gcmr (version 1.0.2)

profile.gcmr: Profile Log-Likelihood for Gaussian Copula Marginal Regression Models

Description

Computes the profile log-likelihood for mean response parameters of a Gaussian copula marginal regression model.

Usage

# S3 method for gcmr
profile(fitted, which, low, up, npoints = 10, 
        display = TRUE, alpha = 0.05, progress.bar = TRUE, ...)

Arguments

fitted

a fitted Gaussian copula marginal regression model of class gcmr.

which

the index of the regression parameter which should be profiled.

low

the lower limit used in computation of the profile log-likelihood. If this is missing, then the lower limit is set equal to the estimate minus three times its standard error.

up

the upper limit used in computation of the profile log-likelihood. If this is missing, then the upper limit is set equal to the estimate plus three times its standard error.

npoints

number of points used in computation of the profile log-likelihood. Default is 10.

display

should the profile log-likelihood be displayed or not? default is TRUE.

alpha

the significance level, default is 0.05.

progress.bar

logical. If TRUE, a text progress bar is displayed.

...

further arguments passed to plot.

Value

A list with the following components:

points

points at which the profile log-likelihood is evaluated.

profile

values of the profile log-likelihood.

Details

If the display is requested, then the profile log-likelihood is smoothed by cubic spline interpolation.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517--1549. http://projecteuclid.org/euclid.ejs/1346421603.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1--26. 10.18637/jss.v077.i08.

See Also

gcmr

Examples

# NOT RUN {
## spatial binomial data
# }
# NOT RUN {
data(malaria)
D <- sp::spDists(cbind(malaria$x, malaria$y))/1000
m <- gcmr(cbind(cases, size-cases) ~ netuse+I(green/100)+phc, data=malaria,
marginal=binomial.marg, cormat=matern.cormat(D), options=gcmr.options(seed=987))
prof <- profile(m, which = 2)
prof
# }