model9analysis.R

rm('degreeResults')
rm('res')
rm('results')

## Collate the data into a neat results dataframe
allAgents <- NULL
for(rd in rawdata) {
  rd$agents$agentCount <- rep(rd$model$agentCount,nrow(rd$agents))
  rd$agents$agentDegree <- rep(rd$model$agentDegree,nrow(rd$agents))
  rd$agents$decisionCount <- rep(rd$model$decisionCount,nrow(rd$agents))
  rd$agents$modelDuration <- rep(rd$duration,nrow(rd$agents))
  rd$agents$meanSensitivity <- rep(rd$model$other$sensitivity,nrow(rd$agents))
  rd$agents$sdSensitivity <- rep(rd$model$other$sensitivitySD,nrow(rd$agents))
  rd$agents$startingEgoBias <- rep(rd$model$other$startingEgoBias,nrow(rd$agents))
  rd$agents$adviceNoise <- rep(rd$model$other$adviceNoise,nrow(rd$agents))
  rd$agents$badAdviceProb <- rep(rd$model$other$badAdviceProb,nrow(rd$agents))
  # only take a subset because of memory limitations
  allAgents <- rbind(allAgents, rd$agents[rd$agents$generation%%50 == 1
                 | (rd$agents$generation%%25 == 1 & rd$agents$generation < 250), ])
}

rm('rawdata')
rm('rd')

if(!require('tidyverse')) {
  install.packages('tidyverse')
  library(tidyverse)
}

style <- theme_light() +
  theme(legend.position = 'top')


# egoBias
ggplot(allAgents, 
       aes(x=generation, y=egoBias, 
           colour = as.factor(badAdviceProb), 
           shape = as.factor(meanSensitivity))) +
  geom_hline(yintercept = 0.5, linetype = 'dashed') +
  stat_summary(geom = 'point', fun.y = mean, size = 3, alpha = 0.5) +
  stat_summary(geom = 'errorbar', fun.data = function(x){data.frame(ymin=min(x), ymax=max(x))}, 
               size = 1, alpha = 0.25) +
  stat_summary(geom = 'errorbar', fun.data = mean_cl_boot, size = 0.25) +
  scale_y_continuous(limits = c(0,1)) +
  facet_grid(startingEgoBias ~ ., labeller = label_both) +
  style 

# fitness
prefix.m <- function(s) paste('m =',s)
prefix.sd <- function(s) paste('sd =',s)
ggplot(allAgents, 
       aes(x=generation, y=fitness/decisionCount,  
           colour = as.factor(badAdviceProb), 
           shape = as.factor(meanSensitivity))) +
  stat_summary(geom = 'point', fun.y = mean, size = 3, alpha = 0.5) +
  stat_summary(geom = 'errorbar', fun.data = mean_cl_boot, size = .5) +
  facet_grid(startingEgoBias ~ .) +
  style 

ggplot(allAgents, 
       aes(x="", y=modelDuration,  
           colour = as.factor(adviceNoise), 
           shape = as.factor(adviceNoise))) +
  geom_point(size = 3, position = position_dodge(width = 0.2)) +
  facet_wrap(~startingEgoBias) +
  style
  
ggplot(allAgents, aes(x = initialDecision, y = finalDecision,  
                      colour = as.factor(adviceNoise), 
                      shape = as.factor(adviceNoise))) +
  geom_point(alpha = 0.05) +
  geom_abline(slope = 1, intercept = 0) +
  coord_fixed() +
  # scale_y_continuous(limits = c(0,100)) +
  # scale_x_continuous(limits = c(0,100)) +
  style