# wfct: Weighting function that can be supplied to the 'weights'... In minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds

 wfct R Documentation

## Weighting function that can be supplied to the weights argument of nlsLM or nls

### Description

wfct can be supplied to the weights argument of nlsLM or nls, and facilitates specification of weighting schemes.

### Usage

wfct(expr)


### Arguments

 expr An expression specifying the weighting scheme as described in the Details section below.

### Details

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.

### Value

The results of evaluation of expr in a new environment, yielding the vector of weights to be applied.

### Author(s)

Andrej-Nikolai Spiess

nlsLM, nls

### 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))



minpack.lm documentation built on Sept. 11, 2023, 9:07 a.m.