likeli: Calculate Likelihood

In likelihood: Methods for Maximum Likelihood Estimation

Calculate Likelihood

Description

Calculate likelihood of a model, given a dataset. Typically this is log likelihood. See the Likelihood Calculation page for details on how likelihood is calculated.

Usage

likeli(model, par, var, source_data, pdf, ...)

Arguments

model	Model function for which to calculate likelihood. See details for how to set up this function.
par	List object of parameters for which to calculate likelihood. The name of each component in par matches the name of an argument in one of the functions passed to likeli (either model, pdf, or another function that does initial calculations). All elements in par must be numeric vectors. This is the same as the argument that you pass to anneal.
var	List object with the source for all other non-parameter arguments and data used by model, pdf, and any other functions. This is the same as the argument that you pass to anneal.
source_data	Data frame containing any needed source data. You can reference the data frame columns by name to likeli.
pdf	Probability density function to use in the likelihood calculation. If you want a log likelihood value, which is usual and matches what anneal does, instruct pdf to calculate the log of its result. This is an option with all of R's built-in PDFs.
...	Any other data that may be needed by model, pdf, or any other function to be called by likeli. This is an alternative to providing the data in var; however, passing values in var is strongly recommended.

Details

See the Likelihood Calculation page for details on how likelihood is calculated. anneal uses the same parameters and is set up in the same way.

The model function is the scientific model, which generally takes as arguments the parameters for which to estimate maximum likelihood. It returns a predicted value of the dependent variable for each record in the source_data dataset, which is compared to the actual (observed) value when likelihood is calculated. Write model so that it returns a vector of numeric values, one for each record in the dataset.

The probability density function calculates the likelihood using the predicted and observed values of the dependent variable. You can provide your own function, but R has many built-in functions that you can use. You can read more about R's probability density functions in the help file “An Introduction to R”, but here is a brief list: dbeta (beta), dexp (exponential), dgamma (gamma), dlnorm (lognormal), dnbinom (negative binomial), dnorm (normal), and dpois (poisson). These all take a log argument which you should set to TRUE in var in order to calculate the log likelihood. If you write your own probability density function, it should return a vector of values, one for each record in the dataset.

If you wish, some of the arguments passed to model or pdf by likeli can be the results of other functions.

likeli handles all function calls and data. You tell likeli how to use your functions and data using par and var. Use par to give likeli the list of parameters for the model. Use var to tell likeli where all other functions and data come from. var and var are lists, and each component's name matches the function argument it should be used for. For example, if the model function takes an argument called “a”, there should be a par$a or a var$a with the value of a. For par, all values must be numeric vectors. For var, the values can be of any data type that makes sense to the function. To indicate that the source of a function argument is a column of data from a dataset, set that value of var to the name of the data frame's column, as a character string (for example, var$dbh<-"DBH"). Case matters! You will get the best results if all function arguments and column names are unique, so that there is no ambiguity. You are also free to reference values directly from the global environment in your functions if you prefer.

The difference between par and var is important to anneal but not to likeli.

The reserved character string “predicted”, used in var, means the predicted value of the dependent variable, as calculated by model.

If you want likeli to pass the results of another function as an argument to the model or pdf functions, define the function and then set the appropriate argument in var to the name of the function. Then provide all arguments to the sub-function in var as well. For instance, if your model function takes an argument called x, and you wish x to be the result of function fun1, then set var$x <- fun1, and add any arguments to fun1 to var. likeli will ensure that all functions are evaluated in the proper order.

Value

A single numeric value for the likelihood. It is possible for this to be NaN or Inf.

Examples

library(likelihood)

## 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
par <- list(a = 1.12, b = 0.07)

## Create a place to put all the other data needed by
## the model and PDF, and indicate that DBH comes from 
## the column marked "DBH" in the dataset
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$mean <- "predicted"
## Use a fixed value of the standard deviation for this example
var$sd <- 0.815585

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

result <- likeli(model, par, var, crown_rad, dnorm)

## Alternately: reference crown_rad$DBH directly in the function without
## using var
model <- function (a, b) {a + b * crown_rad$DBH}
var <- list(x = "Radius",
            mean = "predicted",
            sd = 0.815585,
            log = TRUE)
result <- likeli(model, par, var, crown_rad, dnorm)

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