# profile.gcmr

0th

Percentile

##### Profile Log-Likelihood for Gaussian Copula Marginal Regression Models

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

Keywords
regression, nonlinear
##### 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.

##### Details

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

##### Value

A list with the following components:

points

points at which the profile log-likelihood is evaluated.

profile

values of the profile log-likelihood.

##### 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.

gcmr

• profile.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
# }

Documentation reproduced from package gcmr, version 1.0.2, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.