profile.gcmr | R Documentation |
Computes the profile log-likelihood for mean response parameters of a Gaussian copula marginal regression model.
## S3 method for class 'gcmr' profile(fitted, which, low, up, npoints = 10, display = TRUE, alpha = 0.05, progress.bar = TRUE, ...)
fitted |
a fitted Gaussian copula marginal regression model of class |
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 |
up |
the upper limit used in computation of the profile log-likelihood. If this is |
npoints |
number of points used in computation of the profile log-likelihood. Default is |
display |
should the profile log-likelihood be displayed or not? default is |
alpha |
the significance level, default is |
progress.bar |
logical. If TRUE, a text progress bar is displayed. |
... |
further arguments passed to |
If the display is requested, then the profile log-likelihood is smoothed by cubic spline interpolation.
A list with the following components:
points |
points at which the profile log-likelihood is evaluated. |
profile |
values of the profile log-likelihood. |
Guido Masarotto and Cristiano Varin.
Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.
Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.
gcmr
## 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 ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.