Compute likelihood profiles for a fitted model
proffun(fitted, which = 1:p, maxsteps = 100,
alpha = 0.01, zmax = sqrt(qchisq(1 - alpha/2, p)),
del = zmax/5, trace = FALSE, skiperrs=TRUE,
std.err,
tol.newmin = 0.001, debug=FALSE,
prof.lower, prof.upper,
skip.hessian = TRUE,
continuation = c("none","naive","linear"),
try_harder=FALSE, ...)
# S4 method for mle2
profile(fitted, ...)
A fitted maximum likelihood model of class “mle2”
a numeric or character vector describing which parameters to profile (default is to profile all parameters)
maximum number of steps to take looking for an upper value of the negative log-likelihood
maximum (two-sided) likelihood ratio test confidence level to find
maximum value of signed square root of deviance difference to find (default value corresponds to a 2-tailed chi-squared test at level alpha)
step size for profiling
(logical) produce tracing output?
(logical) ignore errors produced during profiling?
Optional numeric vector of standard errors, for cases when the Hessian is badly behaved. Will be replicated if necessary, and NA values will be replaced by the corresponding values from the fit summary
tolerance for diagnosing a new minimum below the minimum deviance estimated in initial fit is found
(logical) debugging output?
optional vector of lower bounds for profiles
optional vector of upper bounds for profiles
use continuation method to set starting values?
"none" sets starting values to best fit; "naive"
sets starting values to those of previous profiling fit;
"linear" (not yet implemented) would use linear extrapolation
from the previous two profiling fits
skip hessian (defunct?)
(logical) ignore NA and flat spots in the
profile, try to continue anyway?
additional arguments (not used)
proffun is the guts of the profile method, exposed
so that other packages can use it directly.
See the vignette (vignette("mle2",package="bbmle"))
for more technical details of how profiling is done.
profile.mle-class