ProfileLikelihood (version 1.1)

profilelike.lm: Profile Likelihood for Linear Models

Description

This function provides values for a profile likelihood and a normalized profile likelihood for a parameter of interest in a linear model.

Usage

profilelike.lm(formula, data, profile.theta, lo.theta = NULL, hi.theta = NULL, 
	length = 300, round = 2, subset = NULL, weights = NULL, offset = NULL, ...)

Arguments

formula

see corresponding documentation in lm.

data

a data frame. See corresponding documentation in lm.

profile.theta

a parameter of interest, theta; must be a numeric variable.

lo.theta

lower bound for a parameter of interest to obtain values for a profile likelihood.

hi.theta

upper bound for a parameter of interest to obtain values for a profile likelihood.

length

length of numerical grid values for a parameter of interest to obtain values for a profile likelihood.

round

the number of decimal places for round function to automatically define lower and upper bounds of numerical grid for a parameter of interest. If an automatically defined parameter range is not appropriate, increase the number or specify lo.theta and hi.theta.

subset

should not be provided.

weights

should not be provided.

offset

should not be provided.

…

further arguments passed to or from other methods.

Value

theta

numerical grid values for a parameter of interest in a specified range (between lower and upper bounds).

profile.lik

numerical values for a profile likelihood corresponding to theta in a specified range (between lower and upper bounds).

profile.lik.norm

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

Warning

Arguments weights, subset, and offset in the lm function are not comparable.

Missing values should be removed.

Details

This function provides values for a profile likelihood and a normalized profile likelihood for a parameter of interest in a linear model. Users must define a parameter of interest in a linear model. This function can be used for linear models comparable with the lm function. However, arguments weights, subset, and offset should not be provided. A normalized profile likelihood is obtained by a profile likelihood being divided by the maximum value of the profile likelihood so that a normalized profile likelihood ranges from 0 to 1.

See Also

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

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)
# }

Run the code above in your browser using DataCamp Workspace