nlogLik_cauchy {OptimModel} | R Documentation |
Negative log-likelihood function for Cauchy regression
Description
The negative log-likelihood function for Cauchy regression, for use with rout_fitter
. Usually not called by the end user.
Usage
nlogLik_cauchy(theta, x, y, f.model, lbs)
Arguments
theta |
Parameters for f.model and an extra parameter for the scale parameter; e.g., f.model=hill.model |
x |
Explanatory variable(s). Can be vector, matrix, or data.frame |
y |
Response variable. |
f.model |
Name of mean model function. |
lbs |
Logical. lbs = log both sides. See details. |
Details
The function provides the negative log-likelihood for Cauchy regression
Let mu = f.model(theta[1:(p-1)], x) and sigma = exp(theta[p]), where p = number of parameters in theta.
The Cauchy likelihood is
.
The function returns .
If lbs == TRUE
, then is replaced with
.
Value
Returns a single numerical value.
Author(s)
Steven Novick
See Also
Examples
set.seed(123L)
x = rep( c(0, 2^(-4:4)), each=4 )
theta = c(emin=0, emax=100, lec50=log(.5), m=2)
y = hill_model(theta, x) + rnorm( length(x), mean=0, sd=2 )
theta1 = c(theta, lsigma=log(2))
nlogLik_cauchy(theta1, x=x, y=y, f.model=hill_model, lbs=FALSE)
## Cauchy regression via maximum likelihood
optim( theta1, nlogLik_cauchy, x=x, y=y, f.model=hill_model, lbs=FALSE )
[Package OptimModel version 2.0-1 Index]