# eDist: S3 methods for manipulating eDist objects. In ExtDist: Extending the Range of Functions for Probability Distributions

## Description

S3 methods for manipulating eDist objects

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```## S3 method for class 'eDist' logLik(object, ...) ## S3 method for class 'eDist' AIC(object, ..., k = 2) AICc(object) ## S3 method for class 'eDist' AICc(object, ...) ## S3 method for class 'eDist' vcov(object, ..., corr = FALSE) BIC(object) ## S3 method for class 'eDist' BIC(object, ...) MDL(object) ## S3 method for class 'eDist' MDL(object, ...) ## S3 method for class 'eDist' print(x, ...) ## S3 method for class 'eDist' plot(x, ...) ```

## Arguments

 `object,` x An object of class eDist, usually the output of a parameter estimation function. `...` Additional parameters `k` numeric, The penalty per parameter to be used; the default k = 2 is the classical AIC. `corr` logical; should vcov() return correlation matrix (instead of variance-covariance matrix). `x,` A list to be returned as class eDist. `plot` logical; if TRUE histogram, P-P and Q-Q plot of the distribution returned else only parameter estimation is returned.

## Note

The MDL only works for parameter estimation by numerical maximum likelihood.

## Author(s)

A. Jonathan R. Godfrey, Sarah Pirikahu, and Haizhen Wu.

## References

Myung, I. (2000). The Importance of Complexity in Model Selection. Journal of mathematical psychology, 44(1), 190-204.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```X <- rnorm(20) est.par <- eNormal(X, method ="numerical.MLE") logLik(est.par) AIC(est.par) AICc(est.par) BIC(est.par) MDL(est.par) vcov(est.par) vcov(est.par,corr=TRUE) print(est.par) plot(est.par) ```

ExtDist documentation built on May 30, 2017, 12:36 a.m.