eDist: S3 methods for manipulating eDist objects.

Description Usage Arguments Note Author(s) References Examples

Description

S3 methods for manipulating eDist objects

Usage

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## 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

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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.

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