Computes profile traces for fitted max-stable models.
# S3 method for maxstab
profile(fitted, param, range, n = 10, plot = TRUE,
conf = 0.90, method = "RJ", square = "chol", …)
An object of class ``maxstab''. Most often, it will be
the output of the function fitmaxstab
.
A character string giving the model parameter that are to be profiled.
The range for the profiled model parameter that must be explored.
Integer. The number of profiled model parameter that must be considered.
Logical. If TRUE
(default), the profile trace is
plotted.
Numeric giving the confidence interval level.
Character string. Must be one of "CB", "RJ" or "none" for the Chandler and Bate or the Rotnitzky and Jewell approaches respectively. The "none" method simply plots the profile of the log-composite likelihood. See details.
The choice for the matrix square root. This is only useful for the 'CB' method. Must be one of 'chol' (Cholesky) or 'svd' (Singular Value Decomposition).
Extra options that must be passed to the
plot
function.
A matrix. The first column corresponds to the values for which the profiled model parameter is fixed. The second column gives the value of the pairwise log-likelihood. The remaining columns contain the constrained maximum likelihood estimates for the remaining model parameters.
This function can be really time consuming!
The Rotnitzky and Jewell approach consists in adjusting the
distribution of the likelihood ratio statistics - which under
misspecification is no longer
The Chandler and Bate approach adjusts the composite likelihood itself
is such a way that the usual asymptotic
Chandler, R. E. and Bate, S. (2007) Inference for clustered data using the independence loglikelihood Biometrika, 94, 167--183.
Rotnitzky, A. and Jewell, N. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika 77, 485--97.
# NOT RUN {
##Define the coordinates of each location
n.site <- 30
locations <- matrix(rnorm(2*n.site, sd = sqrt(.2)), ncol = 2)
colnames(locations) <- c("lon", "lat")
##Simulate a max-stable process - with unit Frechet margins
data <- rmaxstab(50, locations, cov.mod = "gauss", cov11 = 100, cov12 =
25, cov22 = 220)
##Fit a max-stable process
## 1- using the Smith's model
fitted <- fitmaxstab(data, locations, "gauss", fit.marge = FALSE)
##Plot the profile pairwise log-likelihood for the ''cov11'' parameter
profile(fitted, "cov11", range = c(20, 180))
# }
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