[pOpt,fVal,stat] = NDBASE.LM(dat,func,p0,’Option1’,’Value1’,…)

Levenberg Marquardt curve-fitting function, minimizes sum of weighted squared residuals.

Input:

dat Data to be fitted stored in a structure with fields: dat.x vector of N independent variables, dat.y vector of N data values to be fitted, dat.e vector of N standard deviation (positive numbers) used to weight the fit. If zero or missing 1./dat.y^2 will be assigned to each point, however in this case the refinement is reduced to an optimisation problem. func Function handle with the following definition: y = func(x,p) where x is a vector of N independent variables, p are the M parameters to be optimized and y is the simulated model. p0 Row vector of M initial parameters.

Options:

Options can be given using the modified output of optimset() or as option name string option value pairs.

dp Vector with N or 1 element, defines the fractional increment of
‘p’ when calculating the Jacobian matrix dFunc(x,p)/dp:
dp(j)>0 central differences calculated,
dp(j)<0 one sided ‘backwards’ differences calculated,
dp(j)=0 sets corresponding partials to zero, i.e. holds
p(j) fixed.
Default value if 1e-3.
vary Vector with N elements, if an element is false, the
corresponding parameter will be fixed. Default value is
false(1,N).
win Limits for the independent variabel values where the function
is fitted. Default is [-inf inf].
lb Vector with N elements, lower boundary of the parameters.
Default value is -inf.
ub Vector with N elements, upper boundary of the parameters.
Default value is inf.
MaxIter Maximum number of iterations, default value is 10*M.
MaxFunEvals Maximum number of function evaluations, default value is
100*M.
TolX Convergence tolerance for parameters, defines the maximum of
the relative chande of any parameter value. Default value is
1e-3.
eps1 Convergence tolerance for gradient, default value is 1e-3.
eps2 Convergence tolerance for reduced Chi-square, default value is
1e-2.
eps3 Determines acceptance of a L-M step, default value is 0.1.
lambda0 Initial value of L-M paramter, default value is 1e-2.
nu0 Value that determines the speed of convergence. Default value
is 10. It should be larger than 1.
lUp Factor for increasing lambda, default value is 30.
lDown Factor for decreasing lambda, default value is 7.
update Type of parameter update:
‘lm’ Levenberg-Marquardt lambda update,
‘quadratic’ Quadratic update,
‘nielsen’ Nielsen’s lambda update equations (default).
extraStat Calculates extra statistics: covariance matrix of parameters,
cofficient of multiple determination, asymptotic standard
error of the curve-fit and convergence history.
confLev Confidence level, where the error of the curve fit (sigY) is
calculated. Default is erf(1/sqrt(2))~0.6827 for standard
deviation (+/- 1 sigma).

Output:

pOpt Value of the M optimal parameters. fVal Value of the model function calculated with the optimal parameters at the N independent values of x.

stat Structure, storing the detailed output of the calculation with the following fields: p Least-squares optimal estimate of the parameter values. redX2 Reduced Chi squared error criteria, its value should be close to 1. If the value is larger, the model is not a good description of the data. If the value is smaller, the model is overparameterized and fitting the statistical error of the data. sigP Asymptotic standard error of the parameters. sigY Asymptotic standard error of the curve-fit. corrP Correlation matrix of the parameters. Rsq R-squared cofficient of multiple determination. cvgHst Convergence history. exitFlag The reason, why the code stopped: 1 convergence in r.h.s. (“JtWdy”), 2 convergence in parameters, 3 convergence in reduced Chi-square, 4 maximum Number of iterations reached without convergence message String, one of the above messages. nIter The number of iterations executed during the fit. nFunEvals The number of function evaluations executed during the fit.

See also NDBASE.PSO.