extra.parameter {ssym}

Tool that supports the estimation of the extra parameter.

Description

extra.parameter is used to plot a graph of the behaviour of the overall goodness-of-fit statistic and -2\textsf{L}(\hat{\theta}) versus the extra parameter \zeta in the interval/region defined by the arguments lower and upper. These graphs may be used to choosing the extra parameter value.

Usage

extra.parameter(object, lower, upper, grid)

Arguments

object

an object of the class ssym. This object is returned by the call to ssym.l(), ssym.nl() or ssym.l2()

lower

lower limit(s) of the interest interval/region for the extra parameter.

upper

upper limit(s) of the interest interval/region for the extra parameter.

grid

Number of values of the extra parameter where the overall goodness-of-fit statistic and -2\textsf{L}(\hat{\theta}) are evaluated.

Author(s)

Luis Hernando Vanegas <[e-mail]> and Gilberto A. Paula

References

Vanegas, L.H. and Paula, G.A. (2015b) Log-symmetric distributions: statistical properties and parameter estimation. Brazilian Journal of Probability and Statistics (to appear)

Examples

###################################################################################
############### Textures of snacks Data - a log-Student-t model  #################
###################################################################################
#data("Snacks", package="ssym")
#fit <- extra.parameter(log(texture) ~ type + ncs(week) | type, data=Snacks,
#        family='Student', xi=10)
#summary(fit)
#
############################ Extra parameter ###########################
#extra.parameter(fit,5,50)

###################################################################################
################## Biaxial Fatigue Data - a Birnbaum-Saunders model   #############
###################################################################################
#data("Biaxial", package="ssym")
#fit <- ssym.nl(log(Life) ~ b1*Work^b2, start=c(b1=16, b2=-0.25),
#                data=Biaxial, family='Sinh-normal', xi=1.54)
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,1.3,1.8)