Fitting a sigmoid curve in R

This is a short tutorial on how to fit data points that look like a sigmoid curve using the nls function in R. Let’s assume you have a vector of points you think they fit in a sigmoid curve like the ones in the figure below.

The general form of the logistic or sigmoid function is defined as:

y(x) = A + \frac{K-A}{(1+Qe^{-B(t-M)})^{1/\nu}}

Let’s assume a more simple form in which only three of the parameters K, B and M, are used. Those are the upper asymptote, growth rate and the time of maximum growth respectively.

y(x) = \frac{K}{1+e^{-B(t-M)}}

The following R code estimates the parameters, where y is a vector of data points:

# function needed for visualization purposes
sigmoid = function(params, x) {
params[1] / (1 + exp(-params[2] * (x – params[3])))

# fitting code
y = c( … )
x = 1:53
fitModel = nls(y ~ a / (1 + exp(-b * (x – c))), start = list(a = 1, b = 0.5, c = 25))

# visualization code
# get the coefficients using the coef function
params = coef(fitModel)
y2 = sigmoid(params, x)

Now the data points along with the sigmoid curve look like this, with a = 0.776, b = 0.193, and c = 46.