1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
|
\name{profile.mle2-class}
\docType{class}
\alias{profile.mle2-class}
\alias{confint,profile.mle2-method}
\alias{confint,mle2-method}
\alias{confint.mle2} %% bogus but good ref link
\alias{plot,profile.mle2-method}
\alias{plot,profile.mle2,missing-method}
\alias{show,profile.mle2-method}
\alias{plot.profile.mle2}
\title{Methods for likelihood profiles}
\description{Definition of the mle2 likelihood profile class,
and applicable methods}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("profile.mle2",
...)}, but most often by invoking \code{profile} on an "mle2" object.
}
\section{Slots}{
\describe{
\item{\code{profile}:}{Object of class \code{"list"}. List of
profiles, one for each requested parameter. Each profile is a data
frame with the first column called \code{z} being the signed square
root of the deviance, and the others being the
parameters with names prefixed by \code{par.vals.}}
\item{\code{summary}:}{Object of class \code{"summary.mle2"}. Summary
of object being profiled.}
}
}
\section{Methods}{
\describe{
\item{confint}{\code{signature(object = "profile.mle2")}: Use profile
to generate approximate confidence intervals for parameters.}
\item{plot}{\code{signature(x = "profile.mle2", y = "missing")}: Plot
profiles for each parameter.}
\item{summary}{\code{signature(x = "profile.mle2")}: Plot
profiles for each parameter.}
\item{show}{\code{signature(object = "profile.mle2")}: Show object.}
}
}
\usage{
\S4method{plot}{profile.mle2}(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, \dots)
\S4method{confint}{mle2}(object, parm, level = 0.95, method,
trace=FALSE,quietly=!interactive(),
tol.newmin=0.001,\dots)
\S4method{confint}{profile.mle2}(object, parm, level = 0.95, trace=FALSE, \dots)
}
\arguments{
\item{x}{An object of class \code{profile.mle2}}
\item{object}{An object of class \code{mle2} or \code{profile.mle2}
(as appropriate)}
\item{levels}{levels at which to plot likelihood cutoffs (set by conf
by default)}
\item{level}{level at which to compute confidence interval}
\item{which}{(numeric or character) which parameter profiles to plot}
\item{parm}{(numeric or character) which parameter(s) to find
confidence intervals for}
\item{method}{(character) "spline", "uniroot", or "quad", for
spline-extrapolation-based (default), root-finding, or quadratic
confidence intervals. By default it uses the value of
\code{mle2.options("confint")} -- the factory setting is "spline".}
\item{trace}{trace progress of confidence interval calculation when using
\sQuote{uniroot} method?}
\item{conf}{(1-alpha) levels at which to plot likelihood
cutoffs/confidence intervals}
\item{quietly}{(logical) suppress \dQuote{Profiling ...} message
when computing profile to get confidence interval?}
\item{tol.newmin}{see \code{\link{profile-methods}}}
\item{plot.confstr}{(logical) plot labels showing confidence levels?}
\item{confstr}{(character) labels for confidence levels (by default, constructed from conf levels)}
\item{absVal}{(logical) plot absolute values of signed square
root deviance difference ("V" plot rather than straight-line
plot)?}
\item{add}{(logical) add profile to existing graph?}
\item{col.minval}{color for minimum line}
\item{lty.minval}{line type for minimum line}
\item{col.conf}{color for confidence intervals}
\item{lty.conf}{line type for confidence intervals}
\item{col.prof}{color for profile}
\item{lty.prof}{line type for profile}
\item{xlabs}{x labels}
\item{ylab}{y label}
\item{onepage}{(logical) plot all profiles on one page,
adjusting par(mfcol) as necessary?}
\item{ask}{(logical) pause for user input between plots?}
\item{show.points}{(logical) show computed profile points
as well as interpolated spline?}
\item{main}{(logical) main title}
\item{xlim}{x limits}
\item{ylim}{y limits}
\item{\dots}{other arguments}
}
\seealso{
\code{\link{mle2}}, \code{\link{mle2-class}}, \code{\link{summary.mle2-class}}
}
\details{
The default confidence interval calculation computes a likelihood
profile and uses the points therein, or uses the computed points in
an existing \code{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{
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))
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!
\dontrun{
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)
}
}
}
\keyword{classes}
|
