View source: R/summary.maxlogL.R
summary.maxlogL | R Documentation |
Displays maximum likelihood estimates computed with maxlogL
with
its standard errors, AIC and BIC.
This is a summary
method for maxlogL
object.
## S3 method for class 'maxlogL' summary(object, ...)
object |
an object of |
... |
additional arguments affecting the summary produced. |
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.
A list with information that summarize results of a maxlogL
class object.
Jaime Mosquera GutiƩrrez, jmosquerag@unal.edu.co
maxlogL
, maxlogLreg
,
bootstrap_maxlogL
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) #--------------------------------------------------------------------------------
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