res: Evaluate residuals from an object of class `nlsr`.

Description

Functions nlfb and nlxb return nonlinear least squares solution objects that include (weighted) residuals. If weights are present, the returned quantities are the square roots of the weights times the raw residuals.

Usage

res(object)

Arguments

object

An R object of class nlsr.

Value

The numeric vector with the gradient of the sum of squares at the paramters.

Details

resgr calls resfn to compute residuals and jacfn to compute the Jacobian at the parameters prm using external data in the dot arguments. It then computes the gradient using t(Jacobian) . residuals.

Note that it appears awkward to use this function in calls to optimization routines. The author would like to learn why.

References

Nash, J. C. (1979, 1990) _Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation._ Adam Hilger./Institute of Physics Publications

Examples

Run this code

# NOT RUN {
shobbs.res  <-  function(x){ # scaled Hobbs weeds problem -- residual
  # This variant uses looping
  if(length(x) != 3) stop("hobbs.res -- parameter vector n!=3")
  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  <-  1:12
  res  <-  100.0*x[1]/(1+x[2]*10.*exp(-0.1*x[3]*tt)) - y
}

shobbs.jac  <-  function(x) { # scaled Hobbs weeds problem -- Jacobian
  jj  <-  matrix(0.0, 12, 3)
  tt  <-  1:12
  yy  <-  exp(-0.1*x[3]*tt)
  zz  <-  100.0/(1+10.*x[2]*yy)
  jj[tt,1]   <-   zz
  jj[tt,2]   <-   -0.1*x[1]*zz*zz*yy
  jj[tt,3]   <-   0.01*x[1]*zz*zz*yy*x[2]*tt
  attr(jj, "gradient") <- jj
  jj
}

st  <-  c(b1=1, b2=1, b3=1)
RG <- resgr(st, shobbs.res, shobbs.jac)
RG
# }

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning