confint.glsm {glsm} | R Documentation |
Confidence Intervals for Coefficients in glsm
Objects
Description
Calculates confidence intervals for the coefficients in a fitted glsm
model. Includes exponentiated intervals (Odds Ratios) for easier interpretation.
Usage
## S3 method for class 'glsm'
confint(object, parm, level = 0.95, ...)
Arguments
object |
The type of prediction required. The default is on the scale of the linear predictors. The alternative |
parm |
calculate confidence intervals for the coefficients |
level |
It gives the desired confidence level for the confidence interval. For example, a default value is level = 0.95, which will generate a 95% confidence interval."
The alternative |
... |
further arguments passed to or from other methods. |
Details
Confint Method for 'glsm'
The saturated model is characterized by the assumptions 1 and 2 presented in section 2.3 by Llinas (2006, ISSN:2389-8976).
Value
An object of class "confint.glsm"
, which is a list containing:
object |
a |
parm |
calculate confidence intervals for the coefficients. |
level |
confidence levels |
Author(s)
Humberto Llinás (Universidad del Norte, Barranquilla-Colombia; author), Jorge Villalba (Universidad Tecnológica de Bolívar, Cartagena-Colombia; author and creator), Jorge Borja (Universidad del Norte, Barranquilla-Colombia; author and creator), Jorge Tilano (Universidad del Norte, Barranquilla-Colombia; author)
References
Hosmer, D., Lemeshow, S., & Sturdivant, R. (2013). Applied Logistic Regression (3rd ed.). New York: Wiley. ISBN: 978-0-470-58247-3 Llinás, H. (2006). Precisiones en la teoría de los modelos logísticos. Revista Colombiana de Estadística, 29(2), 239–265. Llinás, H., & Carreño, C. (2012). The Multinomial Logistic Model for the Case in Which the Response Variable Can Assume One of Three Levels and Related Models. Revista Colombiana de Estadística, 35(1), 131–138. Orozco, E., Llinás, H., & Fonseca, J. (2020). Convergence theorems in multinomial saturated and logistic models. Revista Colombiana de Estadística, 43(2), 211–231. Llinás, H., Arteta, M., & Tilano, J. (2016). El modelo de regresión logística para el caso en que la variable de respuesta puede asumir uno de tres niveles: estimaciones, pruebas de hipótesis y selección de modelos. Revista de Matemática: Teoría y Aplicaciones, 23(1), 173–197.
Examples
# Load the glsm package and example dataset
library(glsm)
data("hsbdemo", package = "glsm")
# Fit a multinomial logistic regression model using glsm()
model <- glsm(prog ~ ses + gender, data = hsbdemo)
# Get confidence intervals for all model coefficients (default 95% level)
confint(model)
# Get confidence intervals for a specific coefficient
params <- names(model$coefficients)
results <- lapply(params, function(p) {
cat("\nConfidence interval for:", p, "\n")
print(confint(model, parm = p, level = 0.95))
})