loglik_xy {gofreg} | R Documentation |
Negative log-likelihood function for a parametric regression model
Description
The log-likelihood function for a parametric regression model with data (x,y) is given by the sum of the logarithm of the conditional density of Y given X=x evaluated at y.
This function is one option that can be used to fit a ParamRegrModel. It
returns the negative log-likelihood value in order for optim()
to
maximize (instead of minimize).
Usage
loglik_xy(data, model, params)
Arguments
data |
|
model |
ParamRegrModel to use for the likelihood function |
params |
vector with model parameters to compute likelihood function for |
Value
Value of the negative log-likelihood function
Examples
# Create an example dataset
n <- 100
x <- cbind(runif(n), rbinom(n, 1, 0.5))
model <- NormalGLM$new()
params.true <- list(beta = c(2,3), sd = 1)
y <- model$sample_yx(x, params = params.true)
data <- dplyr::tibble(x = x, y = y)
# Compute negative log likelihood for true parameters
loglik_xy(data, model, params.true)
# Compute negative log likelihood for wrong parameters (should be higher)
loglik_xy(data, model, params = list(beta = c(1,2), sd = 0.5))
[Package gofreg version 1.0.0 Index]