mle2-class | R Documentation |
This class encapsulates results of a generic maximum likelihood procedure.
Objects can be created by calls of the form new("mle2", ...)
, but
most often as the result of a call to mle2
.
call
:(language) The call to mle2
.
call.orig
:(language) The call to mle2
,
saved in its original form (i.e. without data arguments
evaluated).
coef
:(numeric) Vector of estimated parameters.
data
:(data frame or list) Data with which to evaluate the negative log-likelihood function
fullcoef
:(numeric) Fixed and estimated parameters.
vcov
:(numeric matrix) Approximate variance-covariance matrix, based on the second derivative matrix at the MLE.
min
:(numeric) Minimum value of objective function = minimum negative log-likelihood.
details
:(list) Return value from optim
.
minuslogl
:(function) The negative log-likelihood function.
optimizer
:(character) The optimizing function used.
method
:(character) The optimization method used.
formula
:(character) If a formula was specified, a character vector giving the formula and parameter specifications.
signature(object = "mle2")
: Extract coefficients.
If exclude.fixed=TRUE
(it is FALSE
by default),
only the non-fixed parameter values are returned.
signature(object = "mle2")
: Confidence
intervals from likelihood profiles, or quadratic approximations,
or root-finding.
signature(object = "mle2")
: Display object
briefly.
signature(object = "summary.mle2")
: Display object briefly.
signature(object = "mle2")
: Generate object summary.
signature(object = "mle2")
: Update fit.
signature(object = "mle2")
: Extract
variance-covariance matrix.
signature(object="mle2")
: Extract formula
signature(object="profile.mle2,missing")
: Plot
profile.
When the parameters in the original fit are constrained using
lower
or upper
, or when prof.lower
or
prof.upper
are set, and the confidence intervals lie
outside the constraint region, confint
will return NA
.
This may be too conservative – in some cases, the appropriate
answer would be to set the confidence limit to the lower/upper
bound as appropriate – but it is the most general answer.
(If you have a strong opinion about the need for a new
option to confint
that sets the bounds to the limits
automatically, please contact the package maintainer.)
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
lowerbound <- c(a=2,b=-0.2)
d <- data.frame(x,y)
fit1 <- mle2(y~dpois(lambda=exp(a+b*x)),start=list(a=0,b=2),data=d,
method="L-BFGS-B",lower=c(a=2,b=-0.2))
(cc <- confint(fit1,quietly=TRUE))
## to set the lower bounds to the limit
na_lower <- is.na(cc[,1])
cc[na_lower,1] <- lowerbound[na_lower]
cc
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