profile.gcmr
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 at which the profile log-likelihood is evaluated.
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.
See Also
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
# }