bayesianCorrelatedT.test.RdThis function performs the Bayesian correlated t-test
bayesianCorrelatedT.test( x, y = NULL, rho = 1/length(x), rope.min = -0.01, rope.max = 0.01 )
| x | First vector of observations |
|---|---|
| y | Second vector of observations |
| rho | Expectated correlation. |
| rope.min | Lower limit of the rope considered |
| rope.max | Upper limit of the rope considered |
List with probabilities for each region and a sample of posterior distribution.
bayesianCorrelatedT.test(results.rf[1, ], results.knn[1, ])#> $probabilities #> left rope right #> 4.962298e-05 4.407245e-04 9.995097e-01 #> #> $rope #> [1] -0.01 0.01 #> #> $dist #> x y #> 1 -0.0200000000 1.930679e-05 #> 2 -0.0184298049 2.274742e-05 #> 3 -0.0168596098 2.686221e-05 #> 4 -0.0152894147 3.179496e-05 #> 5 -0.0137192196 3.772259e-05 #> 6 -0.0121490245 4.486329e-05 #> 7 -0.0105788293 5.348675e-05 #> 8 -0.0090086342 6.392723e-05 #> 9 -0.0074384391 7.659997e-05 #> 10 -0.0058682440 9.202214e-05 #> 11 -0.0042980489 1.108394e-04 #> 12 -0.0027278538 1.338598e-04 #> 13 -0.0011576587 1.620970e-04 #> 14 0.0004125364 1.968257e-04 #> 15 0.0019827315 2.396525e-04 #> 16 0.0035529266 2.926069e-04 #> 17 0.0051231217 3.582577e-04 #> 18 0.0066933168 4.398635e-04 #> 19 0.0082635120 5.415650e-04 #> 20 0.0098337071 6.686332e-04 #> 21 0.0114039022 8.277879e-04 #> 22 0.0129740973 1.027606e-03 #> 23 0.0145442924 1.279047e-03 #> 24 0.0161144875 1.596119e-03 #> 25 0.0176846826 1.996734e-03 #> 26 0.0192548777 2.503789e-03 #> 27 0.0208250728 3.146521e-03 #> 28 0.0223952679 3.962209e-03 #> 29 0.0239654630 4.998266e-03 #> 30 0.0255356582 6.314791e-03 #> 31 0.0271058533 7.987614e-03 #> 32 0.0286760484 1.011184e-02 #> 33 0.0302462435 1.280582e-02 #> 34 0.0318164386 1.621542e-02 #> 35 0.0333866337 2.051816e-02 #> 36 0.0349568288 2.592675e-02 #> 37 0.0365270239 3.269100e-02 #> 38 0.0380972190 4.109679e-02 #> 39 0.0396674141 5.146039e-02 #> 40 0.0412376092 6.411579e-02 #> 41 0.0428078043 7.939265e-02 #> 42 0.0443779995 9.758260e-02 #> 43 0.0459481946 1.188926e-01 #> 44 0.0475183897 1.433862e-01 #> 45 0.0490885848 1.709160e-01 #> 46 0.0506587799 2.010568e-01 #> 47 0.0522289750 2.330508e-01 #> 48 0.0537991701 2.657834e-01 #> 49 0.0553693652 2.978048e-01 #> 50 0.0569395603 3.274122e-01 #> 51 0.0585097554 3.527938e-01 #> 52 0.0600799505 3.722229e-01 #> 53 0.0616501456 3.842734e-01 #> 54 0.0632203408 3.880164e-01 #> 55 0.0647905359 3.831568e-01 #> 56 0.0663607310 3.700773e-01 #> 57 0.0679309261 3.497809e-01 #> 58 0.0695011212 3.237452e-01 #> 59 0.0710713163 2.937201e-01 #> 60 0.0726415114 2.615133e-01 #> 61 0.0742117065 2.288015e-01 #> 62 0.0757819016 1.969940e-01 #> 63 0.0773520967 1.671588e-01 #> 64 0.0789222918 1.400081e-01 #> 65 0.0804924870 1.159273e-01 #> 66 0.0820626821 9.503221e-02 #> 67 0.0836328772 7.723695e-02 #> 68 0.0852030723 6.232037e-02 #> 69 0.0867732674 4.998348e-02 #> 70 0.0883434625 3.989443e-02 #> 71 0.0899136576 3.172050e-02 #> 72 0.0914838527 2.514887e-02 #> 73 0.0930540478 1.989814e-02 #> 74 0.0946242429 1.572338e-02 #> 75 0.0961944380 1.241664e-02 #> 76 0.0977646331 9.804746e-03 #> 77 0.0993348283 7.745679e-03 #> 78 0.1009050234 6.124356e-03 #> 79 0.1024752185 4.848409e-03 #> 80 0.1040454136 3.844256e-03 #> 81 0.1056156087 3.053617e-03 #> 82 0.1071858038 2.430537e-03 #> 83 0.1087559989 1.938897e-03 #> 84 0.1103261940 1.550378e-03 #> 85 0.1118963891 1.242804e-03 #> 86 0.1134665842 9.988308e-04 #> 87 0.1150367793 8.048904e-04 #> 88 0.1166069745 6.503703e-04 #> 89 0.1181771696 5.269631e-04 #> 90 0.1197473647 4.281594e-04 #> 91 0.1213175598 3.488522e-04 #> 92 0.1228877549 2.850287e-04 #> 93 0.1244579500 2.335304e-04 #> 94 0.1260281451 1.918667e-04 #> 95 0.1275983402 1.580694e-04 #> 96 0.1291685353 1.305800e-04 #> 97 0.1307387304 1.081614e-04 #> 98 0.1323089255 8.982975e-05 #> 99 0.1338791206 7.480040e-05 #> 100 0.1354493158 6.244624e-05 #> #> $method #> [1] "Bayesian Correlated t-test" #> #> attr(,"class") #> [1] "PosteriorT"bayesianCorrelatedT.test(results.rf[5, ], results.knn[5, ], rope.min=-0.05, rope.max = 0.05)#> $probabilities #> left rope right #> 0.07473412 0.72083360 0.20443228 #> #> $rope #> [1] -0.05 0.05 #> #> $dist #> x y #> 1 -0.252812340 6.244624e-05 #> 2 -0.247411444 7.386321e-05 #> 3 -0.242010548 8.756243e-05 #> 4 -0.236609652 1.040373e-04 #> 5 -0.231208756 1.238955e-04 #> 6 -0.225807860 1.478867e-04 #> 7 -0.220406963 1.769380e-04 #> 8 -0.215006067 2.121981e-04 #> 9 -0.209605171 2.550931e-04 #> 10 -0.204204275 3.073966e-04 #> 11 -0.198803379 3.713188e-04 #> 12 -0.193402483 4.496184e-04 #> 13 -0.188001586 5.457441e-04 #> 14 -0.182600690 6.640140e-04 #> 15 -0.177199794 8.098408e-04 #> 16 -0.171798898 9.900175e-04 #> 17 -0.166398002 1.213076e-03 #> 18 -0.160997106 1.489738e-03 #> 19 -0.155596209 1.833482e-03 #> 20 -0.150195313 2.261246e-03 #> 21 -0.144794417 2.794307e-03 #> 22 -0.139393521 3.459360e-03 #> 23 -0.133992625 4.289838e-03 #> 24 -0.128591729 5.327515e-03 #> 25 -0.123190832 6.624411e-03 #> 26 -0.117789936 8.245026e-03 #> 27 -0.112389040 1.026889e-02 #> 28 -0.106988144 1.279340e-02 #> 29 -0.101587248 1.593678e-02 #> 30 -0.096186352 1.984102e-02 #> 31 -0.090785455 2.467434e-02 #> 32 -0.085384559 3.063273e-02 #> 33 -0.079983663 3.793974e-02 #> 34 -0.074582767 4.684344e-02 #> 35 -0.069181871 5.760941e-02 #> 36 -0.063780975 7.050804e-02 #> 37 -0.058380078 8.579501e-02 #> 38 -0.052979182 1.036835e-01 #> 39 -0.047578286 1.243079e-01 #> 40 -0.042177390 1.476805e-01 #> 41 -0.036776494 1.736431e-01 #> 42 -0.031375598 2.018214e-01 #> 43 -0.025974702 2.315882e-01 #> 44 -0.020573805 2.620479e-01 #> 45 -0.015172909 2.920509e-01 #> 46 -0.009772013 3.202469e-01 #> 47 -0.004371117 3.451802e-01 #> 48 0.001029779 3.654190e-01 #> 49 0.006430675 3.797047e-01 #> 50 0.011831572 3.870986e-01 #> 51 0.017232468 3.870986e-01 #> 52 0.022633364 3.797047e-01 #> 53 0.028034260 3.654190e-01 #> 54 0.033435156 3.451802e-01 #> 55 0.038836052 3.202469e-01 #> 56 0.044236949 2.920509e-01 #> 57 0.049637845 2.620479e-01 #> 58 0.055038741 2.315882e-01 #> 59 0.060439637 2.018214e-01 #> 60 0.065840533 1.736431e-01 #> 61 0.071241429 1.476805e-01 #> 62 0.076642326 1.243079e-01 #> 63 0.082043222 1.036835e-01 #> 64 0.087444118 8.579501e-02 #> 65 0.092845014 7.050804e-02 #> 66 0.098245910 5.760941e-02 #> 67 0.103646806 4.684344e-02 #> 68 0.109047703 3.793974e-02 #> 69 0.114448599 3.063273e-02 #> 70 0.119849495 2.467434e-02 #> 71 0.125250391 1.984102e-02 #> 72 0.130651287 1.593678e-02 #> 73 0.136052183 1.279340e-02 #> 74 0.141453080 1.026889e-02 #> 75 0.146853976 8.245026e-03 #> 76 0.152254872 6.624411e-03 #> 77 0.157655768 5.327515e-03 #> 78 0.163056664 4.289838e-03 #> 79 0.168457560 3.459360e-03 #> 80 0.173858456 2.794307e-03 #> 81 0.179259353 2.261246e-03 #> 82 0.184660249 1.833482e-03 #> 83 0.190061145 1.489738e-03 #> 84 0.195462041 1.213076e-03 #> 85 0.200862937 9.900175e-04 #> 86 0.206263833 8.098408e-04 #> 87 0.211664730 6.640140e-04 #> 88 0.217065626 5.457441e-04 #> 89 0.222466522 4.496184e-04 #> 90 0.227867418 3.713188e-04 #> 91 0.233268314 3.073966e-04 #> 92 0.238669210 2.550931e-04 #> 93 0.244070107 2.121981e-04 #> 94 0.249471003 1.769380e-04 #> 95 0.254871899 1.478867e-04 #> 96 0.260272795 1.238955e-04 #> 97 0.265673691 1.040373e-04 #> 98 0.271074587 8.756243e-05 #> 99 0.276475484 7.386321e-05 #> 100 0.281876380 6.244624e-05 #> #> $method #> [1] "Bayesian Correlated t-test" #> #> attr(,"class") #> [1] "PosteriorT"