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)