---
title: "R Notebook"
output: html_notebook
---

# Packages used

```{r}

library(tidyverse)
library(tidybayes)
library(cmdstanr)
library(bayesplot)
library(broom)

library(readxl)
library(brms)
library(ggeffects)
library(ggdist)           # Special geoms for posterior distributions

library(jtools)
library(sjPlot)
library(emmeans)

library(easystats)
library(rstanarm)
library(modelbased)

library(priorsense)

```

# Dataset organization

```{r}
hdgood <- read_xlsx("SupplementaryData1.xlsx", sheet = "GoodData_corr", na = c("", "#N/A", "NA"))

hdgood_hab2 <- hdgood %>% 
  filter(!is.na(ID)) %>% 
  filter(block != 4) %>%
  mutate(block = factor(block)) %>% 
  mutate(sound_condition = fct_relevel(sound_condition, c("noise", "dog", "chimp", "hum", "music")))%>% 
  filter(!is.na(aggr)) %>% 
  filter(!is.na(hab)) %>% 
  mutate(hab = factor(hab)) %>% 
  filter(hab != "h0") %>%
  mutate(hab = fct_relevel(hab, c("h1", "h2", "dog", "hum", "chimp", "music"))) %>% 
  filter(hab != "h1")

```

# Modelling

## Full modell

```{r}

bayesbin_newpri <- brm(aggr ~ block*species*hab2 + (1|ID),
               data = hdgood_hab2,
               family = bernoulli(link = "logit"),
               priors <- c(
                        prior(student_t(3, 0, 2.5), class = "Intercept"),
                        prior(cauchy(0, 2.5), class = "b"),
                        prior(student_t(3, 0, 2.5), class = "sd")
                      ),
               cores = 4,
               chains = 4,
               iter = 20000,
               backend = "cmdstanr",
               seed = 1221,
               control=list(adapt_delta=0.99, step_size = 0.01, max_treedepth =15),
               file = "habdishab_upd_newpriors")

```

### Diagnostics

```{r}

fixef(bayesbin_newpri) %>%
  inv_logit_scaled() %>% 
  round(digits = 3)
```


```{r}
plot(bayesbin_newpri)
```



```{r}

pp_check(bayesbin_newpri, type = "dens_overlay", ndraws = 100)

```

```{r}
summary(bayesbin_newpri)
```


```{r}

conditional_effects(bayesbin_newpri, dpar = "mu", points = T)

```


## Model selection with reduced models

```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri <- update(bayesbin_newpri, formula. = . ~ . - block:species:hab2,
               file = "habdishab_1a")

# Compare models using LOO
loo_full <- loo(bayesbin_newpri)
loo_reduced <- loo(reduced_model_newpri)
loo_compare(loo_full, loo_reduced)

```


```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bs <- update(reduced_model_newpri, formula. = . ~ . - block:species,
               file = "habdishab_2a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri)
loo_reduced <- loo(reduced_model_newpri_bs)
loo_compare(loo_full, loo_reduced)

```



```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_hs <- update(reduced_model_newpri, formula. = . ~ . - species:hab2,
               file = "habdishab_3a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri)
loo_reduced <- loo(reduced_model_newpri_hs)
loo_compare(loo_full, loo_reduced)

```


```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bh <- update(reduced_model_newpri, formula. = . ~ . - block:hab2,
               file = "habdishab_4a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri)
loo_reduced <- loo(reduced_model_newpri_bh)
loo_compare(loo_full, loo_reduced)

```


```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bs_hs <- update(reduced_model_newpri_bs, formula. = . ~ . - species:hab2,
               file = "habdishab_5a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri_bs)
loo_reduced <- loo(reduced_model_newpri_bs_hs)
loo_compare(loo_full, loo_reduced)

```




```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bs_bh <- update(reduced_model_newpri_bs, formula. = . ~ . - block:hab2,
               file = "habdishab_6a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri_bs)
loo_reduced <- loo(reduced_model_newpri_bs_bh)
loo_compare(loo_full, loo_reduced)

```

```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bs_bh_hs <- update(reduced_model_newpri_bs_bh, formula. = . ~ . - species:hab2,
               file = "habdishab_7a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri_bs_bh)
loo_reduced <- loo(reduced_model_newpri_bs_bh_hs)
loo_compare(loo_full, loo_reduced)

```




```{r}
# Fit a reduced model without the interaction term
reduced_model_newpri_bs_bh_b <- update(reduced_model_newpri_bs_bh, formula. = . ~ . - block,
               file = "habdishab_8a")

# Compare models using LOO
loo_full <- loo(reduced_model_newpri_bs_bh)
loo_reduced <- loo(reduced_model_newpri_bs_bh_b)
loo_compare(loo_full, loo_reduced)

```

### Model comparisons

```{r}

loo_reduced_bs_bh_hs <- loo(reduced_model_newpri_bs_bh_hs)
loo_reduced_bs_bh <- loo(reduced_model_newpri_bs_bh)
loo_reduced_bs_hs <- loo(reduced_model_newpri_bs_hs)
loo_reduced_bs <- loo(reduced_model_newpri_bs)
loo_reduced_bh <- loo(reduced_model_newpri_bh)
loo_reduced_hs <- loo(reduced_model_newpri_hs)
loo_reduced <- loo(reduced_model_newpri)
loo_full <- loo(bayesbin_newpri)

loo_compare(loo_full, loo_reduced, loo_reduced_hs, loo_reduced_bh, loo_reduced_bs, loo_reduced_bs_hs, loo_reduced_bs_bh, loo_reduced_bs_bh_hs)



```

### prior diagnostics

```{r}
powerscale_sensitivity(reduced_model_newpri_bs)
```

```{r}

conditional_effects(reduced_model_newpri_bs, dpar = "mu", points = T)

```


```{r}
##Formatting Fig4

ce <- conditional_effects(reduced_model_newpri_bs,
                          dpar = "mu",
                          effects = "block:hab2",
                          prob = 0.89)

# 
plots <- plot(ce, points = FALSE, ask = FALSE)  
p <- plots[[1]]
p

p <- p +
  theme_classic(base_size = 22) +
  labs(
    x = "Block",
    y = "Probability of reaction",
    colour = "Sound condition",
    fill   = "Sound condition"
  ) +
  
  scale_colour_manual(
    values = c(TestSound = "#0072B2", h2 = "#D55E00"),          
    labels = c(TestSound = "Test sound", h2 = "Noise"),         
    breaks = c("TestSound", "h2")                              
  ) +
  scale_fill_manual(
    values = c(TestSound = "#56B4E9", h2 = "#E69F00"),          
    labels = c(TestSound = "Test sound", h2 = "Noise"),         
    breaks = c("TestSound", "h2")                              
  )+
theme(
  axis.title.x = element_text(face = "bold"),
  axis.title.y = element_text(face = "bold")
)

p

library(cowplot)
plot_grid(p, labels = c("a"), label_size = 16, label_fontface = "bold")
fig <- plot_grid(
  p,
  labels = c(""),
  label_size = 22,
  label_fontface = "bold"
)

ggsave(
  filename = "Fig4.tiff",   
  plot = fig,                
  width = 10, height = 6,     
  units = "in",              
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)

```

```{r}
##Formatting Fig6
ce_species <- conditional_effects(
  reduced_model_newpri_bs,
  dpar    = "mu",
  effects = "species:hab2",
  prob    = 0.89
)

# 
p_species <- plot(ce_species, points = FALSE)[[1]] +
  theme_classic(base_size = 22) +
  labs(
    x = "Sound type",
    y = "Probability of reaction",
    colour = "Sound condition",
    fill   = "Sound condition"
  ) +
  scale_colour_manual(
    values = c(TestSound = "#0072B2", h2 = "#D55E00"),          
    labels = c(TestSound = "Test sound", h2 = "Noise"),         
    breaks = c("TestSound", "h2")                              
  ) +
  scale_fill_manual(
    values = c(TestSound = "#56B4E9", h2 = "#E69F00"),          
    labels = c(TestSound = "Test sound", h2 = "Noise"),         
    breaks = c("TestSound", "h2")                              
  ) +
  scale_x_discrete(
    limits = c("dog","hum","chimp","music"),                    
    labels = c(
      "chimp" = "Chimpanzee",
      "dog"   = "Dog",
      "hum"   = "Human",
      "music" = "Music"
    )
  ) +
  theme(
    axis.title.x = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold")
  )

p_species
plot_grid(p_species, labels = c(""), label_size = 16, label_fontface = "bold")
fig <- plot_grid(
  p_species,
  labels = c(""),
  label_size = 22,
  label_fontface = "bold"
)

####save
ggsave(
  filename = "Fig6.tiff",   
  plot = fig,                
  width = 10, height = 6,     
  units = "in",              
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)

```

### Post-hoc tests

```{r}
prior_model <- unupdate(reduced_model_newpri_bs)
```



```{r}

reduced_model_newpri_bs |> 
  emmeans(pairwise ~ block|hab2, type = "response", level = .89)

```


```{r}

ph1 <- emmeans(reduced_model_newpri_bs, ~ block|hab2, level = .89)

pd(ph1)

rope_res <- rope(ph1, range = c(-0.18, 0.18), ci = .89)
rope_res
plot(rope_res)


ph1a <- emmeans(prior_model, ~ block|hab2, level = .89)

bf <- bayesfactor_parameters(ph1, prior = ph1a)
bf
interpret_bf(exp(bf$log_BF), include_value = TRUE)

```


```{r}
p2 <- plot(rope_res) +
  theme_classic(base_size = 22) +
  labs(
    x = "Contrast",
    y = "Sound condition x Block",
    title = "",
    fill = "CrI"   # CI -> CrI
  ) +
  scale_y_discrete(
    limits = c(
      # ALUL: NOISE -> FELÜL: TEST SOUND
      "3 h2", "2 h2", "1 h2",
      "3 TestSound", "2 TestSound", "1 TestSound"
    ),
    labels = c(
      "1 TestSound" = "TS – B1",
      "2 TestSound" = "TS – B2",
      "3 TestSound" = "TS – B3",
      "1 h2"        = "N – B1",
      "2 h2"        = "N – B2",
      "3 h2"        = "N – B3"
    )
  ) +
  theme(
    axis.title.x = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold"),
    plot.title   = element_text(hjust = 0.5, face = "bold")
  )


#
p2


# (a)
fig2 <- plot_grid(
  p2,
  labels = c("a"),
  label_size = 22,
  label_fontface = "bold"
)

###save
ggsave(
  filename = "Fig5a.tiff",
  plot = fig2,
  width = 10,
  height = 6,
  units = "in",
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)

```



```{r}
contr <- pairs(ph1)

pd(contr, as_p = F)

rope(contr, range = c(-0.18, 0.18), ci = .89)

ph1b <- pairs(ph1a)

bf <- bayesfactor_parameters(contr, prior = ph1b)
bf
interpret_bf(exp(bf$log_BF), include_value = TRUE)

plot(rope(contr, range = c(-0.18, 0.18), ci = .89))


```



```{r}

reduced_model_newpri_bs |> 
  emmeans( ~ hab2|block, type = "response", level = .89) |>
  pairs(reverse = T)

```

```{r}

ph2 <- emmeans(reduced_model_newpri_bs, ~ hab2|block, level = .89)

ph2a <- emmeans(prior_model, ~ hab2|block, level = .89)

pd(ph2)

rope(ph2, range = c(-0.18, 0.18), ci = .89)


plot(rope(ph2, range = c(-0.18, 0.18), ci = .89))



```

```{r}
contr2 <- pairs(ph2, reverse = T)

pd(contr2, as_p = F)

rope_res <- rope(contr2, range = c(-0.18, 0.18), ci = .89)
rope_res
ph2b <- pairs(ph2a)

bf <- bayesfactor_parameters(contr2, prior = ph2b)
bf
interpret_bf(exp(bf$log_BF), include_value = TRUE)

plot(rope_res)
```


```{r}
###Formatting Fig5b


p3 <- plot(rope_res) +
  theme_classic(base_size = 22) +
  labs(
    x = "Contrast",
    y = "Sound condition × Block",
    title = "",
    fill = "CrI"
  ) +
  scale_y_discrete(
    labels = c(
      "TS – N – B3",
      "TS – N – B2",
      "TS – N – B1"
    )
  ) +
  theme(
    axis.title.x = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold"),
    plot.title   = element_text(hjust = 0.5, face = "bold")
  )

#
p3

plot_grid(p3, labels = c("b"), label_size = 22, label_fontface = "bold")
fig3 <- plot_grid(
  p3,
  labels = c("b"),
  label_size = 22,
  label_fontface = "bold"
)

####Save
ggsave(
  filename = "Fig5b.tiff",   
  plot = fig3,                
  width = 10, height = 6,     
  units = "in",              
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)

```



```{r}

reduced_model_newpri_bs |> 
  emmeans( ~ species|hab2, type = "response", level = .89)

reduced_model_newpri_bs |> 
  emmeans( ~ species|hab2, type = "response", level = .89)|> 
  pairs(reverse = T)

```


```{r}

ph1 <- emmeans(reduced_model_newpri_bs, ~ species*hab2, level = .89)
ph1a <- emmeans(prior_model, ~ species*hab2, level = .89)

pd(ph1)

rope_res <- rope(ph1, range = c(-0.18, 0.18), ci = .89)

plot(rope_res)

bf <- bayesfactor_parameters(ph1, prior = ph1a)
bf
interpret_bf(exp(bf$log_BF), include_value = TRUE)


```

```{r}
###Formatting Fig7a

p4 <- plot(rope_res) +
  theme_classic(base_size = 22) +
  labs(
    x = "Contrast",
    y = "Sound type x Sound condition",
    title = "",
    fill = "CrI"
  ) +
  scale_y_discrete(
    limits = c(
      # alulról felfelé! -> így felül D–H–C–M lesz
      "music h2", "chimp h2", "hum h2", "dog h2",                 # N blokk
      "music TestSound", "chimp TestSound", "hum TestSound", "dog TestSound"  # TS blokk
    ),
    labels = c(
      "dog h2"          = "D – N",
      "hum h2"          = "H – N",
      "chimp h2"        = "C – N",
      "music h2"        = "M – N",
      "dog TestSound"   = "D – TS",
      "hum TestSound"   = "H – TS",
      "chimp TestSound" = "C – TS",
      "music TestSound" = "M – TS"
    )
  )+
  theme(
    axis.title.x = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold"),
    plot.title   = element_text(hjust = 0.5, face = "bold")
  )

#
p4

plot_grid(p4, labels = c("a"), label_size = 22, label_fontface = "bold")
fig4 <- plot_grid(
  p4,
  labels = c("a"),
  label_size = 22,
  label_fontface = "bold"
)


ggsave(
  filename = "Fig7a.tiff",   
  plot = fig4,                
  width = 10, height = 6,     
  units = "in",              
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)


```


```{r}

contr <- pairs(ph1, by = "hab2", reverse = T)

pd(contr, as_p = F)

r <- rope(contr, range = c(-0.18, 0.18), ci = .89)
r
plot(r)

ph1b <- pairs(ph1a)

bf <- bayesfactor_parameters(contr, prior = ph1b)
bf
interpret_bf(exp(bf$log_BF), include_value = TRUE)
```


```{r}
####Formatting Fig7b


# 2) Plot 
#    
abbr_label <- function(x) {
  # 
  src_map  <- c(dog = "D", hum = "H", music = "M", chimp = "C")
  cond_map <- c(h2 = "N", testsound = "TS")
  
  vapply(x, function(s) {
    # 
    m  <- regexec("^\\s*([A-Za-z]+)\\s*-\\s*([A-Za-z]+)\\s+([A-Za-z0-9]+)\\s*$", s)
    mm <- regmatches(s, m)[[1]]
    if (length(mm) == 4) {
      a <- tolower(mm[2]); b <- tolower(mm[3]); cnd <- tolower(mm[4])
      aL <- src_map[a]; bL <- src_map[b]; cL <- cond_map[cnd]
      if (is.na(aL) || is.na(bL) || is.na(cL)) return(s)
      paste(aL, "-", bL, "-", cL)
    } else {
      s
    }
  }, character(1))
}

p5 <- plot(r) +
  theme_classic(base_size = 22) +
  labs(x = "Contrast", y = "Sound type x Sound condition", title = "", fill = "CrI") +
  scale_y_discrete(
    limits = function(x) rev(x),   
    labels = abbr_label            
  ) +
  theme(
    axis.title.x = element_text(face = "bold"),
    axis.title.y = element_text(face = "bold"),
    plot.title   = element_text(hjust = 0.5, face = "bold")
  )

###save
fig7b <- cowplot::plot_grid(p5, labels = "b", label_size = 22, label_fontface = "bold")
ggsave("Fig7b.tiff", plot = p5, width = 10, height = 6, units = "in", dpi = 600, device = "tiff",
       compression = "lzw")

```



```{r}
reduced_model_newpri_bs |> 
  emmeans( ~ hab2|species, dpar = "mu", type = "response", level = .89)|> 
  pairs(reverse = T)

```



```{r}

contr <- pairs(ph1, by = "species", reverse = T)

pd(contr, as_p = F)

rope(contr, range = c(-0.18, 0.18), ci = .89)

plot(rope(contr, range = c(-0.18, 0.18), ci = .89))



```