setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Fabricio/Yesid/growth")
datos<-read.table("ult.csv", header=T, sep=',')
datos$gen<-as.factor(datos$gen)
datos$Shade<-as.factor(datos$Shade)
datos$bloque<-as.factor(datos$bloque)
attach(datos)
library(ggplot2)
library(emmeans)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
##
## 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 3.1.6 ✓ purrr 0.3.4
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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## x dplyr::rename() masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
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## mutate
library(rstatix)
##
## Attaching package: 'rstatix'
## The following objects are masked from 'package:plyr':
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## desc, mutate
## The following object is masked from 'package:stats':
##
## filter
##Summary statistics diameter
summ<-datos %>%
group_by(Shade, gen) %>%
get_summary_stats(diam, type = "mean_ci")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 9 × 6
## Shade gen variable n mean ci
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 C. pyriformis CCN51 diam 18 5.04 1.10
## 2 C. pyriformis TCS01 diam 18 6.79 0.648
## 3 C. pyriformis TCS19 diam 18 5.7 0.734
## 4 T. rosea CCN51 diam 18 6.2 0.797
## 5 T. rosea TCS01 diam 18 5.56 0.648
## 6 T. rosea TCS19 diam 17 5.61 0.772
## 7 T. superba CCN51 diam 18 5.67 0.755
## 8 T. superba TCS01 diam 18 6.49 0.528
## 9 T. superba TCS19 diam 18 5.53 0.611
summ
## # A tibble: 9 × 6
## Shade gen variable n mean ci
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 C. pyriformis CCN51 diam 18 5.04 1.10
## 2 C. pyriformis TCS01 diam 18 6.79 0.648
## 3 C. pyriformis TCS19 diam 18 5.7 0.734
## 4 T. rosea CCN51 diam 18 6.2 0.797
## 5 T. rosea TCS01 diam 18 5.56 0.648
## 6 T. rosea TCS19 diam 17 5.61 0.772
## 7 T. superba CCN51 diam 18 5.67 0.755
## 8 T. superba TCS01 diam 18 6.49 0.528
## 9 T. superba TCS19 diam 18 5.53 0.611
#Modelo diam
aov.diam<-aov(diam~gen*Shade+bloque)
summary(aov.diam)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 2 15.26 7.63 4.143 0.01774 *
## Shade 2 0.31 0.16 0.085 0.91851
## bloque 2 66.29 33.15 18.001 9.84e-08 ***
## gen:Shade 4 26.92 6.73 3.654 0.00717 **
## Residuals 150 276.20 1.84
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Contrastes Posthoc
contrast <- emmeans(aov.diam, ~gen|Shade)
diammc<-plot(contrast, comparisons = TRUE, xlab = expression ("Estimated marginal mean, diameter"),
ylab = expression ("Genotype"))
diammc
# Letras marginal means diameter
medias.Shade.gen <- emmeans(aov.diam, pairwise ~ gen*Shade)
medias.Shade.gen
## $emmeans
## gen Shade emmean SE df lower.CL upper.CL
## CCN51 C. pyriformis 5.04 0.320 150 4.41 5.68
## TCS01 C. pyriformis 6.79 0.320 150 6.16 7.42
## TCS19 C. pyriformis 5.70 0.320 150 5.07 6.33
## CCN51 T. rosea 6.20 0.320 150 5.57 6.83
## TCS01 T. rosea 5.56 0.320 150 4.92 6.19
## TCS19 T. rosea 5.62 0.329 150 4.97 6.27
## CCN51 T. superba 5.67 0.320 150 5.04 6.30
## TCS01 T. superba 6.49 0.320 150 5.86 7.12
## TCS19 T. superba 5.53 0.320 150 4.90 6.16
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 C. pyriformis - TCS01 C. pyriformis -1.7444 0.452 150 -3.857 0.0052
## CCN51 C. pyriformis - TCS19 C. pyriformis -0.6556 0.452 150 -1.449 0.8765
## CCN51 C. pyriformis - CCN51 T. rosea -1.1556 0.452 150 -2.555 0.2151
## CCN51 C. pyriformis - TCS01 T. rosea -0.5111 0.452 150 -1.130 0.9687
## CCN51 C. pyriformis - TCS19 T. rosea -0.5778 0.459 150 -1.259 0.9414
## CCN51 C. pyriformis - CCN51 T. superba -0.6278 0.452 150 -1.388 0.9008
## CCN51 C. pyriformis - TCS01 T. superba -1.4444 0.452 150 -3.193 0.0439
## CCN51 C. pyriformis - TCS19 T. superba -0.4833 0.452 150 -1.069 0.9778
## TCS01 C. pyriformis - TCS19 C. pyriformis 1.0889 0.452 150 2.407 0.2877
## TCS01 C. pyriformis - CCN51 T. rosea 0.5889 0.452 150 1.302 0.9294
## TCS01 C. pyriformis - TCS01 T. rosea 1.2333 0.452 150 2.727 0.1475
## TCS01 C. pyriformis - TCS19 T. rosea 1.1667 0.459 150 2.542 0.2209
## TCS01 C. pyriformis - CCN51 T. superba 1.1167 0.452 150 2.469 0.2558
## TCS01 C. pyriformis - TCS01 T. superba 0.3000 0.452 150 0.663 0.9991
## TCS01 C. pyriformis - TCS19 T. superba 1.2611 0.452 150 2.788 0.1277
## TCS19 C. pyriformis - CCN51 T. rosea -0.5000 0.452 150 -1.105 0.9726
## TCS19 C. pyriformis - TCS01 T. rosea 0.1444 0.452 150 0.319 1.0000
## TCS19 C. pyriformis - TCS19 T. rosea 0.0778 0.459 150 0.169 1.0000
## TCS19 C. pyriformis - CCN51 T. superba 0.0278 0.452 150 0.061 1.0000
## TCS19 C. pyriformis - TCS01 T. superba -0.7889 0.452 150 -1.744 0.7182
## TCS19 C. pyriformis - TCS19 T. superba 0.1722 0.452 150 0.381 1.0000
## CCN51 T. rosea - TCS01 T. rosea 0.6444 0.452 150 1.425 0.8866
## CCN51 T. rosea - TCS19 T. rosea 0.5778 0.459 150 1.259 0.9414
## CCN51 T. rosea - CCN51 T. superba 0.5278 0.452 150 1.167 0.9621
## CCN51 T. rosea - TCS01 T. superba -0.2889 0.452 150 -0.639 0.9993
## CCN51 T. rosea - TCS19 T. superba 0.6722 0.452 150 1.486 0.8604
## TCS01 T. rosea - TCS19 T. rosea -0.0667 0.459 150 -0.145 1.0000
## TCS01 T. rosea - CCN51 T. superba -0.1167 0.452 150 -0.258 1.0000
## TCS01 T. rosea - TCS01 T. superba -0.9333 0.452 150 -2.063 0.5022
## TCS01 T. rosea - TCS19 T. superba 0.0278 0.452 150 0.061 1.0000
## TCS19 T. rosea - CCN51 T. superba -0.0500 0.459 150 -0.109 1.0000
## TCS19 T. rosea - TCS01 T. superba -0.8667 0.459 150 -1.888 0.6230
## TCS19 T. rosea - TCS19 T. superba 0.0944 0.459 150 0.206 1.0000
## CCN51 T. superba - TCS01 T. superba -0.8167 0.452 150 -1.806 0.6785
## CCN51 T. superba - TCS19 T. superba 0.1444 0.452 150 0.319 1.0000
## TCS01 T. superba - TCS19 T. superba 0.9611 0.452 150 2.125 0.4606
##
## Results are averaged over the levels of: bloque
## P value adjustment: tukey method for comparing a family of 9 estimates
cld_Shade_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_Shade_gen
## Shade = C. pyriformis:
## gen emmean SE df lower.CL upper.CL .group
## TCS01 6.79 0.320 150 6.16 7.42 A
## TCS19 5.70 0.320 150 5.07 6.33 B
## CCN51 5.04 0.320 150 4.41 5.68 B
##
## Shade = T. rosea:
## gen emmean SE df lower.CL upper.CL .group
## CCN51 6.20 0.320 150 5.57 6.83 A
## TCS19 5.62 0.329 150 4.97 6.27 A
## TCS01 5.56 0.320 150 4.92 6.19 A
##
## Shade = T. superba:
## gen emmean SE df lower.CL upper.CL .group
## TCS01 6.49 0.320 150 5.86 7.12 A
## CCN51 5.67 0.320 150 5.04 6.30 A
## TCS19 5.53 0.320 150 4.90 6.16 A
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
ggsave("mgdiam.tiff", plot= diammc, width = 16, height = 12, units= c("cm"), dpi = 1000)
##Summary statistics height
summ<-datos %>%
group_by(Shade, gen) %>%
get_summary_stats(alt_m, type = "mean_ci")
summ %>% as_tibble() %>% print(n=Inf)
## # A tibble: 9 × 6
## Shade gen variable n mean ci
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 C. pyriformis CCN51 alt_m 18 1.96 0.436
## 2 C. pyriformis TCS01 alt_m 18 2.43 0.355
## 3 C. pyriformis TCS19 alt_m 18 1.83 0.208
## 4 T. rosea CCN51 alt_m 18 2.29 0.228
## 5 T. rosea TCS01 alt_m 18 2.00 0.233
## 6 T. rosea TCS19 alt_m 17 1.86 0.254
## 7 T. superba CCN51 alt_m 18 2.21 0.188
## 8 T. superba TCS01 alt_m 18 2.34 0.145
## 9 T. superba TCS19 alt_m 18 1.74 0.215
summ
## # A tibble: 9 × 6
## Shade gen variable n mean ci
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 C. pyriformis CCN51 alt_m 18 1.96 0.436
## 2 C. pyriformis TCS01 alt_m 18 2.43 0.355
## 3 C. pyriformis TCS19 alt_m 18 1.83 0.208
## 4 T. rosea CCN51 alt_m 18 2.29 0.228
## 5 T. rosea TCS01 alt_m 18 2.00 0.233
## 6 T. rosea TCS19 alt_m 17 1.86 0.254
## 7 T. superba CCN51 alt_m 18 2.21 0.188
## 8 T. superba TCS01 alt_m 18 2.34 0.145
## 9 T. superba TCS19 alt_m 18 1.74 0.215
#Modelo diam
aov.alt<-aov(alt_m~gen*Shade+bloque)
summary(aov.alt)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 2 5.85 2.926 12.908 6.72e-06 ***
## Shade 2 0.07 0.034 0.152 0.8594
## bloque 2 8.94 4.472 19.724 2.48e-08 ***
## gen:Shade 4 3.06 0.765 3.374 0.0112 *
## Residuals 150 34.00 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Contrastes Posthoc
contrast <- emmeans(aov.alt, ~gen|Shade)
altc<-plot(contrast, comparisons = TRUE, xlab = expression ("Estimated marginal mean, height" ),
ylab = expression ("Genotype"))
altc
# Letras marginal means height
medias.Shade.gen <- emmeans(aov.alt, pairwise ~ gen*Shade)
medias.Shade.gen
## $emmeans
## gen Shade emmean SE df lower.CL upper.CL
## CCN51 C. pyriformis 1.96 0.112 150 1.74 2.18
## TCS01 C. pyriformis 2.43 0.112 150 2.21 2.65
## TCS19 C. pyriformis 1.83 0.112 150 1.61 2.05
## CCN51 T. rosea 2.29 0.112 150 2.06 2.51
## TCS01 T. rosea 2.00 0.112 150 1.77 2.22
## TCS19 T. rosea 1.87 0.116 150 1.64 2.10
## CCN51 T. superba 2.21 0.112 150 1.99 2.43
## TCS01 T. superba 2.34 0.112 150 2.12 2.57
## TCS19 T. superba 1.74 0.112 150 1.51 1.96
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 C. pyriformis - TCS01 C. pyriformis -0.4700 0.159 150 -2.961 0.0829
## CCN51 C. pyriformis - TCS19 C. pyriformis 0.1256 0.159 150 0.791 0.9970
## CCN51 C. pyriformis - CCN51 T. rosea -0.3283 0.159 150 -2.069 0.4985
## CCN51 C. pyriformis - TCS01 T. rosea -0.0383 0.159 150 -0.242 1.0000
## CCN51 C. pyriformis - TCS19 T. rosea 0.0878 0.161 150 0.545 0.9998
## CCN51 C. pyriformis - CCN51 T. superba -0.2528 0.159 150 -1.593 0.8074
## CCN51 C. pyriformis - TCS01 T. superba -0.3861 0.159 150 -2.433 0.2742
## CCN51 C. pyriformis - TCS19 T. superba 0.2222 0.159 150 1.400 0.8962
## TCS01 C. pyriformis - TCS19 C. pyriformis 0.5956 0.159 150 3.752 0.0075
## TCS01 C. pyriformis - CCN51 T. rosea 0.1417 0.159 150 0.893 0.9931
## TCS01 C. pyriformis - TCS01 T. rosea 0.4317 0.159 150 2.720 0.1498
## TCS01 C. pyriformis - TCS19 T. rosea 0.5578 0.161 150 3.463 0.0194
## TCS01 C. pyriformis - CCN51 T. superba 0.2172 0.159 150 1.369 0.9078
## TCS01 C. pyriformis - TCS01 T. superba 0.0839 0.159 150 0.529 0.9998
## TCS01 C. pyriformis - TCS19 T. superba 0.6922 0.159 150 4.362 0.0008
## TCS19 C. pyriformis - CCN51 T. rosea -0.4539 0.159 150 -2.860 0.1072
## TCS19 C. pyriformis - TCS01 T. rosea -0.1639 0.159 150 -1.033 0.9821
## TCS19 C. pyriformis - TCS19 T. rosea -0.0377 0.161 150 -0.234 1.0000
## TCS19 C. pyriformis - CCN51 T. superba -0.3783 0.159 150 -2.384 0.3006
## TCS19 C. pyriformis - TCS01 T. superba -0.5117 0.159 150 -3.224 0.0402
## TCS19 C. pyriformis - TCS19 T. superba 0.0967 0.159 150 0.609 0.9995
## CCN51 T. rosea - TCS01 T. rosea 0.2900 0.159 150 1.827 0.6641
## CCN51 T. rosea - TCS19 T. rosea 0.4162 0.161 150 2.584 0.2023
## CCN51 T. rosea - CCN51 T. superba 0.0756 0.159 150 0.476 0.9999
## CCN51 T. rosea - TCS01 T. superba -0.0578 0.159 150 -0.364 1.0000
## CCN51 T. rosea - TCS19 T. superba 0.5506 0.159 150 3.469 0.0191
## TCS01 T. rosea - TCS19 T. rosea 0.1262 0.161 150 0.783 0.9972
## TCS01 T. rosea - CCN51 T. superba -0.2144 0.159 150 -1.351 0.9138
## TCS01 T. rosea - TCS01 T. superba -0.3478 0.159 150 -2.191 0.4167
## TCS01 T. rosea - TCS19 T. superba 0.2606 0.159 150 1.642 0.7802
## TCS19 T. rosea - CCN51 T. superba -0.3406 0.161 150 -2.115 0.4673
## TCS19 T. rosea - TCS01 T. superba -0.4739 0.161 150 -2.943 0.0870
## TCS19 T. rosea - TCS19 T. superba 0.1344 0.161 150 0.834 0.9956
## CCN51 T. superba - TCS01 T. superba -0.1333 0.159 150 -0.840 0.9954
## CCN51 T. superba - TCS19 T. superba 0.4750 0.159 150 2.993 0.0763
## TCS01 T. superba - TCS19 T. superba 0.6083 0.159 150 3.833 0.0057
##
## Results are averaged over the levels of: bloque
## P value adjustment: tukey method for comparing a family of 9 estimates
cld_Shade_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_Shade_gen
## Shade = C. pyriformis:
## gen emmean SE df lower.CL upper.CL .group
## TCS01 2.43 0.112 150 2.21 2.65 A
## CCN51 1.96 0.112 150 1.74 2.18 B
## TCS19 1.83 0.112 150 1.61 2.05 B
##
## Shade = T. rosea:
## gen emmean SE df lower.CL upper.CL .group
## CCN51 2.29 0.112 150 2.06 2.51 A
## TCS01 2.00 0.112 150 1.77 2.22 AB
## TCS19 1.87 0.116 150 1.64 2.10 B
##
## Shade = T. superba:
## gen emmean SE df lower.CL upper.CL .group
## TCS01 2.34 0.112 150 2.12 2.57 A
## CCN51 2.21 0.112 150 1.99 2.43 A
## TCS19 1.74 0.112 150 1.51 1.96 B
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
ggsave("mgalt.tiff", plot= altc, width = 16, height = 12, units= c("cm"), dpi = 1000)