ProfileLikelihood (version 1.1)

profilelike.summary: Summary for the Maximum Profile Likelihood Estimate and Likelihood Support Intervals

Description

The function provides the maximum profile likelihood estimate and likelihood support intervals (Royall, 1997).

Usage

profilelike.summary(k, theta = theta, profile.lik.norm = profile.lik.norm, 
		round = 2)

Arguments

k

strength of evidence for the kth likelihood support interval.

theta

numerical grid values for a parameter of interest in a specified range.

profile.lik.norm

numerical values for a normalized profile likelihood ranging from 0 to 1.

round

the number of decimal places for round function for presentation of the maximum profile likelihood estimate and the kth likelihood support intervals.

Value

k

strength of evidence for the kth likelihood support interval.

mle

the maximum profile likelihood estimate.

LI.k

the kth likelihood support interval.

LI.norm

likelihood support interval (k=6.8) corresponding to a 95% confidence interval based on a normal approximation.

Details

The function provides the maximum profile likelihood estimate and likelihood support intervals for a profile likelihood obtained from profilelike.lm, profilelike.glm, profilelike.polr, profilelike.gls and profilelike.lme. The kth likelihood support interval and the likelihood support interval (k=6.8) corresponding to a 95% confidence interval based on a normal approximation are provided.

References

Royall, Richard M. (1997). Statistical Evidence: A Likelihood Paradiam. Chapman & Hall/CRC.

Pawitan, Yudi (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press.

See Also

profilelike.plot, profilelike.lm, profilelike.glm, profilelike.polr, profilelike.gls, profilelike.lme

Examples

# NOT RUN {
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- c(rep(0,10), rep(1,10))
weight <- c(ctl, trt)
dd <- data.frame(group=group, weight=weight)
xx <- profilelike.lm(formula = weight ~ 1, data=dd, profile.theta="group",
				lo.theta=-2, hi.theta=1, length=500)
profilelike.plot(theta=xx$theta, profile.lik.norm=xx$profile.lik.norm, round=2)
profilelike.summary(k=8, theta=xx$theta, profile.lik.norm=xx$profile.lik.norm, round=3)
# }