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+---
+title: "R Notebook for effects of fluoxetine on zebrafish aggression (LaNeC)"
+author:
+- Caio Maximino^[Universidade Federal do Sul e Sudeste do Pará]
+- Monica Gomes Lima^[Universidade do Estado do Pará]
+- Hellen Barbosa^[Universidade Federal do Sul e Sudeste do Pará]
+output:
+  github_document 
+subtitle: From project "Role of the serotonergic system on aggressive behavior in two zebrafish phenotypes"
+tags:
+- aggression
+- zebrafish
+- fluoxetine
+abstract: |
+  Behavioral data in the mirror-induced aggression test using fluoxetine in two zebrafish phenotypes (longfin and leopard)
+---
+
+This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook for the data analysis of the research project "Role of the serotonergic system on aggressive behavior in two zebrafish phenotypes".
+
+Data is produced by members from Laboratório de Neurociências e Comportamento "Frederico Guilherme Graeff", affiliated to Universidade Federal do Sul e Sudeste do Pará and Universidade do Estado do Pará. The package will include primary data for a behavioral experiment on the effects of fluoxetine on zebrafish aggressive behavior. In Experiment 1, basline aggression levels were compared between phenotype; in Experiment 2, the effects of fluoxetine were tested.
+
+When you execute code within the notebook, the results appear beneath the code. 
+
+* Load needed libraries:
+```{r}
+if(!require(ggplot2)){
+    install.packages("ggplot2")
+    library(ggplot2)
+}
+if(!require(coin)){
+    install.packages("coin")
+    library(coin)
+}
+if(!require(RCurl)){
+    install.packages("RCurl")
+    library(RCurl)
+}
+
+if(!require(plyr)){
+    install.packages("plyr")
+    library(plyr)
+}
+
+if(!require(rcompanion)){
+    install.packages("rcompanion")
+    library(rcompanion)
+}
+
+if(!require(WRS2)){
+    install.packages("WRS2")
+    library(WRS2)
+}
+
+if(!require(psych)){
+    install.packages("psychh")
+    library(psych)
+}
+```
+
+* Load data
+```{r}
+x1 <- getURL("https://raw.githubusercontent.com/lanec-unifesspa/5HT-aggression/master/exp1.csv")
+exp1 <- read.csv(text = x1)
+exp1$Phenotype <- as.factor(exp1$Phenotype)
+View(exp1)
+
+x2 <- getURL("https://raw.githubusercontent.com/lanec-unifesspa/5HT-aggression/master/exp2.csv")
+exp2 <- read.csv(text = x2)
+exp2$Phenotype <- as.factor(exp2$Phenotype)
+exp2$Dose <- as.factor(exp2$Dose)
+View(exp2)
+```
+
+* Run Approximative Two-Sample Fisher-Pitman Permutation Tests on data from Experiment 1
+
+1) Latency data
+
+```{r}
+oneway_test(Latency ~ Phenotype, data = exp1, distribution="approximate"(B=10000))
+```
+
+2) Display duration data
+
+```{r}
+oneway_test(Dur.Display ~ Phenotype, data = exp1, distribution="approximate"(B=10000))
+```
+
+3) Display frequency data
+
+```{r}
+oneway_test(N.Display ~ Phenotype, data = exp1, distribution="approximate"(B=10000))
+```
+
+4) Time near mirror data
+
+```{r}
+oneway_test(T.Mirror ~ Phenotype, data = exp1, distribution="approximate"(B=10000))
+```
+
+5) Locomotion data
+
+```{r}
+oneway_test(N.Quad ~ Phenotype, data = exp1, distribution="approximate"(B=10000))
+```
+
+Produce figures on ggplot2
+```{r}
+ggplot(exp1, aes(x = factor(Phenotype), y = Latency, colour = Phenotype)) + geom_boxplot() + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "", y = "Latency to first display (s)", color = "Phenotype")
+
+ggplot(exp1, aes(x = factor(Phenotype), y = Dur.Display, colour = Phenotype)) + geom_boxplot() + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "", y = "Display duration (s)", color = "Phenotype")
+
+ggplot(exp1, aes(x = factor(Phenotype), y = N.Display, colour = Phenotype)) + geom_boxplot() + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "", y = "Display frequency (N)", color = "Phenotype")
+
+ggplot(exp1, aes(x = factor(Phenotype), y = T.Mirror, colour = Phenotype)) + geom_boxplot() + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "", y = "Time near mirror (s)", color = "Phenotype")
+
+ggplot(exp1, aes(x = factor(Phenotype), y = N.Quad, colour = Phenotype)) + geom_boxplot() + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "", y = "Squares crossed (N)", color = "Phenotype")
+```
+
+* Run bootstrapped ANOVA for main and interaction effects on 2-way ANOVA, in Experiment 2 (based on https://rcompanion.org/rcompanion/d_08a.html)
+
+1) Latency data
+1.1) Run two-way ANOVAs on M-estimators
+```{r}
+pbad2way(Latency ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est="mom", nboot = 5000)
+```
+
+1.2) Produce post-hoc tests for main effects
+```{r}
+postLat <- mcp2a(Latency ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est = "mom", nboot = 5000)
+postLat$contrasts
+postLat
+```
+
+1.3) Produce post-hoc tests for interaction effect
+```{r}
+exp2$Factor.int <- interaction(exp2$Dose, exp2$Phenotype)
+exp2$Factor.int <- factor(exp2$Factor.int, levels = c("0 mg/kg.LOF", "2.5 mg/kg.LOF", "5.0 mg/kg.LOF", "0 mg/kg.LEO", "2.5 mg/kg.LEO", "5.0 mg/kg.LEO"))
+headTail(exp2)
+PTLat <- pairwiseRobustTest(Latency ~ Factor.int, data = exp2, est = "mom", nboot = 5000, method = "fdr")
+PTLat
+```
+
+1.4) Draw graph of non-transformed data
+```{r}
+LatInt <- ddply(exp2, .(Dose, Phenotype), summarise, val = mean(Latency))
+ggplot(exp2, aes(x = factor(Dose), y = Latency, colour = Phenotype)) + geom_boxplot(outlier.shape = NA) + geom_point(data = LatInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_line(data = LatInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "Dose", y = "Latency to first display (s)", color = "Phenotype")
+```
+
+1.5) Draw graph of Huber M-estimators and CIs
+```{r}
+SumLat = groupwiseHuber(data = exp2copy, group = c("Phenotype", "Dose"), var = "Latency", conf.level = 0.95, conf.type="wald")
+ggplot(SumLat, aes(x=Dose, y = M.Huber, color = Phenotype)) + geom_errorbar(aes(ymin=lower.ci, ymax=upper.ci), width = 0.2, size = 0.7, position = position_dodge(.2)) + geom_point(position = position_dodge(.2)) + geom_line(data = SumLat, aes(y = M.Huber, group = Phenotype), position = position_dodge(.2))
+```
+
+2. Display duration data
+2.1) Run two-way ANOVAs on M-estimators
+```{r}
+pbad2way(Dur.Display ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est="mom", nboot = 5000)
+```
+
+2.2) Produce post-hoc tests for main effects
+```{r}
+postDur.Display <- mcp2a(Dur.Display ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est = "mom", nboot = 5000)
+postDur.Display$contrasts
+postDur.Display
+```
+
+2.3) Produce post-hoc tests for interaction effect
+```{r}
+exp2$Factor.int <- interaction(exp2$Dose, exp2$Phenotype)
+exp2$Factor.int <- factor(exp2$Factor.int, levels = c("0 mg/kg.LOF", "2.5 mg/kg.LOF", "5.0 mg/kg.LOF", "0 mg/kg.LEO", "2.5 mg/kg.LEO", "5.0 mg/kg.LEO"))
+headTail(exp2)
+PTDur.Display <- pairwiseRobustTest(Dur.Display ~ Factor.int, data = exp2, est = "mom", nboot = 5000, method = "fdr")
+PTDur.Display
+```
+
+2.4) Draw graph of non-transformed data
+```{r}
+Dur.DisplayInt <- ddply(exp2, .(Dose, Phenotype), summarise, val = mean(Dur.Display))
+ggplot(exp2, aes(x = factor(Dose), y = Dur.Display, colour = Phenotype)) + geom_boxplot(outlier.shape = NA) + geom_point(data = Dur.DisplayInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_line(data = Dur.DisplayInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "Dose", y = "Display duration (s)", color = "Phenotype")
+```
+
+2.5) Draw graph of Huber M-estimators and CIs
+```{r}
+SumDur.Display = groupwiseHuber(data = exp2copy, group = c("Phenotype", "Dose"), var = "Dur.Display", conf.level = 0.95, conf.type="wald")
+ggplot(SumDur.Display, aes(x=Dose, y = M.Huber, color = Phenotype)) + geom_errorbar(aes(ymin=lower.ci, ymax=upper.ci), width = 0.2, size = 0.7, position = position_dodge(.2)) + geom_point(position = position_dodge(.2)) + geom_line(data = SumDur.Display, aes(y = M.Huber, group = Phenotype), position = position_dodge(.2))
+```
+
+3. Display frequency data
+3.1) Run two-way ANOVAs on M-estimators
+```{r}
+pbad2way(N.Display ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est="mom", nboot = 5000)
+```
+
+3.2) Produce post-hoc tests for main effects
+```{r}
+postN.Display <- mcp2a(N.Display ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est = "mom", nboot = 5000)
+postN.Display$contrasts
+postN.Display
+```
+
+3.3) Produce post-hoc tests for interaction effect
+```{r}
+exp2$Factor.int <- interaction(exp2$Dose, exp2$Phenotype)
+exp2$Factor.int <- factor(exp2$Factor.int, levels = c("0 mg/kg.LOF", "2.5 mg/kg.LOF", "5.0 mg/kg.LOF", "0 mg/kg.LEO", "2.5 mg/kg.LEO", "5.0 mg/kg.LEO"))
+headTail(exp2)
+PTN.Display <- pairwiseRobustTest(N.Display ~ Factor.int, data = exp2, est = "mom", nboot = 5000, method = "fdr")
+PTN.Display
+```
+
+3.4) Draw graph of non-transformed data
+```{r}
+N.DisplayInt <- ddply(exp2, .(Dose, Phenotype), summarise, val = mean(N.Display))
+ggplot(exp2, aes(x = factor(Dose), y = N.Display, colour = Phenotype)) + geom_boxplot(outlier.shape = NA) + geom_point(data = N.DisplayInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_line(data = N.DisplayInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "Dose", y = "Display frequency (N)", color = "Phenotype")
+```
+
+3.5) Draw graph of Huber M-estimators and CIs
+```{r}
+SumN.Display = groupwiseHuber(data = exp2copy, group = c("Phenotype", "Dose"), var = "N.Display", conf.level = 0.95, conf.type="wald")
+ggplot(SumN.Display, aes(x=Dose, y = M.Huber, color = Phenotype)) + geom_errorbar(aes(ymin=lower.ci, ymax=upper.ci), width = 0.2, size = 0.7, position = position_dodge(.2)) + geom_point(position = position_dodge(.2)) + geom_line(data = SumN.Display, aes(y = M.Huber, group = Phenotype), position = position_dodge(.2))
+```
+
+4. Time near mirror data
+4.1) Run two-way ANOVAs on M-estimators
+```{r}
+pbad2way(T.Mirror ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est="mom", nboot = 5000)
+```
+
+4.2) Produce post-hoc tests for main effects
+```{r}
+postT.Mirror <- mcp2a(T.Mirror ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est = "mom", nboot = 5000)
+postT.Mirror$contrasts
+postT.Mirror
+```
+
+4.3) Produce post-hoc tests for interaction effect
+```{r}
+exp2$Factor.int <- interaction(exp2$Dose, exp2$Phenotype)
+exp2$Factor.int <- factor(exp2$Factor.int, levels = c("0 mg/kg.LOF", "2.5 mg/kg.LOF", "5.0 mg/kg.LOF", "0 mg/kg.LEO", "2.5 mg/kg.LEO", "5.0 mg/kg.LEO"))
+headTail(exp2)
+PTT.Mirror <- pairwiseRobustTest(T.Mirror ~ Factor.int, data = exp2, est = "mom", nboot = 5000, method = "fdr")
+PTT.Mirror
+```
+
+4.4) Draw graph of non-transformed data
+```{r}
+T.MirrorInt <- ddply(exp2, .(Dose, Phenotype), summarise, val = mean(T.Mirror))
+ggplot(exp2, aes(x = factor(Dose), y = T.Mirror, colour = Phenotype)) + geom_boxplot(outlier.shape = NA) + geom_point(data = T.MirrorInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_line(data = T.MirrorInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "Dose", y = "Time near mirror (s)", color = "Phenotype")
+```
+
+4.5) Draw graph of Huber M-estimators and CIs
+```{r}
+SumT.Mirror = groupwiseHuber(data = exp2copy, group = c("Phenotype", "Dose"), var = "T.Mirror", conf.level = 0.95, conf.type="wald")
+ggplot(SumT.Mirror, aes(x=Dose, y = M.Huber, color = Phenotype)) + geom_errorbar(aes(ymin=lower.ci, ymax=upper.ci), width = 0.2, size = 0.7, position = position_dodge(.2)) + geom_point(position = position_dodge(.2)) + geom_line(data = SumT.Mirror, aes(y = M.Huber, group = Phenotype), position = position_dodge(.2))
+```
+
+5. Locomotion data
+5.1) Run two-way ANOVAs on M-estimators
+```{r}
+pbad2way(Quad ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est="mom", nboot = 5000)
+```
+
+5.2) Produce post-hoc tests for main effects
+```{r}
+postQuad <- mcp2a(Quad ~ Phenotype + Dose + Phenotype:Dose, data = exp2, est = "mom", nboot = 5000)
+postQuad$contrasts
+postQuad
+```
+
+5.3) Produce post-hoc tests for interaction effect
+```{r}
+exp2$Factor.int <- interaction(exp2$Dose, exp2$Phenotype)
+exp2$Factor.int <- factor(exp2$Factor.int, levels = c("0 mg/kg.LOF", "2.5 mg/kg.LOF", "5.0 mg/kg.LOF", "0 mg/kg.LEO", "2.5 mg/kg.LEO", "5.0 mg/kg.LEO"))
+headTail(exp2)
+PTQuad <- pairwiseRobustTest(Quad ~ Factor.int, data = exp2, est = "mom", nboot = 5000, method = "fdr")
+PTQuad
+```
+
+4.4) Draw graph of non-transformed data
+```{r}
+QuadInt <- ddply(exp2, .(Dose, Phenotype), summarise, val = mean(Quad))
+ggplot(exp2, aes(x = factor(Dose), y = Quad, colour = Phenotype)) + geom_boxplot(outlier.shape = NA) + geom_point(data = QuadInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_line(data = QuadInt, aes(y = val, group = Phenotype), position = position_dodge(width = 0.75)) + geom_jitter(position = "dodge", alpha = 0.3) + labs(x = "Dose", y = "Number of squares crossed (N)", color = "Phenotype")
+```
+
+4.5) Draw graph of Huber M-estimators and CIs
+```{r}
+SumQuad = groupwiseHuber(data = exp2copy, group = c("Phenotype", "Dose"), var = "Quad", conf.level = 0.95, conf.type="wald")
+ggplot(SumQuad, aes(x=Dose, y = M.Huber, color = Phenotype)) + geom_errorbar(aes(ymin=lower.ci, ymax=upper.ci), width = 0.2, size = 0.7, position = position_dodge(.2)) + geom_point(position = position_dodge(.2)) + geom_line(data = SumQuad, aes(y = M.Huber, group = Phenotype), position = position_dodge(.2))
+```