GarchDistributionFits {fSeries} | R Documentation |
A collection and description of maximum likelihood
estimators to fit the parameters of a distribution
and to compute basic statistical properties. Included
are estimators for the symmetric and skew normal,
the Student-t, and the generalized error distributions.
The functions are:
normFit | MLE parameter fit for a Normal distribution, |
snormFit | MLE parameter fit for a skew Normal distribution, |
stdFit | MLE parameter fit for a Sudent-t distribution, |
stdFit | MLE parameter fit for a skew Sudent-t distribution, |
gedFit | MLE parameter fit for a generalized error distribution, |
nigFit | MLE parameter fit for a skew generalized error distribution. |
normFit(x, ...) snormFit(x, ...) stdFit(x, ...) sstdFit(x, ...) gedFit(x, ...) sgedFit(x, ...)
x |
a numeric vector. |
... |
parameters parsed to the optimization function nlm .
|
The function nlm
is used to minimize the "negative" maximum
log-likelihood function. nlm
carries out a minimization using
a Newton-type algorithm.
The functions *Fit
return a list with the following components:
estimate |
the point at which the maximum value of the log liklihood function is obtained. |
objective |
the value of the estimated maximum, i.e. the value of the log liklihood function. |
message |
an integer indicating why the optimization process terminated. |
code |
an integer indicating why the optimization process terminated. 1: relative gradient is close to zero, current iterate is probably solution; 2: successive iterates within tolerance, current iterate is probably solution; 3: last global step failed to locate a point lower than estimate .
Either estimate is an approximate local minimum of the
function or steptol is too small; 4: iteration limit exceeded; 5: maximum step size stepmax exceeded five consecutive times.
Either the function is unbounded below, becomes asymptotic to a
finite value from above in some direction or stepmax
is too small.
|
gradient |
the gradient at the estimated maximum. |
steps |
number of function calls. |
Diethelm Wuertz for the Rmetrics R-port.
## snormFit - xmpSeries("\nStart: MLE Fit to skew Normal Density > ") options(warn = -1) # suppress negative logs from nlm normFit(rnorm(1000)) snormFit(rnorm(1000)) ## sstdFit - xmpSeries("\nNext: MLE Fit to skew Student-t Density > ") sstdFit(rsstd(1000, mean = 1, sd = 1.5, nu = 7, xi = 2/3)) ## sgedFit - xmpSeries("\nNext: MLE Fit to skew Generalized Error Density > ") sgedFit(rsged(1000, mean = -1, sd = 0.5, nu = 3, xi = 3/2), print.level = 2)