inst/testdata/testdata_mi_bootstrap.R

In jpkrooney/rcorex: An Implementation of Total Correlation Explanation

#### Input variables ####

data_mi_bootstrap <- structure(c(0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,
            1, 0, 1), .Dim = 4:5)

log_p_y_mi_bootstrap <- structure(c(-1.30131233762492, -0.976258809526706, -0.504280872127337,
                       -1.1934620796358, -2.0883943231501, -1.13908336313752),
                     .Dim = 2:3)

theta_mi_bootstrap_gaussian <- list(
    list(mean_ml = structure(c(0.485065836713681, 0.652429296866257, 0.549159919734141,
                               0.377327181451717, 0.292808206499491, 0.436770637223431
    ), .Dim = 2:3),
    sig_ml = structure(c(3.07166862731226, 0.67403283287192, 0.422359821392027,
                         1.34014258508784, 10.2482872274822, 1.12329745214559
    ), .Dim = 2:3)),
    list(mean_ml = structure(c(0.485065836713681, 0.652429296866257, 0.549159919734141,
                               0.377327181451717, 0.292808206499491, 0.436770637223431),
                             .Dim = 2:3),
         sig_ml = structure(c(3.07166862731226, 0.67403283287192, 0.422359821392027,
                              1.34014258508784, 10.2482872274822,1.12329745214559),
                            .Dim = 2:3)),
    list(mean_ml = structure(c(0.485065836713681, 0.652429296866257, 0.549159919734141,
                               0.377327181451717, 0.292808206499491, 0.436770637223431),
                             .Dim = 2:3),
         sig_ml = structure(c(3.07166862731226, 0.67403283287192, 0.422359821392027,
                              1.34014258508784, 10.2482872274822, 1.12329745214559),
                            .Dim = 2:3)),
    list(mean_ml = structure(c(0.544348069962135, 0.503791656067947, 0.45478936738257,
                               0.593484480213392, 0.623215432246983, 0.407004543860708),
                             .Dim = 2:3),
         sig_ml = structure(c(3.0502249514598, 0.743052446088727, 0.422995615430913,
                              1.37613013535667, 11.6214986931416, 1.10206364242556),
                            .Dim = 2:3)),
    list(mean_ml = structure(c(0.544348069962135, 0.503791656067947, 0.45478936738257,
                               0.593484480213392, 0.623215432246983, 0.407004543860708),
                             .Dim = 2:3),
         sig_ml = structure(c(3.0502249514598, 0.743052446088727, 0.422995615430913,
                              1.37613013535667, 11.6214986931416, 1.10206364242556),
                            .Dim = 2:3)))


p_y_given_x_3d_mi_bootstrap <- structure(c(0.211864406779661, 0.211096075778079, 0.348648648648649,
                              0.312693498452012, 0.284119106699752, 0.536690647482014,
                              0.243881856540084, 0.446521287642783, 0.738135593220339,
                              0.384303112313938, 0.351351351351351, 0.370743034055728,
                              0.579404466501241, 0.10863309352518, 0.747679324894515,
                              0.348909657320872, 0.05, 0.404600811907984, 0.3,
                              0.31656346749226, 0.136476426799007, 0.354676258992806,
                              0.00843881856540085, 0.204569055036345),
                            .Dim = c(2L, 4L, 3L))


#### Results ####
mi_bootstrap_gaussian_nosmooth_result <-  list(bias = structure(c(-0.076963051961783, 0.000765413606523662,
                        -0.076963051961783, 0.000765413606523662, -0.076963051961783,
                        0.000765413606523662, -0.0847703135462, 0.00437939857052696,
                        -0.0847703135462, 0.00437939857052696), .Dim = c(2L, 5L)),
     sig = structure(c(-0.0511625974963909, 0.00954192205367079, -0.0511625974963909,
                       0.00954192205367079, -0.0511625974963909, 0.00954192205367079,
                       -0.0511625974963909,  0.00954192205367092, -0.0511625974963909,
                       0.00954192205367092
                        ), .Dim = c(2L, 5L))) # seed = 934