summary.maxlogL {EstimationTools} R Documentation

## 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 class 'maxlogL'
summary(object, ...)


### Arguments

 object an object of maxlogL class which summary is desired. ... additional arguments affecting the summary produced.

### 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.

### Value

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

### Author(s)

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),
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),

## 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),

#--------------------------------------------------------------------------------
# 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,

## 'summary' method
summary(norm_mod)

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



[Package EstimationTools version 4.0.0 Index]