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14_model_param_table.R
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# table of model parameters
# 3.29.21 KLS updated 3.4.22
# init ####
# load required packages
library(here)
library(tidyverse)
library(ggplot2)
# load source functions
source(here::here('scr', 'concat_clean.R'))
source(here::here('scr', 'AIC_functions.R'))
source(here::here('scr', 'BIC_functions.R'))
# set hard-coded variables
# read data in
d1 <- read.csv(here::here('output', 'two_alpha_model_params.csv' ))
d2 <- read.csv(here::here('output', 'single_alpha_model_params.csv' ))
d3 <- read.csv(here::here('output', 'baseline_model_params.csv'))
o4 <- read.csv(here::here('output', 'two_alpha_with_decay_model_params_older.csv'))
y4 <- read.csv(here::here('output', 'two_alpha_with_decay_model_params_younger.csv'))
o5 <- read.csv(here::here('output', 'prior_model_params_older.csv'))
y5 <- read.csv(here::here('output', 'prior_model_params_younger.csv'))
d6 <- read.csv(here::here('output', 'single_alpha_with_decay_model_params.csv'))
d4 <- rbind(o4, y4); rm(o4, y4)
d5 <- rbind(o5, y5); rm(o5, y5)
resp_summary <- read.csv(here::here('output', 'responses.csv'))
# calculate parameter means by age group
# gain-loss learning ####
d1 <- merge(d1, resp_summary, by = 'id') #adds response summary and gets rid of cut participants
d1$AIC <- calc_AIC(d1$n_resp,3,d1$X.LLH) # calc AIC
d1$BIC <- calc_BIC(d1$n_resp,3,d1$X.LLH) # calc BIC
t1 <- d1 %>% group_by(agegrp) %>%
summarise(a_gain_mean = mean(alpha_gain),
a_gain_sd = sd(alpha_gain),
a_loss_mean = mean(alpha_loss),
a_loss_sd = sd(alpha_loss),
beta_mean = mean(beta),
beta_sd = sd(beta),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# general learning model ####
d2 <- merge(d2, resp_summary, by = 'id')
d2$AIC <- calc_AIC(d2$n_resp, 2, d2$X.LLH)
d2$BIC <- calc_BIC(d2$n_resp, 2, d2$X.LLH)
t2 <- d2 %>% group_by(agegrp) %>%
summarise(alpha_mean = mean(alpha),
alpha_sd = sd(alpha),
beta_mean = mean(beta),
beta_sd = sd(beta),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# baseline model ####
d3 <- merge(d3, resp_summary, by = 'id')
d3$AIC <- calc_AIC(d3$n_resp,1,d3$X.LLH) # calc AIC
d3$BIC <- calc_BIC(d3$n_resp,1,d3$X.LLH) # calc BIC
t3 <- d3 %>% group_by(agegrp) %>%
summarise(beta_mean = mean(beta),
beta_sd = sd(beta),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# decay model ####
d4 <- merge(d4, resp_summary, by = 'id')
d4$AIC <- calc_AIC(d4$n_resp,4,d4$X.LLH) # calc AIC
d4$BIC <- calc_BIC(d4$n_resp,4,d4$X.LLH) # calc BIC
t4 <- d4 %>% group_by(agegrp) %>%
summarise(a_gain_mean = mean(alpha_gain),
a_gain_sd = sd(alpha_gain),
a_loss_mean = mean(alpha_loss),
a_loss_sd = sd(alpha_loss),
beta_mean = mean(beta),
beta_sd = sd(beta),
decay_mean = mean(decay),
decay_sd = sd(decay),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# prior model ####
d5 <- merge(d5, resp_summary, by = 'id')
d5$AIC <- calc_AIC(d5$n_resp,6,d5$X.LLH) # calc AIC
d5$BIC <- calc_BIC(d5$n_resp,6,d5$X.LLH) # calc BIC
t5 <- d5 %>% group_by(agegrp) %>%
summarise(a_gain_mean = mean(alpha_gain),
a_gain_sd = sd(alpha_gain),
a_loss_mean = mean(alpha_loss),
a_loss_sd = sd(alpha_loss),
beta_mean = mean(beta),
beta_sd = sd(beta),
itrust_mean = mean(iProb_trust),
itrust_sd = sd(iProb_trust),
ineut_mean = mean(iProb_neut),
ineut_sd = sd(iProb_neut),
iuntrust_mean = mean(iProb_untrust),
iuntrust_sd = sd(iProb_untrust),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# general learning with decay####
d6 <- merge(d6, resp_summary, by = 'id') #adds response summary and gets rid of cut participants
d6$AIC <- calc_AIC(d6$n_resp,3,d6$X.LLH) # calc AIC
d6$BIC <- calc_BIC(d6$n_resp,3,d6$X.LLH) # calc BIC
t6 <- d6 %>% group_by(agegrp) %>%
summarise(alpha_mean = mean(alpha),
alpha_sd = sd(alpha),
beta_mean = mean(beta),
beta_sd = sd(beta),
decay_mean = mean(decay),
decay_sd = sd(decay),
AIC_mean = mean(AIC),
AIC_sd = sd(AIC),
BIC_mean = mean(BIC),
BIC_sd = sd(BIC))
# age group make tables ####
# c('baseline', 'general', 'gain-loss', 'decay gain-loss', 'initial gain_loss')
y0 <- data.frame(model = character(),
itrust = character(),
ineut = character(),
iuntrust = character(),
decay = character(),
alpha = character(),
a_gain = character(),
a_loss = character(),
beta = character(),
AIC = character(),
BIC = character())
o0 <- data.frame(model = character(),
itrust = character(),
ineut = character(),
iuntrust = character(),
decay = character(),
alpha = character(),
a_gain = character(),
a_loss = character(),
beta = character(),
AIC = character(),
BIC = character())
# add baseline ####
y0[1,1] <- 'baseline'
y0[1,9] <- paste0(round(t3$beta_mean[1],2), ' (', round(t3$beta_sd[1],2), ')')
y0[1,10] <- paste0(round(t3$AIC_mean[1],2), ' (', round(t3$AIC_sd[1],2), ')')
y0[1,11] <- paste0(round(t3$BIC_mean[1],2), ' (', round(t3$BIC_sd[1],2), ')')
o0[1,1] <- 'baseline'
o0[1,9] <- paste0(round(t3$beta_mean[2],2), ' (', round(t3$beta_sd[2],2), ')')
o0[1,10] <- paste0(round(t3$AIC_mean[2],2), ' (', round(t3$AIC_sd[2],2), ')')
o0[1,11] <- paste0(round(t3$BIC_mean[2],2), ' (', round(t3$BIC_sd[2],2), ')')
# add general learning ####
y0[2,1] <- 'general'
y0[2,6] <- paste0(round(t2$alpha_mean[1],2), ' (', round(t2$alpha_sd[1],2), ')')
y0[2,9] <- paste0(round(t2$beta_mean[1],2), ' (', round(t2$beta_sd[1],2), ')')
y0[2,10] <- paste0(round(t2$AIC_mean[1],2), ' (', round(t2$AIC_sd[1],2), ')')
y0[2,11] <- paste0(round(t2$BIC_mean[1],2), ' (', round(t2$BIC_sd[1],2), ')')
o0[2,1] <- 'general'
o0[2,6] <- paste0(round(t2$alpha_mean[2],2), ' (', round(t2$alpha_sd[2],2), ')')
o0[2,9] <- paste0(round(t2$beta_mean[2],2), ' (', round(t2$beta_sd[2],2), ')')
o0[2,10] <- paste0(round(t2$AIC_mean[2],2), ' (', round(t2$AIC_sd[2],2), ')')
o0[2,11] <- paste0(round(t2$BIC_mean[2],2), ' (', round(t2$BIC_sd[2],2), ')')
# gain-loss learning ####
y0[3,1] <- 'gain-loss'
y0[3,7] <- paste0(round(t1$a_gain_mean[1],2), ' (', round(t1$a_gain_sd[1],2), ')')
y0[3,8] <- paste0(round(t1$a_loss_mean[1],2), ' (', round(t1$a_loss_sd[1],2), ')')
y0[3,9] <- paste0(round(t1$beta_mean[1],2), ' (', round(t1$beta_sd[1],2), ')')
y0[3,10] <- paste0(round(t1$AIC_mean[1],2), ' (', round(t1$AIC_sd[1],2), ')')
y0[3,11] <- paste0(round(t1$BIC_mean[1],2), ' (', round(t1$BIC_sd[1],2), ')')
o0[3,1] <- 'gain-loss'
o0[3,7] <- paste0(round(t1$a_gain_mean[2],2), ' (', round(t1$a_gain_sd[2],2), ')')
o0[3,8] <- paste0(round(t1$a_loss_mean[2],2), ' (', round(t1$a_loss_sd[2],2), ')')
o0[3,9] <- paste0(round(t1$beta_mean[2],2), ' (', round(t1$beta_sd[2],2), ')')
o0[3,10] <- paste0(round(t1$AIC_mean[2],2), ' (', round(t1$AIC_sd[2],2), ')')
o0[3,11] <- paste0(round(t1$BIC_mean[2],2), ' (', round(t1$BIC_sd[2],2), ')')
# decay gain-loss learning ####
y0[4,1] <- 'decay gain-loss'
y0[4,5] <- paste0(round(t4$decay_mean[1],2), ' (', round(t4$decay_sd[1],2), ')')
y0[4,7] <- paste0(round(t4$a_gain_mean[1],2), ' (', round(t4$a_gain_sd[1],2), ')')
y0[4,8] <- paste0(round(t4$a_loss_mean[1],2), ' (', round(t4$a_loss_sd[1],2), ')')
y0[4,9] <- paste0(round(t4$beta_mean[1],2), ' (', round(t4$beta_sd[1],2), ')')
y0[4,10] <- paste0(round(t4$AIC_mean[1],2), ' (', round(t4$AIC_sd[1],2), ')')
y0[4,11] <- paste0(round(t4$BIC_mean[1],2), ' (', round(t4$BIC_sd[1],2), ')')
o0[4,1] <- 'decay gain-loss'
o0[4,5] <- paste0(round(t4$decay_mean[2],2), ' (', round(t4$decay_sd[2],2), ')')
o0[4,7] <- paste0(round(t4$a_gain_mean[2],2), ' (', round(t4$a_gain_sd[2],2), ')')
o0[4,8] <- paste0(round(t4$a_loss_mean[2],2), ' (', round(t4$a_loss_sd[2],2), ')')
o0[4,9] <- paste0(round(t4$beta_mean[2],2), ' (', round(t4$beta_sd[2],2), ')')
o0[4,10] <- paste0(round(t4$AIC_mean[2],2), ' (', round(t4$AIC_sd[2],2), ')')
o0[4,11] <- paste0(round(t4$BIC_mean[2],2), ' (', round(t4$BIC_sd[2],2), ')')
# initial beliefs gain-loss learning ####
y0[5,1] <- 'initial beliefs gain-loss'
y0[5,2] <- paste0(round(t5$itrust_mean[1],2), ' (', round(t5$itrust_sd[1],2), ')')
y0[5,3] <- paste0(round(t5$ineut_mean[1],2), ' (', round(t5$ineut_sd[1],2), ')')
y0[5,4] <- paste0(round(t5$iuntrust_mean[1],2), ' (', round(t5$iuntrust_sd[1],2), ')')
y0[5,7] <- paste0(round(t5$a_gain_mean[1],2), ' (', round(t5$a_gain_sd[1],2), ')')
y0[5,8] <- paste0(round(t5$a_loss_mean[1],2), ' (', round(t5$a_loss_sd[1],2), ')')
y0[5,9] <- paste0(round(t5$beta_mean[1],2), ' (', round(t5$beta_sd[1],2), ')')
y0[5,10] <- paste0(round(t5$AIC_mean[1],2), ' (', round(t5$AIC_sd[1],2), ')')
y0[5,11] <- paste0(round(t5$BIC_mean[1],2), ' (', round(t5$BIC_sd[1],2), ')')
o0[5,1] <- 'initial beliefs gain-loss'
o0[5,2] <- paste0(round(t5$itrust_mean[2],2), ' (', round(t5$itrust_sd[2],2), ')')
o0[5,3] <- paste0(round(t5$ineut_mean[2],2), ' (', round(t5$ineut_sd[2],2), ')')
o0[5,4] <- paste0(round(t5$iuntrust_mean[2],2), ' (', round(t5$iuntrust_sd[2],2), ')')
o0[5,7] <- paste0(round(t5$a_gain_mean[2],2), ' (', round(t5$a_gain_sd[2],2), ')')
o0[5,8] <- paste0(round(t5$a_loss_mean[2],2), ' (', round(t5$a_loss_sd[2],2), ')')
o0[5,9] <- paste0(round(t5$beta_mean[2],2), ' (', round(t5$beta_sd[2],2), ')')
o0[5,10] <- paste0(round(t5$AIC_mean[2],2), ' (', round(t5$AIC_sd[2],2), ')')
o0[5,11] <- paste0(round(t5$BIC_mean[2],2), ' (', round(t5$BIC_sd[2],2), ')')
# add decay general learning ####
y0[6,1] <- 'decay general'
y0[6,5] <- paste0(round(t6$decay_mean[1],2), ' (', round(t6$decay_sd[1],2), ')')
y0[6,6] <- paste0(round(t6$alpha_mean[1],2), ' (', round(t6$alpha_sd[1],2), ')')
y0[6,9] <- paste0(round(t6$beta_mean[1],2), ' (', round(t6$beta_sd[1],2), ')')
y0[6,10] <- paste0(round(t6$AIC_mean[1],2), ' (', round(t6$AIC_sd[1],2), ')')
y0[6,11] <- paste0(round(t6$BIC_mean[1],2), ' (', round(t6$BIC_sd[1],2), ')')
o0[6,1] <- 'decay general'
o0[6,5] <- paste0(round(t6$decay_mean[2],2), ' (', round(t6$decay_sd[2],2), ')')
o0[6,6] <- paste0(round(t6$alpha_mean[2],2), ' (', round(t6$alpha_sd[2],2), ')')
o0[6,9] <- paste0(round(t6$beta_mean[2],2), ' (', round(t6$beta_sd[2],2), ')')
o0[6,10] <- paste0(round(t6$AIC_mean[2],2), ' (', round(t6$AIC_sd[2],2), ')')
o0[6,11] <- paste0(round(t6$BIC_mean[2],2), ' (', round(t6$BIC_sd[2],2), ')')
# full table ####
table <- cbind(y0,o0[2:11])
table <- rbind(c('', 'Younger', '', '','','','','','','', '', 'Older', '','','','','','','','','', '', ''), table)
write.csv(table, here::here('figs', 'model_parameter_grpmeans.csv'))