# Lab 5: Bayes/Stan

Key Word(s): Stan, Poisson Model

Poisson Model PDF

Stan PDF

```{r setup, include=FALSE} knitr::opts_chunk\$set(echo = TRUE, fig.align="center")

```# load libraries and set bayesplot theme
# make sure you have these packages installed
library("rstan")
library("ggplot2")
library("bayesplot")

theme_set(bayesplot::theme_default())
```

Load the count data into R by calling the "source" function on the file "count-data.R" that's provided in the lab materials. Create a variable called "N" that is equal to the number of observations in the count data. Print "N", the data "y", and create a histogram of "y". ```{r count-data, fig.width = 4, fig.height = 4, message=FALSE}

# Loads vector of counts 'y'

```We will now compare our data to draws from Poisson distribution with same mean. Calculate the mean of "y", then use the "rpois" function to draw "N" random samples from a Poisson with a lambda parameter equal to what you calculated. Then, plot the drawn data.
```{r plot-x, fig.width = 4, fig.height = 4, message=FALSE}
# create Poisson draws and plot
```

Create a dataframe titled "plotdata" that consists of both "y" and the sampled Poisson values "x". The dataframe should have two columns: one consisting of "y" and "x" concatenated together, and one consisting of a label representing whether that observation is from "Our data" or "Poisson data". After you create this dataframe, you should be able to visualize the data with the plot functions below. ```{r plot-y-x, message=FALSE}

# Frequency polygons

ggplot(plotdata, aes(x = value, color = variable)) + geom_freqpoly(binwidth = 0.5) + scale_x_continuous(name = "", breaks = 0:max(x,y)) + scale_color_manual(name = "", values = c("gray30", "purple"))

# Side-by-side bar plots

ggplot(plotdata, aes(x = value, fill = variable)) + geom_bar(position = "dodge") + scale_x_continuous(name = "", breaks = 0:max(x,y)) + scale_fill_manual(name = "", values = c("gray30", "purple"))

```# Fit basic Poisson model

Even though we already suspect it won't be a good model for this data, it's
still a good idea to start by fitting the simplest Poisson model. From there we
can then identify in which ways the model is inadequate.

When using Stan, it's usally easiest to store your data in a list, such as the one provided below:
```{r create_list, fig.width = 4, fig.height = 4, message=FALSE}
# create list
stan_list <- list()
stan_list\$Y <- y
stan_list\$N <- N
```

Try your best to fill out the skeleton Stan code provided below for a simple Poisson model with an exponential prior distribution on \$/lambda\$. ```{r stan_code, fig.width = 4, fig.height = 4, message=FALSE}

# stan code

stan_code <- c(" data { // Number of observations // your code here // Count data (integer array) // your code here }

parameters { real lambda; // Poisson rate parameter }

model { // Prior // your code here

// Likelihood // your code here }

generated quantities { int y_rep[N]; // Draws from posterior predictive dist for (n in 1:N) { y_rep[n] = poisson_rng(lambda); } }

")

```Run the cell below to fit the Poisson model.
```{r fit_model, fig.width = 4, fig.height = 4, message=FALSE}
# fit the model
options(mc.cores = parallel::detectCores())
fit <- stan(model_code = stan_code,
data = stan_list,
iter = 2000,
chains = 4)
```

Use the "trace" plotfun to look at the trace plot of the MCMC sampler for \$/lambda\$. ```{r, plot_convergence} plot(fit, plotfun="trace", pars='lambda')

```Look at posterior distribution of lambda
```{r, plot_lambda}
color_scheme_set("brightblue") # check out bayesplot::color_scheme_set
lambda_draws <- as.matrix(fit, pars = "lambda")
mcmc_areas(lambda_draws, prob = 0.95) # color 95% credible interval
```

Compare posterior of lambda to the mean of the data ```{r, print_fit} means <- c("Posterior mean" = mean(lambda_draws), "Data mean" = mean(y)) print(means, digits = 3)

```The model gets the mean right, but, as we'll see next, the model is quite bad
at predicting the outcome.

# Graphical posterior predictive checks
Extract `y_rep` draws from the fitted model object
```{r y_rep}
y_rep <- as.matrix(fit, pars = "y_rep")
```

Compare histogram of `y` to histograms of several `y_rep`s ```{r ppc-hist, message=FALSE} ppc_hist(y, y_rep[1:8, ], binwidth = 1)

```Compare density estimate of `y` to density estimates of a bunch of `y_rep`s
```{r ppc-dens-overlay}
ppc_dens_overlay(y, y_rep[1:50, ])
```

# Normal Model

```Plot the data as before:
```{r examine_normal_data}
X <- mydata\$x
N <- length(X)
print(mean(X))
qplot(X)
```

Generate fake normal data with the same empirical mean/standard deviation and plot it. ```{r fake_normal_data, fig.width = 4, fig.height = 4, message=FALSE} Y <- rnorm(N, mean=mean(X), sd=sd(X)) qplot(Y)

```Compare the two histograms side-by-side.
```{r normal_side_by_side, fig.width = 4, fig.height = 4, message=FALSE}
plotdata <- data.frame(
value = c(X, Y),
variable = rep(c("Our data", "Normal data"), each = N)
)

ggplot(plotdata, aes(x=value)) +
geom_histogram(data=subset(plotdata, variable == 'Our data'),
fill = "red", alpha = 0.25) +
geom_histogram(data=subset(plotdata, variable == 'Normal data'),
fill = "blue", alpha = 0.25)
```

Create a list for this data. ```{r create_normal_list, fig.width = 4, fig.height = 4, message=FALSE}

# create list

stan_list <- list() stan_list\$X <- X stan_list\$N <- N

```Try writing the code for the normal model yourself!
```{r normal_stan_code, fig.width = 4, fig.height = 4, message=FALSE}
# stan code
stan_code <- c("
data {
// write code here
}

parameters {
// write code here
}

model {
// write code here
}

generated quantities {
// (optional) write code here
}

")
```

Fit the normal model. ```{r fit_normal_model, fig.width = 4, fig.height = 4, message=FALSE}

# fit the model

options(mc.cores = parallel::detectCores()) fit <- stan(model_code = stan_code, data = stan_list, iter = 2000, chains = 4) ```

Play around with some of the same plots used on the Poisson data. Does this model seem to fit the data well?