EstimationTools (version 4.0.0)

summary.maxlogL: Summarize Maximum Likelihood Estimation

Description

Displays maximum likelihood estimates computed with maxlogL with its standard errors, AIC and BIC. This is a summary method for maxlogL object.

Usage

# S3 method for maxlogL
summary(object, ...)

Value

A list with information that summarize results of a maxlogL class object.

Arguments

object

an object of maxlogL class which summary is desired.

...

additional arguments affecting the summary produced.

Author

Jaime Mosquera Gutiérrez,

Details

This summary method computes and displays AIC, BIC, estimates and standard errors from a estimated model stored i a maxlogL class object. It also displays and computes Z-score and p values of significance test of parameters.

See Also

maxlogL, maxlogLreg, bootstrap_maxlogL

Examples

library(EstimationTools)

#--------------------------------------------------------------------------------
### First example: One known parameter

x <- rnorm(n = 10000, mean = 160, sd = 6)
theta_1 <- maxlogL(x = x, dist = 'dnorm', control = list(trace = 1),
                 link = list(over = "sd", fun = "log_link"),
                 fixed = list(mean = 160))
summary(theta_1)


#--------------------------------------------------------------------------------
# Second example: Binomial probability parameter estimation with variable
# creation

N <- rbinom(n = 100, size = 10, prob = 0.3)
phat <- maxlogL(x = N, dist = 'dbinom', fixed = list(size = 10),
                link = list(over = "prob", fun = "logit_link"))

## Standard error calculation method
print(phat$outputs$StdE_Method)

## 'summary' method
summary(phat)

#--------------------------------------------------------------------------------
# Third example: Binomial probability parameter estimation with no variable
# creation

N <- rbinom(n = 100, size = 10, prob = 0.3)
summary(maxlogL(x = N, dist = 'dbinom', fixed = list(size = 10),
                link = list(over = "prob", fun = "logit_link")))

#--------------------------------------------------------------------------------
# Fourth example: Estimation in a regression model with simulated normal data
n <- 1000
x <- runif(n = n, -5, 6)
y <- rnorm(n = n, mean = -2 + 3 * x, sd = exp(1 + 0.3* x))
norm_data <- data.frame(y = y, x = x)
formulas <- list(sd.fo = ~ x, mean.fo = ~ x)

norm_mod <- maxlogLreg(formulas, y_dist = y ~ dnorm, data = norm_data,
                       link = list(over = "sd", fun = "log_link"))

## 'summary' method
summary(norm_mod)


#--------------------------------------------------------------------------------