R: Calculate likelihood "slices"

slice {bbmle}

R Documentation

Calculate likelihood "slices"

Description

Computes cross-section(s) of a multi-dimensional likelihood surface

Usage

slice(x, dim=1, ...)
sliceOld(fitted, which = 1:p, maxsteps = 100,
                       alpha = 0.01, zmax = sqrt(qchisq(1 - alpha/2, p)),
                       del = zmax/5, trace = FALSE,
                       tol.newmin=0.001, ...)
slice1D(params,fun,nt=101,lower=-Inf,
                    upper=Inf,verbose=TRUE, tranges=NULL,...)
slice2D(params,fun,nt=31,lower=-Inf,
                    upper=Inf,
                    cutoff=10,verbose=TRUE,
                    tranges=NULL, ...)
slicetrans(params, params2, fun, extend=0.1, nt=401,
                       lower=-Inf, upper=Inf)

Arguments

`x`	a fitted model object of some sort
`dim`	dimensionality of slices (1 or 2)
`params`	a named vector of baseline parameter values
`params2`	a vector of parameter values
`fun`	an objective function
`nt`	(integer) number of slice-steps to take
`lower`	lower bound(s) (stub?)
`upper`	upper bound(s) (stub?)
`cutoff`	maximum increase in objective function to allow when computing ranges
`extend`	(numeric) fraction by which to extend range beyond specified points
`verbose`	print verbose output?
`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?
`tol.newmin`	tolerance for diagnosing a new minimum below the minimum deviance estimated in initial fit is found
`tranges`	a two-column matrix giving lower and upper bounds for each parameter
`...`	additional arguments (not used)

Details

Slices provide a lighter-weight way to explore likelihood surfaces than profiles, since they vary a single parameter rather than optimizing over all but one or two parameters.

slice: is a generic method
slice1D: creates one-dimensional slices, by default of all parameters of a model
slice2D: creates two-dimensional slices, by default of all pairs of parameters in a model
slicetrans: creates a slice along a transect between two specified points in parameter space (see calcslice in the emdbook package)

Value

An object of class slice with

slices

a list of individual parameter (or parameter-pair) slices, each of which is a data frame with elements

var1: name of the first variable
var2: (for 2D slices) name of the second variable
x: parameter values
y: (for 2D slices) parameter values
z: slice values
ranges: a list (?) of the ranges for each parameter
params: vector of baseline parameter values
dim: 1 or 2

sliceOld returns instead a list with elements profile and summary (see profile.mle2)

Author(s)

Ben Bolker

Examples

x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x,y)
fit1 <- mle2(y~dpois(lambda=exp(lymax)/(1+x/exp(lhalf))),
   start=list(lymax=0,lhalf=0),
   data=d)
s1 <- slice(fit1,verbose=FALSE)
s2 <- slice(fit1,dim=2,verbose=FALSE)
require(lattice)
plot(s1)
plot(s2)
## 'transect' slice, from best-fit values to another point
st <- slice(fit1,params2=c(5,0.5))
plot(st)

[Package bbmle version 1.0.19 Index]