How to save estimated parameters from nigfit() in a variable

Question

I want to automatically fit time series returns into a NIG distribution. With nigfit() from the package fBasics I estimate the mu, alpha, beta and delta of the distribution.

>   nigFit(histDailyReturns,doplot=FALSE,trace=FALSE)

Title:
 Normal Inverse Gaussian Parameter Estimation 

Call:
 .nigFit.mle(x = x, alpha = alpha, beta = beta, delta = delta, 
    mu = mu, scale = scale, doplot = doplot, span = span, trace = trace, 
    title = title, description = description)

Model:
 Normal Inverse Gaussian Distribution

Estimated Parameter(s):
       alpha         beta        delta           mu 
48.379735861 -1.648483055  0.012361539  0.001125734 

This works fine, which means that nigfit plots my parameters. However I would like to use the estimated parameters and save them in variables. So I could use them later.

    > variable = nigfit(histDailyReturns,doplot=FALSE,trace=FALSE)

This doesn't work out. 'variable' is an S4 object of class structure fDISTFIT. Calling the variable replots the output of nigfit above. I tried the following notations, to get just one parameter:

    > variable$alpha
    > variable.alpha
    > variable[1]

I couldn't find an answer in the documentation of nigfit. Is it possible to save the estimated parameters in variables? How does it work?

Answer 1

access the output compenents using @. variable has different slots. Get their names using slotNames(). Using the example from the documentation:

    set.seed(1953)
    s <- rnig(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0)
    a <- nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE) 
    slotNames(a)

     [1] "call"        "model"       "data"        "fit"         "title"      
     [6] "description"
    # `fit` is a list with all the goodies. You're looking for the vector, `estimate`:
    a@fit$estimate

         alpha       beta      delta         mu 
     1.6959724  0.3597794  0.5601027 -1.0446402 

Answer 2

Examine the structure of the output object using str(variable):

> variable@fit$par[["alpha"]]
[1] 48.379735861
> variable@fit$par[["beta"]]
[1] -1.648483055
> variable@fit$par[["delta"]]
[1] 0.012361539
> variable@fit$par[["mu"]]
[1] 0.001125734