F Distribution Family Function
Maximum likelihood estimation of the (2-parameter) F distribution.
fff(link = "loglink", idf1 = NULL, idf2 = NULL, nsimEIM = 100,
imethod = 1, zero = NULL)link |
Parameter link function for both parameters.
See |
idf1, idf2 |
Numeric and positive. Initial value for the parameters. The default is to choose each value internally. |
nsimEIM, zero |
See |
imethod |
Initialization method. Either the value 1 or 2.
If both fail try setting values for |
The F distribution is named after Fisher and has a density function
that has two parameters, called df1 and df2 here.
This function treats these degrees of freedom as positive reals
rather than integers.
The mean of the distribution is
df2/(df2-2) provided df2>2,
and its variance is
2*df2^2*(df1+df2-2)/
(df1*(df2-2)^2*(df2-4)) provided df2>4.
The estimated mean is returned as the fitted values.
Although the F distribution can be defined to accommodate a
non-centrality parameter ncp, it is assumed zero here.
Actually it shouldn't be too difficult to handle any known
ncp; something to do in the short future.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm
and vgam.
Numerical problems will occur when the estimates of the parameters are too low or too high.
T. W. Yee
Forbes, C., Evans, M., Hastings, N. and Peacock, B. (2011). Statistical Distributions, Hoboken, NJ, USA: John Wiley and Sons, Fourth edition.
## Not run:
fdata <- data.frame(x2 = runif(nn <- 2000))
fdata <- transform(fdata, df1 = exp(2+0.5*x2),
df2 = exp(2-0.5*x2))
fdata <- transform(fdata, y = rf(nn, df1, df2))
fit <- vglm(y ~ x2, fff, data = fdata, trace = TRUE)
coef(fit, matrix = TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.