SnapBuddy_Results.txt

fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Estimation Method used: MLE"


 Negative Log-Likelihood Value:  233.3215 


 Estimated parameters:
       scale        shape 
3.898304e+02 1.155735e-08 

 Standard Error Estimates:
    scale     shape 
7.2701414 0.1085682 

 Estimated parameter covariance matrix.
            scale       shape
scale 52.85495614 -0.04753294
shape -0.04753294  0.01178705

 AIC = 470.6429 

 BIC = 473.6956 
[1] "the length of array 0 is 419"
[1] "threshold_0 is :  14562.21"
[1] "num. items over threshold t :  34"
[1] "computation time 2.216064453125"
[1] "The mean and std=: 3464.94272076372" "The mean and std=: 4012.86893444386"
[1] "The error for mean and std: 196.041476086365" "The error for mean and std: 138.622257134489"
fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     15431.75 16006.06     16580.36
2.73972602739726-1 unit = 365 sample return level     15523.69 16276.27     17028.84
5.47945205479452-1 unit = 365 sample return level     15398.84 16546.48     17694.11
13.6986301369863-1 unit = 365 sample return level     15427.69 16903.67     18379.66
27.3972602739726-1 unit = 365 sample return level     15253.01 17173.88     19094.76
54.7945205479452-1 unit = 365 sample return level     15135.82 17444.09     19752.37
136.986301369863-1 unit = 365 sample return level     14951.78 17801.29     20650.81

fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     15431.75 16006.06     16580.36
2.73972602739726-1 unit = 365 sample return level     15523.69 16276.27     17028.84
5.47945205479452-1 unit = 365 sample return level     15398.84 16546.48     17694.11
13.6986301369863-1 unit = 365 sample return level     15427.69 16903.67     18379.66
27.3972602739726-1 unit = 365 sample return level     15253.01 17173.88     19094.76
54.7945205479452-1 unit = 365 sample return level     15135.82 17444.09     19752.37
136.986301369863-1 unit = 365 sample return level     14951.78 17801.29     20650.81

[1] "--------------------------"
There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> print("Bayesian Factor Method:")
[1] "Bayesian Factor Method:"
> print(ind)
[1] 2
> print(max_index)
[1] 419
> print(max_observed)
[1] 16712
> print("--------------------------")
[1] "--------------------------"
> print("Actual observed Time:")
[1] "Actual observed Time:"
> # what are actual timing after these steps:
> for(i in c(500, 1000, 2000, 3000, 4000, 5000, 10000))
+ {
+   print(i)
+   print(max(a$V1[1:i], na.rm = TRUE))
+ }
[1] 500
[1] 16712
[1] 1000
[1] 16712
[1] 2000
[1] 16712
[1] 3000
[1] 16838
[1] 4000
[1] 17181
[1] 5000
[1] 17181
[1] 10000
[1] 17181