# fitModel: Fit a nonlinear least squares model In ProjectMOSAIC/mosaic: Project MOSAIC Statistics and Mathematics Teaching Utilities

## Description

Allows you to specify a formula with parameters, along with starting guesses for the parameters. Refines those guesses to find the least-squares fit.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```fitModel(formula, data = parent.frame(), start = list(), ...) model(object, ...) ## S3 method for class 'nlsfunction' model(object, ...) ## S3 method for class 'nlsfunction' summary(object, ...) ## S3 method for class 'nlsfunction' coef(object, ...) ```

## Arguments

 `formula` formula specifying the model `data` dataframe containing the data to be used `start` passed as `start` to `nls()`. If and empty list, a simple starting point is used (thus avoiding the usual warning message). `...` additional arguments passed to `nls()` `object` an R object (typically a the result of fitModel)

## Details

Fits a nonlinear least squares model to data. In contrast to linear models, all the parameters (including linear ones) need to be named in the formula. The function returned simply contains the formula together with pre-assigned arguments setting the parameter value. Variables used in the fitting (as opposed to parameters) are unassigned arguments to the returned function.

a function

## Note

This doesn't work with categorical explanatory variables. Also, this does not work with synthentic data that fit the model perfectly. See `link{nls}` for details.

`linearModel()`, `nls()`
 ```1 2 3 4 5 6 7``` ```if (require(mosaicData)) { f <- fitModel(temp ~ A+B*exp(-k*time), data=CoolingWater, start=list(A=50,B=50,k=1/20)) f(time=50) coef(f) summary(f) model(f) } ```