inst/doc/ggfan_stan.R

In ggfan: Summarise a Distribution Through Coloured Intervals

## ----setup---------------------------------------------------------------
library(rstan)
library(dplyr)
library(magrittr)
library(tidyr)
library(tibble)

library(ggfan)


## ----stan_example--------------------------------------------------------
seed <- 34526
set.seed(seed)

# data 
x <- seq(-5,5,0.1)
N <- length(x)
y <- cbind(rpois(N, exp(sin(x)+2)),rpois(N, exp(sin(x)+2)))

stan_data <- list(N=N, x=x, y=y)

plot(x,y[,1])
points(x,y[,2])

## ----fit_model, results=FALSE, echo=TRUE, eval=FALSE---------------------
#  gp_model_fit <- sampling(compiled_model, data=stan_data, iter=3000,thin=6)

## ----rhat----------------------------------------------------------------
stan_rhat(gp_model_fit)

## ----convert_to_list-----------------------------------------------------

z_samp_mat <- t(as.matrix(gp_model_fit, "z"))
# give names (as_tibble complains if you don't!)
colnames(z_samp_mat) <- paste0("Sim", 1:dim(z_samp_mat)[2])
z_df <- as_tibble(z_samp_mat)
z_df <- z_df %>% mutate(x=x)

z_df_long <- z_df %>% gather(key=Sim, value=z, -x)

ggplot(z_df_long, aes(x=x,y=z)) + geom_fan() + geom_line(data=data.frame(x=x,y=sin(x)+2), aes(x=x,y=y),colour="red") + theme_minimal() + 
  scale_fill_distiller()

## ----observations--------------------------------------------------------

y_samp_mat <- t(as.matrix(gp_model_fit, "y_gen"))
colnames(y_samp_mat) <- paste0("Sim", 1:dim(y_samp_mat)[2])
y_df <- as_tibble(y_samp_mat)
y_df <- y_df %>% mutate(x=x)

y_df_long <- y_df %>% gather(key=Sim, value=y, -x)

obs_data <- data.frame(x=rep(x,2), y=c(y[,1], y[,2]))
ggplot(y_df_long, aes(x=x,y=y)) + geom_fan() + 
  geom_point(data=obs_data, aes(x=x,y=y)) + theme_minimal() + 
  scale_fill_distiller() + geom_interval()