---
output:
  html_document: default
  pdf_document: default
---
```{r setup, include=FALSE, cache=FALSE}
options(
  scipen = 1,
  digits = 3) #set to two decimal 
```
```{r load packages, message=FALSE, warning=FALSE, include=FALSE}
#LIBRERIE
library(tidyverse)
library(ggplot2)
library(BayesFactor)
library(afex)
library(psych)
library("plyr")
# detach(package:plyr)
library(emmeans)
library("EnvStats")
# library('Cairo')
library("nparLD")
library("coin")
library(rstatix)
library(dplyr)
library(rcompanion)
library(polycor)
library("Hmisc")
library("readxl")
library(lsr)
library('ggpattern')
library(stringr)
library("pwr")
library(knitr)
```

```{r read data and plotting, echo=FALSE, message=FALSE, warning=FALSE}
# non spatial ----------------------------
df  <- read_excel("ordinal_nospatial_D26-L26.xlsx")


df <- df%>%filter(is.na(subjectID)==FALSE)
df1 <- df%>%gather(exp,percent,-subjectID, -hatch_condition)
df1[c('test', 'choice')] <- str_split_fixed(df1$exp, '_', 2)
df1$test <- mapvalues(df1$test, from = unique(df1$test),
                      to = c('sagittal',"FP binocular","FP left","FP right"))


test_choice <- ddply(df1, c("test","hatch_condition","choice"), 
                     summarise,  Mean = mean(percent), sd = sd(percent), n = sum(!is.na(percent)), se = sd / sqrt (n))
test_choice$test <- factor(test_choice$test, levels =  c('sagittal',"FP binocular","FP left","FP right"))
test_choice$choice <- factor(test_choice$choice, levels =  c(1:10,"1L","2L","3L","4L","5L",
                                                             "5R","4R","3R","2R","1R"))


# windowsFonts(Times=windowsFont("Arial"))
ggplot(test_choice, aes(x=choice, y=Mean, colour = hatch_condition)) +  
  geom_hline(yintercept=10, color='grey', size=0.4,lty=5) +
  geom_line(aes(group = hatch_condition)) +
  facet_wrap(~test,scales='free_x',ncol=2)+
  geom_point(size = 1) +
  ylim(0,45) +
  geom_pointrange(aes(ymin=(Mean-se), ymax=(Mean+se)), width=0.2, position=position_dodge(0)) +
  labs(title = "Without spatial information", x = "Positions", y="First choice - Percentage (%)") +  
  theme(legend.position="right")+  
  # guides(shape=FALSE)+ 
  theme_classic() + 
  scale_color_manual(values=c("#4b0ffb","#fc7e0f"),name="Hatch Condition")+ 
  theme(text = element_text(size=14, family="Arial"), 
        panel.background = element_rect(fill = "transparent", color = "black"), 
        plot.background = element_rect(fill = "transparent")) + 
  theme(axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 12),
        strip.text.x = element_text(size = 12),
        strip.text.y = element_text(size = 12),
        legend.text = element_text(size = 12)) 
# ggsave("plot/exp2AnonSpatialN.tiff", plot = last_plot(),
#        # path = "C:/Users/aeo/Desktop/ordinal data/flavia",
#        width = 8, height = 5, device='tiff', dpi=300)


# spatial -------------------------------
df2  <- read_csv("ordinal_spatial_D24-L24.csv")

df2 <- df2%>%gather(exp,percent,-subjectID, -hatch_condition)
df2[c('test', 'choice')] <- str_split_fixed(df2$exp, '_', 2)
# unique(df2$test)
df2$test <- mapvalues(df2$test, from = unique(df2$test),
                      to = c("FP binocular","FP right","FP left",'sagittal'))


test_choice2 <- ddply(df2, c("test","hatch_condition","choice"), 
                      summarise,  Mean = mean(percent), sd = sd(percent), n = sum(!is.na(percent)), se = sd / sqrt (n))
test_choice2$test <- factor(test_choice2$test, levels =  c('sagittal',"FP binocular","FP left","FP right"))
test_choice2$choice <- factor(test_choice2$choice, levels =  c(1:10,"1L","2L","3L","4L","5L",
                                                               "5R","4R","3R","2R","1R"))

ggplot(test_choice2, aes(x=choice, y=Mean, colour = hatch_condition)) +  
   geom_hline(yintercept=10, color='grey', size=0.4,lty=5) +
  geom_line(aes(group = hatch_condition)) +
  facet_wrap(~test,scales='free_x',ncol=2)+
  geom_point(size = 1) +
  ylim(0,45) +
  geom_pointrange(aes(ymin=(Mean-se), ymax=(Mean+se)), width=0.2, position=position_dodge(0)) +
  labs(title = "With spatial information", x = "Positions", y="First choice - Percentage (%)") +  
  theme(legend.position="right")+  
  # guides(shape=FALSE)+ 
  theme_classic() + 
  scale_color_manual(values=c("#4b0ffb","#fc7e0f"),name="Hatch Condition")+ 
  theme(text = element_text(size=14, family="Arial"), 
        panel.background = element_rect(fill = "transparent", color = "black"), 
        plot.background = element_rect(fill = "transparent")) + 
  theme(axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 12),
        strip.text.x = element_text(size = 12),
        strip.text.y = element_text(size = 12),
        legend.text = element_text(size = 12)) 
# ggsave("plot/exp1WithSpatialN.tiff", plot = last_plot(),
#        # path = "C:/Users/aeo/Desktop/ordinal data/flavia",
#        width = 8, height = 5, device='tiff', dpi=300)

```

```{r chance level tests, message=FALSE, warning=FALSE, include=FALSE}

# chance level ---------
for (hatchC in c('light','dark')) {
  for (thistest in unique(df1$test)) {
    for (thisposition in unique(df1$choice[df1$test==thistest])) {
      tmp <-t.test(df1$percent[df1$test==thistest&df1$choice==thisposition&df1$hatch_condition==hatchC], mu = 10,
                   alternative = c("greater"),
                   conf.level = 0.95)
      tmp<-wilcox.test(df1$percent[df1$test==thistest&df1$choice==thisposition&df1$hatch_condition==hatchC], 
                       mu = 10,alternative ="greater")
      
      test_choice$r[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC] <- 
        wilcoxonOneSampleR(x=df1$percent[df1$test==thistest&df1$choice==thisposition&df1$hatch_condition==hatchC], mu=10)
      
      test_choice$p[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC] <- tmp['p.value']
      test_choice$p.adj[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC] <- p.adjust(tmp['p.value'], method = "bonferroni", n = 10)
      bf <- ttestBF(
      x = df1$percent[df1$test==thistest&df1$choice==thisposition&df1$hatch_condition==hatchC]-10,
      mu = 0,nullInterval = c(0,Inf))
      test_choice$bf[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC]  <- extractBF(bf)$bf[1]
          
      # df_1$con_int[i] <- tmp['conf.int']
      # df_1$chance[i] <- chance*100
      # test_choice$cohenH[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC] <- 
      test_choice$ES.h[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC] <- ES.h(df1$percent[test_choice$test==thistest&test_choice$choice==thisposition&test_choice$hatch_condition==hatchC]/100,0.10)
      #   df_1$power[i] <- pwr.p.test(h=df_1$cohen_h[i],
      #                               n=df_1$N[i],
      #                               sig.level=0.05,alternative="greater")
    }
  }
}
test_choice$p.sig <- ifelse (test_choice$p<0.05,'*','ns')
test_choice$p.sig[test_choice$p>=0.001&test_choice$p<0.01] <- '**'
test_choice$p.sig[test_choice$p<0.001] <- '***'
test_choice$p.adj.sig <- ifelse (test_choice$p.adj<0.05,'*','ns')
test_choice$p.adj.sig[test_choice$p.adj>=0.001&test_choice$p.adj<0.01] <- '**'
test_choice$p.adj.sig[test_choice$p.adj<0.001] <- '***'
test_choice$bf_cat[test_choice$bf>1] <- 'Anecdotal'
test_choice$bf_cat[test_choice$bf>3] <- 'Moderate'
test_choice$bf_cat[test_choice$bf>10] <- 'Strong'
test_choice$bf_cat[test_choice$bf>30] <- 'Very Strong'
test_choice$bf_cat[test_choice$bf>100] <- 'Extreme'
df11 <-  apply(test_choice,2,as.character)
# write.csv(df11, file='output20240704/summary_nonSpatial.csv')
for (hatchC in c('light','dark')) {
  for (thistest in unique(df2$test)) {
    for (thisposition in unique(df2$choice[df2$test==thistest])) {
      tmp <-t.test(df2$percent[df2$test==thistest&df2$choice==thisposition&df2$hatch_condition==hatchC], mu = 10,
                   alternative = c("greater"),
                   conf.level = 0.95)
      tmp<-wilcox.test(df2$percent[df2$test==thistest&df2$choice==thisposition&df2$hatch_condition==hatchC], 
                       mu = 10,alternative ="greater")
      
      test_choice2$r[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC] <- 
        wilcoxonOneSampleR(x=df2$percent[df2$test==thistest&df2$choice==thisposition&df2$hatch_condition==hatchC], mu=10)
      
      test_choice2$p[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC] <- tmp['p.value']
      test_choice2$p.adj[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC] <- p.adjust(tmp['p.value'], method = "bonferroni", n = 10)
        bf <- ttestBF(x = df2$percent[df2$test==thistest&df2$choice==thisposition&df2$hatch_condition==hatchC]-10,
      mu = 0,nullInterval = c(0,Inf))
      test_choice2$bf[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC]  <- extractBF(bf)$bf[1]
         
      # df_1$con_int[i] <- tmp['conf.int']
      # df_1$chance[i] <- chance*100
      # test_choice2$cohenH[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC] <- 
      test_choice2$ES.h[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC] <- ES.h(df2$percent[test_choice2$test==thistest&test_choice2$choice==thisposition&test_choice2$hatch_condition==hatchC]/100,0.10)
      #   df_1$power[i] <- pwr.p.test(h=df_1$cohen_h[i],
      #                               n=df_1$N[i],
      #                               sig.level=0.05,alternative="greater")
    }
  }
}
test_choice2$p.sig <- ifelse (test_choice2$p<0.05,'*','ns')
test_choice2$p.sig[test_choice2$p>=0.001&test_choice2$p<0.01] <- '**'
test_choice2$p.sig[test_choice2$p<0.001] <- '***'
test_choice2$p.adj.sig <- ifelse (test_choice2$p.adj<0.05,'*','ns')
test_choice2$p.adj.sig[test_choice2$p.adj>=0.001&test_choice2$p.adj<0.01] <- '**'
test_choice2$p.adj.sig[test_choice2$p.adj<0.001] <- '***'
test_choice2$bf_cat[test_choice2$bf>1] <- 'Anecdotal'
test_choice2$bf_cat[test_choice2$bf>3] <- 'Moderate'
test_choice2$bf_cat[test_choice2$bf>10] <- 'Strong'
test_choice2$bf_cat[test_choice2$bf>30] <- 'Very Strong'
test_choice2$bf_cat[test_choice2$bf>100] <- 'Extreme'
test_choice2
```

```{r organize before analysis, message=FALSE, warning=FALSE, include=FALSE}
df21 <-  apply(test_choice2,2,as.character)
# write.csv(df21, file='output20240704/summary_Spatial2.csv')

library(afex)


df2$spatial <-'spatial'
df1$spatial <-'NONspatial'
df <- rbind(df1,df2)
df.acc <- df%>%filter(choice%in% c('4R','4L','4'))

df.acc$hatch_condition <- factor(df.acc$hatch_condition,levels = c('dark','light'))
df.acc$choice <- factor(df.acc$choice,levels = c('4R','4L','4'))
df.acc$spatial <- factor(df.acc$spatial,levels = c('NONspatial','spatial'))

df.accNON <- df.acc %>% subset(spatial=='NONspatial')
df.accSpatial <- df.acc %>% subset(spatial=='spatial')

df.acc$sideCode[df.acc$choice=='4L'] <- 1
df.acc$sideCode[df.acc$choice=='4R'] <- 0

df.acc$exp[df.acc$exp=="sagittal_4"] <- "sag_4"
sum1 <- ddply(df.acc, c("spatial","test","choice"), 
                     summarise,  Mean = mean(percent), sd = sd(percent), n = sum(!is.na(percent)), se = sd / sqrt (n))
# sum12 <-  apply(sum1,2,as.character)
```
# Analysis

## Experiment 1 With spatial cues 

### 1 Sagittal
```{r exp1 sag, echo=FALSE, message=FALSE, warning=FALSE}
lm1 <- lm(percent ~ hatch_condition, data = df.accSpatial%>%subset(test=='sagittal'))
summary(lm1)
# Anova(lm1,type='III')
lmBF(percent ~ hatch_condition, data = df.accSpatial%>%subset(test=='sagittal'))

```

### 1 FP binocular
```{r exp1 FP bin, echo=FALSE, message=FALSE, warning=FALSE}
lm2 <- lm(percent ~ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP binocular'))
summary(lm2)

lm2 <- lm(percent ~ hatch_condition * choice, data = df.accSpatial%>%subset(test=='FP binocular'))
# lm2 <- lmer(percent ~ hatch_condition * choice +(1|subjectID), data = df.accSpatial%>%subset(test=='FP binocular'))
summary(lm2)
# Anova(lm2,type='III')
lm2.emm <- emmeans(lm2, ~ choice*hatch_condition)
pairs(lm2.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm2.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP binocular'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP binocular'))/lmBF(percent ~  hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP binocular'))
```

### 1 FP left
```{r exp1 FP left, echo=FALSE, message=FALSE, warning=FALSE}

lm3 <- lm(percent ~ hatch_condition+ choice, data = df.accSpatial%>%subset(test=='FP left'))
summary(lm3)

lm3 <- lm(percent ~ hatch_condition* choice, data = df.accSpatial%>%subset(test=='FP left'))
summary(lm3)
# Anova(lm3,type='III')
lm3.emm <- emmeans(lm3, ~ choice*hatch_condition)
pairs(lm3.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm3.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP left'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP left'))/lmBF(percent ~  hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP left'))

```

### 1 FP right
```{r exp1 FP right, echo=FALSE, message=FALSE, warning=FALSE}
lm4 <- lm(percent ~ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP right'))
summary(lm4)

lm4 <- lm(percent ~ hatch_condition * choice, data = df.accSpatial%>%subset(test=='FP right'))
summary(lm4)
# Anova(lm4,type='III')
lm4.emm <- emmeans(lm4, ~ choice*hatch_condition)
pairs(lm4.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm4.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP right'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP right'))/lmBF(percent ~  hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP right'))
```

## Experiment 2 (without spatial cues)

### 2 sagittal
```{r exp2 sag, echo=FALSE, message=FALSE, warning=FALSE}

lm1 <- lm(percent ~ hatch_condition, data = df.accNON%>%subset(test=='sagittal'))
summary(lm1)
# Anova(lm1,type='III')
lm1 <- lmBF(percent ~ hatch_condition, data = df.accNON%>%subset(test=='sagittal'))
summary(lm1)

```

### 2 FP binocular
```{r exp2 FP bin, echo=FALSE, message=FALSE, warning=FALSE}
lm2 <- lm(percent ~ hatch_condition + choice, data = df.accNON%>%subset(test=='FP binocular'))
# lm2 <- lmer(percent ~ hatch_condition * choice +(1|subjectID), data = df.accNON%>%subset(test=='FP binocular'))
summary(lm2)

lm2 <- lm(percent ~ hatch_condition * choice, data = df.accNON%>%subset(test=='FP binocular'))
# lm2 <- lmer(percent ~ hatch_condition * choice +(1|subjectID), data = df.accNON%>%subset(test=='FP binocular'))
summary(lm2)
lm2.emm <- emmeans(lm2, ~ choice*hatch_condition)
pairs(lm2.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm2.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP binocular'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP binocular'))/lmBF(percent ~  hatch_condition + choice, data = df.accNON%>%subset(test=='FP binocular'))
# lm2 <- lmer(percent ~ hatch_condition * choice +(1|subjectID), data = df.accNON%>%subset(test=='FP binocular'))

# Anova(lm2,type='III')

```

### 2 FP left
```{r exp2 FP left, echo=FALSE, message=FALSE, warning=FALSE}
lm3 <- lm(percent ~ hatch_condition+ choice, data = df.accNON%>%subset(test=='FP left'))
summary(lm3)

lm3 <- lm(percent ~ hatch_condition* choice, data = df.accNON%>%subset(test=='FP left'))
summary(lm3)
lm3.emm <- emmeans(lm3, ~ choice*hatch_condition)
pairs(lm3.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm3.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP left'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP left'))/lmBF(percent ~  hatch_condition + choice, data = df.accNON%>%subset(test=='FP left'))
# Anova(lm3,type='III')


```

### 2 FP right
```{r exp2 FP right, echo=FALSE, message=FALSE, warning=FALSE}
lm4 <- lm(percent ~ hatch_condition + choice, data = df.accNON%>%subset(test=='FP right'))
summary(lm4)
# Anova(lm4,type='III')
lm4 <- lm(percent ~ hatch_condition * choice, data = df.accNON%>%subset(test=='FP right'))
summary(lm4)
lm4.emm <- emmeans(lm4, ~ choice*hatch_condition)
pairs(lm4.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm4.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

anovaBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP right'))

lmBF(percent ~ hatch_condition * choice+ hatch_condition + choice, data = df.accNON%>%subset(test=='FP right'))/lmBF(percent ~  hatch_condition + choice, data = df.accNON%>%subset(test=='FP right'))

```


```{r 12 plot, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# compare exp1 with exp2 ---------------

exp1exp2 <- ddply(df.acc, c("test","hatch_condition","spatial",'choice'), 
                  summarise,  Mean = mean(percent), sd = sd(percent), n = sum(!is.na(percent)), se = sd / sqrt (n))
exp1exp2$test <- factor(exp1exp2$test, levels =  c('sagittal',"FP binocular","FP left","FP right"))
exp1exp2$choice <- factor(exp1exp2$choice, levels =  c(4,"4L","4R"))
exp1exp2$condition <- paste(exp1exp2$hatch_condition,exp1exp2$spatial,sep = '.')


ggplot(exp1exp2, aes(x=choice, fill=condition, y=Mean)) + 
  facet_wrap(~test,scales='free_x',ncol=2)+
  geom_hline(yintercept=10, color='grey', size=0.4) +
  geom_bar(stat="identity", color="black", position=position_dodge(),width=0.5) +
  geom_errorbar(aes(ymin=Mean, ymax=(Mean+se)), width=0.2, position=position_dodge(.5)) + 
  theme(legend.position="right")+ 
  # scale_fill_grey(start = 0, end = 1, name = "Condition") + 
  ylim(0,50)+
  scale_fill_manual(values=c("#d44000","#ffc93c",
                             "#0061a8", "#8ab6d6"),name="condition")+
  labs(title = "", x = "Positions", y="First choice - Percentage (%)") + 
  theme_classic() + 
  theme(text = element_text(size=14, family="Times"), 
        panel.background = element_rect(fill = "transparent", color = "black"), 
        plot.background = element_rect(fill = "transparent")) + 
  theme(axis.text.x = element_text(size = 8),
        axis.text.y = element_text(size = 10),
        strip.text.x = element_text(size = 10),
        legend.text = element_text(size = 10)) 
# ggsave("plot/exp1exp2.tiff", plot = last_plot(),
#        # path = "C:/Users/aeo/Desktop/ordinal data/flavia",
#        width = 8, height = 6, device='tiff', dpi=300)


### compare 12 sagittal
lm1 <- lm(percent ~ hatch_condition + spatial, data = df.acc%>%subset(test=='sagittal'))
summary(lm1)
lm1 <- lm(percent ~ hatch_condition * spatial, data = df.acc%>%subset(test=='sagittal'))
summary(lm1)
# Anova(lm1,type='III')

### compare 12 FP bin
lm2 <- lm(percent ~ hatch_condition + choice+ spatial, data = df.acc%>%subset(test=='FP binocular'))
summary(lm2)

lm2 <- lm(percent ~ hatch_condition * choice* spatial, data = df.acc%>%subset(test=='FP binocular'))
summary(lm2)
# Anova(lm2,type='III')
lm2.emm <- emmeans(lm2, ~ choice*hatch_condition* spatial)
pairs(lm2.emm, simple = "each",adjust="bonferroni",paired=F)
pairs(lm2.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm2.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

### compare 12 FP left
lm3 <- lm(percent ~ hatch_condition+ choice, data = df.accSpatial%>%subset(test=='FP left'))
summary(lm3)

lm3 <- lm(percent ~ hatch_condition* choice, data = df.accSpatial%>%subset(test=='FP left'))
summary(lm3)
# Anova(lm3,type='III')
lm3.emm <- emmeans(lm3, ~ choice*hatch_condition)
pairs(lm3.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm3.emm, simple = "choice",adjust="bonferroni",paired=TRUE)


### compare 12 FP right
lm4 <- lm(percent ~ hatch_condition + choice, data = df.accSpatial%>%subset(test=='FP right'))
summary(lm4)

lm4 <- lm(percent ~ hatch_condition * choice, data = df.accSpatial%>%subset(test=='FP right'))
summary(lm4)
# Anova(lm4,type='III')
lm4.emm <- emmeans(lm4, ~ choice*hatch_condition)
pairs(lm4.emm, simple = "hatch_condition",adjust="bonferroni",paired=F)
pairs(lm4.emm, simple = "choice",adjust="bonferroni",paired=TRUE)

```
## t tests
### 4L vs. 4R 
4L - 4R 
```{r 4L vs. 4R, echo=FALSE, message=FALSE, warning=FALSE}
t_side <- data.frame()

for (i in unique(df.acc$spatial)) {
  for (j in unique(df.acc$hatch_condition)) {
    for (k in c( "FP binocular", "FP left", "FP right")) {
      df.tmp <- df.acc %>% subset(spatial==i&hatch_condition==j&test==k)
      runTest <- t.test(df.tmp$percent[df.tmp$choice=='4L'],df.tmp$percent[df.tmp$choice=='4R'],paired = T)
      runbf <- ttestBF(df.tmp$percent[df.tmp$choice=='4L'],df.tmp$percent[df.tmp$choice=='4R'],paired = T)
      t_side <- rbind(t_side,
                      c(i,j,k,extractBF(runbf)['bf'][[1]], unlist(runTest[c(1,3,4,5)])))

    }}}
names(t_side) <- c('spatialCue','lateralization','test','bf','t','p','CI_lower','CI_upper','mean_difference')
t_side[c(4:9)] <- as.numeric(unlist(t_side[c(4:9)] ))
kable(t_side, digits = 3)
```

### strong vs. weak lateralized  
strong - weak
```{r strong vs. weak lateralized, echo=FALSE, message=FALSE, warning=FALSE}
t_lateralization <- data.frame()
df.acc$hatch_condition <- factor(df.acc$hatch_condition, levels = c('light','dark'))
for (i in unique(df.acc$spatial)) {
  for (j in unique(df.acc$exp)) {
      df.tmp <- df.acc %>% subset(spatial==i&exp==j)
      runTest <- t.test(percent ~ hatch_condition, df.tmp)
      
      # runTest <- t.test(df.tmp$percent[df.tmp$hatch_condition=='light'],df.tmp$percent[df.tmp$hatch_condition=='dark'],paired = F)
      runbf <- ttestBF(df.tmp$percent[df.tmp$hatch_condition=='light'],df.tmp$percent[df.tmp$hatch_condition=='dark'],paired = F)
      t_lateralization <- rbind(t_lateralization,
                      c(i,j,extractBF(runbf)['bf'][[1]], unlist(runTest[c(1,3,4,5)])))

    }}
names(t_lateralization) <- c('spatialCue','test_choice','bf','t','p','CI_lower','CI_upper','mean_strong','mean_weak')
t_lateralization[c(3:9)] <- as.numeric(unlist(t_lateralization[c(3:9)] ))
kable(t_lateralization, digits = 3)
```

### spatial vs. non spatial  
spatial - non spatial 
```{r spatial vs. non spatial, echo=FALSE, message=FALSE, warning=FALSE}
t_space <- data.frame()
df.acc$spatial <- factor(df.acc$spatial, levels = c('spatial','NONspatial'))
for (i in unique(df.acc$hatch_condition)) {
  for (j in unique(df.acc$exp)) {
      df.tmp <- df.acc %>% subset(hatch_condition==i&exp==j)
      runTest <- t.test(percent ~ spatial, df.tmp)
      
      # runTest <- t.test(df.tmp$percent[df.tmp$hatch_condition=='light'],df.tmp$percent[df.tmp$hatch_condition=='dark'],paired = F)
      runbf <- ttestBF(df.tmp$percent[df.tmp$spatial=='spatial'],df.tmp$percent[df.tmp$spatial=='NONspatial'],paired = F)
      t_space <- rbind(t_space,
                      c(i,j,extractBF(runbf)['bf'][[1]], unlist(runTest[c(1,3,4,5)])))

    }}
names(t_space) <- c('lateralization','test_choice','bf','t','p','CI_lower','CI_upper','mean_spatial','mean_nonspatial')
t_space[c(3:9)] <- as.numeric(unlist(t_space[c(3:9)] ))
kable(t_space, digits = 3)
```

### chance exp1
```{r print chance level tests1, echo=FALSE, message=FALSE, warning=FALSE}
test_choice2
```
### chance exp2
```{r print chance level tests2, echo=FALSE, message=FALSE, warning=FALSE}
test_choice
```