Acer_YII_CBASS
Ana M. Palacio
2020-11-18
General project set-up
# Get all libraries and sources required to run the script
library(dplyr)
library(plyr)
library(reshape2)
library(ggplot2)
library(ggthemes)
theme_set (theme_classic() + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.position="none",
axis.text.x = element_text(angle = 90, vjust = 0.5),
plot.title = element_text(size=12, face="bold"),
#panel.border = element_rect(colour = "black", fill=NA, size=1)
panel.border = element_blank()
))Data and data clean-up
# 1. Import data:
# Long format Ssid YII
YII.Tall<-read.csv("YII_tall.csv")
#summary(YII.Tall)
# 2. Data clean-up an types:
# Variable types
YII.Tall$Time<-as.numeric(YII.Tall$Time)
YII.Tall$Date<-as.Date(YII.Tall$Date, "%Y-%m-%d")
# Treatments
YII.Tall$Nutrients<-factor(YII.Tall$Nutrients,
levels= c("Ambient", "NH4"), ordered=TRUE)
YII.Tall$Heat<-factor(YII.Tall$Heat,
levels= c("No", "Yes"), ordered=TRUE)
YII.Tall$Treatment<-factor(YII.Tall$Treatment,
levels= c("Control", "NH4", "Heat"), ordered=TRUE)
# Replicates
YII.Tall$Genotype<-factor(YII.Tall$Genotype, ordered=FALSE)
summary(YII.Tall)## Time Date Genotype Fragment Nutrients
## Min. :1.0 Min. :2020-11-13 FM14 :14 207 : 2 Ambient:50
## 1st Qu.:1.0 1st Qu.:2020-11-13 FM9 :14 214 : 2 NH4 :26
## Median :1.5 Median :2020-11-13 Elkhorn:12 217 : 2
## Mean :1.5 Mean :2020-11-13 FM19 :12 220 : 2
## 3rd Qu.:2.0 3rd Qu.:2020-11-14 FM6 : 8 224 : 2
## Max. :2.0 Max. :2020-11-14 U44 : 8 225 : 2
## (Other): 8 (Other):64
## Heat Treatment YII Sample
## No :56 Control:30 Min. :0.0670 2020-11-13_207: 1
## Yes:20 NH4 :26 1st Qu.:0.1415 2020-11-13_214: 1
## Heat :20 Median :0.2925 2020-11-13_217: 1
## Mean :0.3060 2020-11-13_220: 1
## 3rd Qu.:0.4718 2020-11-13_224: 1
## Max. :0.5380 2020-11-13_225: 1
## (Other) :70Data exploration
Genotype
# Genotype
YII_Genet<- ggplot(YII.Tall, aes (Time, YII, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
#geom_jitter(alpha=0.5, shape=21)+
theme(legend.position = "bottom")+
scale_y_continuous(limits = c(0, .73),
breaks = seq(0, 0.7,0.2),
expand = c(0.01, 0.01),
name=("YII (Fv/Fm)"))
YII_Genet
YII_Genet+ facet_wrap(~Nutrients)
YII_Genet+ facet_wrap(~Heat)
YII_Genet+ facet_wrap(~Treatment)
Treatment
YII_Treatment<- ggplot(YII.Tall, aes (Time, YII, colour=Treatment)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_point(shape=21)+
#geom_jitter(alpha=0.5, shape=21)+
theme(legend.position = "bottom")+
scale_y_continuous(limits = c(0, .73),
breaks = seq(0, 0.7,0.2),
expand = c(0.01, 0.01),
name=("YII (Fv/Fm)"))
YII_Treatment
YII_Treatment+ facet_wrap(~Genotype)
Fragment
YII_Frag<- ggplot(YII.Tall, aes (Genotype, YII,
colour=factor(Treatment),
shape=factor(Time))) +
geom_point(size=3, alpha=.5)+
scale_y_continuous(limits = c(0.0, .7),
breaks = seq(0, 0.7, 0.2),
expand = c(0, 0),
name=("YII (Fv/Fm)"))+
theme(legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))
YII_Frag
Model selection
# Libraries
library(lme4)
library(multcomp)
library(multcompView)
library(emmeans)
library(effects)
library(lmerTest)
# More complex model
# LM_1 <- lmer(YII ~ Treatment * Time +
# (1|Genotype/Fragment), REML=TRUE, data= YII.Tall)
#
# step(LM_1)
# LM_2 <- lmer(YII ~ Nutrients * Days +
# (Nutrients|Genotype), REML=TRUE, data= YII.Tall)
#
# LM_3 <- lmer(YII ~ Nutrients * Days +
# (1|Genotype), REML=TRUE, data= YII.Tall)
#
# LM_4 <- lm(YII ~ Nutrients * Days, REML = FALSE, data= YII.Tall)
#
# # Select model
#
# anova(LM_1, LM_2, refit=FALSE)
# anova(LM_2, LM_3, refit=FALSE)
# anova(LM_3, LM_4)
#
# # Final mdel
# LM_Nutrients_Days<-lmer(YII ~ Nutrients * Days +
# (Nutrients|Genotype), data= YII.nutrients)
# anova(LM_1)
# summary(LM_1)
# coef(LM_1)
# fitted(LM_1)
#
# layout(matrix(1:4,2,2))
# plot(LM_1)
#
# plot(Effect(c("Nutrients","DayF"), LM_1), x.var="DayF", multiline=T, ci.style="bars")
#
# # Pair-wise comparisons
# cld(emmeans(LM_1, "Nutrients"))
# YIIAcerEmm<-cld(emmeans(LM_1, specs = c("Nutrients", "DayF")))
# write.csv(YIIAcerEmm, "YIIAcerEmm.csv")Packages used
# Creates bibliography
#knitr::write_bib(c(.packages()), "packages.bib")Arnold, Jeffrey B. 2019. Ggthemes: Extra Themes, Scales and Geoms for ’Ggplot2’. https://CRAN.R-project.org/package=ggthemes.
Bates, Douglas, and Martin Maechler. 2019. Matrix: Sparse and Dense Matrix Classes and Methods. https://CRAN.R-project.org/package=Matrix.
Bates, Douglas, Martin Maechler, Ben Bolker, and Steven Walker. 2019. Lme4: Linear Mixed-Effects Models Using ’Eigen’ and S4. https://CRAN.R-project.org/package=lme4.
Fox, John, Sanford Weisberg, and Brad Price. 2018. CarData: Companion to Applied Regression Data Sets. https://CRAN.R-project.org/package=carData.
Fox, John, Sanford Weisberg, Brad Price, Michael Friendly, and Jangman Hong. 2019. Effects: Effect Displays for Linear, Generalized Linear, and Other Models. https://CRAN.R-project.org/package=effects.
Genz, Alan, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, and Torsten Hothorn. 2019. Mvtnorm: Multivariate Normal and T Distributions. https://CRAN.R-project.org/package=mvtnorm.
Graves, Spencer, Hans-Peter Piepho, and Luciano Selzer with help from Sundar Dorai-Raj. 2015. MultcompView: Visualizations of Paired Comparisons. https://CRAN.R-project.org/package=multcompView.
Hothorn, Torsten. 2019. TH.data: TH’s Data Archive. https://CRAN.R-project.org/package=TH.data.
Hothorn, Torsten, Frank Bretz, and Peter Westfall. 2019. Multcomp: Simultaneous Inference in General Parametric Models. https://CRAN.R-project.org/package=multcomp.
Kuznetsova, Alexandra, Per Bruun Brockhoff, and Rune Haubo Bojesen Christensen. 2019. LmerTest: Tests in Linear Mixed Effects Models. https://CRAN.R-project.org/package=lmerTest.
Lenth, Russell. 2019. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Ripley, Brian. 2019. MASS: Support Functions and Datasets for Venables and Ripley’s Mass. https://CRAN.R-project.org/package=MASS.
Therneau, Terry M. 2019. Survival: Survival Analysis. https://CRAN.R-project.org/package=survival.
Wickham, Hadley. 2016. Plyr: Tools for Splitting, Applying and Combining Data. https://CRAN.R-project.org/package=plyr.
———. 2017. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. https://CRAN.R-project.org/package=reshape2.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, and Hiroaki Yutani. 2019. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.