# profile-methods

0th

Percentile

##### Likelihood profiles

Compute likelihood profiles for a fitted model

Keywords
methods
##### Usage
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, …)
##### Arguments
fitted

A fitted maximum likelihood model of class “mle2”

which

a numeric or character vector describing which parameters to profile (default is to profile all parameters)

maxsteps

maximum number of steps to take looking for an upper value of the negative log-likelihood

alpha

maximum (two-sided) likelihood ratio test confidence level to find

zmax

maximum value of signed square root of deviance difference to find (default value corresponds to a 2-tailed chi-squared test at level alpha)

del

step size for profiling

trace

(logical) produce tracing output?

skiperrs

(logical) ignore errors produced during profiling?

std.err

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

tol.newmin

tolerance for diagnosing a new minimum below the minimum deviance estimated in initial fit is found

debug

(logical) debugging output?

prof.lower

optional vector of lower bounds for profiles

prof.upper

optional vector of upper bounds for profiles

continuation

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

skip hessian (defunct?)

try_harder

(logical) ignore NA and flat spots in the profile, try to continue anyway?

##### Details

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