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03_check_fit.Rmd
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03_check_fit.Rmd
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---
title: "Fitting `r params$score` using the `r params$dataset` dataset"
author: "Guillem Hurault"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: html_document
params:
model: "BinMC"
score: "extent"
dataset: "PFDC"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
fig.height = 5,
fig.width = 8,
dpi = 200)
source(here::here("analysis", "00_init.R"))
item_dict <- detail_POSCORAD()
intensity_signs <- detail_POSCORAD("Intensity signs")$Name
score <- match.arg(params$score, item_dict$Name)
dataset <- match.arg(params$dataset, c("Derexyl", "PFDC", "Fake"))
item_dict <- item_dict %>% filter(Name == score)
item_lbl <- as.character(item_dict[["Label"]])
max_score <- item_dict[["Maximum"]]
reso <- item_dict[["Resolution"]]
mdl_name <- match.arg(params$model, available_models(score)$Model)
param <- list_parameters(mdl_name)
```
```{r load-data}
# Data
POSCORAD <- load_dataset(dataset)
# Subset dataset
df <- POSCORAD %>%
# filter(Patient %in% 1:30) %>%
rename(Time = Day, Score = all_of(item_lbl)) %>%
select(Patient, Time, Score) %>%
drop_na()
pt <- unique(df[["Patient"]])
id <- get_index(df)
df <- left_join(df, id, by = c("Patient", "Time"))
```
```{r load-results}
# Results
file_dict <- get_results_files(outcome = score, model = mdl_name, dataset = dataset, root_dir = here())
fit <- readRDS(file_dict$Fit)
par <- readRDS(file_dict$FitPar)
if (mdl_name != "MC") {
par0 <- readRDS(file_dict$PriorPar)
}
```
# Diagnostics
```{r stan-diagnostics}
check_hmc_diagnostics(fit)
par %>%
select(Rhat) %>%
drop_na() %>%
summarise(max(Rhat),
all(Rhat < 1.1))
tryCatch({
pairs(fit, pars = param$Population)
},
error = function(e) {NULL})
plot(fit, pars = param$Population, plotfun = "trace")
```
# Posterior estimates
```{r prior-posterior, fig.height=5, fig.width=10}
if (mdl_name != "MC") {
plot_grid(HuraultMisc::plot_prior_posterior(par0, par, pars = param$Population) + theme_bw(base_size = 15) + theme(legend.position = "top"),
HuraultMisc::plot_prior_influence(par0, par, pars = c(param$Population, param$Patient)) + theme(legend.position = "top"),
nrow = 1, labels = "AUTO")
# ggsave(here("results", paste0("sensitivity_", score, "_", mdl_name, ".jpg")), width = 13, height = 8, units = "cm", dpi = 300, scale = 2)
}
```
```{r posterior-OrderedRW}
if (mdl_name == "OrderedRW") {
plot(fit, pars = "ct")
}
```
```{r posterior-MC}
if (mdl_name == "MC") {
plot(fit, pars = "p")
plot_transition_MC(fit)
}
```
```{r patient-parameters, results='asis'}
if (length(param[["Patient"]]) > 0) {
cat("## Patient-dependent parameters ")
lapply(param[["Patient"]], function(x) {PPC_group_distribution(fit, x, 50)})
}
```
# Posterior Predictive Checks
```{r yrep}
yrep <- rstan::extract(fit, pars = "y_rep")[[1]] * reso
```
## Coverage of posterior predictive distribution
```{r ppc-coverage}
if (mdl_name != "MC") {
HuraultMisc::plot_coverage(yrep[, df[["Index"]]], df[["Score"]])
}
```
## Posterior predictive p-value for well-controlled-days (averaged across-patients)
```{r ppc-wcd}
if (mdl_name != "MC") {
post_pred_pval(yrep[, df[["Index"]]], df[["Score"]], function(x) {mean(x < max_score * 0.1, na.rm = TRUE)}, plot = TRUE)
}
```
## Posterior predictive distribution for normalised amplitude
```{r ppc-amp}
if (mdl_name != "MC") {
lapply(sample(pt, 4),
function(pid) {
tmp <- df %>%
filter(Patient == pid)
post_pred_pval(yrep[, tmp[["Index"]]], tmp[["Score"]], function(x) {(max(x) - min(x)) / max_score}, plot = TRUE)$plot +
coord_cartesian(xlim = c(0, 1)) +
labs(x = "Normalised amplitude", title = paste0("Patient ", pid))
}) %>%
plot_grid(plotlist = ., ncol = 2)
}
```
## Posterior predictive trajectories
```{r ppc-trajectory, fig.height=8, fig.width=12}
patient_ids <- sort(sample(pt, 2))
if (mdl_name == "BinRW") {
# PPC and Latent trajectory
lapply(patient_ids,
function(pid) {
p1 <- plot_ppc_traj_fanchart(yrep, train = df, test = NULL, patient_id = pid, max_score = max_score) +
labs(y = score, title = paste("Patient", pid))
p2 <- par %>%
filter(Patient == pid & Variable == "y_lat") %>%
ggplot(aes(x = Time, y = Mean, ymin = `5%`, ymax = `95%`)) +
geom_line() +
geom_ribbon(alpha = .5) +
coord_cartesian(ylim = c(0, 1)) +
scale_x_continuous(expand = c(0, 0)) +
labs(y = "Latent score") +
theme_bw(base_size = 15)
plot_grid(get_legend(p1 + theme(legend.position = "top")),
p1 + theme(legend.position = "none") + labs(title = paste("Patient", pid)),
p2,
ncol = 1, rel_heights = c(1, 5, 5))
}) %>%
plot_grid(plotlist = ., ncol = 2, labels = "AUTO")
# ggsave(here("results", paste0("ppc_", score, "_", mdl_name, "_62_177.jpg")),
# width = 13, height = 8, units = "cm", dpi = 300, scale = 2)
} else if (mdl_name == "BinMC") {
# PPC fanchart and evolution of p01/ss1
lapply(patient_ids,
function(pid) {
p1 <- plot_ppc_traj_fanchart(yrep, train = df, test = NULL, patient_id = pid, max_score = max_score) +
labs(y = score, title = paste("Patient", pid))
p2 <- par %>%
filter(Patient == pid & Variable == "ss1") %>%
ggplot(aes(x = Time, y = Mean, ymin = `5%`, ymax = `95%`)) +
geom_line() +
geom_ribbon(alpha = .5) +
coord_cartesian(ylim = c(0, 1)) +
scale_x_continuous(expand = c(0, 0)) +
labs(y = expression(pi)) +
theme_bw(base_size = 15)
p3 <- par %>%
filter(Patient == pid & Variable == "lambda") %>%
ggplot(aes(x = Time, y = Mean, ymin = `5%`, ymax = `95%`)) +
geom_line() +
geom_ribbon(alpha = .5) +
coord_cartesian(ylim = c(-1, 1)) +
scale_x_continuous(expand = c(0, 0)) +
labs(y = expression(lambda)) +
theme_bw(base_size = 15)
plot_grid(get_legend(p1 + theme(legend.position = "top")),
p1 + theme(legend.position = "none") + labs(title = paste("Patient", pid)),
p2,
p3,
ncol = 1, rel_heights = c(1, 4, 4, 4))
}) %>%
plot_grid(plotlist = ., ncol = 2, labels = "AUTO")
} else if (mdl_name == "OrderedRW") {
# PPC and latent score
lapply(patient_ids,
function(pid) {
p1 <- plot_ppc_traj_pmf(yrep, train = df, test = NULL, patient_id = pid, max_score = max_score, max_scale = 1) +
labs(y = item_lbl, title = paste("Patient", pid))
p2 <- plot_latent_OrderedRW(fit, id, pid) +
theme(legend.position = "none")
plot_grid(get_legend(p1 + theme(legend.position = "top")),
p1 + theme(legend.position = "none") + labs(title = paste("Patient", pid)),
p2,
ncol = 1, rel_heights = c(1, 5, 5))
}) %>%
plot_grid(plotlist = ., ncol = 2, labels = "AUTO")
# ggsave(paste0("results/ppc_", score, "_", mdl_name, "_62_103.jpg"), width = 13, height = 8, units = "cm", dpi = 300, scale = 2.2)
} else if (mdl_name %in% c("RW", "AR1", "MixedAR1", "Smoothing")) {
pl <- lapply(patient_ids,
function(pid) {
plot_ppc_traj_fanchart(yrep, train = df, test = NULL, patient_id = pid, max_score = max_score) +
labs(title = paste("Patient", pid))
})
plot_grid(get_legend(pl[[1]] + theme(legend.position = "top")),
plot_grid(plotlist = lapply(pl, function(x) {x + theme(legend.position = "none")}),
ncol = 2,
labels = "AUTO"),
ncol = 1,
rel_heights = c(1, 10))
# ggsave(here("results", paste0("ppc_", score, "_", mdl_name, ".jpg")),
# width = 13, height = 8, units = "cm", dpi = 300, scale = 2)
}
```