profile.mle2class {bbmle}  R Documentation 
Definition of the mle2 likelihood profile class, and applicable methods
## S4 method for signature 'profile.mle2'
plot(x,
levels, which=1:p, conf = c(99, 95, 90, 80, 50)/100,
plot.confstr = TRUE,
confstr = NULL, absVal = TRUE, add = FALSE,
col.minval="green", lty.minval=2,
col.conf="magenta", lty.conf=2,
col.prof="blue", lty.prof=1,
xlabs=nm, ylab="z",
onepage=TRUE,
ask=((prod(par("mfcol")) < length(which)) && dev.interactive() &&
!onepage),
show.points=FALSE,
main, xlim, ylim, ...)
## S4 method for signature 'mle2'
confint(object, parm, level = 0.95, method,
trace=FALSE,quietly=!interactive(),
tol.newmin=0.001,...)
## S4 method for signature 'profile.mle2'
confint(object, parm, level = 0.95, trace=FALSE, ...)
x 
An object of class 
object 
An object of class 
levels 
levels at which to plot likelihood cutoffs (set by conf by default) 
level 
level at which to compute confidence interval 
which 
(numeric or character) which parameter profiles to plot 
parm 
(numeric or character) which parameter(s) to find confidence intervals for 
method 
(character) "spline", "uniroot", or "quad", for
splineextrapolationbased (default), rootfinding, or quadratic
confidence intervals. By default it uses the value of

trace 
trace progress of confidence interval calculation when using ‘uniroot’ method? 
conf 
(1alpha) levels at which to plot likelihood cutoffs/confidence intervals 
quietly 
(logical) suppress “Profiling ...” message when computing profile to get confidence interval? 
tol.newmin 
see 
plot.confstr 
(logical) plot labels showing confidence levels? 
confstr 
(character) labels for confidence levels (by default, constructed from conf levels) 
absVal 
(logical) plot absolute values of signed square root deviance difference ("V" plot rather than straightline plot)? 
add 
(logical) add profile to existing graph? 
col.minval 
color for minimum line 
lty.minval 
line type for minimum line 
col.conf 
color for confidence intervals 
lty.conf 
line type for confidence intervals 
col.prof 
color for profile 
lty.prof 
line type for profile 
xlabs 
x labels 
ylab 
y label 
onepage 
(logical) plot all profiles on one page, adjusting par(mfcol) as necessary? 
ask 
(logical) pause for user input between plots? 
show.points 
(logical) show computed profile points as well as interpolated spline? 
main 
(logical) main title 
xlim 
x limits 
ylim 
y limits 
... 
other arguments 
The default confidence interval calculation computes a likelihood
profile and uses the points therein, or uses the computed points in
an existing profile.mle2
object, to construct an interpolation
spline (which by default has three times as many points as were in
the original set of profile points). It then uses linear
interpolation between these interpolated points (!)
Objects can be created by calls of the form new("profile.mle2",
...)
, but most often by invoking profile
on an "mle2" object.
profile
:Object of class "list"
. List of
profiles, one for each requested parameter. Each profile is a data
frame with the first column called z
being the signed square
root of the deviance, and the others being the
parameters with names prefixed by par.vals.
summary
:Object of class "summary.mle2"
. Summary
of object being profiled.
signature(object = "profile.mle2")
: Use profile
to generate approximate confidence intervals for parameters.
signature(x = "profile.mle2", y = "missing")
: Plot
profiles for each parameter.
signature(x = "profile.mle2")
: Plot
profiles for each parameter.
signature(object = "profile.mle2")
: Show object.
mle2
, mle2class
, summary.mle2class
x < 0:10
y < c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d < data.frame(x,y)
## we have a choice here: (1) don't impose boundaries on the parameters,
## put up with warning messages about NaN values:
fit1 < mle2(y~dpois(lambda=ymax/(1+x/xhalf)),
start=list(ymax=1,xhalf=1),
data=d)
p1 < suppressWarnings(profile(fit1))
plot(p1,main=c("first","second"),
xlab=c(~y[max],~x[1/2]),ylab="Signed square root deviance",
show.points=TRUE)
suppressWarnings(confint(fit1)) ## recomputes profile
confint(p1) ## operates on existing profile
suppressWarnings(confint(fit1,method="uniroot"))
## alternatively, we can use box constraints to keep ourselves
## to positive parameter values ...
fit2 < update(fit1,method="LBFGSB",lower=c(ymax=0.001,xhalf=0.001))
## Not run:
p2 < profile(fit2)
plot(p2,show.points=TRUE)
## but the fit for ymax is just bad enough that the spline gets wonky
confint(p2) ## now we get a warning
confint(fit2,method="uniroot")
## bobyqa is a betterbehaved bounded optimizer ...
## BUT recent (development, 2012.5.24) versions of
## optimx no longer allow singleparameter fits!
if (require(optimx)) {
fit3 < update(fit1,
optimizer="optimx",
method="bobyqa",lower=c(ymax=0.001,xhalf=0.001))
p3 < profile(fit3)
plot(p3,show.points=TRUE)
confint(p3)
}
## End(Not run)