Clase 4 de Octubre 2021
Juan José Arcila Henao
4/10/2021
##Just Run This Chunk
require(readxl)## Loading required package: readxlrequire(ggplot2)## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.0.5require(plotly)## Loading required package: plotly## Warning: package 'plotly' was built under R version 4.0.5##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutrequire(emmeans)## Loading required package: emmeansrequire(multcomp)## Loading required package: multcomp## Loading required package: mvtnorm## Loading required package: survival## Loading required package: TH.data## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:plotly':
##
## select##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyserrequire(tidyverse)## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## v purrr 0.3.4## Warning: package 'tibble' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'dplyr' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks plotly::filter(), stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::select() masks MASS::select(), plotly::select()require(ggpubr)## Loading required package: ggpubr## Warning: package 'ggpubr' was built under R version 4.0.5require(rstatix)## Loading required package: rstatix## Warning: package 'rstatix' was built under R version 4.0.5##
## Attaching package: 'rstatix'## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:stats':
##
## filterrequire(lmerTest)## Loading required package: lmerTest## Warning: package 'lmerTest' was built under R version 4.0.5## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steprequire(Rmisc)## Loading required package: Rmisc## Warning: package 'Rmisc' was built under R version 4.0.5## Loading required package: lattice## Loading required package: plyr## Warning: package 'plyr' was built under R version 4.0.5## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:rstatix':
##
## desc, mutate## The following object is masked from 'package:ggpubr':
##
## mutate## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compact## The following objects are masked from 'package:plotly':
##
## arrange, mutate, rename, summariseLeafLength = read_excel("C:/Users/PC/Desktop/La U/Quinto Semestre/Bioestadistica/Clase 10 -/LeafLength.xlsx")
LeafLength$Temp_C02=paste(LeafLength$Temp,"_and_",LeafLength$CO2,sep="")
LeafLength$id=as.factor(paste(LeafLength$Cab,"_",LeafLength$Obs,sep=""))
LeafLength$Genotype=as.factor(LeafLength$Genotype)##Time Series Plot by Temp-CO2 Treatment
gd = summarySE(LeafLength, measurevar="LeafL", groupvars=c("Temp_C02","Time","Genotype"))
g1=ggplot(gd,aes(x=Time,y=LeafL,color=Temp_C02, group =Temp_C02))+ geom_line(data=gd)+geom_point(data=gd, size = 2)+ facet_grid(~Genotype)+theme_bw()+
labs(title = "Leaf Length",
x = "Time in days",
y = "Length in cm")
g1
En la gráfica se observa que en el caso de la variedad PA107 al ser sometida a diferentes temperaturas y concentraciones de dióxido de carbono obtiene la mayor longitud de hojas entre 22 a 31 grados celsius con una concentración de dióxido de carbono de 400
##Anova de Medidas Repetidas
LeafLength$Time=as.factor(LeafLength$Time)
LeafLength$Temp=as.factor(LeafLength$Temp)
LeafLength$CO2=as.factor(LeafLength$CO2)
anova=aov(LeafL~Temp*CO2*Genotype+Time+Error(id/Genotype/Time),data=LeafLength)
summary(anova)##
## Error: id
## Df Sum Sq Mean Sq F value Pr(>F)
## Temp 2 6614 3307 17.188 1.06e-05 ***
## CO2 1 214 214 1.113 0.30
## Temp:CO2 2 396 198 1.028 0.37
## Residuals 30 5772 192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: id:Genotype
## Df Sum Sq Mean Sq F value Pr(>F)
## Genotype 1 871 871 2.016 0.16598
## Temp:Genotype 2 7270 3635 8.409 0.00126 **
## CO2:Genotype 1 46 46 0.108 0.74522
## Temp:CO2:Genotype 2 2673 1337 3.092 0.06013 .
## Residuals 30 12968 432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: id:Genotype:Time
## Df Sum Sq Mean Sq F value Pr(>F)
## Time 18 17707 983.7 140.2 <2e-16 ***
## Residuals 1278 8964 7.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##Postanova CO2
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
color = "CO2", palette = "jco"
)
pwc = LeafLength %>%
group_by(Time) %>%
pairwise_t_test(
LeafL ~ CO2, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(1)", " = ", "1.113, ", "p", " = ", "0.30"),
caption = get_pwc_label(pwc)
)
En esta gráfica se obseva que la cantidades de dioxido de carbono no son muy representativas repsecto al tamaño de la hoja y al tiempo solo el segundo y el tercer dato presentan significancia pero no es muy alta
##POstanova Temp
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
color = "Temp", palette = "jco"
)
pwc = LeafLength %>%
group_by(Time) %>%
pairwise_t_test(
LeafL ~ Temp, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(2)", " = ", "17.188 , ", "p", " = ", "<0.0001 "),
caption = get_pwc_label(pwc)
)
En cuanto a la temperatura si se observan altos niveles de significancia en donde la temperatura que porporciona mayor tamaño en la hoja están entre los 22 y 31 grados
##Postanova Temp-Genotipo
bxp = ggboxplot(
LeafLength, x = "Time", y = "LeafL",
title= "Leaf Length",
xlab ="time (days)", ylab= "Length (cm)",
color = "Temp", palette = "lancet",
facet.by = "Genotype"
)
pwc = LeafLength %>%
group_by(Time, Genotype) %>%
pairwise_t_test(
LeafL ~ Temp, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc = pwc %>% add_xy_position(x = "Time")
bxp +
stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
labs(
subtitle = paste("Anova, ","F(2)", " = ", "8.409 , ", "p", " = ", "0.00126"),
caption = get_pwc_label(pwc)
)
en cuanto a la relación de la temperatura con la longitud de la hoja y los genotipos, se observa que el genotipo SCA6 tiene un alto nivel de significancia con la temperatura, donde los intervalos de 22 a 31 presentan mejores resultados respecto a los otros dos intervalos. En cuanto al genotipo PA107 este no presenta mucha variación al aumentar la temperatura indicando que puede ser plantada en gran variedad de lugares y obtener los mismos resultados