wfct | R Documentation |

`weights`

argument
of `nlsLM`

or `nls`

`wfct`

can be supplied to the `weights`

argument of
`nlsLM`

or `nls`

, and facilitates specification of
weighting schemes.

```
wfct(expr)
```

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

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 modelresid: 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.

Andrej-Nikolai Spiess

`nlsLM`

, `nls`

```
### 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))
```

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