support_limits: Calculate Support Limits

In likelihood: Methods for Maximum Likelihood Estimation

Calculate Support Limits

Description

Calculates asymptotic support limits for parameter maximum likelihood estimates. For a parameter, support limits are the values above and below the maximum likelihood estimate that cause the likelihood to drop by a given number of units, while holding all other parameters at their maximum likelihood values. Two units is standard. 1.92 units roughly corresponds to a 95% confidence interval.

Usage

support_limits(model, par, var, source_data, pdf, par_lo = NULL, 
par_hi = NULL, delta = 100, slimit = 2)

Arguments

model	Model function for which to calculate likelihood. This is the same as the argument that you pass to anneal or likeli.
par	List of parameters for which to find the support limits. The name of each component in par matches the name of an argument in one of the functions passed to support_limits (either model, pdf, or another function that does initial calculations). The value of each component is the maximum likelihood estimate. All components in par must be numeric vectors. Vectors of length greater than one get a set of support limits calculated separately for each vector value. This is the same as the argument that you pass to anneal or likeli.
var	List object with the source for all other arguments and data used by model, pdf, and any other functions. This is the same as the argument that you pass to anneal or likeli.
source_data	Data frame containing any needed source data. This is the same as the argument that you pass to anneal or likeli.
pdf	Probability density function to use in likelihood calculations. This is the same as the argument that you pass to anneal or likeli.
par_lo	List object with lower bounds for the support limit search. The support limit bounds are in general the same as the simulated annealing search bounds. The list component names and sizes should each match a component in par. Any individual component (up to and including the entire par_lo argument) is optional. For any component of par that is omitted, the lower search bound for that parameter is assumed to be negative infinity. (Infinity isn't quite infinity - see details section for more.) This is the same as the argument that you pass to anneal.
par_hi	List object with upper bounds for the support limit search. The support limit bounds are in general the same as the simulated annealing search bounds. The list component names and sizes should each match a component in par. Any individual component (up to and including the entire par_hi argument) is optional. For any component of par that is omitted, the lower search bound for that parameter is assumed to be infinity. (Infinity isn't quite infinity - see details section for more.) This is the same as the argument that you pass to anneal.
delta	Controls the fineness of the search for support limits. Each parameter is divided by this number to arrive at a step size used for “walking” the likelihood function. Bigger numbers mean a finer search. See details for more on how the support limits are determined.
slimit	The number of units of likelihood that define the support limits. If slimit is 2, then the limits are those values that cause the likelihood to drop by 2 on either side of the parameter maximum likelihood estimate.

Details

Support limits are the values on either side of a parameter's maximum likelihood estimate that make the likelihood drop by slimit units, holding all other parameters at their maximum likelihood estimate value. Of course, support limits are only meaningful if the values in par are indeed maximum likelihood estimates. The distance from the maximum likelihood estimate of a parameter to its support limits is an indication of the “pointiness” of the maximum on the likelihood surface.

The algorithm produces support limits for a parameter by holding all other values at their maximum likelihood value and “walking” the likelihood function in the plane of that parameter, seeking to find the first spot that is slimit units below the peak likelihood. It starts by walking in big steps, then in progressively smaller steps, until it reaches that point. The smallest step it takes is found by dividing the parameter value by delta. This controls the overall fineness of the search.

The support limits search is bounded by the values in par_lo and par_hi. The search uses these bounds to control how it searches. This means that different bounds values may produce slightly different results. If a bounds value is omitted, support_limits will attempt an unbounded search, up to infinity. This will work fine as long as the likelihood surface is not completely flat. In practice, “infinity” means the largest and smallest values the computer can work with. To find out what the actual limits are on your computer, use .Machine$double.xmax.

This algorithm works best if the surface produced by the likelihood function is continuous and monotonic from the maximum likelihood value out to the support limits of all parameters. This is often not true. However, in most cases, this will produce reasonably good results with a low amount of total computation time.

Support limits are calculated automatically at the end of an anneal run.

Value

A list object with two components: “upper_limits” and “lower_limits”. upper_limits has the upper support limits for each member in par, with the maximum possible value being that parameter's value in par_hi; lower_limits has the lower support limits, with the minimum possible value being that parameter's value in par_lo.

If the likelihood calculated from par is infinite or NA, then the support limits will also be NA.

Note

The parameter maximum likelihood estimates found by anneal are in the list component called best_pars. These are the values to pass to support_limits for the par argument.

See Also

likeli, anneal

Examples

#################
## Set up for an annealing run
#################
## Use the included crown_rad dataset
data(crown_rad)

## Create our model function - crown radius is a linear function of DBH.
## DBH is a column of data from the crown_rad dataset; a and b are single
## parameter values.
model <- function (a, b, DBH) {a + b * DBH}

## Create our parameters list and set values for a and b, and indicate
## that DBH comes from the column marked "DBH" in the crown_rad dataset
par <- list(a = 1.12, b = 0.07)
var <- list(DBH = "DBH")

## We'll use the normal probability density function dnorm - add its
## arguments to our parameter list

## "x" value in PDF is observed value
var$x <- "Radius"

## The mean is the predicted value, the outcome of the model statement. Use
## the reserved word "predicted"
var$mean <- "predicted"
var$sd <- 0.815585

## Set bounds within which to search for parameters
par_lo <- list(a = 0, b = 0)
par_hi <- list(a = 50, b = 50)

## Have dnorm calculate log likelihood
var$log <- TRUE

## Not run: 
results <- anneal(model, par, var, crown_rad, par_lo, par_hi, dnorm, "Radius", max_iter=20000)

## End(Not run)

##################
## Do support limits - even though there are a set already in results
##################

## Not run: 
limits <- support_limits(model, results$best_pars, var, crown_rad, dnorm, par_lo, par_hi)

## End(Not run)

likelihood documentation built on March 31, 2023, 9:02 p.m.