gkwreg (version 1.0.7)

logLik.gkwreg: Extract Log-Likelihood from a Generalized Kumaraswamy Regression Model

Description

This function extracts the maximized log-likelihood value from a fitted Generalized Kumaraswamy (GKw) regression model object (class "gkwreg"). The result is returned as an object of class "logLik", which includes attributes for degrees of freedom and number of observations, suitable for use with model selection criteria like AIC and BIC.

Usage

# S3 method for gkwreg
logLik(object, ...)

Value

An object of class "logLik" representing the maximized log-likelihood value. It has the following attributes:

• df: (numeric) The number of estimated parameters in the model (coefficients).

• nobs: (numeric) The number of observations used for fitting the model.

Arguments

object

An object of class "gkwreg", typically the result of a call to gkwreg.

...

Additional arguments, currently ignored by this method.

Author

Lopes, J. E.

Details

The log-likelihood value is typically computed during the model fitting process (e.g., by gkwreg) and stored within the resulting object. This method retrieves this stored value. If the value is not directly available, it attempts to calculate it from the stored deviance (\(logLik = -deviance / 2\)).

The log-likelihood for a GKw family model with parameters \(\theta\) is generally defined as the sum of the log-density contributions for each observation: $$l(\theta | y) = \sum_{i=1}^n \log f(y_i; \alpha_i, \beta_i, \gamma_i, \delta_i, \lambda_i)$$ where \(f(y; \dots)\) is the probability density function (PDF) of the specific distribution from the GKw family used in the model (determined by the family argument in gkwreg), and parameters (\(\alpha_i, \dots, \lambda_i\)) may depend on covariates.

The function also extracts the number of estimated parameters (df) and the number of observations (nobs) used in the fit, storing them as attributes of the returned "logLik" object, which is essential for functions like AIC and BIC. It attempts to find df and nobs from various components within the object if they are not directly stored as npar and nobs.

See Also

gkwreg, AIC.gkwreg, BIC.gkwreg, logLik, AIC, BIC

Examples

# \donttest{
# Assume 'df' exists with response 'y' and predictors 'x1', 'x2', 'x3'
# and that rkw() is available and data is appropriate (0 < y < 1).
set.seed(123)
n <- 100
x1 <- runif(n)
x2 <- rnorm(n)
x3 <- factor(rbinom(n, 1, 0.4))
alpha <- exp(0.5 + 0.2 * x1)
beta <- exp(1.0 - 0.1 * x2 + 0.3 * (x3 == "1"))
y <- rkw(n, alpha = alpha, beta = beta) # Placeholder if rkw not available
y <- pmax(pmin(y, 1 - 1e-7), 1e-7)
df <- data.frame(y = y, x1 = x1, x2 = x2, x3 = x3)

# Fit a Kumaraswamy regression model
kw_reg <- gkwreg(y ~ x1 | x2 + x3, data = df, family = "kw")

# Extract log-likelihood object
ll <- logLik(kw_reg)

# Print the log-likelihood value (with attributes)
print(ll)

# Access the value directly
ll_value <- as.numeric(ll)
print(ll_value)

# Get the number of parameters (degrees of freedom)
df_model <- attr(ll, "df")
print(paste("Number of parameters:", df_model))

# Get the number of observations
nobs_model <- attr(ll, "nobs")
print(paste("Number of observations:", nobs_model))

# Use with AIC/BIC
AIC(kw_reg)
BIC(kw_reg)
# }