## This script analyzes the normalized responses of H.chrysoscelis subjects to conspecific calls of varying call duration.  
# By running this script, you will be able to make figures used in Figure5 and perform statistical analysis associated with this figure.

# Read packages
library("dplyr")
library("ggplot2")
library("lme4")
library("lmerTest")
library("binom")

## Read data
data_CallDur <- read.csv("Figure5_CallDurationPreference.csv",header = T, check.names=F) # full data

## Plot and Statistical Analysis

# Plot and psychometric curve in Fig. 5
data_CallDur_grp <- data_CallDur %>% group_by(Call_duration)
summary_CallDur <- data_CallDur_grp %>% summarize(Mean =mean(Normalized_responses), N = n(), CI = 1.96*(sd(Normalized_responses)/sqrt(N)))

CD <- summary_CallDur$Call_duration
Mean <- summary_CallDur$Mean
df <- data.frame(CD,Mean)

p2 <- nls(Mean ~ SSlogis(CD, Asym, xmid, scal), data = df)
pframe2 <- data.frame(CD=10^seq(log10(5),log10(40), length.out=100))
pp <- data.frame(pframe2, Mean = predict(p2,pframe2), wts =FALSE)

ggplot(summary_CallDur, aes(x =CD, y =Mean)) +
  geom_point(aes(y =Mean),size = 4, shape = 16) +
  geom_errorbar(width=.5, aes(ymin=Mean-CI, ymax=Mean+CI)) +
  theme_classic() + ylab("Normalized responses (%)") +
  scale_x_continuous("Pulse number (Pulses/call)",breaks = c(5,10,15,20,25,30,35,40), 
                     labels = paste0(c(5,10,15,20,25,30,35,40))) +
  geom_line(data =pp, aes(linetype=wts), size=1) + scale_y_continuous(breaks=seq(0,150,25)) +
  theme(axis.title.x=element_text(size=16), axis.title.y=element_text(size=16), axis.text.x=element_text(size=14),axis.text.y=element_text(size=14),legend.title=element_blank(),legend.position="none",panel.spacing.x=unit(1, "lines"))

# Statistical analysis for Fig. 5
data_CallDur$Call_duration <- factor(data_CallDur$Call_duration, levels = c(30,5,10,20,40))

lmer.1 <- lmer(Normalized_responses ~ 1 + Call_duration + (1|Frog_id), data_CallDur, REML = FALSE)
lmer.2 <- lmer(Normalized_responses ~ 1  + (1|Frog_id), data_CallDur, REML = FALSE)
anova(lmer.1,lmer.2)
summary(lmer.1)