setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/René/1er_Cd/data")
datos<-read.table("testafin2.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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## 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.testa, 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.testa 3 3.76 0.107
## 2 T3 CCN51 2 cd.testa 3 5.86 1.17
## 3 T3 CCN51 5 cd.testa 3 10.3 1.80
## 4 T3 CCN51 6 cd.testa 3 12.3 0.987
## 5 T3 ICS95 0 cd.testa 3 5.55 0.618
## 6 T3 ICS95 2 cd.testa 3 8.38 1.00
## 7 T3 ICS95 5 cd.testa 3 13.4 1.07
## 8 T3 ICS95 6 cd.testa 3 14.9 0.442
## 9 T3 TCS01 0 cd.testa 3 2.83 0.288
## 10 T3 TCS01 2 cd.testa 3 5.00 1.05
## 11 T3 TCS01 5 cd.testa 3 6.87 0.768
## 12 T3 TCS01 6 cd.testa 3 7.83 1.11
## 13 T1 CCN51 0 cd.testa 3 5.96 1.99
## 14 T1 CCN51 2 cd.testa 3 8.88 1.39
## 15 T1 CCN51 5 cd.testa 3 13.4 1.84
## 16 T1 CCN51 6 cd.testa 3 13.8 0.37
## 17 T1 ICS95 0 cd.testa 3 6.37 0.255
## 18 T1 ICS95 2 cd.testa 3 7.68 0.589
## 19 T1 ICS95 5 cd.testa 3 9.33 0.373
## 20 T1 ICS95 6 cd.testa 3 9.52 0.84
## 21 T1 TCS01 0 cd.testa 3 3.75 0.335
## 22 T1 TCS01 2 cd.testa 3 5.08 0.931
## 23 T1 TCS01 5 cd.testa 3 6.53 0.432
## 24 T1 TCS01 6 cd.testa 3 7.30 0.898
## 25 T2 CCN51 0 cd.testa 3 5.09 0.202
## 26 T2 CCN51 2 cd.testa 3 5.18 0.403
## 27 T2 CCN51 5 cd.testa 3 9.80 0.949
## 28 T2 CCN51 6 cd.testa 3 8.67 0.552
## 29 T2 ICS95 0 cd.testa 3 6.87 0.575
## 30 T2 ICS95 2 cd.testa 3 9.03 0.559
## 31 T2 ICS95 5 cd.testa 3 8.10 0.561
## 32 T2 ICS95 6 cd.testa 3 10.1 1.08
## 33 T2 TCS01 0 cd.testa 3 4.02 0.178
## 34 T2 TCS01 2 cd.testa 3 5.13 0.402
## 35 T2 TCS01 5 cd.testa 3 7.42 0.497
## 36 T2 TCS01 6 cd.testa 3 8.39 1.67
##Visualization
bxp <- ggboxplot(
datos, x = "curva", y = "cd.testa",
color = "diam2", palette = "jco",
facet.by = "gen"
)
bxp
##Check assumptions
##Outliers
datos %>%
group_by(curva, gen, diam2) %>%
identify_outliers(cd.testa)
## [1] curva gen diam2 muestra id cd.testa is.outlier
## [8] 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.testa)
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.testa 0.964 0.637
## 2 T3 CCN51 2 cd.testa 0.925 0.470
## 3 T3 CCN51 5 cd.testa 0.850 0.239
## 4 T3 CCN51 6 cd.testa 0.976 0.703
## 5 T3 ICS95 0 cd.testa 0.835 0.201
## 6 T3 ICS95 2 cd.testa 0.994 0.846
## 7 T3 ICS95 5 cd.testa 0.993 0.846
## 8 T3 ICS95 6 cd.testa 0.992 0.825
## 9 T3 TCS01 0 cd.testa 0.942 0.537
## 10 T3 TCS01 2 cd.testa 0.987 0.785
## 11 T3 TCS01 5 cd.testa 0.993 0.835
## 12 T3 TCS01 6 cd.testa 0.996 0.875
## 13 T1 CCN51 0 cd.testa 0.894 0.367
## 14 T1 CCN51 2 cd.testa 0.940 0.528
## 15 T1 CCN51 5 cd.testa 0.902 0.393
## 16 T1 CCN51 6 cd.testa 0.973 0.686
## 17 T1 ICS95 0 cd.testa 0.959 0.609
## 18 T1 ICS95 2 cd.testa 0.857 0.260
## 19 T1 ICS95 5 cd.testa 0.985 0.763
## 20 T1 ICS95 6 cd.testa 0.999 0.948
## 21 T1 TCS01 0 cd.testa 0.998 0.918
## 22 T1 TCS01 2 cd.testa 0.935 0.509
## 23 T1 TCS01 5 cd.testa 0.817 0.155
## 24 T1 TCS01 6 cd.testa 0.755 0.0106
## 25 T2 CCN51 0 cd.testa 0.883 0.332
## 26 T2 CCN51 2 cd.testa 0.891 0.358
## 27 T2 CCN51 5 cd.testa 0.869 0.293
## 28 T2 CCN51 6 cd.testa 0.920 0.454
## 29 T2 ICS95 0 cd.testa 0.915 0.436
## 30 T2 ICS95 2 cd.testa 0.880 0.326
## 31 T2 ICS95 5 cd.testa 0.962 0.624
## 32 T2 ICS95 6 cd.testa 0.859 0.265
## 33 T2 TCS01 0 cd.testa 0.968 0.657
## 34 T2 TCS01 2 cd.testa 0.802 0.119
## 35 T2 TCS01 5 cd.testa 0.992 0.833
## 36 T2 TCS01 6 cd.testa 0.934 0.503
##Create QQ plot for each cell of design:
ggqqplot(datos, "cd.testa", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
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## the data.
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## The following aesthetics were dropped during statistical transformation: sample
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## The following aesthetics were dropped during statistical transformation: sample
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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
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
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## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
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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.testa ~ 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 1.18 0.365
## 2 2 8 18 0.432 0.887
## 3 5 8 18 0.537 0.814
## 4 6 8 18 0.410 0.900
##Computation
res.aov <- anova_test(
data = datos, dv = cd.testa, wid = id,
within = diam2, between = c(curva, gen)
)
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 curva 2 18 5.325 1.50e-02 * 0.198
## 2 gen 2 18 77.179 1.48e-09 * 0.782
## 3 diam2 3 54 252.797 9.35e-32 * 0.891
## 4 curva:gen 4 18 17.181 5.81e-06 * 0.615
## 5 curva:diam2 6 54 11.339 3.68e-08 * 0.423
## 6 gen:diam2 6 54 8.561 1.51e-06 * 0.356
## 7 curva:gen:diam2 12 54 5.894 2.08e-06 * 0.433
#Table by error
res.aov.error <- aov(cd.testa ~ diam2*curva*gen + Error(id/diam2), datos)
res.aov.error
##
## Call:
## aov(formula = cd.testa ~ diam2 * curva * gen + Error(id/diam2),
## data = datos)
##
## Grand Mean: 7.842315
##
## Stratum 1: id
##
## Terms:
## curva gen curva:gen Residuals
## Sum of Squares 15.19935 220.31264 98.08973 25.69123
## Deg. of Freedom 2 2 4 18
##
## Residual standard error: 1.194693
## 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 502.7973 45.1039 34.0544 46.8891 35.8009
## Deg. of Freedom 3 6 6 12 54
##
## Residual standard error: 0.8142356
## 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 8.08 0.199 18 7.66 8.50
## T1 8.14 0.199 18 7.72 8.55
## T2 7.31 0.199 18 6.89 7.73
##
## 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 -0.0564 0.282 18 -0.200 0.9782
## T3 - T2 0.7661 0.282 18 2.721 0.0356
## T1 - T2 0.8225 0.282 18 2.921 0.0236
##
## 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 8.05 0.345 18 7.33 8.78
## ICS95 10.55 0.345 18 9.83 11.28
## TCS01 5.63 0.345 18 4.91 6.36
##
## curva = T1:
## gen emmean SE df lower.CL upper.CL
## CCN51 10.52 0.345 18 9.80 11.24
## ICS95 8.22 0.345 18 7.50 8.95
## TCS01 5.66 0.345 18 4.94 6.39
##
## curva = T2:
## gen emmean SE df lower.CL upper.CL
## CCN51 7.18 0.345 18 6.46 7.91
## ICS95 8.52 0.345 18 7.79 9.24
## TCS01 6.24 0.345 18 5.51 6.96
##
## 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 -2.498 0.488 18 -5.122 0.0002
## CCN51 - TCS01 2.422 0.488 18 4.965 0.0003
## ICS95 - TCS01 4.920 0.488 18 10.088 <.0001
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 2.297 0.488 18 4.711 0.0005
## CCN51 - TCS01 4.857 0.488 18 9.958 <.0001
## ICS95 - TCS01 2.559 0.488 18 5.247 0.0002
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## CCN51 - ICS95 -1.334 0.488 18 -2.735 0.0346
## CCN51 - TCS01 0.946 0.488 18 1.939 0.1566
## ICS95 - TCS01 2.280 0.488 18 4.675 0.0005
##
## Results are averaged over the levels of: diam2
## P value adjustment: tukey method for comparing a family of 3 estimates
emm_gen_diam2 <- emmeans(res.aov.error, pairwise ~ diam2 | curva*gen)
## Note: re-fitting model with sum-to-zero contrasts
emm_gen_diam2
## $emmeans
## curva = T3, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.76 0.534 62.6 2.69 4.82
## 2 5.86 0.534 62.6 4.79 6.93
## 5 10.26 0.534 62.6 9.20 11.33
## 6 12.33 0.534 62.6 11.27 13.40
##
## curva = T1, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.96 0.534 62.6 4.90 7.03
## 2 8.88 0.534 62.6 7.82 9.95
## 5 13.44 0.534 62.6 12.38 14.51
## 6 13.79 0.534 62.6 12.72 14.86
##
## curva = T2, gen = CCN51:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.09 0.534 62.6 4.02 6.16
## 2 5.18 0.534 62.6 4.11 6.24
## 5 9.80 0.534 62.6 8.73 10.86
## 6 8.67 0.534 62.6 7.60 9.74
##
## curva = T3, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.55 0.534 62.6 4.48 6.62
## 2 8.38 0.534 62.6 7.32 9.45
## 5 13.36 0.534 62.6 12.29 14.43
## 6 14.91 0.534 62.6 13.85 15.98
##
## curva = T1, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 6.37 0.534 62.6 5.30 7.44
## 2 7.68 0.534 62.6 6.61 8.74
## 5 9.33 0.534 62.6 8.26 10.39
## 6 9.52 0.534 62.6 8.45 10.58
##
## curva = T2, gen = ICS95:
## diam2 emmean SE df lower.CL upper.CL
## 0 6.87 0.534 62.6 5.81 7.94
## 2 9.03 0.534 62.6 7.97 10.10
## 5 8.10 0.534 62.6 7.04 9.17
## 6 10.06 0.534 62.6 8.99 11.13
##
## curva = T3, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 2.83 0.534 62.6 1.76 3.90
## 2 5.00 0.534 62.6 3.93 6.06
## 5 6.87 0.534 62.6 5.81 7.94
## 6 7.83 0.534 62.6 6.76 8.89
##
## curva = T1, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 3.75 0.534 62.6 2.68 4.81
## 2 5.08 0.534 62.6 4.01 6.14
## 5 6.53 0.534 62.6 5.47 7.60
## 6 7.30 0.534 62.6 6.23 8.36
##
## curva = T2, gen = TCS01:
## diam2 emmean SE df lower.CL upper.CL
## 0 4.02 0.534 62.6 2.95 5.08
## 2 5.13 0.534 62.6 4.06 6.19
## 5 7.42 0.534 62.6 6.35 8.49
## 6 8.39 0.534 62.6 7.32 9.45
##
## 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 -2.1033 0.665 54 -3.164 0.0132
## 0 - 5 -6.5067 0.665 54 -9.787 <.0001
## 0 - 6 -8.5767 0.665 54 -12.901 <.0001
## 2 - 5 -4.4033 0.665 54 -6.623 <.0001
## 2 - 6 -6.4733 0.665 54 -9.737 <.0001
## 5 - 6 -2.0700 0.665 54 -3.114 0.0152
##
## curva = T1, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -2.9200 0.665 54 -4.392 0.0003
## 0 - 5 -7.4800 0.665 54 -11.251 <.0001
## 0 - 6 -7.8267 0.665 54 -11.773 <.0001
## 2 - 5 -4.5600 0.665 54 -6.859 <.0001
## 2 - 6 -4.9067 0.665 54 -7.380 <.0001
## 5 - 6 -0.3467 0.665 54 -0.521 0.9536
##
## curva = T2, gen = CCN51:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -0.0867 0.665 54 -0.130 0.9992
## 0 - 5 -4.7067 0.665 54 -7.080 <.0001
## 0 - 6 -3.5800 0.665 54 -5.385 <.0001
## 2 - 5 -4.6200 0.665 54 -6.949 <.0001
## 2 - 6 -3.4933 0.665 54 -5.255 <.0001
## 5 - 6 1.1267 0.665 54 1.695 0.3364
##
## curva = T3, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -2.8333 0.665 54 -4.262 0.0005
## 0 - 5 -7.8100 0.665 54 -11.748 <.0001
## 0 - 6 -9.3633 0.665 54 -14.084 <.0001
## 2 - 5 -4.9767 0.665 54 -7.486 <.0001
## 2 - 6 -6.5300 0.665 54 -9.822 <.0001
## 5 - 6 -1.5533 0.665 54 -2.336 0.1024
##
## curva = T1, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -1.3067 0.665 54 -1.965 0.2138
## 0 - 5 -2.9567 0.665 54 -4.447 0.0003
## 0 - 6 -3.1467 0.665 54 -4.733 0.0001
## 2 - 5 -1.6500 0.665 54 -2.482 0.0743
## 2 - 6 -1.8400 0.665 54 -2.768 0.0376
## 5 - 6 -0.1900 0.665 54 -0.286 0.9918
##
## curva = T2, gen = ICS95:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -2.1600 0.665 54 -3.249 0.0104
## 0 - 5 -1.2300 0.665 54 -1.850 0.2617
## 0 - 6 -3.1867 0.665 54 -4.793 0.0001
## 2 - 5 0.9300 0.665 54 1.399 0.5056
## 2 - 6 -1.0267 0.665 54 -1.544 0.4188
## 5 - 6 -1.9567 0.665 54 -2.943 0.0240
##
## curva = T3, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -2.1667 0.665 54 -3.259 0.0102
## 0 - 5 -4.0433 0.665 54 -6.082 <.0001
## 0 - 6 -4.9967 0.665 54 -7.516 <.0001
## 2 - 5 -1.8767 0.665 54 -2.823 0.0327
## 2 - 6 -2.8300 0.665 54 -4.257 0.0005
## 5 - 6 -0.9533 0.665 54 -1.434 0.4841
##
## curva = T1, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -1.3300 0.665 54 -2.001 0.2005
## 0 - 5 -2.7867 0.665 54 -4.192 0.0006
## 0 - 6 -3.5500 0.665 54 -5.340 <.0001
## 2 - 5 -1.4567 0.665 54 -2.191 0.1387
## 2 - 6 -2.2200 0.665 54 -3.339 0.0081
## 5 - 6 -0.7633 0.665 54 -1.148 0.6616
##
## curva = T2, gen = TCS01:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -1.1100 0.665 54 -1.670 0.3495
## 0 - 5 -3.4033 0.665 54 -5.119 <.0001
## 0 - 6 -4.3700 0.665 54 -6.573 <.0001
## 2 - 5 -2.2933 0.665 54 -3.450 0.0059
## 2 - 6 -3.2600 0.665 54 -4.904 0.0001
## 5 - 6 -0.9667 0.665 54 -1.454 0.4720
##
## 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 3.76 0.534 62.6 2.69 4.82
## 2 CCN51 5.86 0.534 62.6 4.79 6.93
## 5 CCN51 10.26 0.534 62.6 9.20 11.33
## 6 CCN51 12.33 0.534 62.6 11.27 13.40
## 0 ICS95 5.55 0.534 62.6 4.48 6.62
## 2 ICS95 8.38 0.534 62.6 7.32 9.45
## 5 ICS95 13.36 0.534 62.6 12.29 14.43
## 6 ICS95 14.91 0.534 62.6 13.85 15.98
## 0 TCS01 2.83 0.534 62.6 1.76 3.90
## 2 TCS01 5.00 0.534 62.6 3.93 6.06
## 5 TCS01 6.87 0.534 62.6 5.81 7.94
## 6 TCS01 7.83 0.534 62.6 6.76 8.89
##
## curva = T1:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 5.96 0.534 62.6 4.90 7.03
## 2 CCN51 8.88 0.534 62.6 7.82 9.95
## 5 CCN51 13.44 0.534 62.6 12.38 14.51
## 6 CCN51 13.79 0.534 62.6 12.72 14.86
## 0 ICS95 6.37 0.534 62.6 5.30 7.44
## 2 ICS95 7.68 0.534 62.6 6.61 8.74
## 5 ICS95 9.33 0.534 62.6 8.26 10.39
## 6 ICS95 9.52 0.534 62.6 8.45 10.58
## 0 TCS01 3.75 0.534 62.6 2.68 4.81
## 2 TCS01 5.08 0.534 62.6 4.01 6.14
## 5 TCS01 6.53 0.534 62.6 5.47 7.60
## 6 TCS01 7.30 0.534 62.6 6.23 8.36
##
## curva = T2:
## diam2 gen emmean SE df lower.CL upper.CL
## 0 CCN51 5.09 0.534 62.6 4.02 6.16
## 2 CCN51 5.18 0.534 62.6 4.11 6.24
## 5 CCN51 9.80 0.534 62.6 8.73 10.86
## 6 CCN51 8.67 0.534 62.6 7.60 9.74
## 0 ICS95 6.87 0.534 62.6 5.81 7.94
## 2 ICS95 9.03 0.534 62.6 7.97 10.10
## 5 ICS95 8.10 0.534 62.6 7.04 9.17
## 6 ICS95 10.06 0.534 62.6 8.99 11.13
## 0 TCS01 4.02 0.534 62.6 2.95 5.08
## 2 TCS01 5.13 0.534 62.6 4.06 6.19
## 5 TCS01 7.42 0.534 62.6 6.35 8.49
## 6 TCS01 8.39 0.534 62.6 7.32 9.45
##
## 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 -2.1033 0.665 54.0 -3.164 0.0938
## 0 CCN51 - 5 CCN51 -6.5067 0.665 54.0 -9.787 <.0001
## 0 CCN51 - 6 CCN51 -8.5767 0.665 54.0 -12.901 <.0001
## 0 CCN51 - 0 ICS95 -1.7933 0.755 62.6 -2.377 0.4365
## 0 CCN51 - 2 ICS95 -4.6267 0.755 62.6 -6.132 <.0001
## 0 CCN51 - 5 ICS95 -9.6033 0.755 62.6 -12.727 <.0001
## 0 CCN51 - 6 ICS95 -11.1567 0.755 62.6 -14.786 <.0001
## 0 CCN51 - 0 TCS01 0.9267 0.755 62.6 1.228 0.9845
## 0 CCN51 - 2 TCS01 -1.2400 0.755 62.6 -1.643 0.8858
## 0 CCN51 - 5 TCS01 -3.1167 0.755 62.6 -4.130 0.0057
## 0 CCN51 - 6 TCS01 -4.0700 0.755 62.6 -5.394 0.0001
## 2 CCN51 - 5 CCN51 -4.4033 0.665 54.0 -6.623 <.0001
## 2 CCN51 - 6 CCN51 -6.4733 0.665 54.0 -9.737 <.0001
## 2 CCN51 - 0 ICS95 0.3100 0.755 62.6 0.411 1.0000
## 2 CCN51 - 2 ICS95 -2.5233 0.755 62.6 -3.344 0.0571
## 2 CCN51 - 5 ICS95 -7.5000 0.755 62.6 -9.939 <.0001
## 2 CCN51 - 6 ICS95 -9.0533 0.755 62.6 -11.998 <.0001
## 2 CCN51 - 0 TCS01 3.0300 0.755 62.6 4.016 0.0082
## 2 CCN51 - 2 TCS01 0.8633 0.755 62.6 1.144 0.9912
## 2 CCN51 - 5 TCS01 -1.0133 0.755 62.6 -1.343 0.9697
## 2 CCN51 - 6 TCS01 -1.9667 0.755 62.6 -2.606 0.2986
## 5 CCN51 - 6 CCN51 -2.0700 0.665 54.0 -3.114 0.1056
## 5 CCN51 - 0 ICS95 4.7133 0.755 62.6 6.246 <.0001
## 5 CCN51 - 2 ICS95 1.8800 0.755 62.6 2.491 0.3643
## 5 CCN51 - 5 ICS95 -3.0967 0.755 62.6 -4.104 0.0062
## 5 CCN51 - 6 ICS95 -4.6500 0.755 62.6 -6.162 <.0001
## 5 CCN51 - 0 TCS01 7.4333 0.755 62.6 9.851 <.0001
## 5 CCN51 - 2 TCS01 5.2667 0.755 62.6 6.980 <.0001
## 5 CCN51 - 5 TCS01 3.3900 0.755 62.6 4.493 0.0017
## 5 CCN51 - 6 TCS01 2.4367 0.755 62.6 3.229 0.0768
## 6 CCN51 - 0 ICS95 6.7833 0.755 62.6 8.990 <.0001
## 6 CCN51 - 2 ICS95 3.9500 0.755 62.6 5.235 0.0001
## 6 CCN51 - 5 ICS95 -1.0267 0.755 62.6 -1.361 0.9667
## 6 CCN51 - 6 ICS95 -2.5800 0.755 62.6 -3.419 0.0468
## 6 CCN51 - 0 TCS01 9.5033 0.755 62.6 12.594 <.0001
## 6 CCN51 - 2 TCS01 7.3367 0.755 62.6 9.723 <.0001
## 6 CCN51 - 5 TCS01 5.4600 0.755 62.6 7.236 <.0001
## 6 CCN51 - 6 TCS01 4.5067 0.755 62.6 5.973 <.0001
## 0 ICS95 - 2 ICS95 -2.8333 0.665 54.0 -4.262 0.0043
## 0 ICS95 - 5 ICS95 -7.8100 0.665 54.0 -11.748 <.0001
## 0 ICS95 - 6 ICS95 -9.3633 0.665 54.0 -14.084 <.0001
## 0 ICS95 - 0 TCS01 2.7200 0.755 62.6 3.605 0.0280
## 0 ICS95 - 2 TCS01 0.5533 0.755 62.6 0.733 0.9998
## 0 ICS95 - 5 TCS01 -1.3233 0.755 62.6 -1.754 0.8357
## 0 ICS95 - 6 TCS01 -2.2767 0.755 62.6 -3.017 0.1280
## 2 ICS95 - 5 ICS95 -4.9767 0.665 54.0 -7.486 <.0001
## 2 ICS95 - 6 ICS95 -6.5300 0.665 54.0 -9.822 <.0001
## 2 ICS95 - 0 TCS01 5.5533 0.755 62.6 7.360 <.0001
## 2 ICS95 - 2 TCS01 3.3867 0.755 62.6 4.488 0.0017
## 2 ICS95 - 5 TCS01 1.5100 0.755 62.6 2.001 0.6908
## 2 ICS95 - 6 TCS01 0.5567 0.755 62.6 0.738 0.9998
## 5 ICS95 - 6 ICS95 -1.5533 0.665 54.0 -2.336 0.4649
## 5 ICS95 - 0 TCS01 10.5300 0.755 62.6 13.955 <.0001
## 5 ICS95 - 2 TCS01 8.3633 0.755 62.6 11.084 <.0001
## 5 ICS95 - 5 TCS01 6.4867 0.755 62.6 8.597 <.0001
## 5 ICS95 - 6 TCS01 5.5333 0.755 62.6 7.333 <.0001
## 6 ICS95 - 0 TCS01 12.0833 0.755 62.6 16.014 <.0001
## 6 ICS95 - 2 TCS01 9.9167 0.755 62.6 13.142 <.0001
## 6 ICS95 - 5 TCS01 8.0400 0.755 62.6 10.655 <.0001
## 6 ICS95 - 6 TCS01 7.0867 0.755 62.6 9.392 <.0001
## 0 TCS01 - 2 TCS01 -2.1667 0.665 54.0 -3.259 0.0744
## 0 TCS01 - 5 TCS01 -4.0433 0.665 54.0 -6.082 <.0001
## 0 TCS01 - 6 TCS01 -4.9967 0.665 54.0 -7.516 <.0001
## 2 TCS01 - 5 TCS01 -1.8767 0.665 54.0 -2.823 0.1998
## 2 TCS01 - 6 TCS01 -2.8300 0.665 54.0 -4.257 0.0043
## 5 TCS01 - 6 TCS01 -0.9533 0.665 54.0 -1.434 0.9512
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 -2.9200 0.665 54.0 -4.392 0.0028
## 0 CCN51 - 5 CCN51 -7.4800 0.665 54.0 -11.251 <.0001
## 0 CCN51 - 6 CCN51 -7.8267 0.665 54.0 -11.773 <.0001
## 0 CCN51 - 0 ICS95 -0.4067 0.755 62.6 -0.539 1.0000
## 0 CCN51 - 2 ICS95 -1.7133 0.755 62.6 -2.271 0.5074
## 0 CCN51 - 5 ICS95 -3.3633 0.755 62.6 -4.457 0.0019
## 0 CCN51 - 6 ICS95 -3.5533 0.755 62.6 -4.709 0.0008
## 0 CCN51 - 0 TCS01 2.2167 0.755 62.6 2.938 0.1531
## 0 CCN51 - 2 TCS01 0.8867 0.755 62.6 1.175 0.9891
## 0 CCN51 - 5 TCS01 -0.5700 0.755 62.6 -0.755 0.9998
## 0 CCN51 - 6 TCS01 -1.3333 0.755 62.6 -1.767 0.8290
## 2 CCN51 - 5 CCN51 -4.5600 0.665 54.0 -6.859 <.0001
## 2 CCN51 - 6 CCN51 -4.9067 0.665 54.0 -7.380 <.0001
## 2 CCN51 - 0 ICS95 2.5133 0.755 62.6 3.331 0.0592
## 2 CCN51 - 2 ICS95 1.2067 0.755 62.6 1.599 0.9029
## 2 CCN51 - 5 ICS95 -0.4433 0.755 62.6 -0.588 1.0000
## 2 CCN51 - 6 ICS95 -0.6333 0.755 62.6 -0.839 0.9994
## 2 CCN51 - 0 TCS01 5.1367 0.755 62.6 6.807 <.0001
## 2 CCN51 - 2 TCS01 3.8067 0.755 62.6 5.045 0.0002
## 2 CCN51 - 5 TCS01 2.3500 0.755 62.6 3.114 0.1018
## 2 CCN51 - 6 TCS01 1.5867 0.755 62.6 2.103 0.6227
## 5 CCN51 - 6 CCN51 -0.3467 0.665 54.0 -0.521 1.0000
## 5 CCN51 - 0 ICS95 7.0733 0.755 62.6 9.374 <.0001
## 5 CCN51 - 2 ICS95 5.7667 0.755 62.6 7.642 <.0001
## 5 CCN51 - 5 ICS95 4.1167 0.755 62.6 5.456 0.0001
## 5 CCN51 - 6 ICS95 3.9267 0.755 62.6 5.204 0.0001
## 5 CCN51 - 0 TCS01 9.6967 0.755 62.6 12.851 <.0001
## 5 CCN51 - 2 TCS01 8.3667 0.755 62.6 11.088 <.0001
## 5 CCN51 - 5 TCS01 6.9100 0.755 62.6 9.158 <.0001
## 5 CCN51 - 6 TCS01 6.1467 0.755 62.6 8.146 <.0001
## 6 CCN51 - 0 ICS95 7.4200 0.755 62.6 9.833 <.0001
## 6 CCN51 - 2 ICS95 6.1133 0.755 62.6 8.102 <.0001
## 6 CCN51 - 5 ICS95 4.4633 0.755 62.6 5.915 <.0001
## 6 CCN51 - 6 ICS95 4.2733 0.755 62.6 5.663 <.0001
## 6 CCN51 - 0 TCS01 10.0433 0.755 62.6 13.310 <.0001
## 6 CCN51 - 2 TCS01 8.7133 0.755 62.6 11.547 <.0001
## 6 CCN51 - 5 TCS01 7.2567 0.755 62.6 9.617 <.0001
## 6 CCN51 - 6 TCS01 6.4933 0.755 62.6 8.605 <.0001
## 0 ICS95 - 2 ICS95 -1.3067 0.665 54.0 -1.965 0.7135
## 0 ICS95 - 5 ICS95 -2.9567 0.665 54.0 -4.447 0.0024
## 0 ICS95 - 6 ICS95 -3.1467 0.665 54.0 -4.733 0.0009
## 0 ICS95 - 0 TCS01 2.6233 0.755 62.6 3.477 0.0400
## 0 ICS95 - 2 TCS01 1.2933 0.755 62.6 1.714 0.8549
## 0 ICS95 - 5 TCS01 -0.1633 0.755 62.6 -0.216 1.0000
## 0 ICS95 - 6 TCS01 -0.9267 0.755 62.6 -1.228 0.9845
## 2 ICS95 - 5 ICS95 -1.6500 0.665 54.0 -2.482 0.3728
## 2 ICS95 - 6 ICS95 -1.8400 0.665 54.0 -2.768 0.2231
## 2 ICS95 - 0 TCS01 3.9300 0.755 62.6 5.208 0.0001
## 2 ICS95 - 2 TCS01 2.6000 0.755 62.6 3.446 0.0436
## 2 ICS95 - 5 TCS01 1.1433 0.755 62.6 1.515 0.9307
## 2 ICS95 - 6 TCS01 0.3800 0.755 62.6 0.504 1.0000
## 5 ICS95 - 6 ICS95 -0.1900 0.665 54.0 -0.286 1.0000
## 5 ICS95 - 0 TCS01 5.5800 0.755 62.6 7.395 <.0001
## 5 ICS95 - 2 TCS01 4.2500 0.755 62.6 5.632 <.0001
## 5 ICS95 - 5 TCS01 2.7933 0.755 62.6 3.702 0.0212
## 5 ICS95 - 6 TCS01 2.0300 0.755 62.6 2.690 0.2554
## 6 ICS95 - 0 TCS01 5.7700 0.755 62.6 7.647 <.0001
## 6 ICS95 - 2 TCS01 4.4400 0.755 62.6 5.884 <.0001
## 6 ICS95 - 5 TCS01 2.9833 0.755 62.6 3.954 0.0099
## 6 ICS95 - 6 TCS01 2.2200 0.755 62.6 2.942 0.1516
## 0 TCS01 - 2 TCS01 -1.3300 0.665 54.0 -2.001 0.6910
## 0 TCS01 - 5 TCS01 -2.7867 0.665 54.0 -4.192 0.0053
## 0 TCS01 - 6 TCS01 -3.5500 0.665 54.0 -5.340 0.0001
## 2 TCS01 - 5 TCS01 -1.4567 0.665 54.0 -2.191 0.5629
## 2 TCS01 - 6 TCS01 -2.2200 0.665 54.0 -3.339 0.0608
## 5 TCS01 - 6 TCS01 -0.7633 0.665 54.0 -1.148 0.9907
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 CCN51 - 2 CCN51 -0.0867 0.665 54.0 -0.130 1.0000
## 0 CCN51 - 5 CCN51 -4.7067 0.665 54.0 -7.080 <.0001
## 0 CCN51 - 6 CCN51 -3.5800 0.665 54.0 -5.385 0.0001
## 0 CCN51 - 0 ICS95 -1.7833 0.755 62.6 -2.363 0.4452
## 0 CCN51 - 2 ICS95 -3.9433 0.755 62.6 -5.226 0.0001
## 0 CCN51 - 5 ICS95 -3.0133 0.755 62.6 -3.993 0.0088
## 0 CCN51 - 6 ICS95 -4.9700 0.755 62.6 -6.587 <.0001
## 0 CCN51 - 0 TCS01 1.0733 0.755 62.6 1.422 0.9545
## 0 CCN51 - 2 TCS01 -0.0367 0.755 62.6 -0.049 1.0000
## 0 CCN51 - 5 TCS01 -2.3300 0.755 62.6 -3.088 0.1085
## 0 CCN51 - 6 TCS01 -3.2967 0.755 62.6 -4.369 0.0026
## 2 CCN51 - 5 CCN51 -4.6200 0.665 54.0 -6.949 <.0001
## 2 CCN51 - 6 CCN51 -3.4933 0.665 54.0 -5.255 0.0002
## 2 CCN51 - 0 ICS95 -1.6967 0.755 62.6 -2.249 0.5225
## 2 CCN51 - 2 ICS95 -3.8567 0.755 62.6 -5.111 0.0002
## 2 CCN51 - 5 ICS95 -2.9267 0.755 62.6 -3.879 0.0125
## 2 CCN51 - 6 ICS95 -4.8833 0.755 62.6 -6.472 <.0001
## 2 CCN51 - 0 TCS01 1.1600 0.755 62.6 1.537 0.9240
## 2 CCN51 - 2 TCS01 0.0500 0.755 62.6 0.066 1.0000
## 2 CCN51 - 5 TCS01 -2.2433 0.755 62.6 -2.973 0.1415
## 2 CCN51 - 6 TCS01 -3.2100 0.755 62.6 -4.254 0.0038
## 5 CCN51 - 6 CCN51 1.1267 0.665 54.0 1.695 0.8627
## 5 CCN51 - 0 ICS95 2.9233 0.755 62.6 3.874 0.0127
## 5 CCN51 - 2 ICS95 0.7633 0.755 62.6 1.012 0.9969
## 5 CCN51 - 5 ICS95 1.6933 0.755 62.6 2.244 0.5255
## 5 CCN51 - 6 ICS95 -0.2633 0.755 62.6 -0.349 1.0000
## 5 CCN51 - 0 TCS01 5.7800 0.755 62.6 7.660 <.0001
## 5 CCN51 - 2 TCS01 4.6700 0.755 62.6 6.189 <.0001
## 5 CCN51 - 5 TCS01 2.3767 0.755 62.6 3.150 0.0935
## 5 CCN51 - 6 TCS01 1.4100 0.755 62.6 1.869 0.7733
## 6 CCN51 - 0 ICS95 1.7967 0.755 62.6 2.381 0.4337
## 6 CCN51 - 2 ICS95 -0.3633 0.755 62.6 -0.482 1.0000
## 6 CCN51 - 5 ICS95 0.5667 0.755 62.6 0.751 0.9998
## 6 CCN51 - 6 ICS95 -1.3900 0.755 62.6 -1.842 0.7885
## 6 CCN51 - 0 TCS01 4.6533 0.755 62.6 6.167 <.0001
## 6 CCN51 - 2 TCS01 3.5433 0.755 62.6 4.696 0.0009
## 6 CCN51 - 5 TCS01 1.2500 0.755 62.6 1.657 0.8804
## 6 CCN51 - 6 TCS01 0.2833 0.755 62.6 0.375 1.0000
## 0 ICS95 - 2 ICS95 -2.1600 0.665 54.0 -3.249 0.0762
## 0 ICS95 - 5 ICS95 -1.2300 0.665 54.0 -1.850 0.7831
## 0 ICS95 - 6 ICS95 -3.1867 0.665 54.0 -4.793 0.0007
## 0 ICS95 - 0 TCS01 2.8567 0.755 62.6 3.786 0.0165
## 0 ICS95 - 2 TCS01 1.7467 0.755 62.6 2.315 0.4775
## 0 ICS95 - 5 TCS01 -0.5467 0.755 62.6 -0.724 0.9999
## 0 ICS95 - 6 TCS01 -1.5133 0.755 62.6 -2.006 0.6879
## 2 ICS95 - 5 ICS95 0.9300 0.665 54.0 1.399 0.9588
## 2 ICS95 - 6 ICS95 -1.0267 0.665 54.0 -1.544 0.9208
## 2 ICS95 - 0 TCS01 5.0167 0.755 62.6 6.648 <.0001
## 2 ICS95 - 2 TCS01 3.9067 0.755 62.6 5.177 0.0002
## 2 ICS95 - 5 TCS01 1.6133 0.755 62.6 2.138 0.5985
## 2 ICS95 - 6 TCS01 0.6467 0.755 62.6 0.857 0.9993
## 5 ICS95 - 6 ICS95 -1.9567 0.665 54.0 -2.943 0.1551
## 5 ICS95 - 0 TCS01 4.0867 0.755 62.6 5.416 0.0001
## 5 ICS95 - 2 TCS01 2.9767 0.755 62.6 3.945 0.0102
## 5 ICS95 - 5 TCS01 0.6833 0.755 62.6 0.906 0.9988
## 5 ICS95 - 6 TCS01 -0.2833 0.755 62.6 -0.375 1.0000
## 6 ICS95 - 0 TCS01 6.0433 0.755 62.6 8.009 <.0001
## 6 ICS95 - 2 TCS01 4.9333 0.755 62.6 6.538 <.0001
## 6 ICS95 - 5 TCS01 2.6400 0.755 62.6 3.499 0.0377
## 6 ICS95 - 6 TCS01 1.6733 0.755 62.6 2.218 0.5437
## 0 TCS01 - 2 TCS01 -1.1100 0.665 54.0 -1.670 0.8738
## 0 TCS01 - 5 TCS01 -3.4033 0.665 54.0 -5.119 0.0002
## 0 TCS01 - 6 TCS01 -4.3700 0.665 54.0 -6.573 <.0001
## 2 TCS01 - 5 TCS01 -2.2933 0.665 54.0 -3.450 0.0457
## 2 TCS01 - 6 TCS01 -3.2600 0.665 54.0 -4.904 0.0005
## 5 TCS01 - 6 TCS01 -0.9667 0.665 54.0 -1.454 0.9464
##
## 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 4.91 0.178 62.6 4.56 5.27
## 2 6.69 0.178 62.6 6.33 7.05
## 5 9.46 0.178 62.6 9.10 9.81
## 6 10.31 0.178 62.6 9.95 10.67
##
## 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 -1.780 0.222 54 -8.031 <.0001
## 0 - 5 -4.547 0.222 54 -20.518 <.0001
## 0 - 6 -5.400 0.222 54 -24.366 <.0001
## 2 - 5 -2.767 0.222 54 -12.488 <.0001
## 2 - 6 -3.620 0.222 54 -16.335 <.0001
## 5 - 6 -0.853 0.222 54 -3.847 0.0018
##
## 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 4.05 0.308 62.6 3.43 4.66
## 2 6.41 0.308 62.6 5.80 7.03
## 5 10.17 0.308 62.6 9.55 10.78
## 6 11.69 0.308 62.6 11.08 12.31
##
## curva = T1:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.36 0.308 62.6 4.74 5.98
## 2 7.21 0.308 62.6 6.60 7.83
## 5 9.77 0.308 62.6 9.15 10.38
## 6 10.20 0.308 62.6 9.59 10.82
##
## curva = T2:
## diam2 emmean SE df lower.CL upper.CL
## 0 5.33 0.308 62.6 4.71 5.94
## 2 6.45 0.308 62.6 5.83 7.06
## 5 8.44 0.308 62.6 7.82 9.06
## 6 9.04 0.308 62.6 8.42 9.65
##
## 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 -2.368 0.384 54 -6.169 <.0001
## 0 - 5 -6.120 0.384 54 -15.944 <.0001
## 0 - 6 -7.646 0.384 54 -19.919 <.0001
## 2 - 5 -3.752 0.384 54 -9.776 <.0001
## 2 - 6 -5.278 0.384 54 -13.750 <.0001
## 5 - 6 -1.526 0.384 54 -3.975 0.0012
##
## curva = T1:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -1.852 0.384 54 -4.826 0.0001
## 0 - 5 -4.408 0.384 54 -11.484 <.0001
## 0 - 6 -4.841 0.384 54 -12.613 <.0001
## 2 - 5 -2.556 0.384 54 -6.658 <.0001
## 2 - 6 -2.989 0.384 54 -7.787 <.0001
## 5 - 6 -0.433 0.384 54 -1.129 0.6734
##
## curva = T2:
## contrast estimate SE df t.ratio p.value
## 0 - 2 -1.119 0.384 54 -2.915 0.0258
## 0 - 5 -3.113 0.384 54 -8.111 <.0001
## 0 - 6 -3.712 0.384 54 -9.671 <.0001
## 2 - 5 -1.994 0.384 54 -5.196 <.0001
## 2 - 6 -2.593 0.384 54 -6.756 <.0001
## 5 - 6 -0.599 0.384 54 -1.560 0.4096
##
## 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.testa)
## [1] gen diam2 curva muestra id cd.testa is.outlier
## [8] 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.testa)
norm1 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.testa 0.964 0.637
## 2 CCN51 2 cd.testa 0.925 0.470
## 3 CCN51 5 cd.testa 0.850 0.239
## 4 CCN51 6 cd.testa 0.976 0.703
## 5 ICS95 0 cd.testa 0.835 0.201
## 6 ICS95 2 cd.testa 0.994 0.846
## 7 ICS95 5 cd.testa 0.993 0.846
## 8 ICS95 6 cd.testa 0.992 0.825
## 9 TCS01 0 cd.testa 0.942 0.537
## 10 TCS01 2 cd.testa 0.987 0.785
## 11 TCS01 5 cd.testa 0.993 0.835
## 12 TCS01 6 cd.testa 0.996 0.875
##Create QQ plot for each cell of design:
ggqqplot(datos.curve1, "cd.testa", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##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.testa ~ 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 0.632 0.563
## 2 2 2 6 0.0108 0.989
## 3 5 2 6 0.276 0.768
## 4 6 2 6 0.565 0.596
##Computation
res.aov1 <- anova_test(
data = datos.curve1, dv = cd.testa, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov1)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2.00 6.00 89.883 3.37e-05 * 0.865
## 2 diam2 1.21 7.23 110.375 8.80e-06 * 0.935
## 3 gen:diam2 2.41 7.23 4.232 5.60e-02 0.526
#CCN51
datos.ccn<-filter(datos.curve1, gen=="CCN51")
res.aov.ccn1 <- anova_test(
data = datos.ccn, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 32.05 0.000433 * 0.926
#ICS95
datos.ics<-filter(datos.curve1, gen=="ICS95")
res.aov.ics1 <- anova_test(
data = datos.ics, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ics1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 101.961 1.55e-05 * 0.969
#TCS01
datos.tcs<-filter(datos.curve1, gen=="TCS01")
res.aov.tcs1 <- anova_test(
data = datos.tcs, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs1)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 15.123 0.003 * 0.879
## Protocol 1 (T1)
datos.curve2<-filter(datos, curva=="T1")
##Check assumptions
##Outliers
datos.curve2 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.testa)
## [1] gen diam2 curva muestra id cd.testa is.outlier
## [8] 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.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.testa 0.894 0.367
## 2 CCN51 2 cd.testa 0.940 0.528
## 3 CCN51 5 cd.testa 0.902 0.393
## 4 CCN51 6 cd.testa 0.973 0.686
## 5 ICS95 0 cd.testa 0.959 0.609
## 6 ICS95 2 cd.testa 0.857 0.260
## 7 ICS95 5 cd.testa 0.985 0.763
## 8 ICS95 6 cd.testa 0.999 0.948
## 9 TCS01 0 cd.testa 0.998 0.918
## 10 TCS01 2 cd.testa 0.935 0.509
## 11 TCS01 5 cd.testa 0.817 0.155
## 12 TCS01 6 cd.testa 0.755 0.0106
##Create QQ plot for each cell of design:
ggqqplot(datos.curve2, "cd.testa", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##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.testa ~ 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 1.35 0.329
## 2 2 2 6 0.397 0.689
## 3 5 2 6 1.12 0.385
## 4 6 2 6 0.246 0.789
##Computation
res.aov2 <- anova_test(
data = datos.curve2, dv = cd.testa, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2 6 33.644 5.49e-04 * 0.849
## 2 diam2 3 18 66.218 6.39e-10 * 0.847
## 3 gen:diam2 6 18 7.330 4.41e-04 * 0.550
#CCN51
datos.ccn<-filter(datos.curve2, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 29.892 0.000527 * 0.872
#ICS95
datos.ics<-filter(datos.curve2, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 26.763 0.000716 * 0.888
#TCS01
datos.tcs<-filter(datos.curve2, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 17.325 0.002 * 0.85
## Protocol 2 (T2)
datos.curve3<-filter(datos, curva=="T2")
##Check assumptions
##Outliers
datos.curve3 %>%
group_by(gen, diam2) %>%
identify_outliers(cd.testa)
## [1] gen diam2 curva muestra id cd.testa is.outlier
## [8] 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.testa)
norm2 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 12 × 5
## gen diam2 variable statistic p
## <fct> <fct> <chr> <dbl> <dbl>
## 1 CCN51 0 cd.testa 0.883 0.332
## 2 CCN51 2 cd.testa 0.891 0.358
## 3 CCN51 5 cd.testa 0.869 0.293
## 4 CCN51 6 cd.testa 0.920 0.454
## 5 ICS95 0 cd.testa 0.915 0.436
## 6 ICS95 2 cd.testa 0.880 0.326
## 7 ICS95 5 cd.testa 0.962 0.624
## 8 ICS95 6 cd.testa 0.859 0.265
## 9 TCS01 0 cd.testa 0.968 0.657
## 10 TCS01 2 cd.testa 0.802 0.119
## 11 TCS01 5 cd.testa 0.992 0.833
## 12 TCS01 6 cd.testa 0.934 0.503
##Create QQ plot for each cell of design:
ggqqplot(datos.curve3, "cd.testa", ggtheme = theme_bw()) +
facet_grid(diam2~ curva*gen, labeller = "label_both")
## Warning: The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: sample
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##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.testa ~ 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 0.803 0.491
## 2 2 2 6 0.0730 0.930
## 3 5 2 6 0.212 0.815
## 4 6 2 6 0.488 0.636
##Computation
res.aov2 <- anova_test(
data = datos.curve3, dv = cd.testa, wid = id,
within = diam2, between = gen
)
get_anova_table(res.aov2)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 gen 2 6 11.505 9.00e-03 * 0.699
## 2 diam2 3 18 90.448 4.78e-11 * 0.856
## 3 gen:diam2 6 18 14.004 6.45e-06 * 0.648
#CCN51
datos.ccn<-filter(datos.curve3, gen=="CCN51")
res.aov.ccn2 <- anova_test(
data = datos.ccn, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ccn2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 62.105 6.56e-05 * 0.949
#ICS95
datos.ics<-filter(datos.curve3, gen=="ICS95")
res.aov.ics2 <- anova_test(
data = datos.ics, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.ics2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 40.072 0.000231 * 0.795
#TCS01
datos.tcs<-filter(datos.curve3, gen=="TCS01")
res.aov.tcs2 <- anova_test(
data = datos.tcs, dv = cd.testa, wid = id,
within = diam2
)
get_anova_table(res.aov.tcs2)
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 diam2 3 6 25.837 0.000789 * 0.85
## 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.testa)) +
geom_point(aes(y=cd.testa)) +
scale_y_continuous(name = expression("Cd (mg*kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "día", 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.testa", groupvars = c("curva", "gen","diam2"))
write.csv(datos2, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Env_muestra/data/Cd_testa_mean.csv")
pht2<- ggplot(datos2, aes(x = diam2)) +
facet_grid(curva~gen) +
geom_errorbar(aes(ymin=cd.testa-ci, ymax=cd.testa+ci), width=.1) +
geom_line(aes(y=cd.testa)) +
geom_point(aes(y=cd.testa)) +
scale_y_continuous(name = expression("Cd (mg*kg"^"-1)")) + # Etiqueta de la variable continua
scale_x_continuous(name = "día", 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
