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MBMF_DA_OP_Analysis.r
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MBMF_DA_OP_Analysis.r
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## Effects of dopamine D2 and opioid receptor antagonism on model-based behaviour in healthy volunteers ----
# Analysis script,
# Nace Mikus | nace.mikus@univie.ac.at, nace.mikus@cas.au.dk
# load packages -----------------------------------------------------------
library(tidyverse)
library(cowplot)
library(ggthemes)
library(nlme)
library(brms)
library(broom)
library(loo)
library(rstanarm)
library(rstan)
library(patchwork)
library(lme4)
library(lmerTest)
library(bayesplot)
# aux functions ----
source("Utility scripts/plotting_functions.r")
source("Utility scripts/theme_functions_MB.r")
source("Utility scripts/aux_functions.r")
# the aux script for running the models in stan,
source("Utility scripts/run_stan_models_function.r")
# note that these take long to run,
# an already estimated model can be sent upon inquiry (nace.mikus@univie.ac.at)
# load data ---------------------------------------------------------------
data_group<- readRDS("Data/data_group.rds")
data_beh <- readRDS("Data/data_beh.rds")
# options for stan ---------------------------------------------------------------
rstan_options(auto_write = TRUE)
options(mc.cores = 4)
############################################################################################################
# Behavioral analysis ######################################################################################
## stats from the brm model ----------------------------------------------
# run model
if (FALSE) { # this might take long to run
if(!file.exists("Brms_stay_beh_full.rds")){
fit_model_beh <- brm(stay_num|trials(1) ~ (session*prev_state_diff*prev_points|ID) + session*prev_points*prev_state_diff*ami_dummy+
session*prev_points*prev_state_diff*nal_dummy,
data = data_beh, family = binomial(),
prior = c(set_prior("cauchy(0,2)", class = "sd"),
set_prior("normal(0,3)", class = "b"),
set_prior("lkj(2)", class = "cor")),
#set_prior("lkj(2)", class = "cor")),
warmup = 1, iter =2, chains =1,
control = list(adapt_delta = 0.80))
saveRDS(fit_model_beh, file = "Brms_stay_beh_full.rds")
fit_model_beh %>% fixef() %>% round(2) %>% write.csv(file = "Beh_model_results.csv")
}else {
fit_model_beh <- readRDS("Brms_stay_beh_full.rds")
}
}
post_sam <- posterior_samples(fit_model_beh, pars = c("^b_", "sd_", "sigma"))
# get the regressiors
beta_mat <- post_sam %>%
transmute(h_beta_prevpt = (b_prev_points+ 1/2*`b_session2:prev_points` +
`b_prev_points:nal_dummyNal` + `b_prev_points:ami_dummyAmi` +
1/2*`b_session2:prev_points:nal_dummyNal` +1/2*`b_session2:prev_points:ami_dummyAmi`),
g_beta_pp_prevpt= (`b_prev_points:prev_state_diffdifferent` +1/2*`b_session2:prev_points:prev_state_diffdifferent`+
1/2* `b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal` +1/2*`b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi` +
`b_prev_points:prev_state_diffdifferent:nal_dummyNal` +`b_prev_points:prev_state_diffdifferent:ami_dummyAmi`),
e_delta_pp_prevpt_ami_same= (`b_session2:prev_points:ami_dummyAmi` ),
d_delta_pp_prevpt_ami_different= (`b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi` +
`b_session2:prev_points:ami_dummyAmi` ),
f_delta_pp_prevpt_ss_ami= `b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi`,
b_delta_pp_prevpt_nal_same = `b_session2:prev_points:nal_dummyNal`,
a_delta_pp_prevpt_nal_different = ( `b_session2:prev_points:nal_dummyNal`+`b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal`),
c_delta_pp_prevpt_ss_nal = `b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal`)
# print the regressors in odds scale :
beta_mat %>% exp() %>% apply(2, sf) %>% t()
# in logodds scale
beta_mat %>% apply(2, sf) %>% t()
## create the stats plot -----
g_stats <- beta_mat %>% select(h_beta_prevpt, g_beta_pp_prevpt, f_delta_pp_prevpt_ss_ami, c_delta_pp_prevpt_ss_nal) %>%
# convert them to the long format, group, and get the posterior summaries
pivot_longer(everything()) %>%
group_by(name) %>%
summarise(mean = mean(value),
ll = quantile(value, prob = .025),
ul = quantile(value, prob = .975),
lls = quantile(value, prob = .10),
uls = quantile(value, prob = .90)) %>% # since the `key` variable is really two variables in one, here we split them up
# plot!
ggplot(aes(x = mean, xmin = ll, xmax = ul, y = name)) +
geom_errorbar(aes(xmin = lls, xmax = uls), size = 1.5,color = "firebrick", width = 0)+
geom_vline(xintercept = 0, linetype = "dashed") +
geom_pointrange(color = "firebrick") +
labs( y = NULL) + # subtitle = "Effect sizes (95% and 50% quantiles)",
theme_Publication(base_size = 15) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(size=12),
strip.background = element_rect(fill = "transparent", color = "transparent"),
axis.title.x = element_blank()) +
scale_y_discrete(labels = rev(c("Prev points", "PrevPoints:PrevState",
"Ami:Session:PrevPoints:PrevState",
"Nal:Session:PrevPoints:PrevState")))
## plot beh model posterior predictions -------------------------------------------------------
post <- posterior_samples(fit_model_beh)
# define helper functions
post_fu <- function(x, funct_var) {
prev_points = x[1]
ami_dummy = as.numeric(x[2] == 1)
nal_dummy = as.numeric(x[2] == 2)
prev_state = x[3]
session = x[4]
fitted = post$b_Intercept + post$b_prev_points * prev_points + post$b_prev_state_diffdifferent*prev_state+ post$b_session2*session + post$b_ami_dummyAmi*ami_dummy + post$b_nal_dummyNal*nal_dummy+
post$`b_session2:prev_points`* prev_points*session + post$`b_session2:prev_state_diffdifferent`*session*prev_state + post$`b_prev_points:prev_state_diffdifferent`*prev_points*prev_state+
post$`b_session2:ami_dummyAmi`*session*ami_dummy + post$`b_prev_points:ami_dummyAmi`*prev_points*ami_dummy + post$`b_prev_state_diffdifferent:ami_dummyAmi`*ami_dummy*prev_state +
post$`b_session2:nal_dummyNal`*session*nal_dummy+ post$`b_prev_points:nal_dummyNal`*prev_points*nal_dummy + post$`b_prev_state_diffdifferent:nal_dummyNal`*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent`*session*prev_state*prev_points + post$`b_session2:prev_points:ami_dummyAmi`*session*prev_points*ami_dummy +
post$`b_session2:prev_state_diffdifferent:ami_dummyAmi`*prev_state*session*ami_dummy + post$`b_prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_state*prev_points*ami_dummy +
post$`b_session2:prev_points:nal_dummyNal`*session*prev_points*nal_dummy + post$`b_session2:prev_state_diffdifferent:nal_dummyNal`*session*prev_state*nal_dummy +
post$`b_prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_points*prev_state*ami_dummy*session +
post$`b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy*session
# y_lo <- funct_var(fitted)
y <- inv_logit(funct_var(fitted))
return(y)
}
nd <- expand_grid(prev_points = c(-4:5), drug = c(1,2,3), prev_state = c(0,1), session = c(0,1) )
# nd <- nd %>% mutate(median=NA, CI_l=NA, CI_u=NA)
nd_mat <- nd %>% as.matrix()
# nd_mat <- nd_mat
# nd_mean_CI = apply(nd_mat,1, post_fu)
nd_mean = apply(nd_mat,1, post_fu, funct_var = mean)
nd_lCI = apply(nd_mat,1, post_fu , funct_var = function(x) quantile(x, probs = 0.2))
nd_uCI = apply(nd_mat,1, post_fu, funct_var = function(x) quantile(x, probs = 0.8))
nd_sd = apply(nd_mat,1, post_fu, funct_var = sd)
nd_new <- nd %>% mutate(mean_value = nd_mean,
low_CI = nd_lCI,
up_CI = nd_uCI,
sd_value = nd_sd,
drug = factor(drug, levels= c(1,2,3), labels = c( "Ami","Nal", "Pla")),
session = factor(session+1)
# ami_dummy = factor(drug=="Ami", levels= c(FALSE,TRUE), labels = c("Pla", "Ami")),
# nal_dummy = factor(drug=="Ami", levels= c(0,1), labels = c("Pla", "Nal"))
)
data_beh <- data_beh%>% mutate(prev_points_5levels = floor(prev_points/2)*2 + 0.5)
data_same <- data_beh %>% filter(!is.na(stay), prev_state_diff == "same") %>%
group_by(drug, prev_points,session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
data_diff<- data_beh %>% filter(!is.na(stay), prev_state_diff != "same") %>%
group_by(drug, prev_points, session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
g_different <- nd_new %>% filter(prev_state == 1) %>% ggplot(aes(x= prev_points, y = mean_value, group = session)) +
geom_errorbar(data=data_diff, aes(x = prev_points, y = mean_stay,ymin = mean_stay - se_stay, ymax =mean_stay + se_stay),colour = "black", width = 0, position = position_dodge(0.2))+
geom_point(data=data_diff, aes(x = prev_points, y = mean_stay, colour = session),position = position_dodge(0.2)) +
geom_ribbon(aes(ymin = low_CI, ymax = up_CI, group = session), alpha = 0.2) +
# geom_ribbon(aes(ymin = mean_value-sd_value, ymax = mean_value+sd_value), alpha = 0.3) +
geom_line(aes(colour = session)) + theme_Publication() + scale_color_manual(values=c('#999999','#E69F00'))+
theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks = element_blank(), strip.background=element_blank(), strip.text = element_blank())+
xlab("Previous Points")+ ylab("P(stay) after \ndifferent first state") + facet_wrap(~drug) + coord_cartesian(ylim=c(0.20,1)) +
scale_y_continuous(breaks = c(0.2, 0.5, 0.8)) + geom_hline(yintercept =0.5, linetype = "dashed")
g_same <- nd_new %>% filter(prev_state == 0) %>% ggplot(aes(x= prev_points, y = mean_value, group = session)) +
geom_errorbar(data=data_same, aes(x = prev_points, y = mean_stay,ymin = mean_stay - se_stay, ymax =mean_stay + se_stay),colour = "black", width = 0, position = position_dodge(0.2))+
geom_point(data=data_same, aes(x = prev_points, y = mean_stay, colour = session),position = position_dodge(0.2)) +
geom_ribbon(aes(ymin = low_CI, ymax = up_CI, group = session), alpha = 0.2) +
# geom_ribbon(aes(ymin = mean_value-sd_value, ymax = mean_value+sd_value), alpha = 0.3) +
geom_line(aes(colour = session)) + theme_Publication() + scale_color_manual(values=c('#999999','#E69F00'))+
theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks = element_blank()) + xlab("Previous Points")+
ylab("P(stay) after \nsame first state") + facet_wrap(vars(drug)) + coord_cartesian(ylim=c(0.20,1)) +
scale_y_continuous(breaks = c(0.2, 0.5, 0.8)) + geom_hline(yintercept =0.5, linetype = "dashed")
# preview plot
# (g_same / g_different)
## plot beh model posterior predictions for session differences ----
post_fu_sess_difference <- function(x, funct_var) {
prev_points = x[1]
ami_dummy = as.numeric(x[2] == 1)
nal_dummy = as.numeric(x[2] == 2)
prev_state = x[3]
session = 0
fitted_1 = post$b_Intercept + post$b_prev_points * prev_points + post$b_prev_state_diffdifferent*prev_state+ post$b_session2*session + post$b_ami_dummyAmi*ami_dummy + post$b_nal_dummyNal*nal_dummy+
post$`b_session2:prev_points`* prev_points*session + post$`b_session2:prev_state_diffdifferent`*session*prev_state + post$`b_prev_points:prev_state_diffdifferent`*prev_points*prev_state+
post$`b_session2:ami_dummyAmi`*session*ami_dummy + post$`b_prev_points:ami_dummyAmi`*prev_points*ami_dummy + post$`b_prev_state_diffdifferent:ami_dummyAmi`*ami_dummy*prev_state +
post$`b_session2:nal_dummyNal`*session*nal_dummy+ post$`b_prev_points:nal_dummyNal`*prev_points*nal_dummy + post$`b_prev_state_diffdifferent:nal_dummyNal`*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent`*session*prev_state*prev_points + post$`b_session2:prev_points:ami_dummyAmi`*session*prev_points*ami_dummy +
post$`b_session2:prev_state_diffdifferent:ami_dummyAmi`*prev_state*session*ami_dummy + post$`b_prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_state*prev_points*ami_dummy +
post$`b_session2:prev_points:nal_dummyNal`*session*prev_points*nal_dummy + post$`b_session2:prev_state_diffdifferent:nal_dummyNal`*session*prev_state*nal_dummy +
post$`b_prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_points*prev_state*ami_dummy*session +
post$`b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy*session
session = 1
fitted_2 = post$b_Intercept + post$b_prev_points * prev_points + post$b_prev_state_diffdifferent*prev_state+ post$b_session2*session + post$b_ami_dummyAmi*ami_dummy + post$b_nal_dummyNal*nal_dummy+
post$`b_session2:prev_points`* prev_points*session + post$`b_session2:prev_state_diffdifferent`*session*prev_state + post$`b_prev_points:prev_state_diffdifferent`*prev_points*prev_state+
post$`b_session2:ami_dummyAmi`*session*ami_dummy + post$`b_prev_points:ami_dummyAmi`*prev_points*ami_dummy + post$`b_prev_state_diffdifferent:ami_dummyAmi`*ami_dummy*prev_state +
post$`b_session2:nal_dummyNal`*session*nal_dummy+ post$`b_prev_points:nal_dummyNal`*prev_points*nal_dummy + post$`b_prev_state_diffdifferent:nal_dummyNal`*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent`*session*prev_state*prev_points + post$`b_session2:prev_points:ami_dummyAmi`*session*prev_points*ami_dummy +
post$`b_session2:prev_state_diffdifferent:ami_dummyAmi`*prev_state*session*ami_dummy + post$`b_prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_state*prev_points*ami_dummy +
post$`b_session2:prev_points:nal_dummyNal`*session*prev_points*nal_dummy + post$`b_session2:prev_state_diffdifferent:nal_dummyNal`*session*prev_state*nal_dummy +
post$`b_prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy +
post$`b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi`*prev_points*prev_state*ami_dummy*session +
post$`b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal`*prev_points*prev_state*nal_dummy*session
# y_lo <- funct_var(fitted)
y <- inv_logit(fitted_2) - inv_logit(fitted_1)
y <- funct_var(y)
return(y)
}
nd_ses <- expand_grid(prev_points = c(-4:5), drug = c(1,2,3), prev_state = c(0,1))
nd_mat <- nd_ses %>% as.matrix()
nd_mean = apply(nd_mat,1, post_fu_sess_difference, funct_var = mean)
nd_lCI = apply(nd_mat,1, post_fu_sess_difference , funct_var = function(x) quantile(x, probs = 0.2))
nd_uCI = apply(nd_mat,1, post_fu_sess_difference, funct_var = function(x) quantile(x, probs = 0.8))
# nd_sd = apply(nd_ses,1, post_fu, funct_var = sd)
nd_new <- nd_ses %>% mutate(mean_value = nd_mean,
low_CI = nd_lCI,
up_CI = nd_uCI,
# sd_value = nd_sd,
drug = factor(drug, levels= c(1,2,3), labels = c( "Ami","Nal", "Pla")))
## plot difference in staying across 5 reward levels and overlay the model predictions over it -------------------------
### reward level (-2,-1,0,1,2)
data_beh$reward_level <- floor((data_beh$prev_points + 4)/2) -2
g_stay_session_diff <- data_beh %>%
filter(!is.na(prev_points))%>%
group_by(ID, session, reward_level, prev_state_diff, stay) %>%
summarise(N=n(), ami_dummy = ami_dummy[1],
nal_dummy = nal_dummy[1],
drug = drug[1],
dat= dat[1],
darpp = darpp[1]) %>%
mutate(freq = N/sum(N)) %>%
filter(stay==1) %>%
mutate(freq_diff = -10)
# View(g_stay_session_diff)
subjects = unique(g_stay_session_diff$ID)
reward_levels = c(-2,-1,0,1,2)
for (n in subjects) {
# print(n)
for (r in reward_levels) {
if (sum(g_stay_session_diff$session[g_stay_session_diff$ID ==n &
g_stay_session_diff$reward_level ==r] == 1) ==2 &
sum(g_stay_session_diff$session[g_stay_session_diff$ID ==n &
g_stay_session_diff$reward_level ==r] == 2) ==2) {
g_stay_session_diff$freq_diff[g_stay_session_diff$session==2 & g_stay_session_diff$ID ==n &
g_stay_session_diff$reward_level ==r] <- g_stay_session_diff$freq[g_stay_session_diff$session==2 & g_stay_session_diff$ID ==n &
g_stay_session_diff$reward_level ==r] -
g_stay_session_diff$freq[g_stay_session_diff$session==1 &
g_stay_session_diff$ID ==n &
g_stay_session_diff$reward_level ==r]
} else
cat("sub ", n, "and reward ", r, "\n")
}
}
g_stay_session_diff$reward_level <- as.factor(g_stay_session_diff$reward_level )
g_beh_diff_stay <- g_stay_session_diff %>%
filter(session ==2, freq_diff !=-10, prev_state_diff=="different") %>%
group_by(drug, reward_level) %>% summarize(N=n(),
mean_freq_diff = mean(freq_diff, na.rm = TRUE),
se_freq_diff = sd(freq_diff, na.rm = TRUE)/sqrt(N))
nd_new_diff <- nd_new %>% filter(prev_state == 1)
# nd_new_1 %>% View()
g_different_diff <-
g_beh_diff_stay %>% mutate(reward_level = as.numeric(reward_level)*2 - 5.5) %>%
ggplot(aes(x=reward_level, y = mean_freq_diff, fill = drug)) +
geom_ribbon(data =nd_new_diff, aes(x= prev_points, y = mean_value, ymin = low_CI, ymax = up_CI, group = drug), alpha = 0.2) +
geom_line(data = nd_new_diff, aes(x= prev_points, y = mean_value, colour = drug), colour = "black") +
geom_point(aes(colour = drug),stat = "identity", position = position_dodge(width = 0.1), size = 3)+
geom_hline(yintercept =0, linetype = "dashed")+
geom_errorbar(aes(ymin= mean_freq_diff - se_freq_diff, ymax = mean_freq_diff +se_freq_diff), width = 0, position = position_dodge(0.9))+
# scale_x_discrete(breaks = NULL) + #labels = c("-2"="-4","-1"= "","0"= "0", "1" = "", "2"="5"))+
# scale_y_continuous(breaks = c(0,0.1,-0.1), limits= c(-0.15,0.15)) +
facet_wrap(vars(drug)) + ylab("\u0394 P(stay) after\ndifferent first state") +# labs(subtitle = "Different first state") +
theme_Publication() + theme(legend.position = "none", strip.background=element_blank(), strip.text = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank()) +
xlab("Previous Points") + coord_cartesian(ylim = c(-0.1,0.15)) + scale_y_continuous(breaks = c(-0.1, 0.0, 0.1))
# (aes(x = drug, y= mean_par_t0, colour = drug), position = position_dodge(width = 0.1), stat = "identity", size = 3
g_beh_same_stay <- g_stay_session_diff %>%
filter(session ==2, freq_diff !=-10, prev_state_diff=="same") %>%
group_by(drug, reward_level) %>% summarize(N=n(),
mean_freq_diff = mean(freq_diff, na.rm = TRUE),
se_freq_diff = sd(freq_diff, na.rm = TRUE)/sqrt(N))
nd_new_same<- nd_new %>% filter(prev_state == 0)
g_same_diff <-
g_beh_same_stay %>% mutate(reward_level = as.numeric(reward_level)*2 - 5.5) %>%
ggplot(aes(x=reward_level, y = mean_freq_diff, fill = drug)) +
geom_ribbon(data =nd_new_same, aes(x= prev_points, y = mean_value, ymin = low_CI, ymax = up_CI, group = drug), alpha = 0.2) +
geom_line(data = nd_new_same, aes(x= prev_points, y = mean_value, colour = drug), colour = "black") +
geom_point(aes(colour = drug),stat = "identity", position = position_dodge(width = 0.1), size = 3)+
geom_hline(yintercept =0, linetype = "dashed")+
geom_errorbar(aes(ymin= mean_freq_diff - se_freq_diff, ymax = mean_freq_diff +se_freq_diff), width = 0, position = position_dodge(0.9))+
# scale_x_discrete(breaks = NULL) + #labels = c("-2"="-4","-1"= "","0"= "0", "1" = "", "2"="5"))+
# scale_y_continuous(breaks = c(0,0.1,-0.1), limits= c(-0.15,0.15)) +
facet_wrap(vars(drug)) + ylab("\u0394 P(stay) after\nsame first state") + #labs(subtitle = "Different first state") +
theme_Publication() + theme(legend.position = "none",axis.text.x = element_blank(), axis.ticks = element_blank())+
xlab("Previous Points") + coord_cartesian(ylim = c(-0.1,0.15)) + scale_y_continuous(breaks = c(-0.1, 0.0, 0.1))
## plot figure 3: behavior ----
plot_grid(plot_grid(g_same/g_different, g_same_diff/g_different_diff, nrow = 1 , labels = c("a", "b")),
g_stats + theme(axis.text = element_text(size = 10), panel.grid.major.y = element_line( size=.1, color="black", linetype = "dashed")),
nrow = 2,
rel_heights = c(1,0.3), labels = c("", "c"))
## stay with amisulpride serum levels ------------------------------------------------------
data_beh$serum_f = data_beh$serum > 603.9999
data_beh$serum_ami_high = case_when( data_beh$serum >= 604 ~ 1,
TRUE ~ 0)
data_beh$serum_ami_low = case_when( data_beh$serum < 604 ~ 1,
TRUE ~ 0)
data_beh$nal = case_when( data_beh$nal_dummy == "Nal"~ 1,
TRUE ~ 0)
# data_beh %>% filter(trials == 1, session == 2, exclude_serum == F) %>% group_by(ID) %>% summarize(serum_ami_high =serum_ami_high[1],
# serum_ami_low = serum_ami_low[1],
# serum = serum[1],
# nal = nal[1],
# drug = drug[1],
# serum_pla = serum[1]) %>% View
data_beh$exclude_serum = data_beh$ami_dummy =="Ami" & is.na(data_beh$serum)
data_group$exclude_serum = data_group$ami_dummy =="Ami" & is.na(data_group$serum)
data_beh$stay_num <- as.numeric(data_beh$stay)
# subjList = data_group$s %>% unique()
if(!file.exists("Brms_stay_beh_serum.rds")){
# if(FALSE){
Brms_stay_beh_serum <- brm(stay_num|trials(1) ~ (session*prev_state_diff*prev_points|ID) + session*prev_points*prev_state_diff*(serum_ami_low + serum_ami_high+ nal),
data = data_beh%>% filter( exclude_serum == F) , family = binomial(),
prior = c(set_prior("cauchy(0,2)", class = "sd"),
set_prior("normal(0,3)", class = "b"),
set_prior("lkj(2)", class = "cor")),
#set_prior("lkj(2)", class = "cor")),
warmup = , iter =3000, chains =4,
control = list(adapt_delta = 0.90))
saveRDS(fit_model_beh, file = "Brms_stay_beh_serum.rds")
} else
Brms_stay_beh_serum <- readRDS("Brms_stay_beh_serum.rds")
post_sam <- posterior_samples(Brms_stay_beh_serum, pars = c("^b_", "sd_", "sigma"))
# Brms_stay_beh_serum %>% mcmc_plot( pars = c("^b_"))
# diff- same
( post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_high`) %>% sf(1)
(post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_low`) %>% sf(1)
(( post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_high`) -(post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_low`)) %>% sf(1)
# diff
(post_sam$`b_session2:prev_points:serum_ami_high` + post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_high`) %>% sf(1)
(post_sam$`b_session2:prev_points:serum_ami_low` + post_sam$`b_session2:prev_points:prev_state_diffdifferent:serum_ami_low`) %>% sf(1)
# same
(post_sam$`b_session2:prev_points:serum_ami_high`) %>% sf(1)
(post_sam$`b_session2:prev_points:serum_ami_low`) %>% sf(1)
((post_sam$`b_session2:prev_points:serum_ami_high`) - (post_sam$`b_session2:prev_points:serum_ami_low`) )%>% sf(1)
###
data_group$serum_ami_high = case_when( data_group$serum >= 604 ~ 1,
TRUE ~ 0)
data_group$serum_ami_low = case_when( data_group$serum < 604 ~ 1,
TRUE ~ 0)
data_group$nal = case_when( data_group$nal_dummy == "Nal"~ 1,
TRUE ~ 0)
rand_eff <- Brms_stay_beh_serum %>% ranef()
rand_eff <- rand_eff$ID # %>% glimpse
rand_eff %>% dim
data_group <- data_group[order(data_group$ID),]
data_group_temp = data_group %>% filter(exclude_serum == F)
data_group_temp %>% View
cor.test(rand_eff[1:dim(rand_eff)[1],1,6] , data_group_temp$sess_w_rstan)
cor.test(rand_eff[1:dim(rand_eff)[1],1,8] , data_group_temp$sess_w_rstan)
cor.test(rand_eff[1:dim(rand_eff)[1],1,8] + rand_eff[1:dim(rand_eff)[1],1,6] , data_group_temp$sess_w_rstan)
cor.test(rand_eff[1:dim(rand_eff)[1],1,6] , data_group_temp$sess_beta1_rstan)
cor.test(rand_eff[1:dim(rand_eff)[1],1,8] , data_group_temp$sess_beta1_rstan)
cor.test(rand_eff[1:dim(rand_eff)[1],1,8] + rand_eff[1:dim(rand_eff)[1],1,6] , data_group_temp$sess_beta1_rstan)
df = tibble(slope_same = rand_eff[1:dim(rand_eff)[1],1,6] %>% ave(FUN = scale),
slope_different = (rand_eff[1:dim(rand_eff)[1],1,8] +rand_eff[1:dim(rand_eff)[1],1,6]) %>% ave(FUN = scale),
diff_in_slopes = rand_eff[1:dim(rand_eff)[1],1,8]%>% ave(FUN = scale),
sess_beta1_rstan = data_group_temp$sess_beta1_rstan%>% ave(FUN = scale),
sess_w_rstan=data_group_temp$sess_w_rstan%>% ave(FUN = scale),
sess_g_rstan=data_group_temp$sess_g_rstan%>% ave(FUN = scale),
serum = data_group_temp$serum,
serum_ami_low = data_group_temp$serum_ami_low)
df = tibble(slope_same = (rand_eff[1:112,1,6]+ rand_eff[1:112,1,4]) %>% ave(FUN = scale),
slope_different = (rand_eff[1:112,1,8] + rand_eff[1:112,1,7]+rand_eff[1:112,1,6]+ rand_eff[1:112,1,4]) %>% ave(FUN = scale),
diff_in_slopes = (rand_eff[1:112,1,8]+ rand_eff[1:112,1,7])%>% ave(FUN = scale),
beta1_rstan = data_group$beta1_rstan%>% ave(FUN = scale),
w_rstan=data_group$w_rstan%>% ave(FUN = scale),
g_rstan=data_group$g_rstan%>% ave(FUN = scale)
)
#############################################################################################################
# Computational modelling ########################################################################
## stan models were estimated using the analysis_main.r scripts and custom written stan models
## run all stan models
if (FALSE) { # this will take some time - already estimated models are available upon request (nace.mikus@univie.ac.at)
# M1 model
run_model_fit(modelfile = "Stan Scripts/M_new.stan", "Stan Model Results/M_new.rds")
# kool 1lr model
run_model_fit(modelfile = "Stan Scripts/M_kool_1lr.stan", "Stan Model Results/M_kool_1lr.rds")
# kool 2lr model
run_model_fit(modelfile = "Stan Scripts/M_kool_2lr.stan", "Stan Model Results/M_kool_2lr.rds")
# M1 model with serum
run_model_fit(modelfile = "Stan Scripts/M_new_serum.stan", "Stan Model Results/M_new_serum.rds")
# M1 model with serum and stickiness parameters
run_model_fit(modelfile = "Stan Scripts/M_new_sticky_serum.stan", "Stan Model Results/M_new_sticky_serum.rds")
}
## posterior distributions of parameters from M1 model ##################
fit_model <- readRDS("Stan Model Results/M_new.rds")
pars_all <- grep("^beta", names(fit_model), value = T)
pars_all <- pars_all[1:6]
# glimpse results
stan_plot(fit_model, pars_all)
# extract the numbers
Par_extract = rstan::extract(fit_model, pars = c('mu_p', 'sigma', 'sess_w', 'w_mean', 'beta_nal',
'beta_ami', 'w', 'L_Omega','beta_ami_noise', 'beta_nal_noise',
'beta_ami_g', 'beta_nal_g'))
# in parameter estimation space:
w_ami <- Par_extract$beta_ami
w_ami %>% sf(1)
w_nal <- Par_extract$beta_nal
w_nal %>% sf(1)
g_ami <- Par_extract$beta_ami_g
g_nal <- Par_extract$beta_nal_g
b_ami<- Par_extract$beta_ami_noise
b_ami %>% sf(1)
b_nal <- Par_extract$beta_nal_noise
# effect sizes
sd_w_ses = Par_extract$sigma[5]
sd_w = Par_extract$sigma[2]
sd_b_ses = Par_extract$sigma[4]
sd_b = Par_extract$sigma[1]
sd_g_ses = Par_extract$sigma[6]
sd_g = Par_extract$sigma[3]
d_ami_w <- (Par_extract$beta_ami/sqrt(sd_w_ses^2 + sd_w^2))
d_ami_w %>% sf(1)
d_nal_w <- (Par_extract$beta_nal/sqrt(sd_w_ses^2 + sd_w^2))
d_diff_ami_nal_w <- (Par_extract$beta_ami - Par_extract$beta_nal)/sqrt(sd_w_ses^2 + sd_w^2)
(Par_extract$beta_ami - Par_extract$beta_nal) %>% sf(1)
d_ami_noise <- Par_extract$beta_ami_noise/sqrt(sd_b_ses^2 + sd_b^2)
d_ami_noise %>% sf(1)
d_nal_noise <- Par_extract$beta_nal_noise/sqrt(sd_b_ses^2 + sd_b^2)
d_ami_g <- Par_extract$beta_ami_g/sqrt(sd_g_ses^2 + sd_g^2)
d_nal_g <- Par_extract$beta_nal_g/sqrt(sd_g_ses^2 + sd_g^2)
temp_mat <- bind_cols(A = d_nal_g,
B = d_ami_g,
C = d_nal_noise,
D = d_ami_noise,
E = d_diff_ami_nal_w,
F = d_nal_w,
G = d_ami_w)
g_stats <- temp_mat %>%
# convert them to the long format, group, and get the posterior summaries
pivot_longer(everything()) %>%
group_by(name) %>%
summarise(mean = mean(value),
ll = quantile(value, prob = .025),
ul = quantile(value, prob = .975),
lls = quantile(value, prob = .10),
uls = quantile(value, prob = .90)) %>% # since the `key` variable is really two variables in one, here we split them up
# plot!
ggplot(aes(x = mean, xmin = ll, xmax = ul, y = name)) +
geom_errorbar(aes(xmin = lls, xmax = uls), size = 1.5,color = "firebrick", width = 0)+
geom_vline(xintercept = 0, linetype = "dashed") +
geom_pointrange(color = "firebrick") +
labs( y = NULL) + # subtitle = "Effect sizes (95% and 50% quantiles)",
theme_Publication(base_size = 15) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(size=12),
strip.background = element_rect(fill = "transparent", color = "transparent")) +
scale_y_discrete(labels = rev(c("Ami \U1D714","Nal \U1D714","Ami - Nal \U1D714","Ami \U1D702","Nal \U1D702","Ami \U1D6FE","Nal \U1D6FE")))
g_stats + xlab("Effect sizes (95% and 80% CI)")
# d_diff_ami_nal_w %>% sf(1)
# phi_approx <- function(x) inv_logit(0.07056*x^3 + 1.5976*x)
## results M1 (figure 4) ----------------------------------------------------
par ="w"
# parname = "\U1D714"
plot_par_results <- function(par, data= data_group, title_text = list(NA,NA,NA)) {
g= list()
parname = case_when(par == "w"~ "\U1D714",
par == "beta1" ~ "\U1D702",
par == "g" ~ "\U1D6FE",
TRUE~ par)
par_rstan <- paste(par,"rstan",sep= "_")
par_t0_rstan <- paste(par,"t0_rstan",sep= "_")
# par_rstan <- paste(par,"raw",sep= "_")
# par_t0_rstan <- paste(par,"t0_raw",sep= "_")
par_sess <- paste("sess",par,"rstan", sep="_")
# data %>% dplyr::select(drug == 1) %>%
# data.frame(w= data$w_rstan,w_raw = data$w_raw) %>% View()
par_rstan <- sym(par_rstan)
par_t0_rstan <- sym(par_t0_rstan)
par_sess <- sym(par_sess)
data_barplot <- data %>%
group_by(drug) %>%
summarise(N = n(),
mean_par = mean(!!par_rstan),
mean_par_t0 = mean(!!par_t0_rstan),
se_par = sd(!!par_rstan)/sqrt(N),
se_par_t0 = sd(!!par_t0_rstan)/sqrt(N),
max_par = max(c(!!par_rstan, !!par_t0_rstan)),
min_par = min(c(!!par_rstan, !!par_t0_rstan)))
# View(data_barplot)
min_par_no <-min(min(data_barplot$min_par) - 0.1*abs(min(data_barplot$min_par)) ,0)
max_par_no <- max(max(data_barplot$max_par) + 0.1*abs(max(data_barplot$max_par)) ,1)
jw_par = 1
trans_par = 0.2
g[[1]] <- ggplot(data_barplot, aes(x = drug, y= mean_par_t0, fill = drug))+
# geom_bar(position = position_dodge(), stat = "identity") +
geom_point(data = data, aes(x=drug, y = !!par_rstan, fill = drug), alpha =trans_par, position = position_jitterdodge(dodge.width= 0.9, jitter.width =jw_par), shape = 21, colour = "black", size = 2, stroke =1) +
geom_errorbar(aes(ymin= mean_par_t0 - se_par_t0, ymax = mean_par_t0+se_par_t0), width = 0.2, position = position_dodge(0.9)) +
geom_point(aes(x = drug, y= mean_par_t0, colour = drug), position = position_dodge(width = 0.1), stat = "identity", size = 3, shape = 21, colour = "black") +
ylab(paste(parname, 'in session 1') ) + #ylim(min_par_no, max_par_no) +
theme_Publication() +labs(subtitle = title_text[[1]])+
theme(legend.position = "none", plot.subtitle = element_text(size = rel(0.8), hjust = 0),
axis.line = element_line(colour = "black"), axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
scale_y_continuous(breaks = c(0, 0.5,1), limits= c(0,1))
# +
# scale_x_discrete(breaks=c("-4","0","5"))
# g2
# plot.subtitle = element_text(size = rel(rel_height), hjust = 0),
g[[2]] <- ggplot(data_barplot, aes(x = drug, y= mean_par, fill = drug)) +
# geom_bar(position = position_dodge(), stat = "identity") +
# geom_point(aes(x = drug, y= mean_par, colour = drug), position = position_dodge(width = 0.1), stat = "identity", size = 3) +
geom_point(data = data, aes(x=drug, y = !!par_rstan, fill = drug), alpha = trans_par, position = position_jitterdodge(dodge.width= 0.9, jitter.width = jw_par), shape = 21, colour = "black", size = 2, stroke =1) +
geom_errorbar(aes(ymin= mean_par - se_par, ymax = mean_par+se_par), width = 0.2, position = position_dodge(0.9)) +
# geom_point(data = data_change_id, aes(x = Backtransfer_f, y = Change, fill= Treatment, colour= Treatment), alpha = 0.3, position = position_jitterdodge(dodge.width= 0.9, jitter.width = 0.3), shape = 21, colour = "black", size = 2, stroke =1)+
geom_point(aes(x = drug, y= mean_par, colour = drug), position = position_dodge(width = 0.1), stat = "identity", size = 3, shape = 21, colour = "black") +
theme(legend.position = "none") +
#ylim(min_par_no, max_par_no) +
ylab(paste(parname, ' in session 2')) +
theme_Publication()+labs(subtitle = title_text[[2]])+
theme(legend.position = "none", plot.subtitle = element_text(size = rel(0.8), hjust = 0),
axis.line = element_line(colour = "black"), axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
scale_y_continuous(breaks = c(0, 0.5,1), limits= c(0,1))
# g1
g[[3]] <- ggplot(data = data, aes(x = drug, y = !!par_sess))+ # group = ID, linetype = Genotype))
# geom_violin(aes(fill = drug), alpha = 0.3, width = 0.5, colour = "NA" )+
geom_boxplot(aes(fill = drug), width=0.5, color="black", alpha=0.9, outlier.shape=NA)+
geom_point(data = data, aes(x=drug, y = !!par_sess, fill = drug), alpha = trans_par, position = position_jitterdodge(dodge.width= 0.9, jitter.width = jw_par), shape = 21, colour = "black", size = 2, stroke =1) +
# geom_jitter(size = 0.5, width = 0.2) +
theme_Publication()+labs(subtitle = title_text[[3]])+
theme(legend.position = "none", plot.subtitle = element_text(size = rel(0.8), hjust = 0),
axis.line = element_line(colour = "black"), axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) + ylab(paste0("\u0394", parname, "'"))
# g
return(g)
}
g_b <- plot_par_results("beta1", title_text = list(NA, NA, "Inverse temperature") )
g_g <- plot_par_results("g", title_text =list("", "", "Devaluation parameter"))
# g_b_s <- plot_par_results("beta1", data = data_group%>% filter(!is.na(serum_f)))
g_w <- plot_par_results("w", title_text = list("Model-based/model-free weight", "",""))
# g_w_s <- plot_par_results("w", data = data_group%>% filter(!is.na(serum_f)))
# g_b_s[[3]]+facet_wrap(~serum_f)
# g_b_s[[2]]+facet_wrap(~serum_f)
g_all <- plot_grid(g_w[[1]],
g_w[[2]],
g_w[[3]],
label_x = 0.5,
rel_widths = c(1,1,1),
ncol = 3)
g_fig_3 <- plot_grid(g_w[[1]],
g_w[[2]],
g_w[[3]],
g_b[[3]],
g_g[[3]],
g_stats + xlab("Effect sizes"), ncol = 3, labels = c("a","", "", "b", "c", "d")
)
g_fig_3
ggsave("g_all_admin_model.png", plot = g_all, device = NULL, path = NULL,
scale = 1, width = 10, height = 5, dpi = 300, limitsize = TRUE)
## correct predictions + plot -------------------------------------------------------
get_correct_predictions <- function(fit_model) {
# add_criterion(M_g_final, waic)
# fit_model <- M_g_admin #M_g_final
y_pred_mats <- rstan::extract(fit_model, pars = c("y_pred", "y_pred_t0"))
# y_pred_mats
# id <- 45 #110
df = tibble(
perc_correct = rep(0, length=length(subjList)),
perc_correct_mat = rep(0, length=length(subjList)) ,
perc_correct_t0 = rep(0, length=length(subjList)) ,
perc_correct_mat_t0 = rep(0, length=length(subjList))
)
for (l in 1:length(subjList)) {
data_id <- data_beh %>%
filter(s == subjList[l], session == 2) %>% mutate(choice01 = choice12-1)
y_pred_id <- y_pred_mats$y_pred %>% colMeans()
y_pred_id <- y_pred_id[l ,data_id$trials]
corr_vect <- data_id$choice01 == as.numeric(y_pred_id >0.5)
# # y_pred_id_same <- y_pred_id[data_id$prev_state_diff=="same"]
# y_pred_id_diff <- y_pred_id[data_id$prev_state_diff=="different"]
y_pred_id <- y_pred_mats$y_pred
y_pred_id <- y_pred_id[,l ,data_id$trials]
corr_mat <- apply(y_pred_id, 1, function(x) x == data_id$choice01) %>% t()
# rbind(rbind(corr_mat, data_id$choice01), y_pred_id) %>% View()
data_id_t0<- data_beh %>%
filter(s == subjList[l], session == 1) %>% mutate(choice01 = choice12-1)
y_pred_id_t0 <- y_pred_mats$y_pred_t0 %>% colMeans()
y_pred_id_t0 <- y_pred_id_t0[l ,data_id_t0$trials]
corr_vect_t0 <- data_id_t0$choice01 == as.numeric(y_pred_id_t0 >0.5)
# # y_pred_id_same <- y_pred_id[data_id$prev_state_diff=="same"]
# y_pred_id_diff <- y_pred_id[data_id$prev_state_diff=="different"]
y_pred_id_t0 <- y_pred_mats$y_pred_t0
y_pred_id_t0 <- y_pred_id_t0[,l ,data_id_t0$trials]
corr_mat_t0 <- apply(y_pred_id_t0, 1, function(x) x == data_id_t0$choice01) %>% t()
df$perc_correct[l] <- mean(corr_vect)
df$perc_correct_mat[l] <- sum(corr_mat)/length(corr_mat)
df$perc_correct_t0[l] <- sum(corr_vect_t0)/length(data_id_t0$choice01)
df$perc_correct_mat_t0[l] <- sum(corr_mat_t0)/length(corr_mat_t0)
# perc_correct_same[l] <- sum( corr_vect[data_id$prev_state_diff=="same"])/length(corr_vect[data_id$prev_state_diff=="same"])
# perc_correct_diff[l] <- sum(corr_vect[data_id$prev_state_diff=="different"])/length(corr_vect[data_id$prev_state_diff=="different"])
}
return(df)
}
df_m1 = get_correct_predictions(fit_model)
data_group$perc_correct = df_m1$perc_correct
#
## plot the perc correctgraph
g <- ggplot(data = data_group, aes(x = drug, y =perc_correct))+ # group = ID, linetype = Genotype))
# geom_violin(aes(fill = drug))+
geom_boxplot(aes(fill = drug), width=0.5, color="grey", alpha=1)+
geom_jitter(size = 0.1) +
theme_minimal(base_size = 25, ) +
theme(legend.position = "none", plot.title = element_text(face = "bold",size = rel(1.2), hjust = 0.5),
axis.line = element_line(colour = "black")) + ylab("% correct prediction") + scale_x_discrete(name = "")
g
if(!file.exists("brms_perc_correct.rds")) {
brms_perc_correct<- brm(log(perc_correct/(1-perc_correct)) ~ ami_dummy + nal_dummy,
data = data_group,
family = gaussian,
prior = c(set_prior("normal(0,3)", class = "b")),
warmup = 500, iter = 2000, chains = 4)
saveRDS(brms_perc_correct, file ="brms_perc_correct.rds")
} else {brms_perc_correct <- readRDS(file ="brms_perc_correct.rds") }
brms_perc_correct %>% fixef() %>% round(2) %>% write.csv(file = "model_perc_correct.csv")
## generate posterior predictions of the stan models -------------------------------------------------
# individual s = 26, with all trials, for predictions.
data_id_wow <- data_beh[data_beh$s == 26,]
no_of_samples <- 1 # no of parameter sets to samples per individual
if (FALSE) source("Util_scripts/Model_stimulations.r") ## takes some time, need this to produce data_pred
for (l in 1:length(subjList)) { #
print(l)
data_id <- y_pred[1:no_of_samples,l,]
data_id_t0 <- y_pred_t0[1:no_of_samples,l,]
no_of_trials = length(y_pred_t0[1,l,])
reward_id <- y_reward[1:no_of_samples,l,]
reward_id_t0 <- y_reward_t0[1:no_of_samples,l,]
drug_id_t0 <- unique(data_beh$drug[data_beh$s == subjList[l] & data_beh$session ==1])
drug_id <- unique(data_beh$drug[data_beh$s == subjList[l] & data_beh$session ==2])
data_pred_id <- rbind(tibble(
PredChoice01 = as.vector(t(data_id_t0)),
PredPrevChoice01 = lag(as.vector(t(data_id_t0))),
PredStay = as.vector(t(data_id_t0)) == lag(as.vector(t(data_id_t0))),
sim = as.vector(t(matrix(rep(1: no_of_samples, no_of_trials),no_of_samples))),
count_sim = (l-1)*no_of_samples + as.vector(t(matrix(rep(1: no_of_samples, no_of_trials),no_of_samples))),
# prev_points = rep(data_id_wow$prev_points[data_id_wow$session == 1],no_of_samples),
points = as.vector(t(reward_id_t0)),
prev_points = lag(as.vector(t(reward_id_t0))),
prev_state_diff = rep(data_id_wow$prev_state_diff[data_id_wow$session == 1],no_of_samples),
trial =rep(1:no_of_trials,no_of_samples),
s= subjList[l],
drug = drug_id_t0,
session = 1),
tibble(
PredChoice01 = as.vector(t(data_id)),
PredPrevChoice01 = lag(as.vector(t(data_id))),
PredStay = as.vector(t(data_id)) == lag(as.vector(t(data_id))),
sim = as.vector(t(matrix(rep(1: no_of_samples, no_of_trials),nrow=no_of_samples))),
# prev_points = rep(data_id_wow$prev_points[data_id_wow$session == 2],no_of_samples),
points = as.vector(t(reward_id)),
prev_points = lag(as.vector(t(reward_id))),
prev_state_diff = rep(data_id_wow$prev_state_diff[data_id_wow$session == 2],no_of_samples),
trial =rep(1:no_of_trials,no_of_samples),
s= subjList[l],
count_sim = (l-1)*no_of_samples + as.vector(t(matrix(rep(1: no_of_samples, no_of_trials),no_of_samples))),
drug = drug_id,
session = 2) )
data_pred <- rbind(data_pred,data_pred_id)
}
data_pred <- within(data_pred, {
ID_sim <- factor(count_sim)
nal_dummy<-factor(drug == "Nal", levels = c(FALSE,TRUE), labels = c("Pla","Nal" ))
ami_dummy<-factor(drug == "Ami", levels = c(FALSE,TRUE), labels = c("Pla","Ami" ))
})
## plot model predictions straight lines -----------------------------------
data_pred <- data_pred %>% mutate(ID = count_sim, stay = PredStay)
data_pred$session <- as.factor(data_pred$session)
g1 <- data_pred %>%
filter(prev_state_diff == "different") %>%
group_by(ID, session, prev_points, stay) %>%
dplyr::summarize(N=n(),
ami_dummy = ami_dummy[1],
nal_dummy = nal_dummy[1],
drug = drug[1])%>%
mutate(freq=N/sum(N)) %>%
filter(stay == 1) %>% ggplot( aes(x = prev_points, y = freq,group = session, color = session)) +
geom_smooth(method=lm)+
# ylim(c(0,1))+#, se = TRUE) +
scale_x_continuous(breaks=NULL)+
scale_y_continuous(breaks=c(0.3,0.5,0.7), limits = c(0,1))+
facet_wrap(vars(drug))+
#"#F8766D" "#00BA38" "#619CFF
scale_color_manual(values=c('#999999','#E69F00'))+
labs(x = "", y= "P(stay)")+
theme_minimal(base_size=25) +
theme(legend.position = "none",
axis.line = element_line(colour = "black"),
plot.subtitle = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = 18)) + coord_cartesian(ylim=c(0.25, 1))
g2 <- data_pred %>%
filter(prev_state_diff == "same") %>%
group_by(ID, session, prev_points, stay) %>%
summarize(N=n(),
ami_dummy = ami_dummy[1],
nal_dummy = nal_dummy[1],
drug = drug[1]) %>%
mutate(freq=N/sum(N)) %>%
filter(stay == 1)%>%
ggplot(aes(x = prev_points, y = freq, color = session)) +
geom_smooth(method=lm)+
theme(axis.text.x = element_text(face="bold", size = 15)) +
scale_x_continuous(breaks=NULL)+
scale_y_continuous(breaks=c(0.3,0.5,0.7), limits = c(0,1))+
facet_wrap(vars(drug)) +
#"#F8766D" "#00BA38" "#619CFF
scale_color_manual(values=c('#999999','#E69F00'))+
labs(x = "Previous Points", y= "P(stay)")+
theme_minimal(base_size=25) +
theme(legend.position = "none",
axis.line = element_line(colour = "black"),
plot.subtitle = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x = element_text(size=18)) + coord_cartesian(ylim=c(0.25,1))
g2
g_stay_lines <- plot_grid(g1,
g2,
label_x = 0.5,
nrow = 2)
g_stay_lines
## plot model predictions over data... ----
data_pred <- data_pred %>% mutate(ID = count_sim, stay = PredStay)
data_pred$session <- as.factor(data_pred$session)
data_same_model <- data_pred %>% filter(!is.na(stay), prev_state_diff == "same") %>%
group_by(drug, prev_points,session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
data_diff_model<- data_pred %>% filter(!is.na(stay), prev_state_diff != "same") %>%
group_by(drug, prev_points, session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
data_same <- data_beh %>% filter(!is.na(stay), prev_state_diff == "same") %>%
group_by(drug, prev_points,session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
data_diff<- data_beh %>% filter(!is.na(stay), prev_state_diff != "same") %>%
group_by(drug, prev_points, session) %>%
summarize(N = n(),
mean_stay = mean(stay),
se_stay = sd(stay)/sqrt(N))
g_different_model <- ggplot(data=data_diff, aes(x= prev_points, y = mean_stay, group = session)) +
geom_errorbar(aes(x = prev_points, y = mean_stay,ymin = mean_stay - se_stay, ymax =mean_stay + se_stay),colour = "black", width = 0, position = position_dodge(0.2))+
geom_point(aes(x = prev_points, y = mean_stay, colour = session),position = position_dodge(0.2)) +
geom_ribbon(data=data_diff_model, aes(ymin = mean_stay - se_stay, ymax =mean_stay + se_stay, group = session), alpha = 0.2) +
# geom_ribbon(aes(ymin = mean_value-sd_value, ymax = mean_value+sd_value), alpha = 0.3) +
geom_line(data=data_diff_model, aes(colour = session)) + theme_Publication() + scale_color_manual(values=c('#999999','#E69F00'))+
theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks = element_blank(), strip.background=element_blank(), strip.text = element_blank())+
xlab("Previous Points")+ ylab("P(stay) after \ndifferent first state") + facet_wrap(~drug) + coord_cartesian(ylim=c(0.20,1)) +
scale_y_continuous(breaks = c(0.2, 0.5, 0.8)) + geom_hline(yintercept =0.5, linetype = "dashed")
g_same_model <- ggplot(data=data_same, aes(x= prev_points, y = mean_stay, group = session)) +
geom_errorbar(aes(x = prev_points, y = mean_stay,ymin = mean_stay - se_stay, ymax =mean_stay + se_stay),colour = "black", width = 0, position = position_dodge(0.2))+
geom_point(aes(x = prev_points, y = mean_stay, colour = session),position = position_dodge(0.2)) +
geom_ribbon(data=data_same_model, aes(ymin = mean_stay - se_stay, ymax =mean_stay + se_stay, group = session), alpha = 0.2) +
# geom_ribbon(data=data_same_model, aes(ymin = ci_l, ymax = ci_u), alpha = 0.3) +
geom_line(data=data_same_model, aes(colour = session)) + theme_Publication() + scale_color_manual(values=c('#999999','#E69F00'))+
theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks = element_blank())+
xlab("Previous Points")+ ylab("P(stay) after \nsame first state") + facet_wrap(~drug) + coord_cartesian(ylim=c(0.20,1)) +
scale_y_continuous(breaks = c(0.2, 0.5, 0.8)) + geom_hline(yintercept =0.5, linetype = "dashed")
(g_same_model / g_different_model)
## stats from model predictions -----
data_pred$stay_num <- as.numeric(data_pred$stay)
if (FALSE) {
brm.simulated.behavior <- brm(stay_num|trials(1) ~ (session+prev_state_diff+prev_points|ID) + session*prev_points*prev_state_diff*ami_dummy+
session*prev_points*prev_state_diff*nal_dummy,
data = data_pred, family = binomial(),
prior = c(set_prior("cauchy(0,2)", class = "sd"),
set_prior("normal(0,3)", class = "b"),
set_prior("lkj(2)", class = "cor")),
#set_prior("lkj(2)", class = "cor")),
warmup = 1000, iter =3000, chains =4,
control = list(adapt_delta = 0.99))
}
# plot re-estimated model results
pars_set = c("b_prev_points", "b_prev_points:prev_state_diffdifferent",
"b_session2:prev_points:prev_state_diffdifferent:nal_dummyNal",
"b_session2:prev_points:prev_state_diffdifferent:ami_dummyAmi")
probs = c(.5, .025, .975)
posterior <- as.matrix(brm.simulated.behavior)
library("bayesplot")
plot_title <- ggtitle("Posterior distributions", "with medians and 95% intervals")
# g_stat_plot <- mcmc_areas(posterior,
# pars = pars_set,
# prob = 0.95) + theme_Publication() #+ theme(# +
g_stat_plot_model <- mcmc_intervals(posterior,prob = 0.8, prob_outer = 0.95,
pars = pars_set) + theme_Publication() #+ theme(# +
g_stat_plot_model <- g_stat_plot_model + scale_y_discrete(name = "", limits = rev(pars_set), labels = c("Ami:Session:PrevPoints:PrevState",
"Nal:Session:PrevPoints:PrevState",
"PrevPoints:PrevState",
"PrevPoints")) + geom_vline(xintercept = 0, size =0.5, alpha= 0.5, linetype= 2)