| recnormal1 {VGAM} | R Documentation |
Maximum likelihood estimation of the two parameters of a univariate normal distribution when the observations are upper record values.
recnormal1(lmean="identity", lsd="loge",
imean=NULL, isd=NULL, method.init=1, zero=NULL)
lmean, lsd |
Link functions applied to the mean and sd parameters.
See Links for more choices.
|
imean, isd |
Numeric. Optional initial values for the mean and sd.
The default value NULL means they are computed internally,
with the help of method.init.
|
method.init |
Integer, either 1 or 2 or 3. Initial method, three algorithms are
implemented. Choose the another value if convergence fails, or use
imean and/or isd.
|
zero |
An integer vector, containing the value 1 or 2. If so, the mean or
standard deviation respectively are modelled as an intercept only.
Usually, setting zero=2 will be used, if used at all.
The default value NULL means both linear/additive predictors
are modelled as functions of the explanatory variables.
|
The response must be a vector or one-column matrix with strictly increasing values.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
This family function tries to solve a difficult problem, and the
larger the data set the better.
Convergence failure can commonly occur, and
convergence may be very slow, so set maxit=200, trace=TRUE, say.
Inputting good initial values are advised.
This family function uses the BFGS quasi-Newton update formula for the
working weight matrices. Consequently the estimated variance-covariance
matrix may be inaccurate or simply wrong! The standard errors must be
therefore treated with caution; these are computed in functions such
as vcov() and summary().
T. W. Yee
Arnold, B. C. and Balakrishnan, N. and Nagaraja, H. N. (1998) Records, New York: John Wiley & Sons.
n = 10000
mymean = 100
# First value is reference value or trivial record
rawy = c(mymean, rnorm(n, me=mymean, sd=16))
# Keep only observations that are records
delete = c(FALSE, rep(TRUE, len=n))
for(i in 2:length(rawy))
if(rawy[i] > max(rawy[1:(i-1)])) delete[i] = FALSE
(y = rawy[!delete])
fit = vglm(y ~ 1, recnormal1, trace=TRUE, maxit=200)
coef(fit, matrix=TRUE)
Coef(fit)
summary(fit)