Description Usage Arguments Details Value Warnings Author(s) References Examples
Computes profile traces for fitted max-stable models.
1 2 3 |
fitted |
An object of class “maxstab”. Most often, it will be
the output of the function |
param |
A character string giving the model parameter that are to be profiled. |
range |
The range for the profiled model parameter that must be explored. |
n |
Integer. The number of profiled model parameter that must be considered. |
plot |
Logical. If |
conf |
Numeric giving the confidence interval level. |
method |
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. |
square |
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
|
The Rotnitzky and Jewell approach consists in adjusting the distribution of the likelihood ratio statistics - which under misspecification is no longer χ^2 distributed.
The Chandler and Bate approach adjusts the composite likelihood itself is such a way that the usual asymptotic χ^2 null distribution is preserved. Note that in the current code, we use the singular value decomposition for the computation of matrix square roots to preserve asymmetry in the profile composite likelihood.
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!
Mathieu Ribatet
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## 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))
## End(Not run)
|
llik cov11 cov12 cov22
[1,] -32645.38 20.00000 1.866475 476.9397
[2,] -29332.61 37.77778 6.975929 435.8882
[3,] -27907.35 55.55556 14.539194 401.4623
[4,] -27193.48 73.33333 23.365510 375.2561
[5,] -26826.53 91.11111 32.692081 354.4248
[6,] -26652.31 108.88889 42.822252 337.8081
[7,] -26594.08 126.66667 53.137934 324.6270
[8,] -26609.52 144.44444 63.677889 314.6852
[9,] -26673.44 162.22222 74.245576 305.2768
[10,] -26770.17 180.00000 84.533050 297.9062
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