Calculate the log likelihood of the binomial function (and its derivatives)

Usage

llikBeta(x, shape1, shape2, full = FALSE)

Arguments

x: Observation
shape1, shape2: non-negative parameters of the Beta distribution.
full: Add the data frame showing x, mean, sd as well as the fx and derivatives

Value

data frame with fx for the log pdf value of with dShape1 and dShape2 that has the derivatives with respect to the parameters at the observation time-point

Author

Matthew L. Fidler

Examples


x <- seq(1e-4, 1 - 1e-4, length.out = 21)

llikBeta(x, 0.5, 0.5)
#>             fx     dShape1     dShape2
#> 1   3.46049030 -7.82404601  1.38619436
#> 2   0.37793108 -1.60763953  1.33490633
#> 3   0.05888752 -0.91549105  1.28084495
#> 4  -0.11510253 -0.51035907  1.22369308
#> 5  -0.22855163 -0.22284360  1.16307581
#> 6  -0.30780832  0.00019998  1.09854562
#> 7  -0.36444410  0.18245488  1.02956227
#> 8  -0.40444714  0.33655795  0.95546529
#> 9  -0.43118004  0.47005363  0.87543540
#> 10 -0.44655956  0.58780889  0.78843918
#> 11 -0.45158271  0.69314718  0.69314718
#> 12 -0.44655956  0.78843918  0.58780889
#> 13 -0.43118004  0.87543540  0.47005363
#> 14 -0.40444714  0.95546529  0.33655795
#> 15 -0.36444410  1.02956227  0.18245488
#> 16 -0.30780832  1.09854562  0.00019998
#> 17 -0.22855163  1.16307581 -0.22284360
#> 18 -0.11510253  1.22369308 -0.51035907
#> 19  0.05888752  1.28084495 -0.91549105
#> 20  0.37793108  1.33490633 -1.60763953
#> 21  3.46049030  1.38619436 -7.82404601

llikBeta(x, 1, 3, TRUE)
#>          x shape1 shape2           fx     dShape1     dShape2
#> 1  0.00010      1      3   1.09841228 -7.37700704  0.33323333
#> 2  0.05009      1      3   0.99583622 -1.16060056  0.28194530
#> 3  0.10008      1      3   0.88771347 -0.46845208  0.22788392
#> 4  0.15007      1      3   0.77340972 -0.06332009  0.17073205
#> 5  0.20006      1      3   0.65217518  0.22419538  0.11011478
#> 6  0.25005      1      3   0.52311481  0.44723895  0.04558459
#> 7  0.30004      1      3   0.38514811  0.62949385 -0.02339876
#> 8  0.35003      1      3   0.23695415  0.78359692 -0.09749574
#> 9  0.40002      1      3   0.07689437  0.91709260 -0.17752562
#> 10 0.45001      1      3  -0.09709808  1.03484786 -0.26452185
#> 11 0.50000      1      3  -0.28768207  1.14018615 -0.35981385
#> 12 0.54999      1      3  -0.49835866  1.23547815 -0.46515214
#> 13 0.59998      1      3  -0.73386918  1.32247438 -0.58290740
#> 14 0.64997      1      3  -1.00086054  1.40250426 -0.71640308
#> 15 0.69996      1      3  -1.30906667  1.47660124 -0.87050615
#> 16 0.74995      1      3  -1.67357647  1.54558459 -1.05276105
#> 17 0.79994      1      3  -2.11966363  1.61011478 -1.27580462
#> 18 0.84993      1      3  -2.69469457  1.67073205 -1.56332009
#> 19 0.89992      1      3  -3.50495854  1.72788392 -1.96845208
#> 20 0.94991      1      3  -4.88925549  1.78194530 -2.66060056
#> 21 0.99990      1      3 -17.32206846  1.83323333 -8.87700704