How do I code dose-response (4PL) curve fitting with optimize.minimize()

Question:

I want to optimize a dose-response curve (4 parameter logistic) using a data set. I need to use the Powell algorithm, therefore, I have to use optimize.minimize() instead of curve_fit or least square.
I wrote the following code:

import numpy as np
from scipy.optimize import minimize

ydata = np.array([0.1879, 0.4257, 0.80975, 1.3038, 1.64305, 1.94055, 2.21605, 2.3917])
xdata = np.array([40, 100, 250, 400, 600, 800, 1150, 1400])
initParams = [2.4, 0.2, 600.0, 1.0]

def logistic(params):
    A = params[0]
    B = params[1]   
    C = params[2]
    D = params[3]

    logistic4 = ((A-D)/(1.0+((xdata/C)**B))) + D
    sse = np.sum(np.square(ydata-logistic4))
    print sse

results = minimize(logistic, initParams, method='Powell')
print results

Theoretically, this minimizes the sse of the experimental and theoretical data sets iterating the 4 parameters initially entered using the Powell algorithm.
Practically, it does not work: it starts and the last error, in a fairly long list, is

TypeError: unsupported operand type(s) for -: 'NoneType' and 'NoneType'.

Any ideas on how to code this?

Asked By: Sebas

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Source

Answers:

Here is a graphical Python solver for your data and equation, it uses minimize() with ‘Powell’ and also has a commented-out call to curve_fit. I could not get a good fit with the initial parameter estimates that you supplied, so those are commented out here and replaced with my own values. My equation search confirms that this is an excellent equation to use in modeling this data set.

plot

import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.optimize import minimize

xData = numpy.array([40, 100, 250, 400, 600, 800, 1150, 1400], dtype=float)
yData = numpy.array([0.1879, 0.4257, 0.80975, 1.3038, 1.64305, 1.94055, 2.21605, 2.3917], dtype=float)


def func(xdata, A, B, C, D):
    return ((A-D)/(1.0+((xdata/C)**B))) + D

# minimize() requires a function to be minimized, unlike curve_fit()
def SSE(inParameters): # function to minimize, here sum of squared errors
    predictions = func(xData, *inParameters) 
    errors = predictions - yData
    return numpy.sum(numpy.square(errors))


#initialParameters = numpy.array([2.4, 0.2, 600.0, 1.0])
initialParameters = numpy.array([3.0, -1.5, 500.0, 0.1])


# curve fit the data with curve_fit()
#fittedParameters, pcov = curve_fit(func, xData, yData, initialParameters)

# curve fit the data with minimize()
resultObject = minimize(SSE, initialParameters, method='Powell')
fittedParameters = resultObject.x


modelPredictions = func(xData, *fittedParameters) 

absError = modelPredictions - yData

SE = numpy.square(absError) # squared errors
MSE = numpy.mean(SE) # mean squared errors
RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))

print('Parameters:', fittedParameters)
print('RMSE:', RMSE)
print('R-squared:', Rsquared)

print()


##########################################################
# graphics output section
def ModelAndScatterPlot(graphWidth, graphHeight):
    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
    axes = f.add_subplot(111)

    # first the raw data as a scatter plot
    axes.plot(xData, yData,  'D')

    # create data for the fitted equation plot
    xModel = numpy.linspace(min(xData), max(xData))
    yModel = func(xModel, *fittedParameters)

    # now the model as a line plot
    axes.plot(xModel, yModel)

    axes.set_xlabel('X Data') # X axis data label
    axes.set_ylabel('Y Data') # Y axis data label

    plt.show()
    plt.close('all') # clean up after using pyplot

graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)
Answered By: James Phillips

Shouldn’t the correct Hill equation used in the function "func" use the term (C/x)**B rather than (x/C)**B where x=dose, C=IC50, and B is Hill coefficient?

Answered By: xspensiv