**Climate Change Ecology » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

As those of you who read my last post know, I’m at the NIMBioS-CAMBAM workshop on linking mathematical models to biological data here at UT Knoxville. Day 1 (today) was on parameter estimation and model identifiability. Specifically, we (quickly) covered maximum likelihood estimation and how to program our own estimation procedures in MATLAB.

If you’ve read this blog in the past, you know I’m an open-source kind of person. Naturally, I re-programmed all of the MATLAB examples in R. When I did, I noticed something funny. The optimization procedures in MATLAB gave different estimates than those in R. I asked a post-doc there, who seemed equally stumped but did mention that R’s optimization procedures are little funky. So, I took the next logical step and programmed the ML optimization routine into Python, using Scipy and Numpy, just to double check.

The model is an SIR epidemiological model that predicts the number of Susceptible, Infected, and Recovering people with, in this case, cholera. It relies on four parameters, Bi, Bw, e, and k. I won’t give the model here, you’ll see the formula in the code below. When optimizing, I made sure that MATLAB, R, and Python all used Nelder-Mead algorithms and, when possible, equivalent ODE solvers (ode45 in MATLAB and R).

I won’t post the MATLAB code here, because I didn’t write it and it’s multiple files etc etc, but I’ve gone over it line by line to make sure it’s identical to my R and Python code. It is.

MATLAB outputs estimates for Bi (0.2896), Bw (1.0629), e (0.0066) and k (0.0001). If you want the MATLAB files, or the data, I’ll send them to you.

In R, first import the data (see the Python code below for the actual data). Then, run the ode solver and optimization code:

## Main code for the SIR model example library(deSolve) # Load the library to solve the ode ## Set initial parameter values Bi <- 0.75 Bw <- 0.75 e <- 0.01 k <- 1/89193.18 ## Combine parameters into a vector params <- c(Bi, Bw, e, k) names(params) <- c('Bi', 'Bw', 'e', 'k') # Make a function for running the ODE SIRode <- function(t, x, params){ S <- x[1] I <- x[2] W <- x[3] R <- x[4] Bi <- params[1] Bw <- params[2] e <- params[3] k <- params[4] dS <- -Bi*S*I - Bw*S*W dI <- Bi*S*I + Bw*S*W - 0.25*I dW <- e*(I - W) dR <- 0.25*I output <- c(dS, dI, dW, dR) list(output) } # Set initial conditions I0 <- data[1]*k R0 <- 0 S0 <- (1-I0) W0 <- 0 initCond <- c(S0, I0, W0, R0) # Simulate the model using our initial parameter guesses initSim <- ode(initCond, tspan, SIRode, params, method='ode45') plot(tspan, initSim[,3]/k, type='l') points(tspan, data) # Make a function for optimzation of the parameters LLode <- function(params){ k <- params[4] I0 <- data[1]*k R0 <- 0 S0 <- 1 - I0 W0 <- 0 initCond <- c(S0, I0, W0, R0) # Run the ODE odeOut <- ode(initCond, tspan, SIRode, params, method='ode45') # Measurement variable y <- odeOut[,3]/k diff <- (y - data) LL <- t(diff) %*% diff return(LL) } # Run the optimization procedure MLresults <- optim(params, LLode, method='Nelder-Mead') ## Resimulate the ODE estParms <- MLresults$par estParms MLresults$value I0est <- data[1]*estParms[4] S0est <- 1 - I0est R0 <- 0 W0 <- 0 initCond <- c(S0est, I0est, W0, R0) odeEstOut <- ode(initCond, tspan, SIRode, estParms, method='ode45') estY <- odeEstOut[,3]/estParms[4] plot(tspan, data, pch=16, xlab='Time', ylab='Number Infected') lines(tspan, estY)

Running this code gives me estimates of Bi (0.30), Bw (1.05), e (0.0057), and k (0.0001).

I rounded the values, but the unrounded values are even closer to MATLAB. So far, I’m fairly satisfied.

We can run the same procedure in Python:

from scipy.optimize import minimize from scipy import integrate import pylab as py ## load data tspan = np.array([0, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91, 98, 105, 112, 119, 126, 133, 140, 147, 154, 161]) data = np.array([ 113, 60, 70, 140, 385, 2900, 4600, 5400, 5300, 6350, 5350, 4400, 3570, 2300, 1900, 2200, 1700, 1170, 830, 750, 770, 520, 550, 380 ]) ## define ODE equations def SIRode(N, t, Bi, Bw, e, k): return( -Bi*N[0]*N[1] - Bw*N[0]*N[2] , +Bi*N[0]*N[1] + Bw*N[0]*N[2] - 0.25*N[1] , +e*(N[1] - N[2]) , +0.25*N[1] ) # Set parameter values Bi = 0.75 Bw = 0.75 e = 0.01 k = 1/89193.18 # Set initial conditions I0 = data[0]*k S0 = 1 - I0 N = [S0, I0, 0] # Run the ode Nt = integrate.odeint(SIRode, N, tspan, args=(Bi, Bw, e, k)) # Get the second column of data corresponding to I INt = [row[1] for row in Nt] INt = np.divide(INt, k) py.clf() py.plot(tspan, data, 'o') py.plot(tspan, INt) py.show() def LLode(x): Bi = x[0] Bw = x[1] e = x[2] k = x[3] I0 = data[0]*k S0 = 1-I0 N0 = [S0, I0, k] Nt = integrate.odeint(SIRode, N0, tspan, args=(Bi, Bw, e, k)) INt = [row[1] for row in Nt] INt = np.divide(INt, k) difference = data - INt LL = np.dot(difference, difference) return LL x0 = [Bi, Bw, e, k] results = minimize(LLode, x0, method='nelder-mead') print results.x estParams = results.x Bi = estParams[0] Bw = estParams[1] e = estParams[2] k = estParams[3] I0 = data[0]*k S0 = 1 - I0 N = [S0, I0, 0] Nt = integrate.odeint(SIRode, N, tspan, args=(Bi, Bw, e, k)) INt = [row[1] for row in Nt] INt = np.divide(INt, k) py.clf() py.plot(tspan, data, 'o') py.plot(tspan, INt) py.show()

Python gave me estimates of Bi (0.297), Bw (1.106), e (0.0057), and k (0.00001).

Overall, I have to say I’m pretty satisfied with the performance of both R and Python. I also didn’t find programming these sorts of optimization procedures in R or Python to be any more difficult than in MATLAB (discounting for the fact that I’m not terribly familiar with MATLAB, but I’m also only somewhat familiar with Python, so they’re roughly equivalent here). I initially wrote this post because I could not get R to sync up with MATLAB, so I tried it in Python and got the same results as in R. I then found out that something was wrong with the MATLAB code, so that all three matched up pretty well.

I’ll say it again, R + Python = awesome.

**UPDATE:**

I’ve had a couple of requests for time comparisons between MATLAB, R, and Python. I don’t have those for MATLAB, I don’t know MATLAB well.

For Python, if I wrap the mimize function in:

t0 = time.time() miminize....# run the optimizer t1 = time.time() print t1 - t0

I get 3.17 seconds.

In R, if I use system.time( ) to time the optim( ) function, I get about 39 seconds. That pretty much matches my feeling that R is just laboriously slow compared with how quickly Python evaluates the function.

**leave a comment**for the author, please follow the link and comment on their blog:

**Climate Change Ecology » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.