cd.grano.c, 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 6 169.964 5.22e-06 * 0.973
## 2 diam2 3 18 60.864 1.27e-09 * 0.787
## 3 gen:diam2 6 18 2.952 3.50e-02 * 0.263
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.grano.c, 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 3 6 14.126 0.004 * 0.668
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.grano.c, 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 3 6 8.365 0.015 * 0.777
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.grano.c, 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 3 6 90.215 2.21e-05 * 0.888
## 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=cd.grano.c)) +
geom_point(aes(y=cd.grano.c)) +
scale_y_continuous(name = expression("Cd (mg*kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
size = 0.25)) +
theme(text = element_text(size = 12))
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pht
## Gráfica por genotipo
datos2<-summarySE (datos, measurevar = "cd.grano.c", groupvars = c("curva", "gen","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data_out/Cd_grano_mean.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=cd.grano.c-ci, ymax=cd.grano.c+ci), width=.1) +
geom_line(aes(y=cd.grano.c)) +
geom_point(aes(y=cd.grano.c)) +
scale_y_continuous(name = expression("Cd (mg*kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "day", breaks=seq(0,7,1)) + # Etiqueta de los grupos
theme(axis.line = element_line(colour = "black", # Personalización del tema
size = 0.25)) +
theme(text = element_text(size = 15))
pht2
