jdpar.asymp.Rdjdpar.asymp takes input object from dr_asympar for asymptotic bayesian distribution.
It returns objects for joint mutivariate density of parameters across several thresholds.
Check for positive definiteness of the covariance matrix and exclude thresholds yielding
negative eigen values.
jdpar.asymp(drabj, data, jdF = FALSE, vcovfn = "vcovHC", ...)
| drabj | object from |
|---|---|
| data | dataframe, first column is the outcome |
| jdF | logical to return joint density of F(yo) across thresholds in drabj |
| vcovfn | a string denoting the function to extract the variance-covariance. Defaults at "vcov". Other variance-covariance estimators in the sandwich package are usable. |
| ... | additional input to pass to |
mean vector Theta and variance-covariance matrix vcovpar of parameters across
thresholds and if jdF=TRUE,
a mean vector mnF and a variance-covariance matrix vcovF of F(yo)
y = faithful$waiting x = scale(cbind(faithful$eruptions,faithful$eruptions^2)) qtaus = quantile(y,c(0.05,0.25,0.5,0.75,0.95)) drabj<- dr_asympar(y=y,x=x,thresh = qtaus); data = data.frame(y,x) (drjasy = jdpar.asymp(drabj=drabj,data=data,jdF=TRUE))#> $Theta #> [1] -12.529504 7.552197 -16.412033 -6.222253 4.537972 -10.468049 #> [7] 2.048402 -13.100002 8.855666 2.522160 -6.544941 4.100806 #> [13] 8.356307 -21.168439 13.933436 #> #> $vcovpar #> [,1] [,2] [,3] [,4] [,5] #> [1,] 108.9067266 -155.2065192 246.3682334 0.13125022 -0.00209570 #> [2,] -155.2065192 247.2232407 -378.4328698 -0.19510939 0.10646607 #> [3,] 246.3682334 -378.4328698 586.0628795 0.30531646 -0.11268514 #> [4,] 0.1312502 -0.1951094 0.3053165 3.15340158 -7.61883056 #> [5,] -0.0020957 0.1064661 -0.1126851 -7.61883056 29.30944645 #> [6,] 0.1027580 -0.2605866 0.3516794 10.32514625 -36.16117443 #> [7,] 0.5023403 -0.6909663 1.1066747 0.01920583 -0.04187999 #> [8,] -0.2583467 0.4558942 -0.6534718 -0.04081865 0.13236808 #> [9,] 0.7602264 -1.1262124 1.7473638 0.05107603 -0.14468128 #> [10,] 0.6194481 -0.8426163 1.3581362 0.02002443 -0.03907318 #> [11,] -1.0870316 1.5429345 -2.4450419 -0.05061806 0.12731614 #> [12,] 1.4389064 -2.0190087 3.2167802 0.05986334 -0.14321280 #> [13,] 2.0617833 -3.0299233 4.6124610 0.19154970 -0.47163981 #> [14,] -7.9041868 11.8688397 -17.8356426 -0.84559049 2.15312222 #> [15,] 6.4465708 -9.5992343 14.5229972 0.63941363 -1.62396298 #> [,6] [,7] [,8] [,9] [,10] #> [1,] 0.10275800 0.5023402688 -0.258346739 0.760226402 0.6194480693 #> [2,] -0.26058660 -0.6909662721 0.455894200 -1.126212389 -0.8426162830 #> [3,] 0.35167944 1.1066747380 -0.653471805 1.747363817 1.3581362141 #> [4,] 10.32514625 0.0192058269 -0.040818646 0.051076029 0.0200244265 #> [5,] -36.16117443 -0.0418799935 0.132368083 -0.144681280 -0.0390731773 #> [6,] 45.38227590 0.0546187366 -0.146545920 0.172436649 0.0547134404 #> [7,] 0.05461874 0.4245404549 -2.495012901 1.971820088 0.0006279511 #> [8,] -0.14654592 -2.4950129015 17.847465782 -14.624882189 -0.0029906143 #> [9,] 0.17243665 1.9718200875 -14.624882189 12.109001500 0.0022769653 #> [10,] 0.05471344 0.0006279511 -0.002990614 0.002276965 0.0889907723 #> [11,] -0.16331240 -0.0038409754 0.030939625 -0.025922593 -0.3164031405 #> [12,] 0.18903782 0.0031144113 -0.026943359 0.022934557 0.2281976071 #> [13,] 0.49355090 0.0281009153 -0.088614670 0.055804039 0.0163908878 #> [14,] -2.22750649 -0.1313575089 0.462125220 -0.305749115 -0.0737309628 #> [15,] 1.70002594 0.0961152521 -0.348146083 0.233371475 0.0533859902 #> [,11] [,12] [,13] [,14] [,15] #> [1,] -1.087031557 1.438906449 2.061783e+00 -7.90418677 6.44657075 #> [2,] 1.542934479 -2.019008685 -3.029923e+00 11.86883971 -9.59923432 #> [3,] -2.445041891 3.216780217 4.612461e+00 -17.83564255 14.52299717 #> [4,] -0.050618060 0.059863338 1.915497e-01 -0.84559049 0.63941363 #> [5,] 0.127316142 -0.143212804 -4.716398e-01 2.15312222 -1.62396298 #> [6,] -0.163312400 0.189037823 4.935509e-01 -2.22750649 1.70002594 #> [7,] -0.003840975 0.003114411 2.810092e-02 -0.13135751 0.09611525 #> [8,] 0.030939625 -0.026943359 -8.861467e-02 0.46212522 -0.34814608 #> [9,] -0.025922593 0.022934557 5.580404e-02 -0.30574912 0.23337148 #> [10,] -0.316403140 0.228197607 1.639089e-02 -0.07373096 0.05338599 #> [11,] 4.510830091 -4.055196765 -2.027979e-02 0.12899538 -0.10171321 #> [12,] -4.055196765 3.724876360 3.057156e-03 -0.04907088 0.04353567 #> [13,] -0.020279793 0.003057156 3.141960e+01 -155.15256215 115.34941957 #> [14,] 0.128995383 -0.049070877 -1.551526e+02 783.42357303 -585.62768957 #> [15,] -0.101713205 0.043535671 1.153494e+02 -585.62768957 438.43861272 #> #> $mnF #> [1] 0.05882353 0.25735294 0.52573529 0.78308824 0.95588235 #> #> $vcovF #> [,1] [,2] [,3] [,4] [,5] #> [1,] 1.202606e-03 4.971103e-06 6.092426e-07 1.045437e-06 1.042017e-08 #> [2,] 4.971103e-06 1.719992e-03 2.443587e-06 2.216778e-06 1.792418e-07 #> [3,] 6.092426e-07 2.443587e-06 1.509185e-03 5.525976e-06 3.926673e-06 #> [4,] 1.045437e-06 2.216778e-06 5.525976e-06 1.744816e-03 4.359817e-06 #> [5,] 1.042017e-08 1.792418e-07 3.926673e-06 4.359817e-06 9.716957e-04 #>