The Poisson Approximation (DC) approach is requested with method = "Poisson". It is based on a Poisson distribution, whose parameter is the sum of the probabilities of success.
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
dpbinom(NULL, pp, wt, "Poisson")
#> [1] 2.263593e-16 8.154460e-15 1.468798e-13 1.763753e-12 1.588454e-11
#> [6] 1.144462e-10 6.871428e-10 3.536273e-09 1.592402e-08 6.373926e-08
#> [11] 2.296169e-07 7.519830e-07 2.257479e-06 6.255718e-06 1.609704e-05
#> [16] 3.865908e-05 8.704191e-05 1.844490e-04 3.691482e-04 6.999128e-04
#> [21] 1.260697e-03 2.162661e-03 3.541299e-03 5.546660e-03 8.325631e-03
#> [26] 1.199704e-02 1.662255e-02 2.217842e-02 2.853445e-02 3.544609e-02
#> [31] 4.256414e-02 4.946284e-02 5.568342e-02 6.078674e-02 6.440607e-02
#> [36] 6.629115e-02 6.633610e-02 6.458699e-02 6.122916e-02 5.655755e-02
#> [41] 5.093630e-02 4.475488e-02 3.838734e-02 3.216003e-02 2.633059e-02
#> [46] 2.107875e-02 1.650760e-02 1.265269e-02 9.495953e-03 6.981348e-03
#> [51] 5.029979e-03 3.552981e-03 2.461424e-03 1.673044e-03 1.116119e-03
#> [56] 7.310458e-04 4.702766e-04 2.972182e-04 1.846053e-04 1.127169e-04
#> [61] 6.767601e-05 3.996702e-05
ppbinom(NULL, pp, wt, "Poisson")
#> [1] 2.263593e-16 8.380820e-15 1.552606e-13 1.919013e-12 1.780355e-11
#> [6] 1.322498e-10 8.193925e-10 4.355666e-09 2.027968e-08 8.401894e-08
#> [11] 3.136359e-07 1.065619e-06 3.323097e-06 9.578815e-06 2.567585e-05
#> [16] 6.433494e-05 1.513768e-04 3.358259e-04 7.049740e-04 1.404887e-03
#> [21] 2.665584e-03 4.828245e-03 8.369543e-03 1.391620e-02 2.224184e-02
#> [26] 3.423887e-02 5.086142e-02 7.303984e-02 1.015743e-01 1.370204e-01
#> [31] 1.795845e-01 2.290474e-01 2.847308e-01 3.455175e-01 4.099236e-01
#> [36] 4.762147e-01 5.425508e-01 6.071378e-01 6.683670e-01 7.249245e-01
#> [41] 7.758608e-01 8.206157e-01 8.590031e-01 8.911631e-01 9.174937e-01
#> [46] 9.385724e-01 9.550800e-01 9.677327e-01 9.772287e-01 9.842100e-01
#> [51] 9.892400e-01 9.927930e-01 9.952544e-01 9.969275e-01 9.980436e-01
#> [56] 9.987746e-01 9.992449e-01 9.995421e-01 9.997267e-01 9.998394e-01
#> [61] 9.999071e-01 1.000000e+00A comparison with exact computation shows that the approximation quality of the PA procedure increases with smaller probabilities of success. The reason is that the Poisson Binomial distribution approaches a Poisson distribution when the probabilities are very small.
set.seed(1)
# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "Poisson")
#> [1] 0.0000150619 0.0001672374 0.0009284471 0.0034362888 0.0095385726
#> [6] 0.0211820073 0.0391985129 0.0621763578 0.0862956727 0.1064633767
#> [11] 0.1182099310 0.1193204840 0.1104046811 0.0942969970 0.0747865595
#> [16] 0.0553587178 0.0384166744 0.0250913815 0.0154776776 0.0090449448
#> [21] 0.0050214559
dpbinom(NULL, pp)
#> [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#> [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "Poisson") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -9.555e-02 1.506e-05 9.038e-03 -2.461e-04 2.407e-02 4.379e-02
# U(0, 0.01) random probabilities of success
pp <- runif(20, 0, 0.01)
dpbinom(NULL, pp, method = "Poisson")
#> [1] 9.095763e-01 8.620639e-02 4.085167e-03 1.290592e-04 3.057942e-06
#> [6] 5.796418e-08 9.156063e-10 1.239684e-11 1.468661e-13 1.546605e-15
#> [11] 1.465817e-17 1.262953e-19 9.974852e-22 7.272161e-24 4.923067e-26
#> [16] 3.110605e-28 1.842575e-30 1.027251e-32 5.408845e-35 2.698058e-37
#> [21] 1.278562e-39
dpbinom(NULL, pp)
#> [1] 9.093051e-01 8.672423e-02 3.861917e-03 1.066765e-04 2.048094e-06
#> [6] 2.902198e-08 3.145829e-10 2.667571e-12 1.794592e-14 9.656258e-17
#> [11] 4.170114e-19 1.444465e-21 3.994453e-24 8.738444e-27 1.490372e-29
#> [16] 1.938487e-32 1.859939e-35 1.249654e-38 5.381374e-42 1.245845e-45
#> [21] 9.511846e-50
summary(dpbinom(NULL, pp, method = "Poisson") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -5.178e-04 0.000e+00 0.000e+00 0.000e+00 6.000e-10 2.712e-04The Arithmetic Mean Binomial Approximation (AMBA) approach is requested with method = "Mean". It is based on a Binomial distribution, whose parameter is the arithmetic mean of the probabilities of success.
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
mean(rep(pp, wt))
#> [1] 0.5905641
dpbinom(NULL, pp, wt, "Mean")
#> [1] 2.204668e-24 1.939788e-22 8.393759e-21 2.381049e-19 4.979863e-18
#> [6] 8.188480e-17 1.102354e-15 1.249300e-14 1.216331e-13 1.033156e-12
#> [11] 7.749086e-12 5.182139e-11 3.114432e-10 1.693217e-09 8.373498e-09
#> [16] 3.784379e-08 1.569327e-07 5.991812e-07 2.112610e-06 6.896287e-06
#> [21] 2.088890e-05 5.882491e-05 1.542694e-04 3.773093e-04 8.616897e-04
#> [26] 1.839474e-03 3.673702e-03 6.868933e-03 1.203071e-02 1.974641e-02
#> [31] 3.038072e-02 4.382068e-02 5.925587e-02 7.510979e-02 8.921887e-02
#> [36] 9.927353e-02 1.034154e-01 1.007871e-01 9.181496e-02 7.810121e-02
#> [41] 6.195859e-02 4.577391e-02 3.143980e-02 2.003761e-02 1.182352e-02
#> [46] 6.442647e-03 3.232269e-03 1.487928e-03 6.259647e-04 2.395401e-04
#> [51] 8.292214e-05 2.579729e-05 7.155695e-06 1.752667e-06 3.745215e-07
#> [56] 6.875325e-08 1.062521e-08 1.344354e-09 1.337294e-10 9.807924e-12
#> [61] 4.715599e-13 1.115034e-14
ppbinom(NULL, pp, wt, "Mean")
#> [1] 2.204668e-24 1.961834e-22 8.589942e-21 2.466948e-19 5.226557e-18
#> [6] 8.711136e-17 1.189465e-15 1.368247e-14 1.353155e-13 1.168472e-12
#> [11] 8.917558e-12 6.073895e-11 3.721822e-10 2.065399e-09 1.043890e-08
#> [16] 4.828268e-08 2.052154e-07 8.043966e-07 2.917007e-06 9.813294e-06
#> [21] 3.070220e-05 8.952711e-05 2.437965e-04 6.211058e-04 1.482796e-03
#> [26] 3.322270e-03 6.995972e-03 1.386490e-02 2.589561e-02 4.564203e-02
#> [31] 7.602274e-02 1.198434e-01 1.790993e-01 2.542091e-01 3.434279e-01
#> [36] 4.427015e-01 5.461169e-01 6.469040e-01 7.387189e-01 8.168201e-01
#> [41] 8.787787e-01 9.245526e-01 9.559924e-01 9.760300e-01 9.878536e-01
#> [46] 9.942962e-01 9.975285e-01 9.990164e-01 9.996424e-01 9.998819e-01
#> [51] 9.999648e-01 9.999906e-01 9.999978e-01 9.999995e-01 9.999999e-01
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00A comparison with exact computation shows that the approximation quality of the AMBA procedure increases when the probabilities of success are closer to each other. The reason is that, although the expectation remains unchanged, the distribution’s variance becomes smaller the less the probabilities differ. Since this variance is minimized by equal probabilities (but still underestimated), the AMBA method is best suited for situations with very similar probabilities of success.
set.seed(1)
# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "Mean")
#> [1] 9.203176e-08 2.297178e-06 2.723611e-05 2.039497e-04 1.081780e-03
#> [6] 4.320318e-03 1.347977e-02 3.364646e-02 6.823695e-02 1.135495e-01
#> [11] 1.558851e-01 1.768638e-01 1.655492e-01 1.271454e-01 7.934094e-02
#> [16] 3.960811e-02 1.544760e-02 4.536271e-03 9.435709e-04 1.239589e-04
#> [21] 7.735255e-06
dpbinom(NULL, pp)
#> [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#> [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.801e-02 2.290e-06 6.360e-04 0.000e+00 8.837e-03 1.662e-02
# U(0.3, 0.5) random probabilities of success
pp <- runif(20, 0.3, 0.5)
dpbinom(NULL, pp, method = "Mean")
#> [1] 4.348271e-05 5.672598e-04 3.515127e-03 1.375712e-02 3.813748e-02
#> [6] 7.960444e-02 1.298114e-01 1.693472e-01 1.795010e-01 1.561137e-01
#> [11] 1.120132e-01 6.642197e-02 3.249439e-02 1.304339e-02 4.253984e-03
#> [16] 1.109919e-03 2.262438e-04 3.472347e-05 3.774915e-06 2.591904e-07
#> [21] 8.453263e-09
dpbinom(NULL, pp)
#> [1] 4.015121e-05 5.344728e-04 3.370391e-03 1.338738e-02 3.756479e-02
#> [6] 7.915145e-02 1.299445e-01 1.702071e-01 1.806555e-01 1.569062e-01
#> [11] 1.121277e-01 6.604356e-02 3.200604e-02 1.269255e-02 4.078679e-03
#> [16] 1.045709e-03 2.088926e-04 3.133484e-05 3.320483e-06 2.216332e-07
#> [21] 7.008006e-09
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.155e-03 1.400e-09 1.735e-05 0.000e+00 3.508e-04 5.727e-04
# U(0.39, 0.41) random probabilities of success
pp <- runif(20, 0.39, 0.41)
dpbinom(NULL, pp, method = "Mean")
#> [1] 3.638616e-05 4.854405e-04 3.076305e-03 1.231262e-02 3.490673e-02
#> [6] 7.451247e-02 1.242621e-01 1.657824e-01 1.797056e-01 1.598344e-01
#> [11] 1.172824e-01 7.112295e-02 3.558286e-02 1.460687e-02 4.871885e-03
#> [16] 1.299951e-03 2.709859e-04 4.253314e-05 4.728746e-06 3.320414e-07
#> [21] 1.107470e-08
dpbinom(NULL, pp)
#> [1] 3.636149e-05 4.851935e-04 3.075192e-03 1.230970e-02 3.490204e-02
#> [6] 7.450845e-02 1.242626e-01 1.657891e-01 1.797153e-01 1.598415e-01
#> [11] 1.172840e-01 7.112011e-02 3.557873e-02 1.460374e-02 4.870251e-03
#> [16] 1.299328e-03 2.708111e-04 4.249771e-05 4.723809e-06 3.316172e-07
#> [21] 1.105772e-08
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -9.641e-06 1.700e-11 1.747e-07 0.000e+00 2.844e-06 4.689e-06The Geometric Mean Binomial Approximation (Variant A) (GMBA-A) approach is requested with method = "GeoMean". It is based on a Binomial distribution, whose parameter is the geometric mean of the probabilities of success: \[\hat{p} = \sqrt[n]{p_1 \cdot ... \cdot p_n}\]
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
prod(rep(pp, wt))^(1/sum(wt))
#> [1] 0.4669916
dpbinom(NULL, pp, wt, "GeoMean")
#> [1] 2.141782e-17 1.144670e-15 3.008684e-14 5.184208e-13 6.586057e-12
#> [6] 6.578175e-11 5.379195e-10 3.703028e-09 2.189958e-08 1.129911e-07
#> [11] 5.147813e-07 2.091103e-06 7.633772e-06 2.520966e-05 7.572779e-05
#> [16] 2.078916e-04 5.236606e-04 1.214475e-03 2.601021e-03 5.157435e-03
#> [21] 9.489168e-03 1.623184e-02 2.585712e-02 3.841422e-02 5.328923e-02
#> [26] 6.909972e-02 8.382634e-02 9.520502e-02 1.012875e-01 1.009827e-01
#> [31] 9.437363e-02 8.268481e-02 6.791600e-02 5.229152e-02 3.772988e-02
#> [36] 2.550094e-02 1.613623e-02 9.552467e-03 5.285892e-03 2.731219e-03
#> [41] 1.316117e-03 5.906156e-04 2.464113e-04 9.539397e-05 3.419132e-05
#> [46] 1.131690e-05 3.448772e-06 9.643463e-07 2.464308e-07 5.728188e-08
#> [51] 1.204491e-08 2.276152e-09 3.835067e-10 5.705775e-11 7.406038e-12
#> [56] 8.258409e-13 7.752374e-14 5.958061e-15 3.600079e-16 1.603823e-17
#> [61] 4.683928e-19 6.727527e-21
ppbinom(NULL, pp, wt, "GeoMean")
#> [1] 2.141782e-17 1.166088e-15 3.125293e-14 5.496737e-13 7.135731e-12
#> [6] 7.291748e-11 6.108370e-10 4.313865e-09 2.621345e-08 1.392046e-07
#> [11] 6.539859e-07 2.745088e-06 1.037886e-05 3.558852e-05 1.113163e-04
#> [16] 3.192079e-04 8.428685e-04 2.057343e-03 4.658364e-03 9.815799e-03
#> [21] 1.930497e-02 3.553681e-02 6.139393e-02 9.980815e-02 1.530974e-01
#> [26] 2.221971e-01 3.060234e-01 4.012285e-01 5.025160e-01 6.034986e-01
#> [31] 6.978723e-01 7.805571e-01 8.484731e-01 9.007646e-01 9.384945e-01
#> [36] 9.639954e-01 9.801316e-01 9.896841e-01 9.949700e-01 9.977012e-01
#> [41] 9.990173e-01 9.996080e-01 9.998544e-01 9.999498e-01 9.999840e-01
#> [46] 9.999953e-01 9.999987e-01 9.999997e-01 9.999999e-01 1.000000e+00
#> [51] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00It is known that the geometric mean of the probabilities of success is always smaller than their arithmetic mean. Thus, we get a stochastically smaller binomial distribution. A comparison with exact computation shows that the approximation quality of the GMBA-A procedure increases when the probabilities of success are closer to each other:
set.seed(1)
# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "GeoMean")
#> [1] 4.557123e-06 7.742984e-05 6.249130e-04 3.185359e-03 1.150098e-02
#> [6] 3.126602e-02 6.640491e-02 1.128282e-01 1.557610e-01 1.764351e-01
#> [11] 1.648790e-01 1.273387e-01 8.113517e-02 4.241734e-02 1.801777e-02
#> [16] 6.122779e-03 1.625497e-03 3.249263e-04 4.600672e-05 4.114199e-06
#> [21] 1.747603e-07
dpbinom(NULL, pp)
#> [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#> [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.11151 -0.01493 0.00000 0.00000 0.01140 0.10279
# U(0.4, 0.6) random probabilities of success
pp <- runif(20, 0.4, 0.6)
dpbinom(NULL, pp, method = "GeoMean")
#> [1] 1.317886e-06 2.551200e-05 2.345875e-04 1.362363e-03 5.604265e-03
#> [6] 1.735823e-02 4.200318e-02 8.131092e-02 1.278907e-01 1.650496e-01
#> [11] 1.757292e-01 1.546280e-01 1.122499e-01 6.686047e-02 3.235759e-02
#> [16] 1.252775e-02 3.789307e-03 8.629936e-04 1.392173e-04 1.418425e-05
#> [21] 6.864565e-07
dpbinom(NULL, pp)
#> [1] 1.046635e-06 2.098187e-05 1.993006e-04 1.192678e-03 5.043114e-03
#> [6] 1.601621e-02 3.964022e-02 7.829406e-02 1.253351e-01 1.642218e-01
#> [11] 1.770816e-01 1.574210e-01 1.151700e-01 6.896627e-02 3.347297e-02
#> [16] 1.296524e-02 3.913788e-03 8.873960e-04 1.421738e-04 1.435144e-05
#> [21] 6.864565e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.0029201 -0.0004375 0.0000000 0.0000000 0.0005612 0.0030169
# U(0.49, 0.51) random probabilities of success
pp <- runif(20, 0.49, 0.51)
dpbinom(NULL, pp, method = "GeoMean")
#> [1] 9.491177e-07 1.899145e-05 1.805052e-04 1.083550e-03 4.607292e-03
#> [6] 1.475040e-02 3.689366e-02 7.382266e-02 1.200193e-01 1.601024e-01
#> [11] 1.761970e-01 1.602558e-01 1.202494e-01 7.403508e-02 3.703527e-02
#> [16] 1.482120e-02 4.633845e-03 1.090839e-03 1.818935e-04 1.915586e-05
#> [21] 9.582517e-07
dpbinom(NULL, pp)
#> [1] 9.472606e-07 1.895984e-05 1.802539e-04 1.082315e-03 4.603107e-03
#> [6] 1.474011e-02 3.687497e-02 7.379784e-02 1.199969e-01 1.600932e-01
#> [11] 1.762060e-01 1.602781e-01 1.202742e-01 7.405383e-02 3.704562e-02
#> [16] 1.482542e-02 4.635093e-03 1.091093e-03 1.819256e-04 1.915775e-05
#> [21] 9.582517e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -2.485e-05 -4.219e-06 0.000e+00 0.000e+00 4.185e-06 2.482e-05The Geometric Mean Binomial Approximation (Variant B) (GMBA-B) approach is requested with method = "GeoMeanCounter". It is based on a Binomial distribution, whose parameter is 1 minus the geometric mean of the probabilities of failure: \[\hat{p} = 1 - \sqrt[n]{(1 - p_1) \cdot ... \cdot (1 - p_n)}\]
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
1 - prod(1 - rep(pp, wt))^(1/sum(wt))
#> [1] 0.7275426
dpbinom(NULL, pp, wt, "GeoMeanCounter")
#> [1] 3.574462e-35 5.822379e-33 4.664248e-31 2.449471e-29 9.484189e-28
#> [6] 2.887121e-26 7.195512e-25 1.509685e-23 2.721134e-22 4.279009e-21
#> [11] 5.941642e-20 7.356037e-19 8.184508e-18 8.237686e-17 7.541858e-16
#> [16] 6.310225e-15 4.844429e-14 3.424255e-13 2.235148e-12 1.350769e-11
#> [21] 7.574609e-11 3.948978e-10 1.917264e-09 8.681177e-09 3.670379e-08
#> [26] 1.450549e-07 5.363170e-07 1.856461e-06 6.019586e-06 1.829121e-05
#> [31] 5.209921e-05 1.391205e-04 3.482749e-04 8.172712e-04 1.797236e-03
#> [36] 3.702208e-03 7.139892e-03 1.288219e-02 2.172588e-02 3.421374e-02
#> [41] 5.024851e-02 6.872559e-02 8.738947e-02 1.031108e-01 1.126377e-01
#> [46] 1.136267e-01 1.055364e-01 8.994057e-02 7.004907e-02 4.962603e-02
#> [51] 3.180393e-02 1.831737e-02 9.406320e-03 4.265268e-03 1.687339e-03
#> [56] 5.734528e-04 1.640669e-04 3.843049e-05 7.077304e-06 9.609416e-07
#> [61] 8.553338e-08 3.744258e-09
ppbinom(NULL, pp, wt, "GeoMeanCounter")
#> [1] 3.574462e-35 5.858123e-33 4.722829e-31 2.496699e-29 9.733859e-28
#> [6] 2.984460e-26 7.493958e-25 1.584624e-23 2.879597e-22 4.566969e-21
#> [11] 6.398339e-20 7.995871e-19 8.984095e-18 9.136095e-17 8.455467e-16
#> [16] 7.155772e-15 5.560007e-14 3.980256e-13 2.633173e-12 1.614086e-11
#> [21] 9.188695e-11 4.867847e-10 2.404049e-09 1.108523e-08 4.778901e-08
#> [26] 1.928440e-07 7.291610e-07 2.585622e-06 8.605207e-06 2.689642e-05
#> [31] 7.899562e-05 2.181161e-04 5.663910e-04 1.383662e-03 3.180899e-03
#> [36] 6.883107e-03 1.402300e-02 2.690519e-02 4.863107e-02 8.284481e-02
#> [41] 1.330933e-01 2.018189e-01 2.892084e-01 3.923192e-01 5.049569e-01
#> [46] 6.185836e-01 7.241200e-01 8.140606e-01 8.841097e-01 9.337357e-01
#> [51] 9.655396e-01 9.838570e-01 9.932633e-01 9.975286e-01 9.992159e-01
#> [56] 9.997894e-01 9.999534e-01 9.999919e-01 9.999989e-01 9.999999e-01
#> [61] 1.000000e+00 1.000000e+00It is known that the geometric mean of the probabilities of failure is always smaller than their arithmetic mean. As a result, 1 minus the geometric mean is larger than 1 minus the arithmetic mean. Thus, we get a stochastically larger binomial distribution. A comparison with exact computation shows that the approximation quality of the GMBA-B procedure again increases when the probabilities of success are closer to each other:
set.seed(1)
# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#> [1] 4.401037e-11 2.019854e-09 4.403304e-08 6.062685e-07 5.912743e-06
#> [6] 4.341843e-05 2.490859e-04 1.143179e-03 4.262876e-03 1.304297e-02
#> [11] 3.292337e-02 6.868258e-02 1.182069e-01 1.669263e-01 1.915269e-01
#> [16] 1.758024e-01 1.260695e-01 6.807004e-02 2.603394e-02 6.288561e-03
#> [21] 7.215333e-04
dpbinom(NULL, pp)
#> [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#> [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.469e-01 -1.724e-02 -3.200e-07 0.000e+00 2.592e-02 1.528e-01
# U(0.4, 0.6) random probabilities of success
pp <- runif(20, 0.4, 0.6)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#> [1] 1.046635e-06 2.073844e-05 1.951870e-04 1.160254e-03 4.885321e-03
#> [6] 1.548796e-02 3.836059e-02 7.600922e-02 1.223688e-01 1.616443e-01
#> [11] 1.761588e-01 1.586582e-01 1.178895e-01 7.187414e-02 3.560358e-02
#> [16] 1.410928e-02 4.368234e-03 1.018282e-03 1.681387e-04 1.753458e-05
#> [21] 8.685930e-07
dpbinom(NULL, pp)
#> [1] 1.046635e-06 2.098187e-05 1.993006e-04 1.192678e-03 5.043114e-03
#> [6] 1.601621e-02 3.964022e-02 7.829406e-02 1.253351e-01 1.642218e-01
#> [11] 1.770816e-01 1.574210e-01 1.151700e-01 6.896627e-02 3.347297e-02
#> [16] 1.296524e-02 3.913788e-03 8.873960e-04 1.421738e-04 1.435144e-05
#> [21] 6.864565e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.0029663 -0.0005283 0.0000000 0.0000000 0.0004544 0.0029079
# U(0.49, 0.51) random probabilities of success
pp <- runif(20, 0.49, 0.51)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#> [1] 9.472606e-07 1.895800e-05 1.802225e-04 1.082065e-03 4.601880e-03
#> [6] 1.473596e-02 3.686475e-02 7.377926e-02 1.199722e-01 1.600709e-01
#> [11] 1.761969e-01 1.602871e-01 1.202964e-01 7.407854e-02 3.706427e-02
#> [16] 1.483571e-02 4.639289e-03 1.092334e-03 1.821786e-04 1.918963e-05
#> [21] 9.601293e-07
dpbinom(NULL, pp)
#> [1] 9.472606e-07 1.895984e-05 1.802539e-04 1.082315e-03 4.603107e-03
#> [6] 1.474011e-02 3.687497e-02 7.379784e-02 1.199969e-01 1.600932e-01
#> [11] 1.762060e-01 1.602781e-01 1.202742e-01 7.405383e-02 3.704562e-02
#> [16] 1.482542e-02 4.635093e-03 1.091093e-03 1.819256e-04 1.915775e-05
#> [21] 9.582517e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -2.467e-05 -4.159e-06 0.000e+00 0.000e+00 4.196e-06 2.470e-05The Normal Approximation (NA) approach is requested with method = "Normal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean and variance of the input probabilities of success.
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
dpbinom(NULL, pp, wt, "Normal")
#> [1] 2.552770e-32 1.207834e-30 5.219650e-29 2.022022e-27 7.021785e-26
#> [6] 2.185917e-24 6.100302e-23 1.526188e-21 3.423032e-20 6.882841e-19
#> [11] 1.240755e-17 2.005270e-16 2.905604e-15 3.774712e-14 4.396661e-13
#> [16] 4.591569e-12 4.299381e-11 3.609645e-10 2.717342e-09 1.834224e-08
#> [21] 1.110185e-07 6.025326e-07 2.932337e-06 1.279682e-05 5.007841e-05
#> [26] 1.757379e-04 5.530339e-04 1.560683e-03 3.949650e-03 8.963710e-03
#> [31] 1.824341e-02 3.329786e-02 5.450317e-02 8.000636e-02 1.053238e-01
#> [36] 1.243451e-01 1.316535e-01 1.250080e-01 1.064497e-01 8.129267e-02
#> [41] 5.567468e-02 3.419491e-02 1.883477e-02 9.303614e-03 4.121280e-03
#> [46] 1.637186e-03 5.832371e-04 1.863241e-04 5.337829e-05 1.371282e-05
#> [51] 3.159002e-06 6.525712e-07 1.208800e-07 2.007813e-08 2.990389e-09
#> [56] 3.993563e-10 4.782059e-11 5.134327e-12 4.942641e-13 4.266130e-14
#> [61] 3.301422e-15 2.441468e-16
ppbinom(NULL, pp, wt, "Normal")
#> [1] 2.552770e-32 1.233362e-30 5.342987e-29 2.075452e-27 7.229330e-26
#> [6] 2.258210e-24 6.326123e-23 1.589449e-21 3.581977e-20 7.241039e-19
#> [11] 1.313165e-17 2.136587e-16 3.119262e-15 4.086639e-14 4.805325e-13
#> [16] 5.072102e-12 4.806591e-11 4.090305e-10 3.126373e-09 2.146861e-08
#> [21] 1.324871e-07 7.350197e-07 3.667357e-06 1.646417e-05 6.654258e-05
#> [26] 2.422805e-04 7.953144e-04 2.355997e-03 6.305647e-03 1.526936e-02
#> [31] 3.351276e-02 6.681062e-02 1.213138e-01 2.013201e-01 3.066439e-01
#> [36] 4.309891e-01 5.626426e-01 6.876506e-01 7.941003e-01 8.753930e-01
#> [41] 9.310676e-01 9.652625e-01 9.840973e-01 9.934009e-01 9.975222e-01
#> [46] 9.991594e-01 9.997426e-01 9.999290e-01 9.999823e-01 9.999960e-01
#> [51] 9.999992e-01 9.999999e-01 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00A comparison with exact computation shows that the approximation quality of the NA procedure increases with larger numbers of probabilities of success:
set.seed(1)
# 10 random probabilities of success
pp <- runif(10)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.0053305 -0.0010422 0.0005271 0.0000000 0.0016579 0.0026553
# 1000 random probabilities of success
pp <- runif(1000)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -8.412e-06 0.000e+00 0.000e+00 0.000e+00 0.000e+00 3.815e-06
# 100000 random probabilities of success
pp <- runif(100000)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -4.484e-09 0.000e+00 8.720e-13 0.000e+00 4.914e-10 2.734e-09The Refined Normal Approximation (RNA) approach is requested with method = "RefinedNormal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean, variance and skewness of the input probabilities of success.
set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
dpbinom(NULL, pp, wt, "RefinedNormal")
#> [1] 2.579548e-31 1.128297e-29 4.507210e-28 1.611452e-26 5.156486e-25
#> [6] 1.476806e-23 3.785627e-22 8.685911e-21 1.783953e-19 3.280039e-18
#> [11] 5.399492e-17 7.959230e-16 1.050796e-14 1.242802e-13 1.317210e-12
#> [16] 1.251531e-11 1.066498e-10 8.155390e-10 5.599786e-09 3.455053e-08
#> [21] 1.917106e-07 9.574753e-07 4.308224e-06 1.748069e-05 6.401569e-05
#> [26] 2.117447e-04 6.329842e-04 1.710740e-03 4.180480e-03 9.234968e-03
#> [31] 1.843341e-02 3.322175e-02 5.401115e-02 7.912655e-02 1.043358e-01
#> [36] 1.236782e-01 1.316360e-01 1.256489e-01 1.074322e-01 8.218619e-02
#> [41] 5.618825e-02 3.428872e-02 1.865323e-02 9.032795e-03 3.886960e-03
#> [46] 1.483178e-03 5.004545e-04 1.487517e-04 3.873113e-05 8.757189e-06
#> [51] 1.693868e-06 2.722346e-07 3.388544e-08 2.218356e-09 0.000000e+00
#> [56] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [61] 0.000000e+00 0.000000e+00
ppbinom(NULL, pp, wt, "RefinedNormal")
#> [1] 2.579548e-31 1.154092e-29 4.622620e-28 1.657678e-26 5.322254e-25
#> [6] 1.530028e-23 3.938629e-22 9.079774e-21 1.874750e-19 3.467514e-18
#> [11] 5.746244e-17 8.533855e-16 1.136134e-14 1.356415e-13 1.452852e-12
#> [16] 1.396817e-11 1.206179e-10 9.361569e-10 6.535943e-09 4.108647e-08
#> [21] 2.327971e-07 1.190272e-06 5.498496e-06 2.297918e-05 8.699487e-05
#> [26] 2.987396e-04 9.317238e-04 2.642463e-03 6.822944e-03 1.605791e-02
#> [31] 3.449132e-02 6.771307e-02 1.217242e-01 2.008508e-01 3.051866e-01
#> [36] 4.288648e-01 5.605008e-01 6.861497e-01 7.935820e-01 8.757682e-01
#> [41] 9.319564e-01 9.662451e-01 9.848984e-01 9.939312e-01 9.978181e-01
#> [46] 9.993013e-01 9.998018e-01 9.999505e-01 9.999892e-01 9.999980e-01
#> [51] 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00A comparison with exact computation shows that the approximation quality of the RNA procedure increases with larger numbers of probabilities of success:
set.seed(1)
# 10 random probabilities of success
pp <- runif(10)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.0039538 -0.0006920 0.0003543 0.0000000 0.0017167 0.0023597
# 1000 random probabilities of success
pp <- runif(1000)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -2.974e-06 0.000e+00 0.000e+00 0.000e+00 0.000e+00 2.270e-06
# 100000 random probabilities of success
pp <- runif(100000)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.126e-09 0.000e+00 6.200e-13 0.000e+00 4.616e-10 2.293e-09To assess the performance of the approximation procedures, we use the microbenchmark package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
library(microbenchmark)
set.seed(1)
f1 <- function() dpbinom(NULL, runif(4000), method = "Normal")
f2 <- function() dpbinom(NULL, runif(4000), method = "RefinedNormal")
f3 <- function() dpbinom(NULL, runif(4000), method = "Poisson")
f4 <- function() dpbinom(NULL, runif(4000), method = "Mean")
f5 <- function() dpbinom(NULL, runif(4000), method = "GeoMean")
f6 <- function() dpbinom(NULL, runif(4000), method = "GeoMeanCounter")
f7 <- function() dpbinom(NULL, runif(4000), method = "DivideFFT")
microbenchmark(f1(), f2(), f3(), f4(), f5(), f6(), f7())
#> Unit: microseconds
#> expr min lq mean median uq max neval cld
#> f1() 599.2 637.80 697.765 649.50 669.00 2716.5 100 a
#> f2() 754.1 806.20 867.631 820.05 841.30 2949.5 100 b
#> f3() 909.9 984.95 1035.070 993.30 1008.35 3635.5 100 c
#> f4() 1313.2 1413.40 1481.377 1428.50 1447.95 3635.5 100 d
#> f5() 1464.7 1587.60 1616.613 1600.80 1618.45 2884.4 100 de
#> f6() 1453.8 1572.10 1645.180 1586.45 1605.20 3901.6 100 e
#> f7() 7513.9 7729.15 7988.867 7805.40 7869.70 12405.8 100 fClearly, the NA procedure is the fastest, followed by the RNA and PA methods, which need approximately 25% (RNA) and 50% (PA) more time, and the AMBA and GMBA approaches that need 2.5 times as long as the NA algorithm. AMBA, GMBA-A and GMBA-B procedures exhibit almost equal mean execution speed, with the AMBA algorithm being slightly faster. All of the approximation procedures outperform the fastest exact approach, DC-FFT, by far. Even the slowest approximate algorithm is roughly five times as fast as DC-FFT.
The Generalized Normal Approximation (G-NA) approach is requested with method = "Normal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean and variance of the input probabilities of success (see Introduction.
set.seed(2)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
va <- sample(0:10, 10, TRUE)
vb <- sample(0:10, 10, TRUE)
dgpbinom(NULL, pp, va, vb, wt, "Normal")
#> [1] 5.607923e-34 8.868899e-34 2.266907e-33 5.759009e-33 1.454159e-32
#> [6] 3.649437e-32 9.103112e-32 2.256856e-31 5.561194e-31 1.362016e-30
#> [11] 3.315478e-30 8.021587e-30 1.928965e-29 4.610400e-29 1.095224e-28
#> [16] 2.585931e-28 6.068497e-28 1.415453e-27 3.281403e-27 7.560907e-27
#> [21] 1.731562e-26 3.941418e-26 8.916960e-26 2.005077e-25 4.481212e-25
#> [26] 9.954281e-25 2.197730e-24 4.822684e-24 1.051849e-23 2.280173e-23
#> [31] 4.912836e-23 1.052075e-22 2.239296e-22 4.737247e-22 9.960718e-22
#> [36] 2.081639e-21 4.323844e-21 8.926573e-21 1.831680e-20 3.735634e-20
#> [41] 7.572323e-20 1.525612e-19 3.054984e-19 6.080284e-19 1.202787e-18
#> [46] 2.364851e-18 4.621350e-18 8.976023e-18 1.732802e-17 3.324790e-17
#> [51] 6.340586e-17 1.201834e-16 2.264174e-16 4.239603e-16 7.890246e-16
#> [56] 1.459506e-15 2.683313e-15 4.903282e-15 8.905378e-15 1.607563e-14
#> [61] 2.884254e-14 5.143387e-14 9.116221e-14 1.605945e-13 2.811877e-13
#> [66] 4.893417e-13 8.464047e-13 1.455104e-12 2.486337e-12 4.222561e-12
#> [71] 7.127579e-12 1.195799e-11 1.993996e-11 3.304764e-11 5.443857e-11
#> [76] 8.912982e-11 1.450405e-10 2.345880e-10 3.771137e-10 6.025440e-10
#> [81] 9.568753e-10 1.510330e-09 2.369401e-09 3.694497e-09 5.725614e-09
#> [86] 8.819398e-09 1.350224e-08 2.054578e-08 3.107347e-08 4.670967e-08
#> [91] 6.978689e-08 1.036313e-07 1.529531e-07 2.243755e-07 3.271469e-07
#> [96] 4.740893e-07 6.828536e-07 9.775638e-07 1.390954e-06 1.967117e-06
#> [101] 2.765018e-06 3.862920e-06 5.363935e-06 7.402890e-06 1.015475e-05
#> [106] 1.384482e-05 1.876097e-05 2.526814e-05 3.382528e-05 4.500488e-05
#> [111] 5.951520e-05 7.822512e-05 1.021915e-04 1.326884e-04 1.712386e-04
#> [116] 2.196444e-04 2.800198e-04 3.548195e-04 4.468649e-04 5.593647e-04
#> [121] 6.959275e-04 8.605635e-04 1.057674e-03 1.292025e-03 1.568701e-03
#> [126] 1.893038e-03 2.270537e-03 2.706749e-03 3.207136e-03 3.776912e-03
#> [131] 4.420856e-03 5.143112e-03 5.946968e-03 6.834635e-03 7.807017e-03
#> [136] 8.863494e-03 1.000172e-02 1.121747e-02 1.250446e-02 1.385431e-02
#> [141] 1.525651e-02 1.669842e-02 1.816543e-02 1.964112e-02 2.110749e-02
#> [146] 2.254536e-02 2.393468e-02 2.525505e-02 2.648616e-02 2.760831e-02
#> [151] 2.860294e-02 2.945314e-02 3.014411e-02 3.066363e-02 3.100235e-02
#> [156] 3.115414e-02 3.111624e-02 3.088932e-02 3.047753e-02 2.988830e-02
#> [161] 2.913216e-02 2.822242e-02 2.717477e-02 2.600684e-02 2.473770e-02
#> [166] 2.338736e-02 2.197622e-02 2.052462e-02 1.905228e-02 1.757799e-02
#> [171] 1.611912e-02 1.469141e-02 1.330871e-02 1.198280e-02 1.072335e-02
#> [176] 9.537908e-03 8.431904e-03 7.408807e-03 6.470249e-03 5.616215e-03
#> [181] 4.845254e-03 4.154698e-03 3.540890e-03 2.999407e-03 2.525274e-03
#> [186] 2.113156e-03 1.757538e-03 1.452874e-03 1.193717e-03 9.748208e-04
#> [191] 7.912218e-04 6.382955e-04 5.117942e-04 4.078674e-04 3.230671e-04
#> [196] 2.543411e-04 1.990171e-04 1.547798e-04 1.196432e-04 9.192046e-05
#> [201] 7.019178e-05 5.327340e-05 4.018691e-05 3.013068e-05 2.245346e-05
#> [206] 1.663059e-05 1.224284e-05 8.957907e-06 6.514501e-06 1.614725e-05
pgpbinom(NULL, pp, va, vb, wt, "Normal")
#> [1] 5.607923e-34 1.447682e-33 3.714589e-33 9.473598e-33 2.401518e-32
#> [6] 6.050955e-32 1.515407e-31 3.772263e-31 9.333457e-31 2.295361e-30
#> [11] 5.610840e-30 1.363243e-29 3.292208e-29 7.902608e-29 1.885484e-28
#> [16] 4.471416e-28 1.053991e-27 2.469444e-27 5.750847e-27 1.331175e-26
#> [21] 3.062738e-26 7.004156e-26 1.592112e-25 3.597189e-25 8.078401e-25
#> [26] 1.803268e-24 4.000998e-24 8.823682e-24 1.934217e-23 4.214390e-23
#> [31] 9.127226e-23 1.964798e-22 4.204093e-22 8.941340e-22 1.890206e-21
#> [36] 3.971844e-21 8.295689e-21 1.722226e-20 3.553906e-20 7.289540e-20
#> [41] 1.486186e-19 3.011798e-19 6.066782e-19 1.214707e-18 2.417494e-18
#> [46] 4.782345e-18 9.403695e-18 1.837972e-17 3.570774e-17 6.895564e-17
#> [51] 1.323615e-16 2.525449e-16 4.789624e-16 9.029227e-16 1.691947e-15
#> [56] 3.151453e-15 5.834767e-15 1.073805e-14 1.964343e-14 3.571905e-14
#> [61] 6.456159e-14 1.159955e-13 2.071577e-13 3.677521e-13 6.489399e-13
#> [66] 1.138282e-12 1.984686e-12 3.439790e-12 5.926127e-12 1.014869e-11
#> [71] 1.727627e-11 2.923425e-11 4.917421e-11 8.222186e-11 1.366604e-10
#> [76] 2.257903e-10 3.708308e-10 6.054188e-10 9.825325e-10 1.585076e-09
#> [81] 2.541952e-09 4.052282e-09 6.421683e-09 1.011618e-08 1.584179e-08
#> [86] 2.466119e-08 3.816343e-08 5.870922e-08 8.978268e-08 1.364924e-07
#> [91] 2.062792e-07 3.099106e-07 4.628636e-07 6.872392e-07 1.014386e-06
#> [96] 1.488475e-06 2.171329e-06 3.148893e-06 4.539847e-06 6.506964e-06
#> [101] 9.271982e-06 1.313490e-05 1.849884e-05 2.590173e-05 3.605648e-05
#> [106] 4.990129e-05 6.866226e-05 9.393040e-05 1.277557e-04 1.727606e-04
#> [111] 2.322758e-04 3.105009e-04 4.126924e-04 5.453808e-04 7.166194e-04
#> [116] 9.362638e-04 1.216284e-03 1.571103e-03 2.017968e-03 2.577333e-03
#> [121] 3.273260e-03 4.133824e-03 5.191498e-03 6.483523e-03 8.052224e-03
#> [126] 9.945263e-03 1.221580e-02 1.492255e-02 1.812968e-02 2.190660e-02
#> [131] 2.632745e-02 3.147056e-02 3.741753e-02 4.425217e-02 5.205918e-02
#> [136] 6.092268e-02 7.092440e-02 8.214187e-02 9.464633e-02 1.085006e-01
#> [141] 1.237572e-01 1.404556e-01 1.586210e-01 1.782621e-01 1.993696e-01
#> [146] 2.219150e-01 2.458497e-01 2.711047e-01 2.975909e-01 3.251992e-01
#> [151] 3.538021e-01 3.832553e-01 4.133994e-01 4.440630e-01 4.750653e-01
#> [156] 5.062195e-01 5.373357e-01 5.682250e-01 5.987026e-01 6.285909e-01
#> [161] 6.577230e-01 6.859454e-01 7.131202e-01 7.391271e-01 7.638648e-01
#> [166] 7.872521e-01 8.092283e-01 8.297529e-01 8.488052e-01 8.663832e-01
#> [171] 8.825023e-01 8.971938e-01 9.105025e-01 9.224853e-01 9.332086e-01
#> [176] 9.427465e-01 9.511784e-01 9.585872e-01 9.650575e-01 9.706737e-01
#> [181] 9.755189e-01 9.796736e-01 9.832145e-01 9.862139e-01 9.887392e-01
#> [186] 9.908524e-01 9.926099e-01 9.940628e-01 9.952565e-01 9.962313e-01
#> [191] 9.970225e-01 9.976608e-01 9.981726e-01 9.985805e-01 9.989036e-01
#> [196] 9.991579e-01 9.993569e-01 9.995117e-01 9.996314e-01 9.997233e-01
#> [201] 9.997935e-01 9.998467e-01 9.998869e-01 9.999171e-01 9.999395e-01
#> [206] 9.999561e-01 9.999684e-01 9.999773e-01 9.999839e-01 1.000000e+00A comparison with exact computation shows that the approximation quality of the NA procedure increases with larger numbers of probabilities of success:
set.seed(2)
# 10 random probabilities of success
pp <- runif(10)
va <- sample(0:10, 10, TRUE)
vb <- sample(0:10, 10, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "Normal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.0346309 -0.0042919 0.0001378 0.0000000 0.0038447 0.0317044
# 100 random probabilities of success
pp <- runif(100)
va <- sample(0:100, 100, TRUE)
vb <- sample(0:100, 100, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "Normal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.006e-05 -1.126e-09 0.000e+00 0.000e+00 1.854e-09 2.967e-05
# 1000 random probabilities of success
pp <- runif(1000)
va <- sample(0:1000, 1000, TRUE)
vb <- sample(0:1000, 1000, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "Normal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.152e-08 0.000e+00 3.060e-12 0.000e+00 8.991e-10 3.707e-08The Generalized Refined Normal Approximation (G-RNA) approach is requested with method = "RefinedNormal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean, variance and skewness of the input probabilities of success.
set.seed(2)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
va <- sample(0:10, 10, TRUE)
vb <- sample(0:10, 10, TRUE)
dgpbinom(NULL, pp, va, vb, wt, "RefinedNormal")
#> [1] 5.100768e-32 7.816039e-32 1.959106e-31 4.880045e-31 1.208047e-30
#> [6] 2.971921e-30 7.265798e-30 1.765311e-29 4.262362e-29 1.022751e-28
#> [11] 2.438814e-28 5.779315e-28 1.361012e-27 3.185186e-27 7.407878e-27
#> [16] 1.712136e-26 3.932484e-26 8.975930e-26 2.035985e-25 4.589352e-25
#> [21] 1.028037e-24 2.288476e-24 5.062470e-24 1.112900e-23 2.431235e-23
#> [26] 5.278047e-23 1.138660e-22 2.441116e-22 5.200621e-22 1.101015e-21
#> [31] 2.316333e-21 4.842591e-21 1.006056e-20 2.076983e-20 4.260973e-20
#> [36] 8.686571e-20 1.759748e-19 3.542530e-19 7.086575e-19 1.408697e-18
#> [41] 2.782630e-18 5.461965e-18 1.065359e-17 2.064884e-17 3.976912e-17
#> [46] 7.611065e-17 1.447413e-16 2.735176e-16 5.135966e-16 9.582999e-16
#> [51] 1.776730e-15 3.273256e-15 5.992053e-15 1.089949e-14 1.970017e-14
#> [56] 3.538058e-14 6.313772e-14 1.119541e-13 1.972495e-13 3.453144e-13
#> [61] 6.006676e-13 1.038179e-12 1.782897e-12 3.042246e-12 5.157913e-12
#> [66] 8.688860e-12 1.454315e-11 2.418568e-11 3.996319e-11 6.560867e-11
#> [71] 1.070186e-10 1.734408e-10 2.792769e-10 4.467944e-10 7.101774e-10
#> [76] 1.121527e-09 1.759679e-09 2.743061e-09 4.248282e-09 6.536785e-09
#> [81] 9.992759e-09 1.517660e-08 2.289965e-08 3.432780e-08 5.112383e-08
#> [86] 7.564129e-08 1.111860e-07 1.623661e-07 2.355550e-07 3.394997e-07
#> [91] 4.861107e-07 6.914779e-07 9.771650e-07 1.371840e-06 1.913307e-06
#> [96] 2.651012e-06 3.649099e-06 4.990081e-06 6.779222e-06 9.149662e-06
#> [101] 1.226837e-05 1.634294e-05 2.162919e-05 2.843967e-05 3.715276e-05
#> [106] 4.822249e-05 6.218875e-05 7.968764e-05 1.014618e-04 1.283702e-04
#> [111] 1.613972e-04 2.016606e-04 2.504176e-04 3.090698e-04 3.791651e-04
#> [116] 4.623982e-04 5.606082e-04 6.757744e-04 8.100102e-04 9.655553e-04
#> [121] 1.144767e-03 1.350110e-03 1.584150e-03 1.849543e-03 2.149024e-03
#> [126] 2.485405e-03 2.861561e-03 3.280420e-03 3.744950e-03 4.258135e-03
#> [131] 4.822941e-03 5.442277e-03 6.118927e-03 6.855467e-03 7.654163e-03
#> [136] 8.516833e-03 9.444692e-03 1.043817e-02 1.149671e-02 1.261856e-02
#> [141] 1.380053e-02 1.503782e-02 1.632377e-02 1.764978e-02 1.900514e-02
#> [146] 2.037702e-02 2.175055e-02 2.310888e-02 2.443348e-02 2.570445e-02
#> [151] 2.690096e-02 2.800177e-02 2.898579e-02 2.983278e-02 3.052397e-02
#> [156] 3.104271e-02 3.137515e-02 3.151071e-02 3.144261e-02 3.116818e-02
#> [161] 3.068902e-02 3.001109e-02 2.914456e-02 2.810352e-02 2.690563e-02
#> [166] 2.557147e-02 2.412399e-02 2.258773e-02 2.098813e-02 1.935073e-02
#> [171] 1.770044e-02 1.606093e-02 1.445398e-02 1.289904e-02 1.141287e-02
#> [176] 1.000927e-02 8.699011e-03 7.489773e-03 6.386301e-03 5.390581e-03
#> [181] 4.502114e-03 3.718233e-03 3.034469e-03 2.444914e-03 1.942594e-03
#> [186] 1.519822e-03 1.168521e-03 8.805066e-04 6.477360e-04 4.625001e-04
#> [191] 2.621189e-04 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [196] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [201] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [206] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
pgpbinom(NULL, pp, va, vb, wt, "RefinedNormal")
#> [1] 5.100768e-32 1.291681e-31 3.250786e-31 8.130831e-31 2.021130e-30
#> [6] 4.993051e-30 1.225885e-29 2.991196e-29 7.253558e-29 1.748106e-28
#> [11] 4.186920e-28 9.966236e-28 2.357636e-27 5.542822e-27 1.295070e-26
#> [16] 3.007206e-26 6.939690e-26 1.591562e-25 3.627547e-25 8.216899e-25
#> [21] 1.849727e-24 4.138203e-24 9.200673e-24 2.032968e-23 4.464203e-23
#> [26] 9.742250e-23 2.112885e-22 4.554002e-22 9.754623e-22 2.076477e-21
#> [31] 4.392810e-21 9.235402e-21 1.929596e-20 4.006579e-20 8.267552e-20
#> [36] 1.695412e-19 3.455160e-19 6.997690e-19 1.408427e-18 2.817123e-18
#> [41] 5.599754e-18 1.106172e-17 2.171531e-17 4.236415e-17 8.213328e-17
#> [46] 1.582439e-16 3.029852e-16 5.765028e-16 1.090099e-15 2.048399e-15
#> [51] 3.825129e-15 7.098385e-15 1.309044e-14 2.398993e-14 4.369010e-14
#> [56] 7.907068e-14 1.422084e-13 2.541625e-13 4.514120e-13 7.967264e-13
#> [61] 1.397394e-12 2.435573e-12 4.218470e-12 7.260717e-12 1.241863e-11
#> [66] 2.110749e-11 3.565064e-11 5.983632e-11 9.979950e-11 1.654082e-10
#> [71] 2.724267e-10 4.458675e-10 7.251445e-10 1.171939e-09 1.882116e-09
#> [76] 3.003643e-09 4.763322e-09 7.506383e-09 1.175466e-08 1.829145e-08
#> [81] 2.828421e-08 4.346081e-08 6.636046e-08 1.006883e-07 1.518121e-07
#> [86] 2.274534e-07 3.386394e-07 5.010055e-07 7.365605e-07 1.076060e-06
#> [91] 1.562171e-06 2.253649e-06 3.230814e-06 4.602653e-06 6.515960e-06
#> [96] 9.166972e-06 1.281607e-05 1.780615e-05 2.458537e-05 3.373504e-05
#> [101] 4.600341e-05 6.234634e-05 8.397554e-05 1.124152e-04 1.495680e-04
#> [106] 1.977905e-04 2.599792e-04 3.396668e-04 4.411286e-04 5.694988e-04
#> [111] 7.308960e-04 9.325566e-04 1.182974e-03 1.492044e-03 1.871209e-03
#> [116] 2.333607e-03 2.894215e-03 3.569990e-03 4.380000e-03 5.345555e-03
#> [121] 6.490322e-03 7.840432e-03 9.424583e-03 1.127413e-02 1.342315e-02
#> [126] 1.590855e-02 1.877011e-02 2.205053e-02 2.579549e-02 3.005362e-02
#> [131] 3.487656e-02 4.031884e-02 4.643777e-02 5.329323e-02 6.094740e-02
#> [136] 6.946423e-02 7.890892e-02 8.934709e-02 1.008438e-01 1.134624e-01
#> [141] 1.272629e-01 1.423007e-01 1.586245e-01 1.762743e-01 1.952794e-01
#> [146] 2.156564e-01 2.374070e-01 2.605159e-01 2.849493e-01 3.106538e-01
#> [151] 3.375548e-01 3.655565e-01 3.945423e-01 4.243751e-01 4.548991e-01
#> [156] 4.859418e-01 5.173169e-01 5.488276e-01 5.802702e-01 6.114384e-01
#> [161] 6.421274e-01 6.721385e-01 7.012831e-01 7.293866e-01 7.562922e-01
#> [166] 7.818637e-01 8.059877e-01 8.285754e-01 8.495636e-01 8.689143e-01
#> [171] 8.866147e-01 9.026757e-01 9.171296e-01 9.300287e-01 9.414415e-01
#> [176] 9.514508e-01 9.601498e-01 9.676396e-01 9.740259e-01 9.794165e-01
#> [181] 9.839186e-01 9.876368e-01 9.906713e-01 9.931162e-01 9.950588e-01
#> [186] 9.965786e-01 9.977471e-01 9.986276e-01 9.992754e-01 9.997379e-01
#> [191] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [196] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [201] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [206] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00A comparison with exact computation shows that the approximation quality of the RNA procedure increases with larger numbers of probabilities of success:
set.seed(2)
# 10 random probabilities of success
pp <- runif(10)
va <- sample(0:10, 10, TRUE)
vb <- sample(0:10, 10, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "RefinedNormal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -3.045e-02 -4.084e-03 1.727e-04 1.179e-05 4.324e-03 3.161e-02
# 100 random probabilities of success
pp <- runif(100)
va <- sample(0:100, 100, TRUE)
vb <- sample(0:100, 100, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "RefinedNormal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -8.831e-06 0.000e+00 1.000e-12 9.000e-12 3.642e-07 1.333e-05
# 1000 random probabilities of success
pp <- runif(1000)
va <- sample(0:1000, 1000, TRUE)
vb <- sample(0:1000, 1000, TRUE)
dpn <- dgpbinom(NULL, pp, va, vb, method = "RefinedNormal")
dpd <- dgpbinom(NULL, pp, va, vb)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.980e-08 0.000e+00 4.960e-12 0.000e+00 1.561e-09 3.197e-08To assess the performance of the approximation procedures, we use the microbenchmark package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Windows 10 Education (20H2).
library(microbenchmark)
n <- 1500
set.seed(2)
va <- sample(1:50, n, TRUE)
vb <- sample(1:50, n, TRUE)
f1 <- function() dgpbinom(NULL, runif(n), va, vb, method = "Normal")
f2 <- function() dgpbinom(NULL, runif(n), va, vb, method = "RefinedNormal")
f3 <- function() dgpbinom(NULL, runif(n), va, vb, method = "DivideFFT")
microbenchmark(f1(), f2(), f3())
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> f1() 7.6607 8.07170 9.705939 8.14100 8.64070 106.7701 100 a
#> f2() 9.1814 9.61555 10.250806 9.74215 10.01675 13.7779 100 a
#> f3() 42.7156 43.67715 44.416052 43.92380 44.33145 55.8752 100 bClearly, the G-NA procedure is the fastest, followed by the G-RNA method. Both are roughly 4-5 times as fast as G-DC-FFT.