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combine_model_female.R
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library(tidyverse)
library(rjags)
library(rstan)
library(tidybayes)
library(cowplot)
library(patchwork)
theme_set(theme_classic())
# First indicates result of TPBF, second is result of RFM.
# NP <- Normal for BMI, obese for TPBF.
# PP <- obese for BMI, obese for TPBF
# NP <- obese for BMI, normal for TPBF.
# NP <- Normal for BMI, normal for TPBF.
# y<- c(PP, NP, PN, NN)
## Specify model parameters
female_abn <- list(y = c(177, 40, 79, 277),
N = 573,
p_mu = 0.20,
p_kappa = 2,
mu_se_1 = 0.934,
mu_se_2 = 0.903,
mu_sp_1 = 0.854,
mu_sp_2 = 0.756,
sd_se_1 = 0.010,
sd_se_2 = 0.012,
sd_sp_1 = 0.015,
sd_sp_2 = 0.018)
female_obesity <- list(y = c(48, 12, 55, 458),
N = 573,
p_mu = 0.05,
p_kappa = 2,
mu_se_1 = 0.911,
mu_se_2 = 0.933,
mu_sp_1 = 0.964,
mu_sp_2 = 0.885,
sd_se_1 = 0.012,
sd_se_2 = 0.010,
sd_sp_1 = 0.008,
sd_sp_2 = 0.013)
# To be used in plotting later
cells_fe.ab <- tibble(i = 1:4,
counts = female_abn$y,
TPBF = c('Positive','Negative','Positive','Negative'),
RFM = c('Positive','Positive','Negative','Negative'))
cells_fe.ob <- tibble(i = 1:4,
counts = female_obesity$y,
TPBF = c('Positive','Negative','Positive','Negative'),
RFM = c('Positive','Positive','Negative','Negative'))
# Compile the model
model <- stan_model('combine_prevalence.stan')
# Fit the model
fit_female.ab <- sampling(model, data = female_abn, chains = 20, pars = c('SE1','SE2','SP1','SP2'), iter = 50000 , seed = 12345, include=F)
fit_female.ob <- sampling(model, data = female_obesity, chains = 20, pars = c('SE1','SE2','SP1','SP2'), iter = 50000 , seed = 12345, include=F)
# Write to a little text file for easy reference later
sink('results/fit_female_ab.txt')
sink('results/fit_female_ob.txt')
print(fit_female.ab, digits=3, probs = c(0.025, 0.5, 0.975))
print(fit_female.ob, digits=3, probs = c(0.025, 0.5, 0.975))
sink()
# Make the posterior predictive check plot: Overweight/obesity
make_plot<-function(fit_female.ab,j){
plotme<-fit_female.ab %>%
gather_draws(y_ppc[i]) %>%
left_join(cells_fe.ab) %>%
filter(i==j) %>%
ggplot()+
stat_histinterval(aes(.value),
outline_bars = T,
slab_color = 'gray45',
.width = c(0.8,0.95))+
geom_point(aes(x = counts, y = 0.025), color = 'black', shape=25, fill = 'red')+
labs(x='', y='')+
theme(aspect.ratio = 1,
plot.margin = unit(c(1,1,1,1)*0.05, "cm"),
panel.grid.major = element_line())
if(j==1){
plotme<- plotme +
facet_grid(~TPBF, labeller = function(x) label_both(x,sep = ' ')) +
ylab('Frequency')
}
if(j==2){
plotme<- plotme + facet_grid(RFM~TPBF, labeller = function(x) label_both(x,sep = ' '))
}
if(j==3){
plotme<-plotme + labs(x='Cell Count', y='Frequency')
}
if(j==4){
plotme<- plotme +
facet_grid(RFM~., labeller = function(x) label_both(x,sep = ' ')) +
xlab('Cell Count')
}
plotme
}
pp <- make_plot(fit_female.ab,1)
np <- make_plot(fit_female.ab, 2)
pn <- make_plot(fit_female.ab,3)
nn <- make_plot(fit_female.ab,4)
final <- (pp + np)/(pn + nn)
ggsave('plots/predictive_check_fe_ab.png', height = 5, width = 5)
#---- Make 6x2 Figure For Paper ----
p = rstan::extract(fit_female.ab)
# Create subplots
tiff("plots/posterior_fe_ab.tiff", units="in", width=8, height=5, res=300)
par(mfrow=c(2,3))
hist(p$se_1,
xlab = expression(delta[1]),
main = 'Posterior TPBF Sensitivity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$sp_1,
xlab = expression(gamma[1]),
main = 'Posterior TPBF Specificity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$p,
xlab = expression(pi),
main = 'Posterior Prevalence',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$se_2,
xlab = expression(delta[2]),
main = 'Posterior RFM Sensitivity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$sp_2,
xlab = expression(gamma[2]),
main = 'Posterior RFM Specificity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
dev.off()
fit_female.ab %>%
spread_draws(p, se_2, sp_2,se_1, sp_1) %>%
mutate(p_survey = p*se_1 + (1-p)*(1-sp_1)) %>%
ggplot(aes(p))+
geom_histogram()
## Posterior predictive plot: obesity
theme_set(theme_classic())
make_plot<-function(fit_female.ob,j){
plotme<-fit_female.ob %>%
gather_draws(y_ppc[i]) %>%
left_join(cells_fe.ob) %>%
filter(i==j) %>%
ggplot()+
stat_histinterval(aes(.value),
outline_bars = T,
slab_color = 'gray45',
.width = c(0.8,0.95))+
geom_point(aes(x = counts, y = 0.025), color = 'black', shape=25, fill = 'red')+
labs(x='', y='')+
theme(aspect.ratio = 1,
plot.margin = unit(c(1,1,1,1)*0.05, "cm"),
panel.grid.major = element_line())
if(j==1){
plotme<- plotme +
facet_grid(~TPBF, labeller = function(x) label_both(x,sep = ' ')) +
ylab('Frequency')
}
if(j==2){
plotme<- plotme + facet_grid(RFM~TPBF, labeller = function(x) label_both(x,sep = ' '))
}
if(j==3){
plotme<-plotme + labs(x='Cell Count', y='Frequency')
}
if(j==4){
plotme<- plotme +
facet_grid(RFM~., labeller = function(x) label_both(x,sep = ' ')) +
xlab('Cell Count')
}
plotme
}
pp <- make_plot(fit_female.ob,1)
np <- make_plot(fit_female.ob, 2)
pn <- make_plot(fit_female.ob,3)
nn <- make_plot(fit_female.ob,4)
final <- (pp + np)/(pn + nn)
ggsave('plots/female_ob.png', height = 5, width = 5)
#---- Make 6x2 Figure For Paper ----
p = rstan::extract(fit_female.ob)
# Create subplots
tiff("plots/posterior_fe_ob.tiff", units="in", width=8, height=5, res=300)
par(mfrow=c(2,3))
hist(p$se_1,
xlab = expression(delta[1]),
main = 'Posterior TPBF Sensitivity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$sp_1,
xlab = expression(gamma[1]),
main = 'Posterior TPBF Specificity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$p,
xlab = expression(pi),
main = 'Posterior Prevalence',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$se_2,
xlab = expression(delta[2]),
main = 'Posterior RFM Sensitivity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
hist(p$sp_2,
xlab = expression(gamma[2]),
main = 'Posterior RFM Specificity',
ylab = '',
yaxt = 'n',
breaks = 20,
cex.lab = 1.25)
dev.off()
fit_female.ob %>%
spread_draws(p, se_2, sp_2,se_1, sp_1) %>%
mutate(p_survey = p*se_1 + (1-p)*(1-sp_1)) %>%
ggplot(aes(p))+
geom_histogram()