|
|||||||
DetailsThe weighting function can take 5 different variable definitions and combinations thereof:
For the last two, the model is fit unweighted, fitted values and residuals are extracted and the model is refit by the defined weights. ValueThe results of evaluation of Author(s)Andrej-Nikolai Spiess See Also
Examples### 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)) ## 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))) ## 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)) ## 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)) Results |
|||||||
|