I’ll take my NLS with weights, please…

January 13, 2014

Today I want to advocate weighted nonlinear regression. Why so?
Minimum-variance estimation of the adjustable parameters in linear and non-linear least squares requires that the data be weighted inversely as their variances w_i \propto \sigma^{-2}. Only then \hat{\beta} is the BLUE (Best Linear Unbiased Estimator) for linear regression and nonlinear regression with small errors (http://en.wikipedia.org/wiki/Weighted_least_squares#Weighted_least_squares), an important fact frequently neglected, especially in scenarios with heteroscedasticity.
The variance of a fit s^2 is also characterized by the statistic \chi^2 defined as followed:
\chi^2 \equiv \sum_{i=1}^n \frac{(y_i - f(x_i))^2}{\sigma_i^2}
The relationship between s^2 and \chi^2 can be seen most easily by comparison with the reduced \chi^2: \chi_\nu^2 = \frac{\chi^2}{\nu} = \frac{s^2}{\langle \sigma_i^2 \rangle}
whereas \nu = degrees of freedom (N – p), and \langle \sigma_i^2 \rangle is the weighted average of the individual variances. If the fitting function is a good approximation to the parent function, the value of the reduced chi-square should be approximately unity, \chi_\nu^2 = 1. If the fitting function is not appropriate for describing the data, the deviations will be larger and the estimated variance will be too large, yielding a value greater than 1. A value less than 1 can be a consequence of the fact that there exists an uncertainty in the determination of s^2, and the observed values of \chi_\nu^2 will fluctuate from experiment to experiment. To assign significance to the \chi^2 value, one can use the integral probability P_\chi(\chi^2;\nu) = \int_{\chi^2}^\infty P_\chi(x^2, \nu)dx^2 which describes the probability that a random set of n data points sampled from the parent distribution would yield a value of \chi^2 equal to or greater than the calculated one. This can be calculated by 1 - pchisq(chi^2, nu) in R.

To see that this actually works, we can Monte Carlo simulate some heteroscedastic data with defined variance as a function of y-magnitude and compare unweighted and weighted NLS.
First we take the example from the documentation to nls and fit an enzyme kinetic model:

DNase1 <- subset(DNase, Run == 1)
fm3DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1))

Then we take the fitted values \hat{y} (which are duplicated because of the initial replicates), create a new unique dataset on which we create 20 response values y_i for each concentration x sampled from a normal distribution with 2% random heteroscedastic gaussian noise as a function of the value’s magnitude y_i = \hat{y} + \mathcal{N}(0, 0.02 \cdot \hat{y}):

FITTED <- unique(fitted(fm3DNase1))
DAT <- sapply(FITTED, function(x) rnorm(20, mean = x, sd = 0.02 * x))
matplot(t(DAT), type = "p", pch = 16, lty = 1, col = 1)
lines(FITTED, col = 2)

Now we create the new dataframe to be fitted. For this we have to stack the unique x– and y_i-values into a 2-column dataframe:

CONC <- unique(DNase1$conc)
fitDAT <- data.frame(conc = rep(CONC, each = 20), density = matrix(DAT))

First we create the unweighted fit:

FIT1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = fitDAT,
start = list(Asym = 3, xmid = 0, scal = 1))

Then we fit the data with weights w = 1/var(y). IMPORTANT: we need to replicate the weight values by 20 in order to match the data length.

VAR <- tapply(fitDAT$density, fitDAT$conc, var)
VAR <- rep(VAR, each = 20)
FIT2 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = fitDAT, weights = 1/VAR,
start = list(Asym = 3, xmid = 0, scal = 1))

For calculation of \chi^2_\nu and its corresponding p-value, we use the fitchisq function of my ‘qpcR’ package:

> fitchisq(FIT1)
[1] 191.7566
[1] 1.22138
[1] 0.03074883

> fitchisq(FIT2)
[1] 156.7153
[1] 0.9981866
[1] 0.4913983

Now we see the benefit of weighted fitting: Only the weighted model shows us with it’s reduced chi-square value of almost exactly 1 and its high p-value that our fitted model approximates the parent model. And of course it does, because we simulated our data from it…