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

Description Usage Arguments Details Value Author(s) See Also Examples

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

model2rjfun(modelformula, pvec, data = NULL, jacobian = TRUE, testresult = TRUE, ...)
model2ssgrfun(modelformula, pvec, data = NULL, gradient = TRUE, 
	      testresult = TRUE, ...)
modelexpr(fun)

`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.

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.

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.

John Nash and Duncan Murdoch

nls

  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)