# 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
# }
```

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