# Fitting multivariate ODE models with brms

This article illustrates how ordinary differential equations and multivariate observations can be modelled and fitted with the brms package (Bürkner (2017)) in R1.

As an example I will use the well known Lotka-Volterra model (Lotka (1925), Volterra (1926)) that describes the predator-prey behaviour of lynxes and hares. Bob Carpenter published a detailed tutorial to implement and analyse this model in Stan and so did Richard McElreath in Statistical Rethinking 2nd Edition (McElreath (2020)).

Here I will use brms as an interface to Stan. With brms I can write the model using formulas similar to glm or lmer directly in R, avoiding to code up the model in Stan. Having said that, I will have to write a little bit of Stan for the ODEs and pass them on via the brm function.

# Data

The data to model shows the number of pelts in thousands of Canadian hares and lynxes from 1900 - 1920:

library(data.table)
Lynx_Hare <- data.table(
Year = 1900:1920,
Lynx = c(4, 6.1, 9.8, 35.2, 59.4, 41.7, 19, 13, 8.3, 9.1, 7.4, 8,
12.3, 19.5, 45.7, 51.1, 29.7, 15.8, 9.7, 10.1, 8.6),
Hare = c(30, 47.2, 70.2, 77.4, 36.3, 20.6, 18.1, 21.4, 22, 25.4,
27.1, 40.3, 57, 76.6, 52.3, 19.5, 11.2, 7.6, 14.6, 16.2, 24.7))
head(Lynx_Hare)
##    Year Lynx Hare
## 1: 1900  4.0 30.0
## 2: 1901  6.1 47.2
## 3: 1902  9.8 70.2
## 4: 1903 35.2 77.4
## 5: 1904 59.4 36.3
## 6: 1905 41.7 20.6

# Model

The classic Lotka-Volterra model is a system of two autonomous ordinary differential equations (ODEs), describing the interaction between hares and lynxes. The four ODE parameters related to the birth and mortality rates of the two populations. In addition, there are two parameters for the initial states at time $$t=0$$:

\begin{aligned} \frac{dH}{dt} & = (b_H - m_H L ) H \\ \frac{dL}{dt} & = (b_L H - m_L) L \\ H(0) & = H_0 \\ L(0) & = L_0 \\ \end{aligned}

Note, the data only show the number of pelts, i.e. the number of trapped and killed animals not the actual population statistics.

Assuming no population can become extinct and all parameters have to be positive, I will assume log-normal distributions for the process and the birth and mortality parameters.

# Implementation in brms

Load the R packages needed:

library(brms)
library(cmdstanr)
library(parallel)
nCores <- detectCores()
options(mc.cores = nCores)

## Data preperations

The data was presented as a table with two columns, showing the time series for the hares and lynxes separately - a multivariate time series.

However, in order to model this data with brms the data has to be transformed into a univariate time series. The trick is to introduce a dummy variable for the long format of the data. In addition I add a new time variable, which starts at 1, rather than 1900.

LH <- melt(data.table(Lynx_Hare), id.vars = "Year",
measure.vars = c("Lynx", "Hare"),
variable.name = "Population",
value.name = "Pelts")
LH[, := (
delta = ifelse(Population %in% "Lynx", 1, 0),
Population = factor(Population),
t = Year - min(Year) + 1)]
head(LH)
##    Year Population Pelts delta t
## 1: 1900       Lynx   4.0     1 1
## 2: 1901       Lynx   6.1     1 2
## 3: 1902       Lynx   9.8     1 3
## 4: 1903       Lynx  35.2     1 4
## 5: 1904       Lynx  59.4     1 5
## 6: 1905       Lynx  41.7     1 6

## ODE model in Stan

The implementation of the ODEs in Stan is straightforward, but note below the integration step and the use of the dummy variable delta from my data set and how I use it to select the relevant metric from the bivariate output of the integrated ODEs. For delta = 0 the LV function returns the hare component only and for delta = 1 the lynx component.

LotkaVolterra <- "
// Sepcify dynamical system (ODEs)
vector ode_LV(real t, vector y,  vector theta){
vector[2] dydt;

dydt[1] = (theta[1] - theta[2] * y[2] ) * y[1]; // Hare
dydt[2] = (theta[3] * y[1] - theta[4]) * y[2]; // Lynx

return dydt;
}
// Integrate ODEs and prepare output
real LV(real t, real Hare0, real Lynx0,
real brHare, real mrHare,
real brLynx, real mrLynx,
real delta){
vector[2] y0;     // Initial values
vector[4] theta;  // Parameters
array[1] vector[2]  y;   // ODE solution
// Set initial values
y0[1] = Hare0; y0[2] = Lynx0;
// Set parameters
theta[1] = brHare; theta[2] = mrHare;
theta[3] = brLynx; theta[4] = mrLynx;
// Solve ODEs
y = ode_rk45(ode_LV, y0, 0, rep_array(t, 1), theta);
// Return relevant population values
return (y[1,1] * (1 - delta) + y[1,2] * delta);
}
"

## Formula

To write the model formula in brms I make use of the nlf function, short for non-linear function, not only to map the median behaviour of the process, but also to transform standardised Normal(0, 1) priors to log-normal priors. In addition, I allow for different process variances for the two populations.

frml <-  bf(
Pelts ~ eta,
nlf(eta ~ log(
LV(t, Hare0, Lynx0,
brHare, mrHare,brLynx, mrLynx, delta)
)
),
nlf(Hare0 ~ 10 * exp(stdNHare0)),
nlf(Lynx0 ~ 10 * exp(stdNLynx0)),
nlf(brHare ~ 0.5 * exp(0.25 * stdNbrHare)),
nlf(mrHare ~ 0.025 * exp(0.25 * stdNmrHare)),
nlf(brLynx ~ 0.025 * exp(0.25 * stdNbrLynx)),
nlf(mrLynx ~ 0.8 * exp(0.25 * stdNmrLynx)),
stdNHare0 ~ 1,  stdNLynx0 ~ 1,
stdNbrHare ~ 1, stdNmrHare ~ 1,
stdNbrLynx ~ 1, stdNmrLynx ~ 1,
sigma ~ 0 + Population,
nl = TRUE)

## Priors

Where possible I try to stick to standardised Normal priors and use nlf to transform those to what I think are sensible ranges for the model.

mypriors <- c(
prior(normal(0, 1), nlpar = "stdNHare0"),
prior(normal(0, 1), nlpar = "stdNLynx0"),
prior(normal(0, 1), nlpar = "stdNbrHare"),
prior(normal(0, 1), nlpar = "stdNmrHare"),
prior(normal(0, 1), nlpar = "stdNbrLynx"),
prior(normal(0, 1), nlpar = "stdNmrLynx"),
prior(normal(-1, 0.5), class = "b",
coef= "PopulationHare",  dpar = "sigma"),
prior(normal(-1, 0.5), class = "b",
coef= "PopulationLynx",  dpar = "sigma")
)

## Model run

With the preparations done, I can start running the model with brm. I use cmdstan as the backend and feed the ODE model using the stanvars argument.

mod <- brm(
frml, prior = mypriors,
stanvars = stanvar(scode = LotkaVolterra, block = "functions"),
data = LH, backend = "cmdstan",
family = brmsfamily("lognormal", link_sigma = "log"),
control = list(adapt_delta = 0.99),
seed = 1234, iter = 1000,
chains = 4, cores = nCores,
file = "LotkaVolterraCMDStan.rds")

# Model output

The model run takes about 3 minutes on my 2020 M1 MacBook Air. The output looks promising:

mod
##  Family: lognormal
##   Links: mu = identity; sigma = log
## Formula: Pelts ~ eta
##          eta ~ log(LV(t, Hare0, Lynx0, brHare, mrHare, brLynx, mrLynx, delta))
##          Hare0 ~ 10 * exp(stdNHare0)
##          Lynx0 ~ 10 * exp(stdNLynx0)
##          brHare ~ 0.5 * exp(0.25 * stdNbrHare)
##          mrHare ~ 0.025 * exp(0.25 * stdNmrHare)
##          brLynx ~ 0.025 * exp(0.25 * stdNbrLynx)
##          mrLynx ~ 0.8 * exp(0.25 * stdNmrLynx)
##          stdNHare0 ~ 1
##          stdNLynx0 ~ 1
##          stdNbrHare ~ 1
##          stdNmrHare ~ 1
##          stdNbrLynx ~ 1
##          stdNmrLynx ~ 1
##          sigma ~ 0 + Population
##    Data: LH (Number of observations: 42)
##   Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
##          total post-warmup draws = 2000
##
## Population-Level Effects:
##                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## stdNHare0_Intercept      0.87      0.09     0.70     1.04 1.00     1294
## stdNLynx0_Intercept     -0.42      0.10    -0.62    -0.23 1.00     1103
## stdNbrHare_Intercept     0.20      0.35    -0.52     0.84 1.00      583
## stdNmrHare_Intercept     0.18      0.46    -0.75     1.06 1.00      671
## stdNbrLynx_Intercept    -0.07      0.44    -0.93     0.83 1.00      653
## stdNmrLynx_Intercept     0.09      0.33    -0.54     0.76 1.00      574
## sigma_PopulationLynx    -1.36      0.16    -1.66    -1.02 1.00     1585
## sigma_PopulationHare    -1.39      0.17    -1.70    -1.05 1.01     1202
##                      Tail_ESS
## stdNHare0_Intercept      1243
## stdNLynx0_Intercept      1199
## stdNbrHare_Intercept      833
## stdNmrHare_Intercept      961
## stdNbrLynx_Intercept      802
## stdNmrLynx_Intercept      742
## sigma_PopulationLynx     1307
## sigma_PopulationHare     1026
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Let’s take a look at the parameter distribution plot:

theme_update(text = element_text(family = "sans"))
plot(mod, N=8)

The charts look all sensible, but the parameters don’t look familiar, so let’s transform them back to the original scale.

trnf <- function(m, par, a, b){
x <- as.vector(as_draws_matrix(m, par))
tx <- a * exp(b * x)
c(mean=mean(tx),
se_mean = sd(tx)/sqrt(length(tx)),
sd=sd(tx),
quantile(tx, probs=c(0.025, 0.1, 0.5, 0.9, 0.975)))
}
round(rbind(
Hare0 = trnf(mod, 'b_stdNHare0_Intercept', 10, 1),
Lynx0 = trnf(mod, 'b_stdNLynx0_Intercept', 10, 1),
brHare = trnf(mod, 'b_stdNbrHare_Intercept', 0.5, 0.25),
mrHare = trnf(mod, 'b_stdNmrHare_Intercept', 0.025, 0.25),
brLynx = trnf(mod, 'b_stdNbrLynx_Intercept', 0.025, 0.25),
mrLynx = trnf(mod, 'b_stdNmrLynx_Intercept', 0.8, 0.25),
SigmaHare = trnf(mod, 'b_sigma_PopulationHare', 1, 1),
SigmaLynx = trnf(mod, 'b_sigma_PopulationLynx', 1, 1)
), 3)
##             mean se_mean    sd   2.5%    10%    50%    90%  97.5%
## Hare0     23.853   0.046 2.044 20.047 21.259 23.806 26.441 28.183
## Lynx0      6.578   0.015 0.659  5.376  5.741  6.567  7.426  7.970
## brHare     0.528   0.001 0.045  0.439  0.469  0.527  0.585  0.617
## mrHare     0.026   0.000 0.003  0.021  0.023  0.026  0.030  0.033
## brLynx     0.025   0.000 0.003  0.020  0.021  0.025  0.028  0.031
## mrLynx     0.821   0.002 0.069  0.700  0.738  0.817  0.913  0.967
## SigmaHare  0.251   0.001 0.043  0.182  0.202  0.247  0.307  0.350
## SigmaLynx  0.259   0.001 0.044  0.189  0.208  0.254  0.316  0.360

## Plot posterior simulations

Before I can draw simulations from the posterior predictive distributions, I have to expose the Stan functions to R.

expose_functions(mod, vectorize = TRUE, cacheDir = "~/Downloads/")

Finally, I can run simulations from the posterior predictive model and compare the simulated output with the data.

library(tidybayes)
pred <- predicted_draws(mod, newdata = LH, n= 1000)
ggplot(pred, aes(x = Year, y = Pelts)) +
stat_lineribbon(aes(y = .prediction),
.width = c(.99, .95, .8, .5),
color = "#08519C") +
geom_point(data = pred) + labs(y="Pelts (thousands)") +
scale_fill_brewer() + facet_wrap(~ Population)    

This plot suggests again that the model is a not unreasonable.

# Summary

Although the model seems to describe the data well, it has its limitations:

• The solutions of the ODEs for a fixed set of parameters
are defined by the initial values, i.e. there are only stable orbits
• No population can become extinct, i.e. if the initial value was greater 0
• There are many other factors impacting the birth and death rates of hares and lynxes apart from the interaction between the two populations

However, this example demonstrated how multivariate ODE models can be fitted with brms. For more complex examples of multivariate ODEs with brms see the case studies in section 5 of (Gesmann and Morris (2020)).

# Session Info

session_info <- (sessionInfo()[-8])
utils:::print.sessionInfo(session_info, local=FALSE)
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.2.1
##
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
##
## attached base packages:
## NULL
##
## other attached packages:
## [1] tidybayes_3.0.6     cmdstanr_0.6.1.9000 brms_2.20.4
## [4] Rcpp_1.0.11         ggplot2_3.4.4       data.table_1.14.10
##
## loaded via a namespace (and not attached):
##   [1] gridExtra_2.3        inline_0.3.19        rlang_1.1.2
##   [4] magrittr_2.0.3       matrixStats_1.2.0    compiler_4.3.2
##   [7] loo_2.6.0            vctrs_0.6.5          reshape2_1.4.4
##  [10] stringr_1.5.1        arrayhelpers_1.1-0   pkgconfig_2.0.3
##  [13] fastmap_1.1.1        backports_1.4.1      ellipsis_0.3.2
##  [16] labeling_0.4.3       utf8_1.2.4           threejs_0.3.3
##  [19] promises_1.2.1       rmarkdown_2.25       markdown_1.12
##  [22] ps_1.7.5             purrr_1.0.2          xfun_0.41
##  [25] cachem_1.0.8         jsonlite_1.8.8       highr_0.10
##  [28] later_1.3.2          R6_2.5.1             dygraphs_1.1.1.6
##  [31] RColorBrewer_1.1-3   bslib_0.6.1          stringi_1.8.3
##  [34] StanHeaders_2.26.28  jquerylib_0.1.4      estimability_1.4.1
##  [37] bookdown_0.37        rstan_2.32.3         knitr_1.45
##  [40] zoo_1.8-12           base64enc_0.1-3      bayesplot_1.10.0
##  [43] httpuv_1.6.13        Matrix_1.6-4         igraph_1.6.0
##  [46] tidyselect_1.2.0     rstudioapi_0.15.0    abind_1.4-5
##  [49] yaml_2.3.8           codetools_0.2-19     miniUI_0.1.1.1
##  [52] blogdown_1.18        curl_5.2.0           processx_3.8.3
##  [55] pkgbuild_1.4.3       lattice_0.22-5       tibble_3.2.1
##  [58] plyr_1.8.9           shiny_1.8.0          withr_2.5.2
##  [61] bridgesampling_1.1-2 posterior_1.5.0      coda_0.19-4
##  [64] evaluate_0.23        RcppParallel_5.1.7   ggdist_3.3.1
##  [67] xts_0.13.1           RcppEigen_0.3.3.9.4  pillar_1.9.0
##  [70] tensorA_0.36.2.1     checkmate_2.3.1      DT_0.31
##  [73] stats4_4.3.2         shinyjs_2.1.0        distributional_0.3.2
##  [76] generics_0.1.3       rstantools_2.3.1.1   munsell_0.5.0
##  [79] scales_1.3.0         gtools_3.9.5         xtable_1.8-4
##  [82] glue_1.6.2           emmeans_1.9.0        tools_4.3.2
##  [85] shinystan_2.6.0      colourpicker_1.3.0   mvtnorm_1.2-4
##  [88] grid_4.3.2           tidyr_1.3.0          QuickJSR_1.0.9
##  [91] crosstalk_1.2.1      colorspace_2.1-0     nlme_3.1-164
##  [94] cli_3.6.2            svUnit_1.0.6         fansi_1.0.6
##  [97] Brobdingnag_1.2-9    dplyr_1.1.4          V8_4.4.1
## [100] gtable_0.3.4         sass_0.4.8           digest_0.6.33
## [103] htmlwidgets_1.6.4    farver_2.1.1         htmltools_0.5.7
## [106] lifecycle_1.0.4      mime_0.12            shinythemes_1.2.0

# References

Bürkner, Paul-Christian. 2017. brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software 80 (1): 1–28. https://doi.org/10.18637/jss.v080.i01.
Gesmann, M., and J. Morris. 2020. Hierarchical Compartmental Reserving Models. Casualty Actuarial Society; https://www.casact.org/sites/default/files/2021-02/compartmental-reserving-models-gesmannmorris0820.pdf.
Lotka, Alfred J. 1925. Principles of Physical Biology. Waverly.
McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Second Edition. CRC Press. https://xcelab.net/rm/statistical-rethinking/.
Volterra, Vito. 1926. “Fluctuations in the Abundance of a Species Considered Mathematically.” Nature 118 (2972): 558–60. https://doi.org/10.1038/118558a0.

### Citation

For attribution, please cite this work as:

Markus Gesmann (Feb 05, 2021) Fitting multivariate ODE models with brms. Retrieved from https://magesblog.com/post/2021-02-08-fitting-multivariate-ode-models-with-brms/

BibTeX citation:

@misc{ 2021-fitting-multivariate-ode-models-with-brms,
author = { Markus Gesmann },
title = { Fitting multivariate ODE models with brms },
url = { https://magesblog.com/post/2021-02-08-fitting-multivariate-ode-models-with-brms/ },
year = { 2021 }
updated = { Feb 05, 2021 }
}