setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("percentage.csv", header=T, sep=',')
datos$curva <- factor(datos$curva, levels = c("1", "2", "3"),
labels = c("T3", "T1", "T2"))
datos$gen<-as.factor(datos$gen)
datos$curva<-as.factor(datos$curva)
datos$id<-as.factor(datos$id)
datos$muestra<-as.factor(datos$muestra)
datos$diam2<-as.factor(datos$diam2)
library(ggplot2)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.1 ✔ forcats 1.0.0
## Warning: package 'tibble' was built under R version 4.1.2
## Warning: package 'tidyr' was built under R version 4.1.2
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library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
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## mutate
library(rstatix)
##
## Attaching package: 'rstatix'
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##
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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(tf, 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 tf 3 0.43 0.028
## 2 T3 CCN51 2 tf 3 0.713 0.146
## 3 T3 CCN51 5 tf 3 1.32 0.209
## 4 T3 CCN51 6 tf 3 1.67 0.117
## 5 T3 ICS95 0 tf 3 0.473 0.06
## 6 T3 ICS95 2 tf 3 0.734 0.117
## 7 T3 ICS95 5 tf 3 1.22 0.06
## 8 T3 ICS95 6 tf 3 1.47 0.062
## 9 T3 TCS01 0 tf 3 0.295 0.025
## 10 T3 TCS01 2 tf 3 0.602 0.124
## 11 T3 TCS01 5 tf 3 0.894 0.157
## 12 T3 TCS01 6 tf 3 1.12 0.19
## 13 T1 CCN51 0 tf 3 0.631 0.226
## 14 T1 CCN51 2 tf 3 1.07 0.214
## 15 T1 CCN51 5 tf 3 1.72 0.316
## 16 T1 CCN51 6 tf 3 1.80 0.066
## 17 T1 ICS95 0 tf 3 0.639 0.06
## 18 T1 ICS95 2 tf 3 0.798 0.007
## 19 T1 ICS95 5 tf 3 1.01 0.086
## 20 T1 ICS95 6 tf 3 1.09 0.066
## 21 T1 TCS01 0 tf 3 0.494 0.089
## 22 T1 TCS01 2 tf 3 0.748 0.096
## 23 T1 TCS01 5 tf 3 1.00 0.119
## 24 T1 TCS01 6 tf 3 1.14 0.091
## 25 T2 CCN51 0 tf 3 0.702 0.049
## 26 T2 CCN51 2 tf 3 0.742 0.029
## 27 T2 CCN51 5 tf 3 1.51 0.161
## 28 T2 CCN51 6 tf 3 1.53 0.105
## 29 T2 ICS95 0 tf 3 0.565 0.035
## 30 T2 ICS95 2 tf 3 0.783 0.061
## 31 T2 ICS95 5 tf 3 0.71 0.058
## 32 T2 ICS95 6 tf 3 0.937 0.147
## 33 T2 TCS01 0 tf 3 0.448 0.029
## 34 T2 TCS01 2 tf 3 0.586 0.03
## 35 T2 TCS01 5 tf 3 0.904 0.067
## 36 T2 TCS01 6 tf 3 1.26 0.27
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "tf",
color = "diam2", palette = "jco",
facet.by = "gen", xlab = "Treatment", ylab = "ITF", legend.title = "day"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(tf)
## [1] curva gen diam2 ids trat
## [6] muestra id dia cd.testa cd.grano
## [11] pdcd.grano picd.testa pdph.grano piph.testa ph.testa
## [16] acidez.testa ph.grano acidez.grano tf pi.tf
## [21] 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(tf)
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 tf 0.834 0.198
## 2 T3 CCN51 2 tf 0.994 0.851
## 3 T3 CCN51 5 tf 0.834 0.199
## 4 T3 CCN51 6 tf 0.866 0.285
## 5 T3 ICS95 0 tf 0.984 0.755
## 6 T3 ICS95 2 tf 0.830 0.188
## 7 T3 ICS95 5 tf 0.903 0.396
## 8 T3 ICS95 6 tf 0.985 0.762
## 9 T3 TCS01 0 tf 0.964 0.637
## 10 T3 TCS01 2 tf 0.991 0.817
## 11 T3 TCS01 5 tf 0.972 0.681
## 12 T3 TCS01 6 tf 0.921 0.456
## 13 T1 CCN51 0 tf 0.834 0.199
## 14 T1 CCN51 2 tf 0.787 0.0832
## 15 T1 CCN51 5 tf 0.834 0.198
## 16 T1 CCN51 6 tf 1.00 0.985
## 17 T1 ICS95 0 tf 0.894 0.368
## 18 T1 ICS95 2 tf 0.931 0.494
## 19 T1 ICS95 5 tf 0.974 0.690
## 20 T1 ICS95 6 tf 0.966 0.646
## 21 T1 TCS01 0 tf 1.00 0.988
## 22 T1 TCS01 2 tf 0.978 0.715
## 23 T1 TCS01 5 tf 0.885 0.339
## 24 T1 TCS01 6 tf 0.989 0.798
## 25 T2 CCN51 0 tf 0.756 0.0142
## 26 T2 CCN51 2 tf 0.954 0.588
## 27 T2 CCN51 5 tf 0.963 0.628
## 28 T2 CCN51 6 tf 0.877 0.316
## 29 T2 ICS95 0 tf 0.992 0.825
## 30 T2 ICS95 2 tf 0.759 0.0202
## 31 T2 ICS95 5 tf 0.966 0.647
## 32 T2 ICS95 6 tf 0.811 0.141
## 33 T2 TCS01 0 tf 0.934 0.505
## 34 T2 TCS01 2 tf 0.986 0.777
## 35 T2 TCS01 5 tf 0.884 0.337
## 36 T2 TCS01 6 tf 1.00 0.967
##Create QQ plot for each cell of design:
ggqqplot(datos, "tf", 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.
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## 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
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## the data.
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## 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
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## the data.
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## 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
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## 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(tf ~ 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 0.798 0.612
## 2 2 8 18 0.617 0.753
## 3 5 8 18 0.474 0.859
## 4 6 8 18 0.648 0.728
##Computation
res.aov <- anova_test(
data = datos, dv = tf, 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 5.943 1.00e-02 * 0.203
## 2 gen 2 18 51.875 3.37e-08 * 0.690
## 3 diam2 3 54 275.421 1.07e-32 * 0.904
## 4 curva:gen 4 18 5.460 5.00e-03 * 0.319
## 5 curva:diam2 6 54 5.079 3.39e-04 * 0.257
## 6 gen:diam2 6 54 13.706 2.18e-09 * 0.483
## 7 curva:gen:diam2 12 54 3.138 2.00e-03 * 0.300
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 diam2 0.787 0.549
## 2 curva:diam2 0.787 0.549
## 3 gen:diam2 0.787 0.549
## 4 curva:gen:diam2 0.787 0.549
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 diam2 0.877 2.63, 47.33 6.14e-29 * 1.04 3.12, 56.17
## 2 curva:diam2 0.877 5.26, 47.33 6.95e-04 * 1.04 6.24, 56.17
## 3 gen:diam2 0.877 5.26, 47.33 1.83e-08 * 1.04 6.24, 56.17
## 4 curva:gen:diam2 0.877 10.52, 47.33 3.00e-03 * 1.04 12.48, 56.17
## p[HF] p[HF]<.05
## 1 1.07e-32 *
## 2 3.39e-04 *
## 3 2.18e-09 *
## 4 2.00e-03 *
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2 18 5.943 1.00e-02 * 0.203
## 2 gen 2 18 51.875 3.37e-08 * 0.690
## 3 diam2 3 54 275.421 1.07e-32 * 0.904
## 4 curva:gen 4 18 5.460 5.00e-03 * 0.319
## 5 curva:diam2 6 54 5.079 3.39e-04 * 0.257
## 6 gen:diam2 6 54 13.706 2.18e-09 * 0.483
## 7 curva:gen:diam2 12 54 3.138 2.00e-03 * 0.300
#Table by error
res.aov.error <- aov(tf ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = tf ~ diam2 * curva * gen + Error(id/diam2), data = datos)
##
## Grand Mean: 0.9371656
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 0.2992541 2.6121718 0.5498559 0.4531984
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 0.1586748
## 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 10.999270 0.405681 1.094717 0.501285 0.718853
## Deg. of Freedom 3 6 6 12 54
##
## Residual standard error: 0.115378
## 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 0.911 0.0264 18 0.855 0.966
## T1 1.011 0.0264 18 0.955 1.066
## T2 0.890 0.0264 18 0.834 0.946
##
## 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 -0.0997 0.0374 18 -2.666 0.0398
## T3 - T2 0.0209 0.0374 18 0.560 0.8428
## T1 - T2 0.1207 0.0374 18 3.226 0.0124
##
## 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 1.032 0.0458 18 0.936 1.129
## ICS95 0.974 0.0458 18 0.877 1.070
## TCS01 0.727 0.0458 18 0.631 0.823
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 1.303 0.0458 18 1.207 1.399
## ICS95 0.884 0.0458 18 0.788 0.980
## TCS01 0.845 0.0458 18 0.749 0.941
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 1.122 0.0458 18 1.026 1.218
## ICS95 0.749 0.0458 18 0.652 0.845
## TCS01 0.799 0.0458 18 0.703 0.895
##
## 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 0.0587 0.0648 18 0.906 0.6435
## CCN51 - TCS01 0.3055 0.0648 18 4.716 0.0005
## ICS95 - TCS01 0.2468 0.0648 18 3.810 0.0035
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.4191 0.0648 18 6.469 <.0001
## CCN51 - TCS01 0.4578 0.0648 18 7.067 <.0001
## ICS95 - TCS01 0.0387 0.0648 18 0.597 0.8233
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 0.3733 0.0648 18 5.763 0.0001
## CCN51 - TCS01 0.3228 0.0648 18 4.983 0.0003
## ICS95 - TCS01 -0.0506 0.0648 18 -0.781 0.7193
##
## 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 0.430 0.0737 65.5 0.283 0.578
## 2 0.713 0.0737 65.5 0.566 0.860
## 5 1.318 0.0737 65.5 1.171 1.465
## 6 1.668 0.0737 65.5 1.521 1.815
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.631 0.0737 65.5 0.484 0.778
## 2 1.068 0.0737 65.5 0.921 1.216
## 5 1.716 0.0737 65.5 1.569 1.863
## 6 1.796 0.0737 65.5 1.649 1.943
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.702 0.0737 65.5 0.555 0.849
## 2 0.742 0.0737 65.5 0.595 0.889
## 5 1.514 0.0737 65.5 1.367 1.661
## 6 1.529 0.0737 65.5 1.382 1.677
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.473 0.0737 65.5 0.326 0.620
## 2 0.734 0.0737 65.5 0.587 0.881
## 5 1.221 0.0737 65.5 1.074 1.368
## 6 1.467 0.0737 65.5 1.320 1.614
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.639 0.0737 65.5 0.492 0.786
## 2 0.798 0.0737 65.5 0.651 0.945
## 5 1.007 0.0737 65.5 0.860 1.154
## 6 1.092 0.0737 65.5 0.945 1.239
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.565 0.0737 65.5 0.418 0.712
## 2 0.783 0.0737 65.5 0.636 0.930
## 5 0.710 0.0737 65.5 0.562 0.857
## 6 0.937 0.0737 65.5 0.790 1.085
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.295 0.0737 65.5 0.148 0.443
## 2 0.602 0.0737 65.5 0.455 0.750
## 5 0.894 0.0737 65.5 0.747 1.041
## 6 1.115 0.0737 65.5 0.968 1.263
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.494 0.0737 65.5 0.347 0.642
## 2 0.748 0.0737 65.5 0.601 0.895
## 5 1.002 0.0737 65.5 0.855 1.149
## 6 1.136 0.0737 65.5 0.989 1.283
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 0.448 0.0737 65.5 0.301 0.595
## 2 0.586 0.0737 65.5 0.439 0.733
## 5 0.904 0.0737 65.5 0.757 1.051
## 6 1.259 0.0737 65.5 1.112 1.406
##
## 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 -0.2824 0.0942 54 -2.997 0.0208
## 0 - 5 -0.8873 0.0942 54 -9.419 <.0001
## 0 - 6 -1.2378 0.0942 54 -13.140 <.0001
## 2 - 5 -0.6049 0.0942 54 -6.421 <.0001
## 2 - 6 -0.9555 0.0942 54 -10.142 <.0001
## 5 - 6 -0.3505 0.0942 54 -3.721 0.0026
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.4371 0.0942 54 -4.640 0.0001
## 0 - 5 -1.0844 0.0942 54 -11.511 <.0001
## 0 - 6 -1.1646 0.0942 54 -12.362 <.0001
## 2 - 5 -0.6473 0.0942 54 -6.871 <.0001
## 2 - 6 -0.7275 0.0942 54 -7.722 <.0001
## 5 - 6 -0.0802 0.0942 54 -0.851 0.8298
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.0398 0.0942 54 -0.422 0.9744
## 0 - 5 -0.8122 0.0942 54 -8.621 <.0001
## 0 - 6 -0.8273 0.0942 54 -8.781 <.0001
## 2 - 5 -0.7724 0.0942 54 -8.199 <.0001
## 2 - 6 -0.7875 0.0942 54 -8.359 <.0001
## 5 - 6 -0.0151 0.0942 54 -0.160 0.9985
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.2602 0.0942 54 -2.762 0.0381
## 0 - 5 -0.7474 0.0942 54 -7.933 <.0001
## 0 - 6 -0.9933 0.0942 54 -10.544 <.0001
## 2 - 5 -0.4872 0.0942 54 -5.171 <.0001
## 2 - 6 -0.7331 0.0942 54 -7.782 <.0001
## 5 - 6 -0.2460 0.0942 54 -2.611 0.0550
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.1592 0.0942 54 -1.690 0.3388
## 0 - 5 -0.3681 0.0942 54 -3.908 0.0015
## 0 - 6 -0.4533 0.0942 54 -4.812 0.0001
## 2 - 5 -0.2089 0.0942 54 -2.218 0.1314
## 2 - 6 -0.2941 0.0942 54 -3.122 0.0149
## 5 - 6 -0.0852 0.0942 54 -0.904 0.8028
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.2184 0.0942 54 -2.318 0.1065
## 0 - 5 -0.1448 0.0942 54 -1.537 0.4227
## 0 - 6 -0.3728 0.0942 54 -3.957 0.0012
## 2 - 5 0.0735 0.0942 54 0.781 0.8629
## 2 - 6 -0.1544 0.0942 54 -1.639 0.3660
## 5 - 6 -0.2279 0.0942 54 -2.419 0.0854
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.3070 0.0942 54 -3.259 0.0102
## 0 - 5 -0.5983 0.0942 54 -6.351 <.0001
## 0 - 6 -0.8200 0.0942 54 -8.704 <.0001
## 2 - 5 -0.2913 0.0942 54 -3.092 0.0161
## 2 - 6 -0.5130 0.0942 54 -5.445 <.0001
## 5 - 6 -0.2217 0.0942 54 -2.353 0.0988
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.2539 0.0942 54 -2.695 0.0449
## 0 - 5 -0.5075 0.0942 54 -5.387 <.0001
## 0 - 6 -0.6411 0.0942 54 -6.806 <.0001
## 2 - 5 -0.2536 0.0942 54 -2.692 0.0452
## 2 - 6 -0.3872 0.0942 54 -4.111 0.0008
## 5 - 6 -0.1336 0.0942 54 -1.418 0.4936
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.1381 0.0942 54 -1.465 0.4652
## 0 - 5 -0.4560 0.0942 54 -4.841 0.0001
## 0 - 6 -0.8112 0.0942 54 -8.611 <.0001
## 2 - 5 -0.3180 0.0942 54 -3.376 0.0073
## 2 - 6 -0.6732 0.0942 54 -7.146 <.0001
## 5 - 6 -0.3552 0.0942 54 -3.770 0.0022
##
## 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 0.430 0.0737 65.5 0.283 0.578
## 2 CCN51 0.713 0.0737 65.5 0.566 0.860
## 5 CCN51 1.318 0.0737 65.5 1.171 1.465
## 6 CCN51 1.668 0.0737 65.5 1.521 1.815
## 0 ICS95 0.473 0.0737 65.5 0.326 0.620
## 2 ICS95 0.734 0.0737 65.5 0.587 0.881
## 5 ICS95 1.221 0.0737 65.5 1.074 1.368
## 6 ICS95 1.467 0.0737 65.5 1.320 1.614
## 0 TCS01 0.295 0.0737 65.5 0.148 0.443
## 2 TCS01 0.602 0.0737 65.5 0.455 0.750
## 5 TCS01 0.894 0.0737 65.5 0.747 1.041
## 6 TCS01 1.115 0.0737 65.5 0.968 1.263
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.631 0.0737 65.5 0.484 0.778
## 2 CCN51 1.068 0.0737 65.5 0.921 1.216
## 5 CCN51 1.716 0.0737 65.5 1.569 1.863
## 6 CCN51 1.796 0.0737 65.5 1.649 1.943
## 0 ICS95 0.639 0.0737 65.5 0.492 0.786
## 2 ICS95 0.798 0.0737 65.5 0.651 0.945
## 5 ICS95 1.007 0.0737 65.5 0.860 1.154
## 6 ICS95 1.092 0.0737 65.5 0.945 1.239
## 0 TCS01 0.494 0.0737 65.5 0.347 0.642
## 2 TCS01 0.748 0.0737 65.5 0.601 0.895
## 5 TCS01 1.002 0.0737 65.5 0.855 1.149
## 6 TCS01 1.136 0.0737 65.5 0.989 1.283
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 0.702 0.0737 65.5 0.555 0.849
## 2 CCN51 0.742 0.0737 65.5 0.595 0.889
## 5 CCN51 1.514 0.0737 65.5 1.367 1.661
## 6 CCN51 1.529 0.0737 65.5 1.382 1.677
## 0 ICS95 0.565 0.0737 65.5 0.418 0.712
## 2 ICS95 0.783 0.0737 65.5 0.636 0.930
## 5 ICS95 0.710 0.0737 65.5 0.562 0.857
## 6 ICS95 0.937 0.0737 65.5 0.790 1.085
## 0 TCS01 0.448 0.0737 65.5 0.301 0.595
## 2 TCS01 0.586 0.0737 65.5 0.439 0.733
## 5 TCS01 0.904 0.0737 65.5 0.757 1.051
## 6 TCS01 1.259 0.0737 65.5 1.112 1.406
##
## 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 -0.28238 0.0942 54.0 -2.997 0.1377
## 0 CCN51 - 5 CCN51 -0.88730 0.0942 54.0 -9.419 <.0001
## 0 CCN51 - 6 CCN51 -1.23785 0.0942 54.0 -13.140 <.0001
## 0 CCN51 - 0 ICS95 -0.04295 0.1042 65.5 -0.412 1.0000
## 0 CCN51 - 2 ICS95 -0.30317 0.1042 65.5 -2.910 0.1616
## 0 CCN51 - 5 ICS95 -0.79033 0.1042 65.5 -7.587 <.0001
## 0 CCN51 - 6 ICS95 -1.03628 0.1042 65.5 -9.948 <.0001
## 0 CCN51 - 0 TCS01 0.13495 0.1042 65.5 1.295 0.9769
## 0 CCN51 - 2 TCS01 -0.17206 0.1042 65.5 -1.652 0.8827
## 0 CCN51 - 5 TCS01 -0.46335 0.1042 65.5 -4.448 0.0019
## 0 CCN51 - 6 TCS01 -0.68502 0.1042 65.5 -6.576 <.0001
## 2 CCN51 - 5 CCN51 -0.60493 0.0942 54.0 -6.421 <.0001
## 2 CCN51 - 6 CCN51 -0.95547 0.0942 54.0 -10.142 <.0001
## 2 CCN51 - 0 ICS95 0.23942 0.1042 65.5 2.298 0.4882
## 2 CCN51 - 2 ICS95 -0.02080 0.1042 65.5 -0.200 1.0000
## 2 CCN51 - 5 ICS95 -0.50795 0.1042 65.5 -4.876 0.0004
## 2 CCN51 - 6 ICS95 -0.75390 0.1042 65.5 -7.237 <.0001
## 2 CCN51 - 0 TCS01 0.41732 0.1042 65.5 4.006 0.0082
## 2 CCN51 - 2 TCS01 0.11031 0.1042 65.5 1.059 0.9954
## 2 CCN51 - 5 TCS01 -0.18097 0.1042 65.5 -1.737 0.8442
## 2 CCN51 - 6 TCS01 -0.40264 0.1042 65.5 -3.865 0.0127
## 5 CCN51 - 6 CCN51 -0.35054 0.0942 54.0 -3.721 0.0217
## 5 CCN51 - 0 ICS95 0.84435 0.1042 65.5 8.105 <.0001
## 5 CCN51 - 2 ICS95 0.58413 0.1042 65.5 5.607 <.0001
## 5 CCN51 - 5 ICS95 0.09698 0.1042 65.5 0.931 0.9985
## 5 CCN51 - 6 ICS95 -0.14898 0.1042 65.5 -1.430 0.9530
## 5 CCN51 - 0 TCS01 1.02225 0.1042 65.5 9.813 <.0001
## 5 CCN51 - 2 TCS01 0.71524 0.1042 65.5 6.866 <.0001
## 5 CCN51 - 5 TCS01 0.42396 0.1042 65.5 4.070 0.0067
## 5 CCN51 - 6 TCS01 0.20229 0.1042 65.5 1.942 0.7290
## 6 CCN51 - 0 ICS95 1.19489 0.1042 65.5 11.470 <.0001
## 6 CCN51 - 2 ICS95 0.93468 0.1042 65.5 8.972 <.0001
## 6 CCN51 - 5 ICS95 0.44752 0.1042 65.5 4.296 0.0032
## 6 CCN51 - 6 ICS95 0.20157 0.1042 65.5 1.935 0.7334
## 6 CCN51 - 0 TCS01 1.37280 0.1042 65.5 13.178 <.0001
## 6 CCN51 - 2 TCS01 1.06579 0.1042 65.5 10.231 <.0001
## 6 CCN51 - 5 TCS01 0.77450 0.1042 65.5 7.435 <.0001
## 6 CCN51 - 6 TCS01 0.55283 0.1042 65.5 5.307 0.0001
## 0 ICS95 - 2 ICS95 -0.26022 0.0942 54.0 -2.762 0.2255
## 0 ICS95 - 5 ICS95 -0.74737 0.0942 54.0 -7.933 <.0001
## 0 ICS95 - 6 ICS95 -0.99333 0.0942 54.0 -10.544 <.0001
## 0 ICS95 - 0 TCS01 0.17790 0.1042 65.5 1.708 0.8582
## 0 ICS95 - 2 TCS01 -0.12911 0.1042 65.5 -1.239 0.9835
## 0 ICS95 - 5 TCS01 -0.42039 0.1042 65.5 -4.035 0.0074
## 0 ICS95 - 6 TCS01 -0.64206 0.1042 65.5 -6.163 <.0001
## 2 ICS95 - 5 ICS95 -0.48715 0.0942 54.0 -5.171 0.0002
## 2 ICS95 - 6 ICS95 -0.73311 0.0942 54.0 -7.782 <.0001
## 2 ICS95 - 0 TCS01 0.43812 0.1042 65.5 4.206 0.0043
## 2 ICS95 - 2 TCS01 0.13111 0.1042 65.5 1.259 0.9814
## 2 ICS95 - 5 TCS01 -0.16017 0.1042 65.5 -1.538 0.9242
## 2 ICS95 - 6 TCS01 -0.38185 0.1042 65.5 -3.665 0.0230
## 5 ICS95 - 6 ICS95 -0.24595 0.0942 54.0 -2.611 0.2995
## 5 ICS95 - 0 TCS01 0.92527 0.1042 65.5 8.882 <.0001
## 5 ICS95 - 2 TCS01 0.61826 0.1042 65.5 5.935 <.0001
## 5 ICS95 - 5 TCS01 0.32698 0.1042 65.5 3.139 0.0950
## 5 ICS95 - 6 TCS01 0.10531 0.1042 65.5 1.011 0.9969
## 6 ICS95 - 0 TCS01 1.17123 0.1042 65.5 11.243 <.0001
## 6 ICS95 - 2 TCS01 0.86422 0.1042 65.5 8.296 <.0001
## 6 ICS95 - 5 TCS01 0.57293 0.1042 65.5 5.500 <.0001
## 6 ICS95 - 6 TCS01 0.35126 0.1042 65.5 3.372 0.0523
## 0 TCS01 - 2 TCS01 -0.30701 0.0942 54.0 -3.259 0.0744
## 0 TCS01 - 5 TCS01 -0.59829 0.0942 54.0 -6.351 <.0001
## 0 TCS01 - 6 TCS01 -0.81997 0.0942 54.0 -8.704 <.0001
## 2 TCS01 - 5 TCS01 -0.29128 0.0942 54.0 -3.092 0.1111
## 2 TCS01 - 6 TCS01 -0.51296 0.0942 54.0 -5.445 0.0001
## 5 TCS01 - 6 TCS01 -0.22167 0.0942 54.0 -2.353 0.4540
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 -0.43709 0.0942 54.0 -4.640 0.0013
## 0 CCN51 - 5 CCN51 -1.08440 0.0942 54.0 -11.511 <.0001
## 0 CCN51 - 6 CCN51 -1.16457 0.0942 54.0 -12.362 <.0001
## 0 CCN51 - 0 ICS95 -0.00727 0.1042 65.5 -0.070 1.0000
## 0 CCN51 - 2 ICS95 -0.16649 0.1042 65.5 -1.598 0.9036
## 0 CCN51 - 5 ICS95 -0.37542 0.1042 65.5 -3.604 0.0275
## 0 CCN51 - 6 ICS95 -0.46057 0.1042 65.5 -4.421 0.0021
## 0 CCN51 - 0 TCS01 0.13689 0.1042 65.5 1.314 0.9743
## 0 CCN51 - 2 TCS01 -0.11700 0.1042 65.5 -1.123 0.9925
## 0 CCN51 - 5 TCS01 -0.37061 0.1042 65.5 -3.558 0.0314
## 0 CCN51 - 6 TCS01 -0.50424 0.1042 65.5 -4.840 0.0005
## 2 CCN51 - 5 CCN51 -0.64731 0.0942 54.0 -6.871 <.0001
## 2 CCN51 - 6 CCN51 -0.72748 0.0942 54.0 -7.722 <.0001
## 2 CCN51 - 0 ICS95 0.42982 0.1042 65.5 4.126 0.0056
## 2 CCN51 - 2 ICS95 0.27059 0.1042 65.5 2.598 0.3024
## 2 CCN51 - 5 ICS95 0.06167 0.1042 65.5 0.592 1.0000
## 2 CCN51 - 6 ICS95 -0.02348 0.1042 65.5 -0.225 1.0000
## 2 CCN51 - 0 TCS01 0.57398 0.1042 65.5 5.510 <.0001
## 2 CCN51 - 2 TCS01 0.32009 0.1042 65.5 3.073 0.1115
## 2 CCN51 - 5 TCS01 0.06647 0.1042 65.5 0.638 1.0000
## 2 CCN51 - 6 TCS01 -0.06715 0.1042 65.5 -0.645 1.0000
## 5 CCN51 - 6 CCN51 -0.08017 0.0942 54.0 -0.851 0.9993
## 5 CCN51 - 0 ICS95 1.07713 0.1042 65.5 10.340 <.0001
## 5 CCN51 - 2 ICS95 0.91790 0.1042 65.5 8.811 <.0001
## 5 CCN51 - 5 ICS95 0.70898 0.1042 65.5 6.806 <.0001
## 5 CCN51 - 6 ICS95 0.62383 0.1042 65.5 5.988 <.0001
## 5 CCN51 - 0 TCS01 1.22129 0.1042 65.5 11.724 <.0001
## 5 CCN51 - 2 TCS01 0.96740 0.1042 65.5 9.286 <.0001
## 5 CCN51 - 5 TCS01 0.71378 0.1042 65.5 6.852 <.0001
## 5 CCN51 - 6 TCS01 0.58016 0.1042 65.5 5.569 <.0001
## 6 CCN51 - 0 ICS95 1.15729 0.1042 65.5 11.109 <.0001
## 6 CCN51 - 2 ICS95 0.99807 0.1042 65.5 9.581 <.0001
## 6 CCN51 - 5 ICS95 0.78915 0.1042 65.5 7.575 <.0001
## 6 CCN51 - 6 ICS95 0.70399 0.1042 65.5 6.758 <.0001
## 6 CCN51 - 0 TCS01 1.30146 0.1042 65.5 12.493 <.0001
## 6 CCN51 - 2 TCS01 1.04757 0.1042 65.5 10.056 <.0001
## 6 CCN51 - 5 TCS01 0.79395 0.1042 65.5 7.621 <.0001
## 6 CCN51 - 6 TCS01 0.66033 0.1042 65.5 6.339 <.0001
## 0 ICS95 - 2 ICS95 -0.15922 0.0942 54.0 -1.690 0.8648
## 0 ICS95 - 5 ICS95 -0.36815 0.0942 54.0 -3.908 0.0126
## 0 ICS95 - 6 ICS95 -0.45330 0.0942 54.0 -4.812 0.0007
## 0 ICS95 - 0 TCS01 0.14417 0.1042 65.5 1.384 0.9626
## 0 ICS95 - 2 TCS01 -0.10973 0.1042 65.5 -1.053 0.9956
## 0 ICS95 - 5 TCS01 -0.36334 0.1042 65.5 -3.488 0.0382
## 0 ICS95 - 6 TCS01 -0.49697 0.1042 65.5 -4.771 0.0006
## 2 ICS95 - 5 ICS95 -0.20893 0.0942 54.0 -2.218 0.5447
## 2 ICS95 - 6 ICS95 -0.29408 0.0942 54.0 -3.122 0.1036
## 2 ICS95 - 0 TCS01 0.30339 0.1042 65.5 2.912 0.1608
## 2 ICS95 - 2 TCS01 0.04949 0.1042 65.5 0.475 1.0000
## 2 ICS95 - 5 TCS01 -0.20412 0.1042 65.5 -1.959 0.7179
## 2 ICS95 - 6 TCS01 -0.33775 0.1042 65.5 -3.242 0.0734
## 5 ICS95 - 6 ICS95 -0.08515 0.0942 54.0 -0.904 0.9988
## 5 ICS95 - 0 TCS01 0.51232 0.1042 65.5 4.918 0.0004
## 5 ICS95 - 2 TCS01 0.25842 0.1042 65.5 2.481 0.3701
## 5 ICS95 - 5 TCS01 0.00481 0.1042 65.5 0.046 1.0000
## 5 ICS95 - 6 TCS01 -0.12882 0.1042 65.5 -1.237 0.9838
## 6 ICS95 - 0 TCS01 0.59747 0.1042 65.5 5.735 <.0001
## 6 ICS95 - 2 TCS01 0.34357 0.1042 65.5 3.298 0.0636
## 6 ICS95 - 5 TCS01 0.08996 0.1042 65.5 0.864 0.9993
## 6 ICS95 - 6 TCS01 -0.04367 0.1042 65.5 -0.419 1.0000
## 0 TCS01 - 2 TCS01 -0.25390 0.0942 54.0 -2.695 0.2566
## 0 TCS01 - 5 TCS01 -0.50751 0.0942 54.0 -5.387 0.0001
## 0 TCS01 - 6 TCS01 -0.64114 0.0942 54.0 -6.806 <.0001
## 2 TCS01 - 5 TCS01 -0.25361 0.0942 54.0 -2.692 0.2581
## 2 TCS01 - 6 TCS01 -0.38724 0.0942 54.0 -4.111 0.0068
## 5 TCS01 - 6 TCS01 -0.13363 0.0942 54.0 -1.418 0.9546
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 -0.03978 0.0942 54.0 -0.422 1.0000
## 0 CCN51 - 5 CCN51 -0.81218 0.0942 54.0 -8.621 <.0001
## 0 CCN51 - 6 CCN51 -0.82726 0.0942 54.0 -8.781 <.0001
## 0 CCN51 - 0 ICS95 0.13754 0.1042 65.5 1.320 0.9734
## 0 CCN51 - 2 ICS95 -0.08084 0.1042 65.5 -0.776 0.9997
## 0 CCN51 - 5 ICS95 -0.00730 0.1042 65.5 -0.070 1.0000
## 0 CCN51 - 6 ICS95 -0.23523 0.1042 65.5 -2.258 0.5157
## 0 CCN51 - 0 TCS01 0.25429 0.1042 65.5 2.441 0.3946
## 0 CCN51 - 2 TCS01 0.11624 0.1042 65.5 1.116 0.9929
## 0 CCN51 - 5 TCS01 -0.20175 0.1042 65.5 -1.937 0.7323
## 0 CCN51 - 6 TCS01 -0.55691 0.1042 65.5 -5.346 0.0001
## 2 CCN51 - 5 CCN51 -0.77240 0.0942 54.0 -8.199 <.0001
## 2 CCN51 - 6 CCN51 -0.78748 0.0942 54.0 -8.359 <.0001
## 2 CCN51 - 0 ICS95 0.17732 0.1042 65.5 1.702 0.8607
## 2 CCN51 - 2 ICS95 -0.04106 0.1042 65.5 -0.394 1.0000
## 2 CCN51 - 5 ICS95 0.03248 0.1042 65.5 0.312 1.0000
## 2 CCN51 - 6 ICS95 -0.19545 0.1042 65.5 -1.876 0.7691
## 2 CCN51 - 0 TCS01 0.29407 0.1042 65.5 2.823 0.1948
## 2 CCN51 - 2 TCS01 0.15602 0.1042 65.5 1.498 0.9360
## 2 CCN51 - 5 TCS01 -0.16197 0.1042 65.5 -1.555 0.9186
## 2 CCN51 - 6 TCS01 -0.51713 0.1042 65.5 -4.964 0.0003
## 5 CCN51 - 6 CCN51 -0.01508 0.0942 54.0 -0.160 1.0000
## 5 CCN51 - 0 ICS95 0.94972 0.1042 65.5 9.117 <.0001
## 5 CCN51 - 2 ICS95 0.73134 0.1042 65.5 7.020 <.0001
## 5 CCN51 - 5 ICS95 0.80488 0.1042 65.5 7.726 <.0001
## 5 CCN51 - 6 ICS95 0.57695 0.1042 65.5 5.538 <.0001
## 5 CCN51 - 0 TCS01 1.06647 0.1042 65.5 10.237 <.0001
## 5 CCN51 - 2 TCS01 0.92842 0.1042 65.5 8.912 <.0001
## 5 CCN51 - 5 TCS01 0.61043 0.1042 65.5 5.860 <.0001
## 5 CCN51 - 6 TCS01 0.25527 0.1042 65.5 2.450 0.3888
## 6 CCN51 - 0 ICS95 0.96480 0.1042 65.5 9.261 <.0001
## 6 CCN51 - 2 ICS95 0.74642 0.1042 65.5 7.165 <.0001
## 6 CCN51 - 5 ICS95 0.81996 0.1042 65.5 7.871 <.0001
## 6 CCN51 - 6 ICS95 0.59204 0.1042 65.5 5.683 <.0001
## 6 CCN51 - 0 TCS01 1.08156 0.1042 65.5 10.382 <.0001
## 6 CCN51 - 2 TCS01 0.94351 0.1042 65.5 9.057 <.0001
## 6 CCN51 - 5 TCS01 0.62551 0.1042 65.5 6.004 <.0001
## 6 CCN51 - 6 TCS01 0.27035 0.1042 65.5 2.595 0.3037
## 0 ICS95 - 2 ICS95 -0.21838 0.0942 54.0 -2.318 0.4770
## 0 ICS95 - 5 ICS95 -0.14484 0.0942 54.0 -1.537 0.9230
## 0 ICS95 - 6 ICS95 -0.37276 0.0942 54.0 -3.957 0.0109
## 0 ICS95 - 0 TCS01 0.11676 0.1042 65.5 1.121 0.9926
## 0 ICS95 - 2 TCS01 -0.02130 0.1042 65.5 -0.204 1.0000
## 0 ICS95 - 5 TCS01 -0.33929 0.1042 65.5 -3.257 0.0707
## 0 ICS95 - 6 TCS01 -0.69445 0.1042 65.5 -6.666 <.0001
## 2 ICS95 - 5 ICS95 0.07354 0.0942 54.0 0.781 0.9997
## 2 ICS95 - 6 ICS95 -0.15439 0.0942 54.0 -1.639 0.8866
## 2 ICS95 - 0 TCS01 0.33513 0.1042 65.5 3.217 0.0783
## 2 ICS95 - 2 TCS01 0.19708 0.1042 65.5 1.892 0.7597
## 2 ICS95 - 5 TCS01 -0.12091 0.1042 65.5 -1.161 0.9902
## 2 ICS95 - 6 TCS01 -0.47607 0.1042 65.5 -4.570 0.0013
## 5 ICS95 - 6 ICS95 -0.22793 0.0942 54.0 -2.419 0.4113
## 5 ICS95 - 0 TCS01 0.26159 0.1042 65.5 2.511 0.3517
## 5 ICS95 - 2 TCS01 0.12354 0.1042 65.5 1.186 0.9883
## 5 ICS95 - 5 TCS01 -0.19445 0.1042 65.5 -1.867 0.7747
## 5 ICS95 - 6 TCS01 -0.54961 0.1042 65.5 -5.276 0.0001
## 6 ICS95 - 0 TCS01 0.48952 0.1042 65.5 4.699 0.0008
## 6 ICS95 - 2 TCS01 0.35147 0.1042 65.5 3.374 0.0521
## 6 ICS95 - 5 TCS01 0.03347 0.1042 65.5 0.321 1.0000
## 6 ICS95 - 6 TCS01 -0.32169 0.1042 65.5 -3.088 0.1075
## 0 TCS01 - 2 TCS01 -0.13805 0.0942 54.0 -1.465 0.9435
## 0 TCS01 - 5 TCS01 -0.45605 0.0942 54.0 -4.841 0.0006
## 0 TCS01 - 6 TCS01 -0.81121 0.0942 54.0 -8.611 <.0001
## 2 TCS01 - 5 TCS01 -0.31799 0.0942 54.0 -3.376 0.0554
## 2 TCS01 - 6 TCS01 -0.67315 0.0942 54.0 -7.146 <.0001
## 5 TCS01 - 6 TCS01 -0.35516 0.0942 54.0 -3.770 0.0189
##
## P value adjustment: tukey method for comparing a family of 12 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(tf)
## [1] gen diam2 ids curva trat
## [6] muestra id dia cd.testa cd.grano
## [11] pdcd.grano picd.testa pdph.grano piph.testa ph.testa
## [16] acidez.testa ph.grano acidez.grano tf pi.tf
## [21] 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(tf)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 tf 0.834 0.198
## 2 CCN51 2 tf 0.994 0.851
## 3 CCN51 5 tf 0.834 0.199
## 4 CCN51 6 tf 0.866 0.285
## 5 ICS95 0 tf 0.984 0.755
## 6 ICS95 2 tf 0.830 0.188
## 7 ICS95 5 tf 0.903 0.396
## 8 ICS95 6 tf 0.985 0.762
## 9 TCS01 0 tf 0.964 0.637
## 10 TCS01 2 tf 0.991 0.817
## 11 TCS01 5 tf 0.972 0.681
## 12 TCS01 6 tf 0.921 0.456
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "tf", 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(tf ~ 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 0.702 0.532
## 2 2 2 6 0.0665 0.936
## 3 5 2 6 0.420 0.675
## 4 6 2 6 0.487 0.637
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = tf, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov1)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 31.412 6.63e-04 * 0.636
## 2 diam2 1.24 7.46 111.736 6.23e-06 * 0.939
## 3 gen:diam2 2.49 7.46 2.154 1.80e-01 0.374
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = tf, 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 41.746 0.000205 * 0.947
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = tf, 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 77.505 3.45e-05 * 0.974
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = tf, 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 19.445 0.002 * 0.881
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(tf)
## [1] gen diam2 ids curva trat
## [6] muestra id dia cd.testa cd.grano
## [11] pdcd.grano picd.testa pdph.grano piph.testa ph.testa
## [16] acidez.testa ph.grano acidez.grano tf pi.tf
## [21] 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(tf)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 tf 0.834 0.199
## 2 CCN51 2 tf 0.787 0.0832
## 3 CCN51 5 tf 0.834 0.198
## 4 CCN51 6 tf 1.00 0.985
## 5 ICS95 0 tf 0.894 0.368
## 6 ICS95 2 tf 0.931 0.494
## 7 ICS95 5 tf 0.974 0.690
## 8 ICS95 6 tf 0.966 0.646
## 9 TCS01 0 tf 1.00 0.988
## 10 TCS01 2 tf 0.978 0.715
## 11 TCS01 5 tf 0.885 0.339
## 12 TCS01 6 tf 0.989 0.798
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "tf", 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(tf ~ 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 0.557 0.600
## 2 2 2 6 0.743 0.515
## 3 5 2 6 0.546 0.605
## 4 6 2 6 0.129 0.882
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = tf, 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 18.077 3.00e-03 * 0.751
## 2 diam2 3 18 76.291 1.98e-10 * 0.864
## 3 gen:diam2 6 18 7.253 4.70e-04 * 0.546
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = tf, 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 33.695 0.000377 * 0.873
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = tf, 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 55.863 8.92e-05 * 0.925
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = tf, 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 18.603 0.002 * 0.901
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(tf)
## [1] gen diam2 ids curva trat
## [6] muestra id dia cd.testa cd.grano
## [11] pdcd.grano picd.testa pdph.grano piph.testa ph.testa
## [16] acidez.testa ph.grano acidez.grano tf pi.tf
## [21] 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(tf)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 tf 0.756 0.0142
## 2 CCN51 2 tf 0.954 0.588
## 3 CCN51 5 tf 0.963 0.628
## 4 CCN51 6 tf 0.877 0.316
## 5 ICS95 0 tf 0.992 0.825
## 6 ICS95 2 tf 0.759 0.0202
## 7 ICS95 5 tf 0.966 0.647
## 8 ICS95 6 tf 0.811 0.141
## 9 TCS01 0 tf 0.934 0.505
## 10 TCS01 2 tf 0.986 0.777
## 11 TCS01 5 tf 0.884 0.337
## 12 TCS01 6 tf 1.00 0.967
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "tf", 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(tf ~ 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 0.0639 0.939
## 2 2 2 6 0.173 0.846
## 3 5 2 6 0.823 0.483
## 4 6 2 6 0.665 0.549
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = tf, 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 2.0 6.00 21.676 2.00e-03 * 0.767
## 2 diam2 1.6 9.61 94.187 8.35e-07 * 0.895
## 3 gen:diam2 3.2 9.61 14.091 6.73e-04 * 0.719
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = tf, 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 64.702 5.83e-05 * 0.96
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = tf, 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 22.804 0.001 * 0.784
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = tf, 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 27.949 0.000635 * 0.881
## 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=tf)) +
geom_point(aes(y=tf)) +
scale_y_continuous(name = expression("ITF")) + # 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 = "tf", 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=tf-ci, ymax=tf+ci), width=.1) +
geom_line(aes(y=tf)) +
geom_point(aes(y=tf)) +
scale_y_continuous(name = expression("ITF")) + # 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
