S3 methods for manipulating eDist objects.
eDist.RdS3 methods for manipulating eDist objects
# S3 method for eDist
logLik(object, ...)
# S3 method for eDist
AIC(object, ..., k = 2)
AICc(object)
# S3 method for eDist
AICc(object, ...)
# S3 method for eDist
vcov(object, ..., corr = FALSE)
BIC(object)
# S3 method for eDist
BIC(object, ...)
MDL(object)
# S3 method for eDist
MDL(object, ...)
# S3 method for eDist
print(x, ...)
# S3 method for 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.
References
Myung, I. (2000). The Importance of Complexity in Model Selection. Journal of mathematical psychology, 44(1), 190-204.
Examples
X <- rnorm(20)
est.par <- eNormal(X, method ="numerical.MLE")
logLik(est.par)
#> [1] -28.39302
AIC(est.par)
AICc(est.par)
#> [1] 61.49193
BIC(est.par)
#> [1] 62.77751
MDL(est.par)
#> [1] 31.73391
vcov(est.par)
#> mean sd
#> mean 5.007131e-02 1.541113e-10
#> sd 1.541113e-10 2.503565e-02
vcov(est.par,corr=TRUE)
#> mean sd
#> mean 1.000000e+00 4.352718e-09
#> sd 4.352718e-09 1.000000e+00
print(est.par)
#>
#> Parameters for the Normal distribution.
#> (found using the numerical.MLE method.)
#>
#> Parameter Type Estimate S.E.
#> mean location -0.1843416 0.2237662
#> sd scale 1.0007128 0.1582266
#>
#>
plot(est.par)
