maxBFGS {micEcon} | R Documentation |
This function is a wrapper for optim
where the arguments are
compatible with maxNR
maxBFGS(fn, grad = NULL, theta, print.level = 0, iterlim = 200, tol = 1e-06, ... )
fn |
function to be maximised. 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. If NULL , numeric
gradient is used. For BHHH method it must return a matrix, where
rows corresponds to the gradients of the observations. Note that
this corresponds to
t(numericGradient(fn)) , not numericGradient(fn) .
It is summed over
observations in order to get a single gradient vector. |
theta |
initial values for the parameters to be optimized over. |
print.level |
a larger number prints more working information. |
iterlim |
maximum number of iterations. |
tol |
the absolute convergence tolerance (see optim ). |
... |
further arguments for fn and grad . |
Object of class "maximisation":
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 Poissonian distribution n <- rpois(100, 3) loglik <- function(l) n*log(l) - l - lfactorial(n) # we use numeric gradient summary(maxBFGS(loglik, theta=1)) # you would probably prefer mean(n) instead of that ;-) # Note also that maxLik is better suited for Maximum Likelihood