Standard nonlinear regression assumes homoscedastic data, that is, all response values are distributed normally. In case of heteroscedastic data (i.e. when the variance is dependent on the magnitude of the data), weighting the fit is essential. In *nls* (or *nlsLM* of the minpack.lm package), weighting can be conducted by two different methods:

1) by supplying a vector of weighting values for every that is supplied to the ‘weights’ argument.

2) by modifying the objective function in a way that it has the weights included in the body of the function. An example is given for this one in the documentation to *nls*.

1) is easy of course, but all values have to be derived from somewhere, for example the reciproce of the response values . 2) is a bit more cumbersome and one does not always want to define a new weighted version of the objective function.

I found it about time to design a weighting function that can be supplied to the ‘weights’ argument and that makes weighting by different regimes a breeze. *wfct()* returns a vector of weights that are calculated from a user-defined expression and transfers this vector within *nls*.

wfct <- function(expr)
{
expr <- deparse(substitute(expr))
## create new environment
newEnv <- new.env()
## get call
mc <- sys.calls()[[1]]
mcL <- as.list(mc)
## get data and write to newEnv
DATA <- mcL[["data"]]
DATA <- eval(DATA)
DATA <- as.list(DATA)
NAMES <- names(DATA)
for (i in 1:length(DATA)) assign(NAMES[i], DATA[[i]], envir = newEnv)
## get parameter, response and predictor names
formula <- as.formula(mcL[[2]])
VARS <- all.vars(formula)
RESP <- VARS[1]
RHS <- VARS[-1]
PRED <- match(RHS, names(DATA))
PRED <- names(DATA)[na.omit(PRED)]
## calculate variances for response values if "error" is in expression
## and write to newEnv
if (length(grep("error", expr)) > 0) {
y <- DATA[[RESP]]
x <- DATA[[PRED]]
## test for replication
if (!any(duplicated(x))) stop("No replicates available to calculate error from!")
## calculate error
error <- tapply(y, x, function(e) var(e, na.rm = TRUE))
error <- as.numeric(sqrt(error))
## convert to original repititions
error <- rep(error, as.numeric(table(x)))
assign("error", error, envir = newEnv)
}
## calculate fitted or residual values if "fitted"/"resid" is in expression
## and write to newEnv
if (length(grep("fitted", expr)) > 0 || length(grep("resid", expr)) > 0) {
mc2 <- mc
mc2$weights <- NULL
MODEL <- eval(mc2)
fitted <- fitted(MODEL)
resid <- residuals(MODEL)
assign("fitted", fitted, newEnv)
assign("resid", resid, newEnv)
}
## return evaluation in newEnv: vector of weights
OUT <- eval(parse(text = expr), envir = newEnv)
return(OUT)
}

The weighting function can take 5 different variable definitions and combinations thereof:

the name of the predictor (independent) variable,

the name of the response (dependent) variable,

*error*: if replicates exist, the error ,

*fitted*: the fitted values of the model,

*resid*: the residuals of the model,

For the last two, the model is fit unweighted, fitted values and residuals are extracted and the model is refit by the defined weights.

Let’s have some examples (taken from the *nls* doc):

Treated <- Puromycin[Puromycin$state == "treated", ]

Weighting by inverse of response :

nls(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(1/rate))
Nonlinear regression model
model: rate ~ Vm * conc/(K + conc)
data: Treated
Vm K
209.59669 0.06065
weighted residual sum-of-squares: 12.27
Number of iterations to convergence: 5
Achieved convergence tolerance: 5.2e-06

Weighting by square root of predictor :

nls(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(sqrt(conc)))
Nonlinear regression model
model: rate ~ Vm * conc/(K + conc)
data: Treated
Vm K
216.74134 0.07106
weighted residual sum-of-squares: 332.6
Number of iterations to convergence: 5
Achieved convergence tolerance: 2.403e-06

Weighting by inverse square of fitted values :

nls(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(1/fitted^2))
Nonlinear regression model
model: rate ~ Vm * conc/(K + conc)
data: Treated
Vm K
200.84098 0.04943
weighted residual sum-of-squares: 0.2272
Number of iterations to convergence: 4
Achieved convergence tolerance: 1.47e-06

A common weighting regime is to use the inverse variance :