profile.mle2-class: Methods for likelihood profiles

Description

Definition of the mle2 likelihood profile class, and applicable methods

Usage

# S4 method for 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 mle2
confint(object, parm, level = 0.95, method,
          trace=FALSE,quietly=!interactive(),
          tol.newmin=0.001,...)
# S4 method for profile.mle2
confint(object, parm, level = 0.95, trace=FALSE, ...)

Objects from the Class

Objects can be created by calls of the form new("profile.mle2", ...), but most often by invoking profile on an "mle2" object.

Slots

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.

Methods

confint: signature(object = "profile.mle2"): Use profile to generate approximate confidence intervals for parameters.
plot: signature(x = "profile.mle2", y = "missing"): Plot profiles for each parameter.
summary: signature(x = "profile.mle2"): Plot profiles for each parameter.
show: signature(object = "profile.mle2"): Show object.

Arguments

x: An object of class profile.mle2
object: An object of class mle2 or profile.mle2 (as appropriate)
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 spline-extrapolation-based (default), root-finding, or quadratic confidence intervals. By default it uses the value of mle2.options("confint") -- the factory setting is "spline".
trace: trace progress of confidence interval calculation when using ‘uniroot’ method?
conf: (1-alpha) levels at which to plot likelihood cutoffs/confidence intervals
quietly: (logical) suppress “Profiling ...” message when computing profile to get confidence interval?
tol.newmin: see profile-methods
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 straight-line 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

Details

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 (!)

Examples

Run this code

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="L-BFGS-B",lower=c(ymax=0.001,xhalf=0.001))
if (FALSE) {
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 better-behaved bounded optimizer ...
##  BUT recent (development, 2012.5.24) versions of
##    optimx no longer allow single-parameter 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)
}
}

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