nmsurv2
Calculates maximum likelihood estimates using Nelder Mead's simplex method for bivariate data
Contents
Syntax
[q, info] = nmsurv2(func, p, t, y, Z)
Description
Calculates maximum likelihood estimates using Nelder Mead's simplex method for bivariate data
Input
- func: string with name of user-defined function
f = func (p, t, y) with p: np-vector; t: nt-vector; y: ny-vector f: (nt,ny)-matrix with model-predictions for surviving numbers
- p: (np,2) matrix with
p(:,1) initial guesses for parameter values p(:,2) binaries with yes or no iteration (optional)
- t: (nt,1)-vector with first independent variable (time)
- y: (ny,1)-vector with second independent variable
- Z: (nx,ny)-matrix with surviving numbers
Output
- q: matrix like p, but with maximum likelihood estimates
- info: 1 if convergence has been successful; 0 otherwise
Remarks
Set options with nmsurv_options Similar to scsurv2, but slower and a larger bassin of attraction. See scsurv2 for the definition of the user-defined function, and scsurv and nmsurv for one unidvariate data. It is usually a good idea to run scsurv2 on the result of nmsurv2.