{ "cells": [ { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "options(jupyter.plot_mimetypes = \"image/png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A quick-start introduction to Stan for economists\n", "\n", "### Jim Savage, Lendable Inc\n", "\n", "Stan is a flexible modeling language that makes it straightforward to estimate a very wide range of probability models using Bayesian techniques. There are a few reasons one may want to spend the time to learn Stan: \n", "\n", "- Stan implements efficient estimation of probability models on large data sets using Hamiltonain Monte Carlo, Variational Inference and Penalised Maximum Likelihood. Stan can be called from many environments that users may use for data preparation, including R, Python, Stata, Julia and Matlab. \n", "- Stan allows users to speed up their R code by exporting Stan functions (which are compiled C++ functions) into R. This is especially useful for users who want similar performance to Julia but are tied to R for its large library ecosystem.\n", "- Stan is perhaps the easiest Bayesian library to learn and use, with straightforward syntax and companion libraries for easy model checking and comparison. \n", "- Stan (alongside the workflow that is encouraged by the Stan community) forces users to think more carefully about their models than they may do otherwise. Learning Stan illuminates many aspects of statistics that you may have skipped over; it should help you think more carefully about modeling even if you do not use Stan. \n", "- In Stan there is no penalty from using non-conjugate prior distributions, allowing far richer model specifications than under other MCMC methods. \n", "\n", "By the end of this tutorial, you should feel comfortable with the following: \n", "\n", "1. Understand the Bayesian workflow \n", "2. Know how to write out a Stan model\n", "3. Know how to estimate and check a model in R\n", "4. Know where to get help\n", "\n", "The examples given below will use R's interface with Stan, rstan. While Stan can also be called from Python (using PyStan), Julia (using Stan.jl) and other environments, there are some useful companion libraries that are better developed in R. \n", "\n", "\n", "## 1. The Bayesian Workflow\n", "\n", "A strong culture exists within the Bayesian community around a workflow that promotes high quality modeling. Many of the steps should be familiar to economists, yet a few are distinct to Bayesian modeling.\n", "\n", " - Write out the full probability model. That is, a joint probability model of all data and parameters of the model. \n", " - Simulate the model with known values for the parameters.\n", " - Estimate the model to recover the parameters from simulated data.\n", " - Estimate the model against real data.\n", " - Check that the estimation has run properly.\n", " - Run posterior predictive checking/time series cross validation to evaluate model fit.\n", " - Perform inference and prediction.\n", " - Model comparison.\n", " - Iterate!\n", "\n", "We typically start this workflow with the simplest possible model. Only once this is running through the entire workflow without problems do we add richness. Starting simple and adding layers of complexity decreases the scope for error at each iteration; it also gives you a working model to fall back on if your model falls over, allowing you to more easily pinpoint where problems might happen. \n", "\n", "It is worthwhile to build each layer of complexity on a different feature [git branch](https://www.atlassian.com/git/tutorials/). This minimises the possibility of contamination between models. Once the more complex model is working fine, then merge it back into the master branch. \n", "\n", "While at first it may seem a drag to adhere to this workflow strictly, after a while it should feel natural. It will reduce the number of errors in your work, and help you think through the details of the modeling task. \n", "\n", "Let's walk through each step, gradually introducing Stan along the way. \n", "\n", "### A quick note on notation\n", "\n", "The tutorial below uses probabilistic notation. \n", "\n", "- In probabilistic modeling, each random variable is associated with a probability density function (for continuous random variables) or probability mass function (for discrete random variables), in generic notation $p()$. \n", "- The _density_ at a given point $y_{i}$ is $p(y_{i})$. \n", "- The probability that some particular value of our random variable $y$, $y_{i}$ will fall between two points $lower$ and $upper$ is $\\int_{lower}^{upper}p(y)dy$, the area under the probability density function between those two points. Accordingly the area under a probability density is always equal to 1. \n", "- A distribution of some data $y$ _that does not depend on the value of any other random variables_, is written $p(y)$. Because its distribution is not conditioned on the values of any other random variables, we call this an _unconditional distribution_. An unconditional distribution can still have parameters, but these are treated as fixed values.\n", "- In Bayesian analysis, the unconditional (joint) distribution of the parameters of a model, normally lumped together into a single vector $\\theta$, is called the _prior distribution_, $p(\\theta)$. \n", "- If the distribution of a random variable is conditional on the values of another random variable, we write the probability distribution as being _conditional_. For instance, $p(y\\, |\\, \\theta)$ is the (generic) probability distribution of $y$ given some parameter vector $\\theta$, a given realization of a random variable. \n", "- We can generate random draws from a probability density where the probability of a draw at a given value is proportional to the density at that point. \n", "- In cases where the data $y$ and parameters $\\theta$ are fixed, the notation $L(\\theta) = p(y\\, |\\, \\theta)$ is also used to denote the value of the _likelihood function_, which is the product of the densities for all the data points $y_{i}\\in y$.\n", "- Let $\\theta = (\\mu, \\sigma)$, an expected value (mean) and scale parameter. If we say $y\\, |\\, \\theta \\sim \\mbox{Normal}(\\mu, \\sigma)$, this is read that the variable $y$ _is distributed according to_ a normal distribution with expected value of $\\mu$ and standard deviation of $\\sigma$. \n", "- In this tutorial below we drop the conditional statement on the lefthand and shorten this expression to $y \\sim \\mbox{Normal}(\\mu, \\sigma)$ as it is clear that $y$'s distribution depends on $\\mu$ and $\\sigma$.\n", "- It is common to model elements of our data $y_{i}\\in y$ as being distributed according to their own parameters. For example in the normal case, $y_{i} \\sim \\mbox{Normal}(\\mu_{i}, \\sigma_{i})$. Much statistical modeling is simply coming up with functional forms for $\\mu_{i}$ and $\\sigma_{i}$; for instance in the normal linear model, $\\mu_{i} = X_{i}\\beta$ and $\\sigma_{i} = \\sigma$, where $\\theta = (\\beta, \\sigma)$ is a modeled as a random variable. \n", "- The likelihood of a normal linear model is often written $p(y\\, |\\, \\beta, \\sigma)$. Yet the distribution of $y$ is clearly dependent on the conditional expected value $X\\beta$, so sometimes you will see it written more completely as $p(y\\, |\\, \\beta, \\sigma, X)$. It should be mentioned that our conditioning information $X$ is not a random variable, and so it normally doesn't appear on the right hand side of the pipe (we include it below for clarity).\n", "\n", "\n", "### A) Writing out your model in probability form\n", "\n", "Stan estimates probability models. These are models in which we assume that all unknown parameters and the outcome variable(s) $y$ each come from some (conditional) probability density.\n", "\n", "As a first example, take the linear regression model. In this model we have an ($N$-long) vector of outcomes $y$, a matrix of covariates $X$ ($N\\times P$), a ($P$-long) vector of unknown coefficients $\\beta$, and a ($N$-long) vector of residuals $\\epsilon$. The model is typically written out in matrix notation as\n", "\n", "$$\n", "y = X\\beta + \\epsilon\n", "$$\n", "\n", "This is not yet a probability model. To express it as a probability model, we make an assumption about the distribution of the residuals and parameters. We'll start by assuming the residuals are normally distributed $\\epsilon \\sim \\mbox{Normal}(0, \\sigma)$, where $\\sigma$ is another model parmeter--the standard deviation of the residuals. \n", "\n", "Once we have made this distributional assumption, we can use it to express the linear model above in probability notation.\n", "\n", "$$\n", "y = X\\beta + \\epsilon \\mbox{ and } \\epsilon \\sim \\mbox{Normal}(0, \\sigma) \\implies y \\sim \\mbox{Normal}(X\\beta, \\sigma)\n", "$$\n", "\n", "(This follows from the fact that if we add $X\\beta$ to $\\epsilon$, we have a new distribution with an expected value of $X\\beta$.) \n", "\n", "Note that the normal distribution $\\mbox{Normal}(\\mu, \\sigma)$ is a _two-parameter_ distribution. It has an expected value of $\\mu$ and standard deviation $\\sigma$. This does not stop us from estimating a model that has a function for each parameter that itself may have several parameters--in the case above, we're using a linear function $\\mu = X\\beta$ (with $P$ regression coefficients in $\\beta$) for the mean model.\n", "\n", "The model $y \\sim \\mbox{Normal}(X\\beta, \\sigma)$ is the _data model_. If we knew the value of the parameters $\\beta$ and $\\sigma$ for certain, we could simulate plausible values for an outcome $y_{i}^{\\mathrm{sim}}$ for a given set of covariates $X_{i}$ simply by generating random numbers drawn from $\\mbox{Normal}(X_{i}\\beta, \\sigma)$. (This gets to an important aspect of Bayesian models: there is no _predicted value_, rather a _predictive distribution_). However, because we don't know $\\beta$ and $\\sigma$ for certain, we will generate probabilistic estimates for them. \n", "\n", "In Bayesian statistics we try to learn about a _posterior_ density, in this case (where $\\theta = (\\beta, \\sigma)$) the posterior density is $p(\\theta | y, X)$--a joint density of the parameters given the data. According to Bayes' law, this density is given by \n", "\n", "$$\n", "p(\\theta | y, X) = \\frac{p(y | \\theta, X)\\times p(\\theta)}{p(y)}\n", "$$\n", "\n", "Because the density $p(y)$ does not depend on the values of $\\theta$, we typically ignore the denominator of this expression and write it out up to a constant of proportionality. That is\n", "\n", "$$\n", "p(\\theta | y, X) \\propto p(y | \\theta, X)\\times p(\\theta)\n", "$$\n", "\n", "\n", "If we assume that the prior distributions of $\\beta$ and $\\sigma$ are independent, then $p(\\theta) = p(\\beta)\\times p(\\sigma)$, and this becomes\n", "\n", "$$\n", "p(\\beta, \\sigma | y, X) \\propto p(y | \\beta, \\sigma, X)\\times p(\\beta)\\times p(\\sigma)\n", "$$\n", "\n", "\n", "Now let's examine the term $p(y|\\beta, \\sigma, X)$--the probability density of $y$ given the parameters and covariates. We already made a modeling assumption about this distribution--it is our data model $\\mbox{Normal}(X_{i}\\beta, \\sigma)$.\n", "\n", "#### A2) Choosing priors\n", "\n", "To complete (and estimate) our probability model, we need to specify priors for the parameters $\\beta$ and $\\sigma$. \n", "\n", "First, what do priors do? Informally: \n", "\n", "- Priors pull our estimate of the posterior density away from the likelhood in the direction of the prior. The less information contained in the data, the more pronounced this effect will be. \n", "- Priors help (mathematically, not causally) identify models. For instance, imagine a regression $y = \\alpha + \\beta x + \\epsilon$, where for a particular sample, every observation of $x=0$. $\\beta$ is not identified--it could take on any value without affecting the plausibility of the model. Adding prior information about $\\beta$ identifies this model. Our posterior estimate of $\\beta$ in this case would simply be the prior. \n", "- Priors can help _constrain_ our estimates of posterior distributions. More on this below. \n", "\n", "For regression coefficients that do not vary across groups, we often use univariate normal priors; if coefficients vary across groups, we typically specify multivariate normal priors. The expected value and scale of the prior distributions can be used to include information that is known before observing the data, such as parameter estimates from a meta-study, or a theoretically imposed value. It is also common to use so-called _shrinkage priors_ or _regularising priors_. These are priors that _shrink_ the estimate towards 0 (or a group mean). In many prediction tasks, shrinkage priors help prevent overfitting.\n", "\n", "An important note on prior distributions: if a prior distribution puts no weight on a particular value of the parameter, the posterior distribution cannot put weight on that value either. We can use this property by choosing prior distributions that restrict estimates of $\\beta$ to economically meaningful values. For instance, if we are estimating a very simple cost function $\\mbox{costs} = \\alpha + \\beta\\, \\mbox{quantity_sold} + \\epsilon$, we may want to exclude the possibility of zero or negative fixed costs (that is, we think that $\\alpha> 0$). In such a case, we could give $\\alpha$ a prior distribution that is only defined for positive values, such as a truncated normal distribution, the lognormal distribution, or a gamma distribution. \n", "\n", "Similarly, the prior for $\\sigma$ should be constrained to positive numbers. A convenient prior for standard deviations is the half Cauchy distribution (restricted to positive numbers), which provides some prior information but allows for potentially large standard deviations. \n", "\n", "#### Putting it all together\n", "\n", "We now have our probability model. It is made up of two prior distributions for the parameters (one for the $\\beta$s, one for the $\\sigma$), and a model for the data given the parameters. \n", "\n", "Where $\\mu_{p}, \\sigma_{p}, x_{0}\\mbox{ and } \\gamma$ are fixed numbers (not parameters to be estimated) that summarise prior information and are chosen by the researcher. We have the priors: \n", "\n", "$$\n", "\\mbox{for }p\\in [1 \\dots P] \\mbox{ }\\beta_{p} \\sim\\mbox{Normal}(\\mu_{p}, \\sigma_{p})\n", "$$\n", "\n", "$$\n", "\\sigma \\sim\\mbox{Cauchy}_{+}(x_{0}, \\gamma)\n", "$$\n", "\n", "And the data model\n", "\n", "$$\n", "y \\sim\\mbox{Normal}(X\\beta, \\sigma)\n", "$$\n", "\n", "\n", "#### A slightly richer model\n", "\n", "The illustration above shows _how_ you could put together a simple probability model, but does not motivate _why_ you might want to. The real power of probabilistic modeling is that it is not much harder to define far richer models than simple ones. \n", "\n", "For instance, let's say that we want to make two changes to the model above. First, we'd like to accept that our outcome $y$ might come from a so-called \"fat tail\" distribution like the student's t distribution. Next, we want to restrict the first element of $\\beta$ to be non-negative. We can define this slightly richer model like so: \n", "\n", "Priors: \n", "\n", "$$\n", "\\beta_{1} \\sim\\mbox{Normal}_{+}(\\mu_{1}, \\sigma_{1})\n", "$$\n", "\n", "$$\n", "\\mbox{for }p\\in [2 \\dots P] \\mbox{ }\\beta_{p} \\sim\\mbox{Normal}(\\mu_{p}, \\sigma_{p})\n", "$$\n", "\n", "$$\n", "\\sigma \\sim\\mbox{Cauchy}_{+}(x_{0}, \\gamma)\n", "$$\n", "\n", "And the data model\n", "\n", "$$\n", "y \\sim\\mbox{Student's t}(\\nu, X\\beta, \\sigma)\n", "$$\n", "\n", "Where $\\nu$ is the degrees of freedom of the student's t distribution. We need to give this parameter a prior also, limited below by 0. As the student's t distribution approaches a normal distribution as $\\nu$ gets much above 20, we may want to centre our distribution around 7, with a fairly wide spread. \n", "\n", "$$\n", "\\nu \\sim \\mbox{Cauchy}_{+}(7, 5)\n", "$$\n", "\n", "\n", "\n", "### B) Simulating the model with known parameters (an introduction to Stan)\n", "\n", "We have now specified two probability models. What we will do next is simulate some data _from the second (more complex model)_, and then check to see if we can recover the (known) model parameters by estimating both the correctly specified and incorrectly specified models above. Simulating and recovering known parameters is an important checking procedure in model building; it often helps catch errors in the model and clarifies the model in the mind of the modeler. While it would be trivial to do in your statistics package of choice, we'll use this task as an opportunity to introduce writing out data generating models in Stan. \n", "\n", "A Stan model is comprised of code blocks. Each block is a place for a certain task. The bold blocks below must be present in all Stan programs (even if they contain no arguments): \n", "\n", "1. functions, where we define functions to be used in the blocks below. **This is where we will write out a random number generator that gives us draws from our assumed model**. \n", "2. **data**, declares the data to be used for the model\n", "3. transformed data, makes transformations of the data passed in above\n", "4. **parameters**, defines the unknowns to be estimated, including any restrictions on their values. \n", "5. transformed parameters, often it is preferable to work with transformations of the parameters and data declared above; in this case we define them here. \n", "6. **model**, where the full probability model is defined. \n", "7. generated quantities, generates a range of outputs from the model (posterior predictions, forecasts, values of loss functions, etc.). \n", "\n", "Below is the R and Stan script to perform the task. \n", "\n", "First we need to load some libraries. I normally use dplyr for data munging, ggplot2 for plotting the parameters, rstan, which is R's interface with Stan, and reshape2, which I use to manipulate parameter draws. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# In R:\n", "# Load necessary libraries and set up multi-core processing for Stan\n", "options(warn=-1, message =-1)\n", "library(dplyr); library(ggplot2); library(rstan); library(reshape2)\n", "rstan_options(auto_write = TRUE)\n", "options(mc.cores = parallel::detectCores())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Next we define the data generating process. Note that you can define this as a string defined in R, as I have done below, or as a text file that is saved with a .stan suffix. The script below is annotated. A few things to notice: \n", "\n", "- Stan writes C++ programs, and so requires static typing (that is, defining the data and variables by their type). In the function that we declare below, arguments include vectors using vector an integer using int, some reals real and a matrix, using matrix. \n", "- All random number generators in Stan are distribution names followed by _rng. This includes any functions that you write. \n", "- A very complete account of the Stan language is available in the [Stan Modeling Language User's Guide and Reference Manual](http://mc-stan.org/documentation/). \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# In R, or you could save the contents of the string in a file with .stan file type\n", "\n", "dgp_string <- \"\n", "\n", "functions {\n", " /**\n", " * Return draws from a linear regression with data matrix X,\n", " * coefficients beta, and student-t noise with degrees of freedom nu\n", " * and scale sigma.\n", " *\n", " * @param X Data matrix (N x P)\n", " * @param beta Coefficient vector (P x 1)\n", " * @param nu Residual distribution degrees of freedom.\n", " * @param sigma Residual distribution scale.\n", " * @return Return an N-vector of draws from the model.\n", " */\n", "\n", " vector dgp_rng(matrix X, vector beta, real nu, real sigma) {\n", " vector[rows(X)] y; // define the output vector to be as long as the number of rows in X\n", " \n", " // Now fill it in\n", " for (n in 1:rows(X))\n", " y[n] <- student_t_rng(nu, X[n] * beta, sigma);\n", " return y;\n", " }\n", "}\n", "data {\n", " // If we were estimating a model, we'd define the data inputs here\n", "}\n", "parameters {\n", " // ... and the parameters we want to estimate would go in here\n", "}\n", "model {\n", " // This is where the probability model we want to estimate would go\n", "}\n", "\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have written out the data generating model, let's generate some known parameter and covariates, and simulate the model. First: generate some values for the data and paramaters. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Generate a matrix of random numbers, and values for beta, nu and sigma\n", "\n", "set.seed(42) # Set the random number generator seed so that we get the same parameters\n", "N <- 1000 # Number of observations\n", "P <- 10 # Number of covariates\n", "X <- matrix(rnorm(N*P), N, P) # generate an N*P covariate matrix of random data\n", "nu <- 5 # Set degrees of freedom\n", "sigma <- 5 # And scale parameter\n", "beta <- rnorm(10) # Generate some random coefficients that we'll try to recover\n", "# Make sure the first element of beta is positive as in our chosen DGP\n", "beta[1] <- abs(beta[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the inputs to our model, we should compile the script above. Stan uses templating to turn your script into a C++ script, which is then compiled (this will take a few moments). We're going to compile the script using stan_model(), and then make the function that we declared available to R using expose_stan_functions(). \n", "\n", "This is a useful feature: especially when you are using nested loops, Stan functions can be several orders of magnitude faster than R functions. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compile the script\n", "compiled_function <- stan_model(model_code = dgp_string) # you could use file = \"path/to/yourfile.stan\" if you have saved it as so\n", "# And make the function available to the user in R\n", "expose_stan_functions(compiled_function)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now our dgp_rng() is available in R. Let's use it along with the data we declared above to simulate some fake data. Again--the reason we want to do this is to make sure that we can recover known parameters from our data. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Draw a vector of random numbers for known Xs and parameters\n", "y_sim <- dgp_rng(nu = nu, X = X, sigma = sigma, beta = beta)\n", "\n", "# Plot the data\n", "data_frame(y_sim = y_sim) %>% # Declare a data frame and pipe it into a ggplot\n", " ggplot(aes( x = y_sim)) + # Where we state the x-axis aesthetic (our simulated values)\n", " geom_histogram(binwidth = 3) # And tell ggplot what sort of chart to build" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "scrolled": true }, "source": [ "### C) Estimating the model for known parameters\n", "\n", "Now we have $y$ and $X$, and we want to estimate $\\beta$, $\\sigma$ and, depending on the model, $\\nu$. We have two candidate probability models that we want to estimate and check which one is a more plausible model of the data. To do this, we need to specify both models in Stan and then estimate them.\n", "\n", "Let's jump straight in and define the incorrectly specified model--commented heavily below. It is incorrect as it assumes the draws are coming from a normal distribution. It will also be slightly inefficient as it considers that $\\beta_{1}$ could be of any value (even though we might have some reason to believe $\\beta_{1}$ is positive). " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# In R, or in your .stan file (contents from within the quotes only)\n", "\n", "incorrect_model <- \"\n", "data {\n", " // In this section, we define the data that must be passed to Stan (from whichever environment you are using)\n", "\n", " int N; // number of observations\n", " int P; // number of covariates\n", " matrix[N, P] X; //covariate matrix\n", " vector[N] y; //outcome vector\n", "}\n", "parameters {\n", " // Define the parameters that we will estimate, as well as any restrictions on the parameter values (standard deviations can't be negative...)\n", "\n", " vector[P] beta; // the regression coefficients\n", " real sigma; // the residual standard deviation (note that it's restricted to be non-negative)\n", "}\n", "model {\n", " // This is where we write out the probability model, in very similar form to how we would using paper and pen\n", "\n", " // Define the priors\n", " beta ~ normal(0, 5); // same prior for all betas; we could define a different one for each, or use a multivariate prior\n", " sigma ~ cauchy(0, 2.5);\n", " \n", " // The likelihood\n", " y ~ normal(X*beta, sigma);\n", "}\n", "generated quantities {\n", " // For model comparison, we'll want to keep the likelihood contribution of each point\n", " // We will also generate posterior predictive draws (yhat) for each data point. These will be elaborated on below. \n", " \n", " vector[N] log_lik;\n", " vector[N] y_sim;\n", " for(i in 1:N){\n", " log_lik[i] <- normal_log(y[i], X[i,]*beta, sigma);\n", " y_sim[i] <- normal_rng(X[i,]*beta, sigma);\n", " }\n", "}\n", "\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now we define the correctly specified model. It is the same as above, but with a couple of changes: \n", "\n", "- We define the constrained parameter separately, then join it to the unconstrained parameters\n", "- We define a degrees of freedom parameter nu, and give it a prior. \n", "- The likelihood model assumes the data come from a student t distribution. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# In R, or in your .stan file (contents from within the quotes only)\n", "\n", "correct_model <- \"\n", "data {\n", " int N; // number of observations\n", " int P; // number of covariates\n", " matrix[N, P] X; //covariate matrix\n", " vector[N] y; //outcome vector\n", "}\n", "parameters {\n", " // We need to define two betas--the first is the restricted value, the next are the others. We'll join these in the next block\n", " real beta_1;\n", " vector[P-1] beta_2; // the regression coefficients\n", " real sigma; // the residual scale (note that it's restricted to be non-negative)\n", " real nu; \n", "}\n", "transformed parameters {\n", " vector[P] beta;\n", " beta <- append_row(rep_vector(beta_1, 1), beta_2);\n", "}\n", "model {\n", " // Define the priors\n", " beta ~ normal(0, 5); // same prior for all betas; we could define a different one for each, or use a multivariate prior. The first beta will have a prior of the N+(0, 5)\n", " sigma ~ cauchy(0, 2.5);\n", " nu ~ cauchy(7, 5);\n", "\n", " // The likelihood\n", " y ~ student_t(nu, X*beta, sigma);\n", "}\n", "generated quantities {\n", " // For model comparison, we'll want to keep the likelihood contribution of each point\n", " vector[N] log_lik;\n", " vector[N] y_sim;\n", " for(i in 1:N){\n", " log_lik[i] <- student_t_log(y[i], nu, X[i,]*beta, sigma);\n", " y_sim[i] <- student_t_rng(nu, X[i,]*beta, sigma);\n", " }\n", "}\n", "\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have specified two models, let's estimate them with the $y$ and $X$ we generated above. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "SAMPLING FOR MODEL 'c6d682e386dc051417acee4ec59a765d' NOW (CHAIN 1).\n", "\n", "Chain 1, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 1, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 1, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 1, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 1, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 1, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 1, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 1, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 1, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 1, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 1, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 1, Iteration: 2000 / 2000 [100%] (Sampling)# \n", "# Elapsed Time: 1.6476 seconds (Warm-up)\n", "# 1.22683 seconds (Sampling)\n", "# 2.87443 seconds (Total)\n", "# \n", "\n", "SAMPLING FOR MODEL 'c6d682e386dc051417acee4ec59a765d' NOW (CHAIN 2).\n", "\n", "Chain 2, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 2, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 2, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 2, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 2, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 2, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 2, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 2, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 2, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 2, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 2, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 2, Iteration: 2000 / 2000 [100%] (Sampling)# \n", "# Elapsed Time: 1.52025 seconds (Warm-up)\n", "# 1.40495 seconds (Sampling)\n", "# 2.9252 seconds (Total)\n", "# \n", "\n", "SAMPLING FOR MODEL 'fb5681ca08d43f5523f250d52285c81f' NOW (CHAIN 1).\n", "\n", "Chain 1, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 1, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 1, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 1, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 1, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 1, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 1, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 1, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 1, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 1, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 1, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 1, Iteration: 2000 / 2000 [100%] (Sampling)# \n", "# Elapsed Time: 2.89004 seconds (Warm-up)\n", "# 2.63082 seconds (Sampling)\n", "# 5.52086 seconds (Total)\n", "# \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The following numerical problems occured the indicated number of times after warmup on chain 1\n", " count\n", "Exception thrown at line 26: student_t_log: Degrees of freedom parameter is inf, but must be finite! 2\n", "When a numerical problem occurs, the Metropolis proposal gets rejected.\n", "However, by design Metropolis proposals sometimes get rejected even when there are no numerical problems.\n", "Thus, if the number in the 'count' column is small, do not ask about this message on stan-users.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "SAMPLING FOR MODEL 'fb5681ca08d43f5523f250d52285c81f' NOW (CHAIN 2).\n", "\n", "Chain 2, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 2, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 2, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 2, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 2, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 2, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 2, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 2, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 2, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 2, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 2, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 2, Iteration: 2000 / 2000 [100%] (Sampling)# \n", "# Elapsed Time: 3.02454 seconds (Warm-up)\n", "# 2.4903 seconds (Sampling)\n", "# 5.51483 seconds (Total)\n", "# \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The following numerical problems occured the indicated number of times after warmup on chain 2\n", " count\n", "Exception thrown at line 26: student_t_log: Degrees of freedom parameter is inf, but must be finite! 1\n", "Exception thrown at line 26: student_t_log: Scale parameter is 0, but must be > 0! 1\n", "When a numerical problem occurs, the Metropolis proposal gets rejected.\n", "However, by design Metropolis proposals sometimes get rejected even when there are no numerical problems.\n", "Thus, if the number in the 'count' column is small, do not ask about this message on stan-users.\n" ] } ], "source": [ "# In R\n", "\n", "# Specify the data list that we will pass to Stan. This gives Stan everything declared in the data{} block. \n", "data_list_2 <- list(X = X, N = N, y = y_sim, P = P)\n", "\n", "# Call Stan. You'll need to give it either model_code (like the ones we defined above), a file (.stan file), \n", "# or a fitted Stan object (fit)\n", "# You should also pass Stan a data list, number of cores to estimate on (jupyter only has access to one), \n", "# the number of Markov chains to run (4 by default)\n", "# and number of iterations (2000 by default). \n", "# We use multiple chains to make sure that the posterior distribution that we converge on \n", "# is stable, and not affected by starting values. \n", "\n", "# The first time you run the models, they will take some time to compile before sampling. \n", "# On subsequent runs, it will only re-compile if you change the model code. \n", "\n", "incorrect_fit <- stan(model_code = incorrect_model, data = data_list_2, cores = 1, chains = 2, iter = 2000)\n", "correct_fit <- stan(model_code = correct_model, data = data_list_2, cores = 1, chains = 2, iter = 2000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now fit our two competing models to the data. What has been estimated? \n", "\n", "#### What do these fitted objects contain? \n", "\n", "If you are accustomed to estimating models using OLS, Maximum Likelihood or GMM, then you may expect point estimates for parameters: regression tables contain an estimate of the parameter along with some standard errors. Bayesians tend not to use point estimates, but instead estimate parameter _distributions_. For all but a few models, estimating posterior distributions cannot be done analytically, and so we use the Monte Carlo estimate of the distribution. \n", "\n", "Monte Carlo approximation is quite simple. Let's say a parameter $\\theta$ is distributed according to some distribution $\\mbox{SomeDistribution()}$ for which we have no analytical formula, but from which we can generate draws. We want to make inference about this distribution; we want its expected value, median, standard deviation, quantiles, etc. The Monte Carlo method allows us to make these inferences by simply generating many **independent** draws from the distribution and then calculating the statistic of interest from those draws. An important note: these draws are _from_ the distribution of interest; they will tend to come from the higher probability regions of the distribution. \n", "\n", "For example, we want to estimate the mean of $\\mbox{SomeDistribution()}$. All we need is a large number $N$ of independent draws of $\\theta_{k} \\sim \\mbox{SomeDistribution()}$, then we can estimate $E[\\theta] = \\frac{1}{N}\\sum_{n=1}^{N}\\theta_{n}$. The standard error of this estimate decreases at $O(1/\\sqrt{\\mbox{number of independent draws}})$. \n", "\n", "A fitted Stan object contains draws for every parameter. If the model has fitted correctly, these draws are from the posterior distribution of our model. _These are draws from a joint distribution; correlation between parameters will be present in the joint posterior even if it was not present in the priors._\n", "\n", "In the generated quantities{} block of the two models above, we also created two more variable types. \n", "\n", "- The first, log_lik, is the log-likelihood, which we use for model comparison. We obtain this value for each parameter draw, for each value of $y_{i}$. Thus if you have $N$ observations and iter parameter draws, this will contain $N\\times$ iter log-likelihood values (be warned if estimating models on a many data points). \n", "\n", "- The second, yhat, is a _posterior predictive draw_. For each parameter draw, we draw a plausible value of each observation of $y$ from the data model. So rather than each observation having a \"predicted value\", it now has a predictive distribution that takes into account both the regression residual _and_ uncertainty in the parameter estimates. \n", "\n", "\n", "#### Some remaining questions\n", "\n", "It is premature to take our fitted models and generate inference or prediction. A few questions remain: \n", "\n", "1. Did our estimates of the posterior distribution converge? \n", "2. Were there any other problems in the model fitting? \n", "3. Which model is better?\n", "\n", "### D) Checking model fit with shinystan\n", "\n", "To address questions 1 and 2 above, we need to examine the parameter draws from the model to check for a few common problems: \n", "\n", "- **Lack of mixing**. We have run several Markov chains using different starting values. This is a good way of working out whether we are actually converging on a common posterior distribution. If the chains don't \"mix\", then it's unlikely we're sampling from a well specified posterior. The most common reason for this error is a poorly specified model. \n", "- **Stationarity**. Markov chains should be covariance stationary (mean and variance of the chain should not depend on when you draw the observations). Non-stationarity is normally the consequence of a poorly specified model or insufficient number of iterations. \n", "- **Autocorrelation**. Especially in poorly specified or weakly identified models, a given draw of parameters can be highly dependent on the previous draw of the parameters. This results in Monte Carlo estimates not being reliable. If you have checked the model specification, then _thinning_ (keeping every n-th draw) is one approach to fixing the problem--though not ideal. [Reparameterising the model](http://mc-stan.org/documentation/) is normally the best approach to this problem. (See section 21 of the manual, on Optimizing Stan code). \n", "- **Divergent transitions**. In models with very curved or irregular posterior densities, we often get \"divergent transitions\". This means that the model may not have sampled well, and may require a respecification or changes to the sampling routine. The easiest way of addressing this issue is to use control = list(adapt_delta = 0.99) or some other number close to 1. This will slow down the model fitting, but can help with divergent transitions. \n", "\n", "To check all of these potential problems, we use a tool in R called shinystan. You can install it with install.packages(\"shinystan\", dependencies = T), and run it with shinystan::launch_shinystan(correct_fit). It will bring up an interactive session in your web browser within which you can explore the estimated parameters. More information on shinystan is available [here](https://github.com/stan-dev/shinystan/wiki).\n", "\n", "\n", "\n", "### E) Comparing models\n", "\n", "Let's start by looking at the model outputs. The draws from each parameter can be neatly summarized with print:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inference for Stan model: c6d682e386dc051417acee4ec59a765d.\n", "2 chains, each with iter=2000; warmup=1000; thin=1; \n", "post-warmup draws per chain=1000, total post-warmup draws=2000.\n", "\n", " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", "beta[1] 1.34 0 0.20 0.96 1.21 1.34 1.47 1.72 1655 1\n", "beta[2] -0.22 0 0.20 -0.61 -0.35 -0.22 -0.09 0.17 1767 1\n", "beta[3] 0.03 0 0.20 -0.37 -0.11 0.03 0.16 0.43 1821 1\n", "beta[4] -0.42 0 0.20 -0.82 -0.56 -0.43 -0.28 -0.04 2000 1\n", "beta[5] 0.77 0 0.19 0.40 0.64 0.77 0.91 1.16 1928 1\n", "beta[6] -1.49 0 0.19 -1.86 -1.61 -1.48 -1.37 -1.11 1724 1\n", "beta[7] 0.74 0 0.20 0.34 0.61 0.74 0.87 1.17 2000 1\n", "beta[8] -1.36 0 0.20 -1.74 -1.50 -1.36 -1.22 -0.96 1886 1\n", "beta[9] 0.67 0 0.20 0.29 0.53 0.68 0.82 1.04 2000 1\n", "beta[10] -0.19 0 0.20 -0.58 -0.32 -0.19 -0.05 0.18 1879 1\n", "sigma 6.27 0 0.14 6.01 6.18 6.27 6.36 6.56 1588 1\n", "\n", "Samples were drawn using NUTS(diag_e) at Wed Mar 23 03:20:26 2016.\n", "For each parameter, n_eff is a crude measure of effective sample size,\n", "and Rhat is the potential scale reduction factor on split chains (at \n", "convergence, Rhat=1).\n" ] } ], "source": [ "# In R:\n", "\n", "print(incorrect_fit, pars = c(\"beta\", \"sigma\"))\n", "# Notice that we specify which parameters we want; else we'd get values for log_lik and yhat also\n", "\n", "# Some things to note: \n", "\n", "# - mean is the mean of the draws for each observation\n", "# - se_mean is the Monte Carlo error (standard error of the Monte Carlo estimate from the true mean)\n", "# - sd is the standard deviation of the parameter's draws\n", "# - the quantiles are self-explanatory\n", "# - n_eff is the effective number of independent draws. If there is serial correlation between sequential draws, \n", "# the draws cannot be considered independent. In Stan, high serial correlation is typically a problem in \n", "# poorly specified models\n", "# - Rhat: this is the Gelman Rubin convergence diagnostic. Values close to 1 indicate that the multiple chains\n", "# that you estimated have converged to the same distribution and are \"mixing\" well. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inference for Stan model: fb5681ca08d43f5523f250d52285c81f.\n", "2 chains, each with iter=2000; warmup=1000; thin=1; \n", "post-warmup draws per chain=1000, total post-warmup draws=2000.\n", "\n", " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", "beta[1] 1.19 0.00 0.19 0.82 1.06 1.19 1.32 1.55 2000 1\n", "beta[2] -0.14 0.00 0.18 -0.49 -0.26 -0.14 -0.02 0.22 2000 1\n", "beta[3] -0.04 0.00 0.17 -0.36 -0.16 -0.04 0.07 0.28 2000 1\n", "beta[4] -0.37 0.00 0.18 -0.72 -0.49 -0.37 -0.26 -0.03 2000 1\n", "beta[5] 0.89 0.00 0.17 0.54 0.77 0.89 1.00 1.23 2000 1\n", "beta[6] -1.59 0.00 0.17 -1.95 -1.70 -1.59 -1.47 -1.25 2000 1\n", "beta[7] 0.81 0.00 0.18 0.45 0.69 0.80 0.93 1.19 2000 1\n", "beta[8] -1.31 0.00 0.19 -1.68 -1.43 -1.31 -1.19 -0.95 2000 1\n", "beta[9] 0.63 0.00 0.18 0.29 0.51 0.63 0.75 0.99 2000 1\n", "beta[10] -0.14 0.00 0.18 -0.50 -0.27 -0.14 -0.01 0.21 2000 1\n", "sigma 4.98 0.01 0.20 4.58 4.84 4.98 5.12 5.37 1401 1\n", "nu 5.54 0.03 1.01 3.98 4.84 5.41 6.10 7.96 1455 1\n", "\n", "Samples were drawn using NUTS(diag_e) at Wed Mar 23 03:21:09 2016.\n", "For each parameter, n_eff is a crude measure of effective sample size,\n", "and Rhat is the potential scale reduction factor on split chains (at \n", "convergence, Rhat=1).\n" ] } ], "source": [ "# In R\n", "\n", "print(correct_fit, pars = c(\"beta\", \"sigma\", \"nu\"))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "It's often useful to plot the parameter estimates against the known values. If all is good, your estimated parameter densities should have a reasonable weight on the true value. " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No id variables; using all as measure variables\n" ] }, { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# In R\n", "\n", "# Declare a data frame that contains the known parameter names in one column variable and their known values\n", "known_parameters <- data_frame(variable = c(paste0(\"beta[\",1:P,\"]\"),\"sigma\", \"nu\"), real_value = c(beta, sigma, nu))\n", "\n", "\n", "# Extract params as a (draws * number of chains * number of params) array\n", "extract(correct_fit, permuted = F, pars = c(\"beta\", \"sigma\", \"nu\")) %>% \n", "# Stack the chains on top of one another and drop the chains label\n", " plyr::adply(2) %>% \n", " dplyr::select(-chains) %>% \n", "# Convert from wide form to long form (stack the columns on one another)\n", " melt() %>% \n", "# Perform a left join with the known parameters\n", " left_join(known_parameters, by = \"variable\") %>%\n", "# Generate the plot\n", " ggplot(aes(x = value)) + \n", " geom_density(fill = \"orange\", alpha = 0.5) + # Make it pretty\n", " facet_wrap(~ variable, scales = \"free\") +\n", " geom_vline(aes(xintercept = real_value), colour = \"red\") +\n", " ggtitle(\"Actual parameters and estimates\\ncorrectly specified model\\n\")\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No id variables; using all as measure variables\n" ] }, { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "extract(incorrect_fit, permuted = F, pars = c(\"beta\", \"sigma\")) %>% \n", "# Extract params as a (draws * number of chains * number of params) array\n", " plyr::adply(2) %>% \n", " dplyr::select(-chains) %>% \n", "# Stack the chains on top of one another and drop the chains label\n", " melt() %>% \n", " left_join(known_parameters, by = \"variable\") %>% # Join the known parameter table\n", "# Convert from wide form to long form (stack the columns on one another)\n", "# Write out the plot\n", " ggplot(aes(x = value)) + \n", " geom_density(fill = \"orange\", alpha = 0.5) + # Make it pretty\n", " facet_wrap(~ variable, scales = \"free\") + # small sub-plots of each variable\n", " geom_vline(aes(xintercept = real_value), colour = \"red\") + # red vertical lines for the known parameters\n", " ggtitle(\"Actual parameters and estimates\\nincorrectly specified model\\n\") # A title" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the moment, it seems as though both our models have done about as good a job at estimating the regression coefficients $\\beta$ as one another. But the incorrectly specified model severely overestimates $\\sigma$. This makes sense--a Student-t distribution with $\\nu=5$ will have fat tails, and so a normal distribution will try to replicate the extreme values by having a large variance. \n", "\n", "How else might we compare the two models? \n", "\n", "One approach is to use the loo package. The idea of this package is to approximate each model's leave-one-out (LOO) cross-validation error, allowing model comparison by the \"LOO Information Criterion\". This package estimates $\\sum_{n = 1}^{N}\\log p(y_{n} \\, | \\, y_{1}, ..., y_{n-1}, y_{n+1}, ..., y_{N})$ without re-estimating the model N times. A big upside of this approach is that it enables us to generate probabilistic estimates of the degree to which each model is most likely to produce the best predictions. We use loo like so: " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Incorrect model\"\n", "Computed from 2000 by 1000 log-likelihood matrix\n", "\n", " Estimate SE\n", "elpd_loo -3261.7 33.4\n", "p_loo 11.9 1.1\n", "looic 6523.3 66.7\n", "\n", "All Pareto k estimates OK (k < 0.5)\n" ] }, { "data": { "text/html": [ "'\n", "\n", "Correct model'" ], "text/latex": [ "'\n", "\n", "Correct model'" ], "text/markdown": [ "'\n", "\n", "Correct model'" ], "text/plain": [ "[1] \"\\n\\nCorrect model\"" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Computed from 2000 by 1000 log-likelihood matrix\n", "\n", " Estimate SE\n", "elpd_loo -3226.6 29.1\n", "p_loo 11.8 0.5\n", "looic 6453.2 58.2\n", "\n", "All Pareto k estimates OK (k < 0.5)\n", "elpd_diff se \n", " 35.05 12.51 \n" ] } ], "source": [ "# in R\n", "\n", "library(loo) # Load the library\n", "\n", "# Extract the log likelihoods of both models. Note that we need to declare log_lik in the generated quantities {} block\n", "llik_incorrect <- extract_log_lik(incorrect_fit, parameter_name = \"log_lik\")\n", "llik_correct <- extract_log_lik(correct_fit, parameter_name = \"log_lik\")\n", "\n", "# estimate the leave-one-out cross validation error \n", "loo_incorrect <- loo(llik_incorrect)\n", "loo_correct <- loo(llik_correct)\n", "\n", "# Print the LOO statistics\n", "print(\"Incorrect model\")\n", "print(loo_incorrect)\n", "sprintf(\"\\n\\nCorrect model\")\n", "print(loo_correct)\n", "\n", "# Print the comparison between the two models\n", "print(compare(loo_incorrect, loo_correct), digits = 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The statistic elpd_loo is the expected log pointwise predictive density. It is a rough analogy to the log likelihood of a model. p_loo gives us the effective number of parameters. We can multiply elpd_loo by $-2$ to calculate the looic, which you can think of like AIC or BIC (on the deviance scale), but coming from our Bayesian framework. For further details on these statistics, please consult [this paper](http://arxiv.org/abs/1507.04544). \n", "\n", "The elpd_diff gives us the expected difference in log posterior density between the two (or more) models. A value for elpd_diff greater than zero indicates that the second model generates more plausible predictions than the first model--which is precisely what we expect. \n", "\n", "### F) Generating posterior predictions for our model\n", "\n", "Now we have settled on a model to use for inference and/or prediction, we can go about producing some predictions. \n", "\n", "As mentioned above, in Bayesian analysis we do not have _predicted values_ but _predictive distributions_. \n", "\n", "Recall our data model: $y_{i} \\sim \\mbox{Student-t}(\\nu, X_{i}\\beta, \\sigma)$. Under this model, we are assuming fixed values of the parameters. But we have just estimated a model for which we have many plausible values of $\\nu$, $\\beta$ and $\\sigma$. These plausible values come from our _posterior distribution_. This brings us to the _posterior predictive distribution_. \n", "\n", "Posterior predictions are constructed by: \n", "1. Drawing a set of parameters $\\theta^{\\mathrm{draw}}$ from the posterior distribution\n", "2. For each observation $i$, draw a value $y_{i}^{\\mathrm{sim}}$ from $p(y\\, |\\, \\theta^{\\mathrm{draw}}, X_{i})$\n", "3. Repeat for all parameter draws. \n", "\n", "\n", "For each data point, we end up with as many plausible outcomes as we have draws from the posterior. **These draws take into account both the expected variation in the data model and also our posterior uncertainty**. This allows us use the Monte Carlo method to calculate statistics for prediction. \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No id variables; using all as measure variables\n" ] }, { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# In R\n", "\n", "known_y <- data_frame(variable = paste0(\"y_sim[\",1:N,\"]\"), real_y = y_sim)\n", "\n", "\n", "# Extract params as a (draws * number of chains * number of params) array\n", "plot_data <- extract(correct_fit, permuted = F, pars = c(\"y_sim\")) %>%\n", " plyr::adply(2) %>% \n", " dplyr::select(-chains) %>% \n", "# Stack the chains on top of one another and drop the chains label\n", " melt() %>% \n", " left_join(known_y, by = \"variable\") %>% # Join the known parameter table\n", "# Convert from wide form to long form (stack the columns on one another)\n", "# Write out the plot\n", " group_by(variable) %>%\n", " summarise(median = median(value),\n", " lower = quantile(value, 0.025),\n", " upper = quantile(value, 0.975),\n", " actual = first(real_y)) \n", "\n", "plot_data %>%\n", " ggplot(aes(x = median)) + \n", " geom_ribbon(aes(ymin = lower, ymax = upper), fill = \"orange\", alpha = 0.5) + \n", " geom_line(aes(y = median)) +\n", " geom_point(aes(y = actual)) +\n", " ggtitle(\"Actual outcomes and 95% posterior predictive interval\\n\") # A title" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what proportion of actual values fell within the 95% posterior predictive interval? \n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
proportion_within_95pc
10.946
\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & proportion_within_95pc\\\\\n", "\\hline\n", "\t1 & 0.946\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [1 x 1]\n", "\n", " proportion_within_95pc\n", " (dbl)\n", "1 0.946" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_data %>% summarize(proportion_within_95pc = mean(actual>=lower & actual<=upper))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's not too bad! " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### G) Getting help\n", "\n", "Stan has a well-written manual and thriving online community. I would recommend subscribing to the [Stan Users Group](https://groups.google.com/forum/#!forum/stan-users), which has daily email threads on a wide range of modeling problems and coding issues. \n", "\n", "The [Stan modeling language manual and example models](http://mc-stan.org/documentation/) also contains a huge amount of information on the language, as well as a variety of common models that are ready to go. \n", "\n", "If you are estimating generalised linear models and varying-intercept, varying-slope models, you should use [rstanarm](http://mc-stan.org/interfaces/rstanarm.html). This package uses Stan in the back-end, but does not require the user to write Stan models. \n", "\n", "\n", "### Coming up\n", "\n", "This concludes this introductory tutorial to Stan. I'd love your feedback on how to\n", "\n", "a) make it easier to follow\n", "b) hear about what sort of tutorials you would like in the future. I am planning to write some on: \n", "\n", "- Basic time-series models\n", "- Instrumental variables\n", "- VAR/SVAR\n", "- GARCH/M-GARCH\n", "- Time-varying parameter models\n", "- Reparameterizing models for high-performance sampling.\n", "\n", "If you have any thoughts on which you'd like next, please drop me a line at james@lendable.io\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.3" } }, "nbformat": 4, "nbformat_minor": 0 }