maxBFGS {maxLik} | R Documentation |
These functions are wrappers for optim
where the arguments are
compatible with maxNR
maxBFGS(fn, grad = NULL, hess=NULL, start, print.level = 0, iterlim = 200, tol = 1e-08, reltol=tol, ... ) maxSANN(fn, grad = NULL, hess = NULL, start, print.level = 0, iterlim = 10000, tol = 1e-08, reltol=tol, temp = 10, tmax = 10, parscale = rep(1, length = length(start)), ...) maxNM(fn, grad = NULL, hess = NULL, start, print.level = 0, iterlim = 500, tol = 1e-08, reltol=tol, parscale = rep(1, length = length(start)), alpha = 1, beta = 0.5, gamma = 2, ...)
fn |
function to be maximised. Must have the parameter vector as
the first argument. In order to use numeric gradient
and BHHH method, fn must return vector of
observation-specific likelihood values. Those are summed by maxNR
if necessary. If the parameters are out of range, fn should
return NA . See details for constant parameters. |
grad |
gradient of the function. Must have the parameter vector as
the first argument. If NULL , numeric
gradient is used (only maxBFGS uses gradient). Gradient may return
a matrix, where columns correspond to the parameters and rows to the
observations (useful for maxBHHH). The columns are summed internally. |
hess |
Hessian of the function. Not used by any of these methods, for
compatibility with maxNR . |
start |
initial values for the parameters. |
print.level |
a larger number prints more working information. |
iterlim |
maximum number of iterations. |
tol, reltol |
the relative convergence tolerance (see
optim ). tol is for compatibility with maxNR . |
temp |
controls the '"SANN"' method. It is the starting temperature for the cooling schedule. Defaults to '10'. |
tmax |
is the number of function evaluations at each temperature
for the '"SANN"' method. Defaults to '10'. (see
optim ) |
parscale |
A vector of scaling values for the parameters.
Optimization is performed on 'par/parscale' and these should
be comparable in the sense that a unit change in any element
produces about a unit change in the scaled value. (see
optim ) |
alpha, beta, gamma |
Scaling parameters for the
'"Nelder-Mead"' method. 'alpha' is the reflection factor
(default 1.0), 'beta' the contraction factor (0.5) and
'gamma' the expansion factor (2.0). (see
optim ) |
... |
further arguments for fn and grad . |
Object of class "maxim":
maximum |
value of fn at maximum. |
estimate |
best set of parameters found. |
gradient |
gradient at parameter value estimate . |
hessian |
value of Hessian at optimum. |
code |
integer. Success code, 0 is success (see
optim ). |
message |
character string giving any additional information returned by the optimizer, or NULL. |
iterations |
two-element integer vector giving the number of
calls to fn and gr , respectively.
This excludes those calls needed to
compute the Hessian, if requested, and any calls to fn to compute a
finite-difference approximation to the gradient. |
type |
character string "BFGS maximisation". |
Ott Toomet otoomet@ut.ee
# Maximum Likelihood estimation of the parameter of Poissonian distribution n <- rpois(100, 3) loglik <- function(l) n*log(l) - l - lfactorial(n) # we use numeric gradient summary(maxBFGS(loglik, start=1)) # you would probably prefer mean(n) instead of that ;-) # Note also that maxLik is better suited for Maximum Likelihood