R Under development (unstable) (2024-10-16 r87241 ucrt) -- "Unsuffered Consequences"
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> library(POT)
> set.seed(123)
> 
> mc <- simmc(100, alpha = 0.25)
> mc <- qgpd(mc, 0, 1, 0.25)
> ##A first application when marginal parameter are estimated
> f1 <- fitmcgpd(mc, 0)
> str(f1)
List of 22
 $ fitted.values: Named num [1:3] 0.841 0.331 0.247
  ..- attr(*, "names")= chr [1:3] "scale" "shape" "alpha"
 $ std.err      : Named num [1:3] 0.5453 0.1893 0.0634
  ..- attr(*, "names")= chr [1:3] "scale" "shape" "alpha"
 $ var.cov      : num [1:3, 1:3] 0.2973 -0.0165 -0.0281 -0.0165 0.0358 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:3] "scale" "shape" "alpha"
  .. ..$ : chr [1:3] "scale" "shape" "alpha"
 $ fixed        : NULL
 $ param        : Named num [1:3] 0.841 0.331 0.247
  ..- attr(*, "names")= chr [1:3] "scale" "shape" "alpha"
 $ deviance     : num 42
 $ corr         : NULL
 $ convergence  : chr "successful"
 $ counts       : Named int [1:2] 50 14
  ..- attr(*, "names")= chr [1:2] "function" "gradient"
 $ message      : NULL
 $ threshold    : num 0
 $ nat          : int 98
 $ pat          : num 1
 $ data         : num [1:100] 0.354 0.573 0.509 0.905 1.602 ...
 $ exceed       : num [1:98] 0.573 0.509 0.905 1.602 0.72 ...
 $ call         : language fitmcgpd(data = mc, threshold = 0)
 $ est          : chr "MLE"
 $ model        : chr "log"
 $ logLik       : num -21
 $ var.thresh   : logi FALSE
 $ opt.value    : num -21
 $ chi          : Named num 0.813
  ..- attr(*, "names")= chr "alpha"
 - attr(*, "class")= chr [1:3] "mcpot" "uvpot" "pot"
> f1

Call: fitmcgpd(data = mc, threshold = 0) 
Estimator: MLE 
Dependence Model and Strenght:
	Model : Logistic 
	lim_u Pr[ X_1 > u | X_2 > u] = 0.813 
Deviance: 41.99968 
     AIC: 47.99968 

Threshold Call: 
Number Above: 98 
Proportion Above: 1 

Estimates
 scale   shape   alpha  
0.8409  0.3309  0.2467  

Standard Errors
  scale    shape    alpha  
0.54526  0.18926  0.06336  

Asymptotic Variance Covariance
       scale      shape      alpha    
scale   0.297306  -0.016485  -0.028131
shape  -0.016485   0.035818  -0.004116
alpha  -0.028131  -0.004116   0.004015

Optimization Information
  Convergence: successful 
  Function Evaluations: 50 
  Gradient Evaluations: 14 

> 
> proc.time()
   user  system elapsed 
   0.23    0.12    0.32