# model2rjfun: Create functions to calculate the residual vector or the sum... In nlsr: Functions for Nonlinear Least Squares Solutions

## Description

These functions create functions to evaluate residuals or sums of squares at particular parameter locations.

## Usage

 ```1 2 3 4``` ```model2rjfun(modelformula, pvec, data = NULL, jacobian = TRUE, testresult = TRUE, ...) model2ssgrfun(modelformula, pvec, data = NULL, gradient = TRUE, testresult = TRUE, ...) modelexpr(fun) ```

## Arguments

 `modelformula` A formula describing a nonlinear regression model. `pvec` A vector of parameters. `data` A dataframe, list or environment holding data used in the calculation. `jacobian` Whether to compute the Jacobian matrix. `gradient` Whether to compute the gradient vector. `testresult` Whether to test the function by evaluating it at `pvec`. `fun` A function produced by one of `model2rjfun` or `model2ssgrfun`. `...` Dot arguments, that is, arguments that may be supplied by `name = value` to supply information needed to compute specific quantities in the model.

## Details

If `pvec` does not have names, the parameters will have names generated in the form p_<n>, e.g. `p_1, p_2`. Names that appear in `pvec` will be taken to be parameters of the model.

The `data` argument may be a dataframe, list or environment, or `NULL`. If it is not an environment, one will be constructed using the components of `data` with parent environment set to be the environment of `modelformula`.

## Value

`model2rjfun` returns a function with header `function(prm)`, which evaluates the residuals (and if `jacobian` is `TRUE` the Jacobian matrix) of the model at `prm`. The residuals are defined to be the right hand side of `modelformula` minus the left hand side.

`model2ssgrfun` returns a function with header `function(prm)`, which evaluates the sum of squared residuals (and if `gradient` is `TRUE` the gradient vector) of the model at `prm`.

`modelexpr` returns the expression used to calculate the vector of residuals (and possibly the Jacobian) used in the previous functions.

## Author(s)

John Nash and Duncan Murdoch

`nls`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ``` y <- c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443, 38.558, 50.156, 62.948, 75.995, 91.972) tt <- seq_along(y) # for testing mydata <- data.frame(y = y, tt = tt) f <- y ~ b1/(1 + b2 * exp(-1 * b3 * tt)) p <- c(b1 = 1, b2 = 1, b3 = 1) rjfn <- model2rjfun(f, p, data = mydata) rjfn(p) myexp <- modelexpr(rjfn) cat("myexp:") print(myexp) ssgrfn <- model2ssgrfun(f, p, data = mydata) ssgrfn(p) ```