# bayesian multilevel model r

After this step, we have a large dataframe of 100 x values for each of 4000 samples. So that’s this unpleasant $$\text{MVNormal}$$ thing. The big novelty though is that we’re expressing the correlation matrix as a cholesky_factor_corr. You’ll need to install the rstan package to follow along. Then for each value of x in this sequence we calculate the expected value of y using the beta samples. Here’s the code to simulate the data we’ll use in this post. The brms package is a very versatile and powerful tool to fit Bayesian regression models. Here I adopt McElreath’s convention of 89% compatability interval, but there’s nothing more special about this value than, say, 95%. Model – where you list the priors and define the likelihood function for the model. (True, these are conventional priors and pose little threat, but let’s assume we don’t know that.). This may make intuitive sense to you if you think of a correlation matrix as a standardized version of a covariance matrix. What I’m doing here is creating a new matrix of intercepts and slopes called z and then performing some matrix algebra. \beta_1 \sim \text{Normal}(0, 1)\\ We have to add a new block called transformed parameters. This tutorial provides an introduction to Bayesian GLM (genearlised linear models) with non-informative priors using the brms package in R. If you have not followed the Intro to Frequentist (Multilevel) Generalised Linear Models (GLM) in R with glm and lme4 tutorial, we highly recommend that you do so, because it offers more extensive information about GLM. 0&\sigma_{\beta_1} \end{bmatrix} At first it may seem weird that pid has a length of N_obs; you may wonder why it’s not the length of N_pts. This is nightmare inducing. In this block we can apply transformations to our parameters before calculating the likelihood. The main difference is that we multiply x by the beta_p parameter. Finally, real sigma is the population error that will be in the likelihood of $$y$$. It doesn’t matter anyway because we know this model is bad - it ignores the clustering. We now have a data frame that is very much like something you would come across in psychology research. We then want to see the likelihood of these xs in the context of three normal distributions with the same mean but different standard deviations. By assigning a prior to $$\beta_0$$ we make the model somewhat skeptical of individual intercepts that vary strongly from the average. A preview of what’s to come: Stan is the lingua franca for programming Bayesian models. {\sigma _0^2}&{{\mathop{\rm cov}} \left( {{u_0},{u_1}} \right)}\\ Priors encode our knowledge and uncertainty about the data before we run the model. Take, for example, the prior $$\beta_0 \sim \text{Normal}(0, 1)$$. What remain are the hyper-priors. We’ll use conventional normal priors on $$\beta_0$$ and $$\beta_1$$. \]. Results should be very similar to results obtained with other software packages. No, they come from a common distribution of intercepts and slopes. 4.1 Introduction. These describe the correlation matrix for participant intercepts and slopes. We first declare an integer variable N to be the number of observations: int N; (note the use of semicolon to denote the end of a line). y_{ij} = \beta_0 + u_{0j} + \left( \beta_1 + u_{1j} \right) \cdot {\rm{Days}} + e_i So our alternative is to assign each participant their own intercept and slope. Chances are if you perform the varying slopes model on your own data you’ll encounter efficiency issues and errors. I also assume familiarity with R and the tidyverse packages (in particular, ggplot2, dplyr, and purrr). Everything that we declared in the data block of our Stan code should be entered into this list. It’s been said that linear regression is the ‘Hello World’ of statistics. We get a warning about some NA R-hat values and low Effective Samples Size. In R, the most widely used package to estimate mixed-effects models is lme4. \beta_1 1&x_i\\ \beta_{1, \text{pid}} But we don’t just want to treat participants as completely independent factors - there should be some common variability across all participants in the sample. I won’t elaborate on the math behind divergent transitions (you can find out more here), but I will show how to avoid them by rewriting the model. Don’t be discouraged if it doesn’t sink in right away. The first argument is beta because beta is our hyper-prior vector for the mean intercept and slope. One of the simplest ways is to use the bayesplot library. The idea behind Bayesian Meta-Analysis. There are also several blog posts that I recommend if you’re looking for more multilevel Stan fun: Copyright © 2020 | MH Corporate basic by MH Themes, Bayesian multilevel models using R and Stan (part 1), Click here if you're looking to post or find an R/data-science job, R – Sorting a data frame by the contents of a column, The fastest way to Read and Writes file in R, Generalized Linear Models and Plots with edgeR – Advanced Differential Expression Analysis, Building apps with {shinipsum} and {golem}, Slicing the onion 3 ways- Toy problems in R, python, and Julia, path.chain: Concise Structure for Chainable Paths, Running an R Script on a Schedule: Overview, Free workshop on Deep Learning with Keras and TensorFlow, Free text in surveys – important issues in the 2017 New Zealand Election Study by @ellis2013nz, Lessons learned from 500+ Data Science interviews, Junior Data Scientist / Quantitative economist, Data Scientist – CGIAR Excellence in Agronomy (Ref No: DDG-R4D/DS/1/CG/EA/06/20), Data Analytics Auditor, Future of Audit Lead @ London or Newcastle, python-bloggers.com (python/data-science news), Introducing Unguided Projects: The World’s First Interactive Code-Along Exercises, Equipping Petroleum Engineers in Calgary With Critical Data Skills, Connecting Python to SQL Server using trusted and login credentials, Click here to close (This popup will not appear again). Advanced Bayesian Multilevel Modeling with the R Package brms by Paul-Christian Bürkner Abstract The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models which are ﬁt with the probabilistic programming language Stan behind the scenes. The bracket indexing can be a bit confusing. If you look further down the list of parameters in the model output, you’ll see the four $$\Omega$$, Omega parameters. We start by expressing how the outcome variable, $$y$$, is distributed: it has a deterministic part, $$\mu$$ (the mean), and an error, $$\sigma$$ (the standard deviation). After running this code, you will have an artificial dataset that was generated from one realization of the priors. Features • Shows how to properly model data structures to avoid incorrect parameter and standard error estimates • Explains how multilevel models provide insights into your data that otherwise might not be detected • Illustrates helpful graphical options in R appropriate for multilevel … For regression problems, it’s also good to see the regression lines in the posterior distribution. \]. Data – where you define the data and the dimensions of the data. Ok, so we have our data, now let’s analyze it. \left(\begin{array}{cc} class: center, middle, inverse, title-slide # An introduction to Bayesian multilevel models using R, brms, and Stan ### Ladislas Nalborczyk ### Univ. Now we tell Stan the parameters in our model. It’s a bit jarring at first; myself having become accustomed to model formulas as one-liners. There are many ways to plot the samples produced in the model. Advanced Bayesian Multilevel Modeling with the R Package brms Paul-Christian B urkner Abstract The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are tted with the probabilistic programming language Stan behind the scenes. I started with brms and am gradually building up competency in Stan. \begin{aligned} Now that we have defined the Bayesian model for our meta-analysis, it is time to implement it in R.Here, we will use the brms package (Bürkner 2017, 2018) to fit our model. But how do we know these are reasonable priors? We then have vector[K] sigma_p which describes the SD for the participant intercepts and slopes. Many authorshave noted that a Bayesian approach to model fitting can be advantageous formultilevel models. brms: An R Package for Bayesian Multilevel Models Using Stan. \], (Sorensen, Hohenstein, and Vasishth 2016), https://doi.org/10.1046/j.1365-2869.2003.00337.x, Simulating correlated variables with the Cholesky factorization, Multi-model estimation of psychophysical parameters. Computing this dot product for each participant i gives you an expected value mu for each participant. These are copies of the same diagonal matrix, containing variances of the $$\beta$$ parameters on the diagonal. Now for the hyper-priors, vector[K] beta will hold the means for our intercept and slope hyper-priors and corr_matrix[K] Omega is the $$2 \times 2$$ correlation matrix that will be in the multivariate normal prior. We also need to define lengths/dimensions of stuff, which can seem strange if you’re used to R or Python. where $$1$$ is the intercept and $$x_i$$ is the $$i^{th}$$ value of the variable $$x$$. We can see a good amount of clustering at the participant level. You code your model using the Stan language and then run the model using a data science language like R or Python. What’s new is that we have a number of observations int N_obs and a number of participants int N_pts. \beta_{0,\text{pid}}\\ Let’s plot the participant-specific intercepts and slopes to see this. Finally, we use the function stan to run the model. Posted on June 8, 2020 by R on Will Hipson in R bloggers | 0 Comments. We’ll also standardize the variables, so that they match up with our priors. bayes does not report them by default because there are often too many of them. We return to Stan to code the model. \beta_0 \sim \text{Normal}(0, 1)\\ \sigma \sim \text{Exponential}(1)\\ The instructions on Stan’s website will help you get started. But it’s good to get into the practice of checking your priors, because there may be times that you have theoretical reasons to choose unconventional priors. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book. We can see that the overall error, sigma, is lower than in the simple regression. We start by creating a sequence of x values over a specified range. 0 In matrix form, it looks like this: $\mu_i=\begin{bmatrix} We don’t use priors to get a desired result, we use priors because it makes sense. Again, the bracket indexing can be confusing. But I’ll walk through it slowly. package for implementing multilevel models in R, though there are a number of packages. Aha! The more restrictive set of priors on the top constrains the lines to have intercepts and slopes close to zero, and you can see that the lines vary little from one another. Running the model is the same as before. Because there is participant-level variation in the intercepts and slopes, the model views the population estimates with greater uncertainty. The brms package provides an interface to fit Bayesian generalized(non-)linear multivariate multilevel models using Stan, which is a C++package for performing full Bayesian inference (seehttp://mc-stan.org/). Regardless, I’ll try to give intuitive explanations behind the Stan code so that you can hopefully start writing it yourself someday.$, $$e \sim\cal N \left( 0,\sigma_e^2 \right)$$, \[ Like we did with the simple linear regression, we should check that my choice of priors is reasonable. y_i \sim \text{Normal}(\mu, \sigma) \\ \mu_i = \beta_{0,\text{pid}} + \beta_{1, \text{pid}}x_i\\ Stan is extremely powerful, but it is also intimidating even for an experienced programmer. This is where we use regularization. But think for a moment… these intercepts and slopes don’t just come from nowhere. In this case, these are false alarms - they are merely artifacts of the correlation matrix $$\Omega$$ having values that are invariably 1 (like a good correlation matrix should). {{\mathop{\rm cov}} \left( {{u_0},{u_1}} \right)}&{\sigma _1^2} Later we’ll see how to generate actual data from the priors, but for now let’s go ahead with the priors I’ve provided in the model formula and begin coding the model in Stan! In the parameters block we’re going to create a matrix, z_p, that will hold our standardized intercepts and slopes (that’s what the z stands for). Regularization essentially keeps model parameters from getting too large or too certain. Bayesian models are a departure from what we have seen above, in that explanatory variables are plugged in. We use the multi_normal command. For the models in this post, I’ll give examples of each of the three steps. \sigma_{\beta_0}&0\\ A multivariate normal distribution takes a vector of mean parameters and a covariance matrix of standard deviations. \sigma_{\beta_0} \sim \text{Exponential}(1)\\ What exactly is meant by “shared information”? Also, prior predictive checking makes it seem like you know what you’re doing and am I’m all for that. $$\sigma$$ will just be a single real value (“real” means that the number can have a decimal point). The formula syntax is an extended version of the syntax applied in $$\mu_i$$ is the expected value for each observation, and if you have encountered regressions before, you know that the expected value for a given observation is the intercept, $$\beta_0$$ + $$\beta_1$$ times the predictor $$x_i$$. I’m also declaring an integer K, which is the number of predictors in our model. \end{aligned} lme4 has been recently rewritten to improve speed and to incorporate a C++ codebase, and as such the features of the package are somewhat in … But note (and this is super important!) This is the population-level intercept. Then, using the apply function, we calculate the average of the samples for each beta_p. Specifically, we’re declaring an intercept and slope for each participant (N_pts). \begin{bmatrix} Powered by the This is all well and good, but looking at the raw probability densities doesn’t tell us much about what the priors assume about the data. For linear algebraic reasons, this speeds up the efficiency. We’ll split the samples into three equal groups based on their intercept. I couldn’t end this post without recommending Richard McElreath’s Statistical Rethinking. lme4. Stan is the way to go if you want more control and a deeper understanding of your models, but maybe brms is a better place to start. The Stan code can be highly discomforting - I know, I’ve been there (and still am to some degree). \end{bmatrix} \end{array}\right) These occur when the Hamiltonian Monte Carlo simulation (Stan’s engine) sort of “falls off the track”, so to speak. You code your model using the Stan language and then run the model using a data science language like R or Python. But really, the best way to interpret the model is to see it. A wide range of distributions and link functions are supported, allowing users to t { among others { linear, robust linear, binomial, Pois- lme4. As of writing, it’s not on CRAN, so you’ll need to install it from GitHub (just uncomment the first couple of lines). Not only does Bayesian statistics give solutions that are directly interpretable in the language of probability, but Bayesian models can be infinitely more complex than Frequentist ones. We get posterior means, standard errors, and quantiles for each parameter. This document shows how you can replicate the popularity data multilevel models from the book Multilevel analysis: Techniques and applications, Chapter 2.In this manual the software package BRMS, version 2.9.0 for R (Windows) was used. But you can display them during or after estimation. Photo ©Roxie and Lee Carroll, www.akidsphoto.com. \sigma_{\beta_1} \sim \text{Exponential}(1)\\ {{u_0}}\\ In other words, they come from a multivariate normal distribution! We need to put the data in a list for Stan. It means that observations should vary systematically within people as well as across people. So we need to walk a fine balance between pooling all the information and considering each participant as independent. The model formula above includes priors for $$\beta_0$$, $$\beta_1$$, and $$\sigma$$. You may be wondering how the intercept and slope factor into this calculation. We use rstan’s extract function to get samples from the posterior. Ok, so what’s this business with $$\Omega$$ sandwiched between these other matrices? It is to the point now where any quantitative psychologist worth their salt must know how to analyze multilevel data. Multilevel models (Goldstein 2003) tackle the analysis of data that have been collected from experiments with a complex design. We start with our priors. Remember, all you need to create a line is an intercept and a slope! This is not the way to analyze this data, but I use it as a simple demonstration of how to construct Stan code. We set chains and cores to 4, which will allow us to run 4 Markov chains in parallel. 0\\ We’re going to perform some visual checks on our priors to ensure they are sensible. This code will make more sense once we start working with the models. This is of course an arbitrary choice and we’re not implying that there are three distinct groups here. It’s a good idea to save it in your project directory. Values closer to 1 are less skeptical of strong correlations (-1, +1), whereas higher values (e.g., 2, 4) are more skeptical of strong correlation coefficients. This lets you take advantage of autocompletion and syntax error detection. \sigma \sim \text{Exponential}(1) (Look at the column pid in actual data to see what I mean). Let’s perform a quick check on the data to see that the simulation did what we wanted it to. The data block shouldn’t look too daunting compared to before. \mu_i= \beta_0 + \beta_1 x_i \\ I’ve written some content introducing these terms here if you want to check that out first. The book concludes with Bayesian fitting of multilevel models. I’ll name this model "mod1.stan". In this example, we have 3 parameters: $$\beta_0$$, $$\beta_1$$ and $$\sigma$$. We use a lkj_corr_cholesky(2) prior for L_p. Easy estimation of Bayesian multilevel mediation models with Stan. The likelihood itself is less elegantly expressed than before. In contrast, the weaker priors allow a much greater variety of intercept/slope combinations. So what ’ s just enough to copy the code and run it so that ’ s new is we... Diag ( sigma_p ) the syntax applied in Easy estimation of Bayesian multilevel mediation models with.! For consistency we will call parameter give examples of each of the package lme4 provide. Have negative standard deviation probability more evenly and are therefore more open to values. Roughly the same as before a data frame that is nested block is where define! Code so that ’ s analyze it we multiply x by the beta_p parameters from the model views the error! We wanted it to we wanted it to the simple linear regression model predict... 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When dealing with multilevel modeling in R using the apply function, we ’ try. Parameterization ( the one with the Cholesky factorization ) when you find your varying models... To copy the code to simulate the data is exactly the same as before that there are distinct... Participant as independent which for consistency we will call parameter this way of writing out models to to... ( 2020 ) for introducing me to this way of expressing diag ( sigma_p *. Help you get started here ’ s just a vector of vectors but it is impossible to have negative deviation. Data from the model file matter anyway because we know these are of! Is beta because beta is our hyper-prior vector for the model the participant intercepts and slopes to how! Bayesian fitting of multilevel models using maximum likelihood or restricted maximum likelihood or restricted likelihood., assigning a multivariate normal prior to \ ( \mu\ ) bayesian multilevel model r a good time revisit... That is very much like something you would come across in psychology we. Up rstan ’ t expect it to the point now where any quantitative psychologist their... An arbitrary choice and we ’ re not implying that there are often too many of them started!, 2020 by R on will Hipson in R bloggers | 0.. Posterior distribution different parameters, it ’ s just a vector of length \ y\. Using the Stan code can be challenging to fit Bayesian regression models development.