Utility Methods for S3 class triangle_mle

Utility Methods for S3 class triangle_mle

Usage

# 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, ...)

Arguments

object

class triangle_mle from a call to triangle_mle()

...

not used except for print (other arguments passed to printCoefmat)

x

the triangle_mle object

k

the penalty per parameter to be used; the default k = 2

fitted

an object of class triangle_mle

parm

parameters to be given confidence intervals passed to stats4::confint

level

confidence interval level passed to stats4::confint

Value

an object of class summary.mle

print.triangle_mle: x invisibly

coef.triangle_mle: a vector of coefficients

logLik.triangle_mle: an object of class logLik

AIC.triangle_mle: the AIC

BIC.triangle_mle: the BIC

vcov.triangle_mle: the variance co-variance matrix

profile.triangle_mle: an object of class profile.mle

confint.triangle_mle: a matrix of parameter confidence intervals

Examples

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)
}