scala-glm: Regression modelling in Scala


As discussed in the previous post, I’ve recently constructed and delivered a short course on statistical computing with Scala. Much of the course is concerned with writing statistical algorithms in Scala, typically making use of the scientific and numerical computing library, Breeze. Breeze has all of the essential tools necessary for building statistical algorithms, but doesn’t contain any higher level modelling functionality. As part of the course, I walked through how to build a small library for regression modelling on top of Breeze, including all of the usual regression diagnostics (such as standard errors, t-statistics, p-values, F-statistics, etc.). While preparing the course materials it occurred to me that it would be useful to package and document this code properly for general use. In advance of the course I packaged the code up into a bare-bones library, but since then I’ve fleshed it out, tidied it up and documented it properly, so it’s now ready for people to use.

The library covers PCA, linear regression modelling and simple one-parameter GLMs (including logistic and Poisson regression). The underlying algorithms are fairly efficient and numerically stable (eg. linear regression uses the QR decomposition of the model matrix, and the GLM fitting uses QR within each IRLS step), though they are optimised more for clarity than speed. The library also includes a few utility functions and procedures, including a pairs plot (scatter-plot matrix).

A linear regression example

Plenty of documentation is available from the scala-glm github repo which I won’t repeat here. But to give a rough idea of how things work, I’ll run through an interactive session for the linear regression example.

First, download a dataset from the UCI ML Repository to disk for subsequent analysis (caching the file on disk is good practice, as it avoids unnecessary load on the UCI server, and allows running the code off-line):

import scalaglm._
import breeze.linalg._

val url = ""
val fileName = "self-noise.csv"

// download the file to disk if it hasn't been already
val file = new
if (!file.exists) {
  val s = new
  val data =
  data.foreach(l => s.write(l.trim.
    split('\t').filter(_ != "").
    mkString("", ",", "\n")))

Once we have a CSV file on disk, we can load it up and look at it.

val mat = csvread(new
// mat: breeze.linalg.DenseMatrix[Double] =
// 800.0    0.0  0.3048  71.3  0.00266337  126.201
// 1000.0   0.0  0.3048  71.3  0.00266337  125.201
// 1250.0   0.0  0.3048  71.3  0.00266337  125.951
// ...
println("Dim: " + mat.rows + " " + mat.cols)
// Dim: 1503 6
val figp = Utils.pairs(mat, List("Freq", "Angle", "Chord", "Velo", "Thick", "Sound"))
// figp: breeze.plot.Figure = breeze.plot.Figure@37718125

We can then regress the response in the final column on the other variables.

val y = mat(::, 5) // response is the final column
// y: DenseVector[Double] = DenseVector(126.201, 125.201, ...
val X = mat(::, 0 to 4)
// X: breeze.linalg.DenseMatrix[Double] =
// 800.0    0.0  0.3048  71.3  0.00266337
// 1000.0   0.0  0.3048  71.3  0.00266337
// 1250.0   0.0  0.3048  71.3  0.00266337
// ...
val mod = Lm(y, X, List("Freq", "Angle", "Chord", "Velo", "Thick"))
// mod: scalaglm.Lm =
// Lm(DenseVector(126.201, 125.201, ...
// Estimate	 S.E.	 t-stat	p-value		Variable
// ---------------------------------------------------------
// 132.8338	 0.545	243.866	0.0000 *	(Intercept)
//  -0.0013	 0.000	-30.452	0.0000 *	Freq
//  -0.4219	 0.039	-10.847	0.0000 *	Angle
// -35.6880	 1.630	-21.889	0.0000 *	Chord
//   0.0999	 0.008	12.279	0.0000 *	Velo
// -147.3005	15.015	-9.810	0.0000 *	Thick
// Residual standard error:   4.8089 on 1497 degrees of freedom
// Multiple R-squared: 0.5157, Adjusted R-squared: 0.5141
// F-statistic: 318.8243 on 5 and 1497 DF, p-value: 0.00000
val fig = mod.plots
// fig: breeze.plot.Figure = breeze.plot.Figure@60d7ebb0

There is a .predict method for generating point predictions (and standard errors) given a new model matrix, and fitting GLMs is very similar – these things are covered in the quickstart guide for the library.


scala-glm is a small Scala library built on top of the Breeze numerical library which enables simple and convenient regression modelling in Scala. It is reasonably well documented and usable in its current form, but I intend to gradually add additional features according to demand as time permits.

Statistical computing with Scala free on-line course

I’ve recently delivered a three-day intensive short-course on Scala for statistical computing and data science. The course seemed to go well, and the experience has convinced me that Scala should be used a lot more by statisticians and data scientists for a range of problems in statistical computing. In particular, the simplicity of writing fast efficient parallel algorithms is reason alone to take a careful look at Scala. With a view to helping more statisticians get to grips with Scala, I’ve decided to freely release all of the essential materials associated with the course: the course notes (as PDF), code fragments, complete examples, end-of-chapter exercises, etc. Although I developed the materials with the training course in mind, the course notes are reasonably self-contained, making the course quite suitable for self-study. At some point I will probably flesh out the notes into a proper book, but that will probably take me a little while.

I’ve written a brief self-study guide to point people in the right direction. For people studying the material in their spare time, the course is probably best done over nine weeks (one chapter per week), and this will then cover material at a similar rate to a typical MOOC.

The nine chapters are:

1. Introduction
2. Scala and FP Basics
3. Collections
4. Scala Breeze
5. Monte Carlo
6. Statistical modelling
7. Tools
8. Apache Spark
9. Advanced topics

For anyone frustrated by the limitations of dynamic languages such as R, Python or Octave, this course should provide a good pathway to an altogether more sophisticated, modern programming paradigm.

Stochastic Modelling for Systems Biology, second edition

The second edition of my textbook, Stochastic Modelling for Systems Biology was published on 7th November, 2011. One of the new features introduced into the new edition is an R package called smfsb which contains all of the code examples discussed in the text, which allow modelling, simulation and inference for stochastic kinetic models. The smfsb R package is the main topic of this post, but it seems appropriate to start off the post with a quick introduction to the book, and the main new features of the second edition.

The first edition was published in April 2006. It provided an introduction to mathematical modelling for systems biology from a stochastic viewpoint. It began with an introduction to biochemical network modelling, then moved on to probability theory, stochastic simulation and Markov processes. After providing all of the necessary background material, the book then introduced the theory of stochastic kinetic modelling and the Gillespie algorithm for exact discrete stochastic event simulation of stochastic kinetic biochemical network models. This was followed by examples and case studies, advanced simulation algorithms, and then a brief introduction to Bayesian inference and its application to inference for stochastic kinetic models.

The first edition proved to be very popular, as it was the first self-contained introduction to the field, and was aimed at an audience without a strong quantitative background. The decision to target an applied audience meant that it contained only the bare essentials necessary to get started with stochastic modelling in systems biology. The second edition was therefore an opportunity not only to revise and update the existing material, but also to add in additional material, especially new material which could provide a more solid foundation for advanced study by students with a more mathematical focus. New material introduced into the second edition includes a greatly expanded chapter on Markov processes, with particular emphasis on diffusion processes and stochastic differential equations, as well as Kolmogorov equations, the Fokker-Planck equation (FPE), Kurtz’s random time change representation of a stochastic kinetic model, an additional derivation of the chemical Langevin equation (CLE), and a derivation of the linear noise approximation (LNA). There is now also discussion of the modelling of “extrinsic” in addition to “intrinsic” noise. The final chapters on inference have also been greatly expanded, including discussion of importance resampling, particle filters, pseudo-marginal “exact approximate” MCMC, likelihood-free techniques and particle MCMC for rate parameter inference. I have tried as far as possible to maintain the informal and accessible style of the first edition, and a couple of the more technical new sections have been flagged as “skippable” by less mathematically trained students. In terms of computing, all of the SBML models have been updated to the new Level 3 specification, and all of the R code has been re-written, extended, documented and packaged as an open source R package. The rest of this post is an introduction to the R package. Although the R package is aimed mainly at owners of the second edition, it is well documented, and should therefore be usable by anyone with a reasonable background knowledge of the area. In particular, the R package should be very easy to use for anyone familiar with the first edition of the book. The introduction given here is closely based on the introductory vignette included with the package.

smfsb: an R package for simulation and inference in stochastic kinetic models


The smfsb package provides all of the R code associated with the book, Wilkinson (2011). Almost all of the code is pure R code, intended to be inspected from the R command line. In order to keep the code short, clean and easily understood, there is almost no argument checking or other boilerplate code.


The package is available from CRAN, and it should therefore be possible to install from the R command prompt using


from any machine with an internet connection.

The package is being maintained on R-Forge, and so it should always be possible to install the very latest nightly build from the R command prompt with


but you should only do this if you have a good reason to, in order not to overload the R-Forge servers (not that I imagine downloads of this package are likely to overload the servers…).

Once installed, the package can be loaded ready for use with


Accessing documentation

I have tried to ensure that the package and all associated functions and datasets are properly documented with runnable examples. So,


will give a brief overview of the package and a complete list of all functions. The list of vignettes associated with the package can be obtained with


At the time of writing, the introductory vignette is the only one available, and can be accessed from the R command line with


Help on functions can be obtained using the usual R mechanisms. For example, help on the function StepGillespie can be obtained with


and the associated example can be run with


The sourcecode for the function can be obtained by typing StepGillespie on a line by itself. In this case, it returns the following R code:

function (N) 
    S = t(N$Post - N$Pre)
    v = ncol(S)
    return(function(x0, t0, deltat, ...) {
        t = t0
        x = x0
        termt = t0 + deltat
        repeat {
            h = N$h(x, t, ...)
            h0 = sum(h)
            if (h0 < 1e-10)
                t = 1e+99 
            else if (h0 > 1e+06) {
                t = 1e+99
                warning("Hazard too big - terminating simulation!")
                t = t + rexp(1, h0)
            if (t >= termt) 
            j = sample(v, 1, prob = h)
            x = x + S[, j]

A list of demos associated with the package can be obtained with


A list of data sets associated with the package can be obtained with


For example, the small table, mytable from the introduction to R in Chapter 4 can by loaded with


After running this command, the data frame mytable will be accessible, and can be examined by typing


at the R command prompt.

Simulation of stochastic kinetic models

The main purpose of this package is to provide a collection of tools for building and simulating stochastic kinetic models. This can be illustrated using a simple Lotka-Volterra predator-prey system. First, consider the prey, X_1 and the predator X_2 as a stochastic network, viz

R_1:\quad X_1 \longrightarrow 2 X_1
R_2:\quad X_1 + X_2\longrightarrow 2X_2
R_3:\quad X_2 \longrightarrow \emptyset.

The first “reaction” represents predator reproduction, the second predator-prey interaction and the third predator death. We can write the stoichiometries of the reactions, together with the rate (or hazard) of each reaction, in tabular form as

Reaction Pre Post Hazard
X_1 X_2 X_1 X_2 h()
R_1 1 0 2 0 \theta_1 x_1
R_2 1 1 0 2 \theta_2 x_1 x_2
R_3 0 1 0 0 \theta_3 x_2

This can be encoded in R as a stochastic Petri net (SPN) using

# SPN for the Lotka-Volterra system
         return(c(th1*x1, th2*x1*x2, th3*x2 ))

This object could be created directly by executing


since the LV model is one of the standard demo models included with the package. Functions for simulating from the transition kernel of the Markov process defined by the SPN can be created easily by passing the SPN object into the appropriate constructor. For example, if simulation using the Gillespie algorithm is required, a simulation function can be created with


This resulting function (closure) can then be used to advance the state of the process. For example, to simulate the state of the process at time 1, given an initial condition of X_1=50, X_2=100 at time 0, use


Alternatively, to simulate a realisation of the process on a regular time grid over the interval [0,100] in steps of 0.1 time units, use

out = simTs(c(x1=50,x2=100),0,100,0.1,stepLV)

which gives the resulting plot

See the help and runnable example for the function StepGillespie for further details, including some available alternative simulation algorithms, such as StepCLE.

Inference for stochastic kinetic models from time course data

Estimating the parameters of stochastic kinetic models using noisy time course measurements on some aspect of the system state is a very important problem. Wilkinson (2011) takes a Bayesian approach to the problem, using particle MCMC methodology. For this, a key aspect is the use of a particle filter to compute an unbiased estimate of marginal likelihood. This is accomplished using the function pfMLLik. Once a method is available for generating unbiased estimates for the marginal likelihood, this may be embedded into a fairly standard marginal Metropolis-Hastings algorithm for parameter estimation. See the help and runnable example for pfMLLik for further details, along with the particle MCMC demo, which can by run using demo(PMCMC). I’ll discuss more about particle MCMC and rate parameter inference in the next post.


  • Wilkinson, D. J. (2006) Stochastic Modelling for Systems Biology, Boca Raton, Florida: Chapman & Hall/CRC Press.
  • Wilkinson, D. J. (2011) Stochastic Modelling for Systems Biology, second edition, Boca Raton, Florida: Chapman & Hall/CRC Press.