wfct can be supplied to the weights argument of
nlsLM or nls, and facilitates specification of
weighting schemes.
wfct(expr)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 \(y_{ij}\) exist, the error \(\sigma(y_{ij})\)
fitted: the fitted values \(\hat{y}_i\) of the model
resid: the residuals \(y_i - \hat{y}_i\) 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.
The results of evaluation of expr in a new
environment, yielding the vector of weights to be applied.
### Examples from 'nls' doc ###
## note that 'nlsLM' below may be replaced with calls to 'nls'
Treated <- Puromycin[Puromycin$state == "treated", ]
## Weighting by inverse of response 1/y_i:
nlsLM(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(1/rate))
#> Error in DATA[[i]]: subscript out of bounds
## Weighting by square root of predictor \sqrt{x_i}:
nlsLM(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(sqrt(conc)))
#> Error in DATA[[i]]: subscript out of bounds
## Weighting by inverse square of fitted values 1/\hat{y_i}^2:
nlsLM(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(1/fitted^2))
#> Error in DATA[[i]]: subscript out of bounds
## Weighting by inverse variance 1/\sigma{y_i}^2:
nlsLM(rate ~ Vm * conc/(K + conc), data = Treated,
start = c(Vm = 200, K = 0.05), weights = wfct(1/error^2))
#> Error in DATA[[i]]: subscript out of bounds