Class "mle2". Result of Maximum Likelihood Estimation.
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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