Summarizing Normal Laplace Distribution Fit

Description

summary Method for class "nlFit".

Usage

  ## S3 method for class 'nlFit'
summary(object, ...)
  ## S3 method for class 'summary.nlFit'
print(x,
        digits = max(3, getOption("digits") - 3), ...)

Arguments

Details

summary.nlFit calculates standard errors for the estimates of mu, sigma, alpha, and beta of the normal laplace distribution parameter vector param if the Hessian from the call to nlFit is available.

Value

If the Hessian is available, summary.nlFit computes standard errors for the estimates of mu, sigma, alpha, and beta, and adds them to object as object$sds. Otherwise, no calculations are performed and the composition of object is unaltered.

summary.nlFit invisibly returns object with class changed to summary.nlFit.

See nlFit for the composition of an object of class nlFit.

print.summary.nlFit prints a summary in the same format as print.nlFit when the Hessian is not available from the fit. When the Hessian is available, the standard errors for the parameter estimates are printed in parentheses beneath the parameter estimates, in the manner of fitdistr in the package MASS.

See Also

nlFit, summary.

Examples

## Continuing the  nlFit() example:
param <- c(2, 2, 1, 1)
dataVector <- rnl(500, param = param)
fit <- nlFit(dataVector, hessian = TRUE)
print(fit)
summary(fit)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(NormalLaplace)
Loading required package: DistributionUtils
Loading required package: RUnit
Loading required package: GeneralizedHyperbolic
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/NormalLaplace/summary.nlFit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.nlFit
> ### Title: Summarizing Normal Laplace Distribution Fit
> ### Aliases: summary.nlFit print.summary.nlFit
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ## Continuing the  nlFit() example:
> param <- c(2, 2, 1, 1)
> dataVector <- rnl(500, param = param)
> fit <- nlFit(dataVector, hessian = TRUE)
Error in dnl(x, param = param) : beta must be non-negative
Calls: nlFit -> optim -> <Anonymous> -> fn -> dnl
Execution halted