Utility Methods for S3 class triangle_mle
# S3 method for triangle_mle
summary(object, ...)
# S3 method for triangle_mle
print(x, ...)
# S3 method for triangle_mle
coef(object, ...)
# S3 method for triangle_mle
logLik(object, ...)
# S3 method for triangle_mle
AIC(object, ..., k = 2)
# S3 method for triangle_mle
BIC(object, ...)
# S3 method for triangle_mle
vcov(object, ...)
# S3 method for triangle_mle
profile(fitted, ...)
# S3 method for triangle_mle
confint(object, parm, level = 0.95, ...)
class triangle_mle from a call to triangle_mle()
not used except for print
(other arguments passed to printCoefmat
)
the triangle_mle
object
the penalty per parameter to be used; the default k = 2
an object of class triangle_mle
parameters
confidence interval level
an object of class summary.mle
x invisibly
a vector of coefficients
an object of class logLik
the AIC
the BIC
the variance co-variance matrix
an object of class profile.mle
an object of class profile.mle
set.seed(1234)
x <- rtriangle(100, 0, 1, 0.5)
mle1 <- triangle_mle(x)
summary(mle1)
#> Maximum likelihood estimation
#>
#> Call:
#> triangle_mle(x = x)
#>
#> Coefficients:
#> Estimate Std. Error
#> a 0.02332696 0.02638939
#> b 0.96376880 0.02408263
#> c 0.38903301 0.02333771
#>
#> -2 log L: -51.57153
print(mle1)
#> Triangle Maximum Likelihood Estimates
#>
#> Call: triangle_mle(x = x)
#>
#> Estimates:
#> Estimate Std.Err
#> a 0.023327 0.0264
#> b 0.963769 0.0241
#> c 0.389033 0.0233
#>
#> Convergence Code: 0
#> CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH
coef(mle1)
#> a b c
#> 0.02332696 0.96376880 0.38903301
logLik(mle1)
#> 'log Lik.' 25.78577 (df=3)
AIC(mle1)
#> [1] -45.57153
BIC(mle1)
#> [1] -37.75602
vcov(mle1)
#> a b c
#> a 6.963999e-04 -4.543361e-05 0.0000000000
#> b -4.543361e-05 5.799729e-04 0.0000000000
#> c 0.000000e+00 0.000000e+00 0.0005446485
if (FALSE) {
prof <- profile(mle1)
stats4::plot(prof)
confint(mle1, 1:3, level = 0.95)
}