Calculates max likelihood estimates using Nelder Mead's simplex method
[q, info] = nmsurv(func, p, varargin)
Calculates max likelihood estimates using Nelder Mead's simplex method similar to nrsurv, but slower and a larger bassin of attraction
- func: string with name of user-defined function
f = func (p, tn) with p: k-vector with parameters; tn: (n,c)-matrix; f: n-vector [f1, f2, ...] = func (p, tn1, tn2, ...) with p: k-vector and tni: (ni,k)-matrix; fi: ni-vector with model predictions The dependent variable in the output f; For tn see below.
- p: (k,2) matrix with
p(:,1) initial guesses for parameter values p(:,2) binaries with yes or no iteration (optional)
- tni (read as tn1, tn2, .. ): (ni,2) matrix with
tni(:,1) time: must be increasing with rows tni(:,2) number of survivors: must be non-increasing with rows tni(:,3, 4, ... ) data-pont specific information data (optional) The number of data matrices tn1, tn2, ... is optional but >0
- q: matrix like p, but with ml-estimates
- info: 1 if convergence has been successful; 0 otherwise
Calls user-defined function 'func' Set options with html nmregr_options See scsurv for the definition of the user-defined function, and scsurv2 and nmsurv2 for 2 independent variables and scsurv3 and nmsurv3 for 3 independent variables. It is usually a good idea to run scsurv on the result of nmsurv.