summary.gkwreg {gkwreg} | R Documentation |
Summary Method for Generalized Kumaraswamy Regression Models
Description
Computes and returns a detailed statistical summary for a fitted Generalized
Kumaraswamy (GKw) regression model object of class "gkwreg"
.
Usage
## S3 method for class 'gkwreg'
summary(object, conf.level = 0.95, ...)
Arguments
object |
An object of class |
conf.level |
Numeric. The desired confidence level for constructing confidence intervals for the regression coefficients. Default is 0.95. |
... |
Additional arguments, currently ignored by this method. |
Details
This method provides a comprehensive summary of the fitted gkwreg
model.
It calculates z-values and p-values for the regression coefficients based on
the estimated standard errors (if available) and computes confidence intervals
at the specified conf.level
. The summary includes:
The model call.
The distribution family used.
A table of coefficients including estimates, standard errors, z-values, and p-values. Note: Significance stars are typically added by the corresponding
print.summary.gkwreg
method.Confidence intervals for the coefficients.
Link functions used for each parameter.
Mean values of the fitted distribution parameters (
\alpha, \beta, \gamma, \delta, \lambda
).A five-number summary (Min, Q1, Median, Q3, Max) plus the mean of the response residuals.
Key model fit statistics (Log-likelihood, AIC, BIC, RMSE, Efron's R^2).
Information about model convergence and optimizer iterations.
If standard errors were not computed (e.g., hessian = FALSE
in the
original gkwreg
call), the coefficient table will only contain estimates,
and confidence intervals will not be available.
Value
An object of class "summary.gkwreg"
, which is a list containing
the following components:
call |
The original function call that created the |
family |
Character string specifying the distribution family. |
coefficients |
A data frame (matrix) containing the coefficient estimates, standard errors, z-values, and p-values. |
conf.int |
A matrix containing the lower and upper bounds of the confidence intervals for the coefficients (if standard errors are available). |
link |
A list of character strings specifying the link functions used. |
fitted_parameters |
A list containing the mean values of the estimated distribution parameters. |
residuals |
A named numeric vector containing summary statistics for the response residuals. |
nobs |
Number of observations used in the fit. |
npar |
Total number of estimated regression coefficients. |
df.residual |
Residual degrees of freedom. |
loglik |
The maximized log-likelihood value. |
aic |
Akaike Information Criterion. |
bic |
Bayesian Information Criterion. |
rmse |
Root Mean Squared Error of the residuals. |
efron_r2 |
Efron's pseudo-R-squared value. |
mean_absolute_error |
Mean Absolute Error of the residuals. |
convergence |
Convergence code from the optimizer. |
iterations |
Number of iterations reported by the optimizer. |
conf.level |
The confidence level used for calculating intervals. |
Author(s)
Lopes, J. E.
See Also
gkwreg
, print.summary.gkwreg
,
coef
, confint
Examples
set.seed(123)
n <- 100
x1 <- runif(n, -2, 2)
x2 <- rnorm(n)
alpha_coef <- c(0.8, 0.3, -0.2)
beta_coef <- c(1.2, -0.4, 0.1)
eta_alpha <- alpha_coef[1] + alpha_coef[2] * x1 + alpha_coef[3] * x2
eta_beta <- beta_coef[1] + beta_coef[2] * x1 + beta_coef[3] * x2
alpha_true <- exp(eta_alpha)
beta_true <- exp(eta_beta)
# Use stats::rbeta as a placeholder if rkw is unavailable
y <- stats::rbeta(n, shape1 = alpha_true, shape2 = beta_true)
y <- pmax(pmin(y, 1 - 1e-7), 1e-7)
df <- data.frame(y = y, x1 = x1, x2 = x2)
# Fit a Kumaraswamy regression model
kw_reg <- gkwreg(y ~ x1 + x2 | x1 + x2, data = df, family = "kw")
# Generate detailed summary using the summary method
summary_kw <- summary(kw_reg)
# Print the summary object (uses print.summary.gkwreg)
print(summary_kw)
# Extract coefficient table directly from the summary object
coef_table <- coef(summary_kw) # Equivalent to summary_kw$coefficients
print(coef_table)