S3 methods for manipulating eDist objects
# 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, ...)
x An object of class eDist, usually the output of a parameter estimation function.
Additional parameters
numeric, The penalty per parameter to be used; the default k = 2 is the classical AIC.
logical; should vcov() return correlation matrix (instead of variance-covariance matrix).
A list to be returned as class eDist.
logical; if TRUE histogram, P-P and Q-Q plot of the distribution returned else only parameter estimation is returned.
The MDL only works for parameter estimation by numerical maximum likelihood.
Myung, I. (2000). The Importance of Complexity in Model Selection. Journal of mathematical psychology, 44(1), 190-204.
X <- rnorm(20)
est.par <- eNormal(X, method ="numerical.MLE")
logLik(est.par)
#> [1] -27.52245
AIC(est.par)
AICc(est.par)
#> [1] 59.75078
BIC(est.par)
#> [1] 61.03636
MDL(est.par)
#> [1] 30.95039
vcov(est.par)
#> mean sd
#> mean 4.589658e-02 2.961946e-09
#> sd 2.961946e-09 2.294828e-02
vcov(est.par,corr=TRUE)
#> mean sd
#> mean 1.000000e+00 9.126659e-08
#> sd 9.126659e-08 1.000000e+00
print(est.par)
#>
#> Parameters for the Normal distribution.
#> (found using the numerical.MLE method.)
#>
#> Parameter Type Estimate S.E.
#> mean location -0.06018004 0.2142349
#> sd scale 0.95808745 0.1514869
#>
#>
plot(est.par)