Apply ResNMTF
apply_resnmtf.RdApply ResNMTF to data for a range of biclusters selecting the optimal number, with optional stability analysis
Usage
apply_resnmtf(
data,
init_f = NULL,
init_s = NULL,
init_g = NULL,
k_val = NULL,
phi = NULL,
xi = NULL,
psi = NULL,
n_iters = NULL,
k_min = 3,
k_max = 8,
distance = "euclidean",
spurious = TRUE,
num_repeats = 5,
no_clusts = FALSE,
sample_rate = 0.9,
n_stability = 5,
stability = TRUE,
stab_thres = 0.4,
remove_unstable = TRUE,
use_parallel = TRUE
)Arguments
- data
list of n_v matrices, data to be factorised. If only one view is supplied, can be given as a matrix.
- init_f
list of matrices, initialisation for F matrices
- init_s
list of matrices, initialisation for S matrices
- init_g
list of matrices, initialisation for G matrices
- k_val
integer, number of clusters to consider in each view if known, default is NULL
- phi
n_v x n_v matrix, default is NULL, restriction matrices for F
- xi
n_v x n_v matrix, default is NULL, restriction matrices for S
- psi
n_v x n_v matrix, default is NULL, restriction matrices for G
- n_iters
integer, default is NULL, number of iterations to run for, otherwise will run until convergence
- k_min
positive integer, default is 3, smallest value of k to be considered initially, must be at least 2,
- k_max
positive integer, default is 6, largest value of k to be considered initially,
- distance
string, default is "euclidean", distance metric to use within the bisilhouette score
- spurious
boolean, default is TRUE, whether or not spurious biclusters should be found and removed
- num_repeats
integer, default is 5, number of repeats to use within stability analysis
- no_clusts
boolean, default is FALSE, whether to return only the factorisation or not,
- sample_rate
numeric, default is 0.9, proportion of data to sample for stability analysis,
- n_stability
integer, default is 5, number of times to repeat stability analysis,
- stability
boolean, default is TRUE, whether to perform stability analysis or not,
- stab_thres
numeric, default is 0.4, threshold for stability analysis,
- remove_unstable
boolean, default is TRUE, whether to remove unstable clusters or not
- use_parallel
boolean, default is TRUE, wheather to use parallelisation, not applicable on Windows or linux machines
Value
list of results from ResNMTF, containing the following: - output_f: list of matrices, F matrices - output_s: list of matrices, S matrices - output_g: list of matrices, G matrices - Error: numeric, mean error - All_Error: numeric, all errors - bisil: numeric, bisilhouette score - row_clusters: list of matrices, row clusters - col_clusters: list of matrices, column clusters - lambda: list of vectors, lambda vectors - mu: list of vectors, mu vectors
Examples
row_clusters <- cbind(
rbinom(100, 1, 0.5),
rbinom(100, 1, 0.5),
rbinom(100, 1, 0.5)
)
col_clusters <- cbind(
rbinom(50, 1, 0.4),
rbinom(50, 1, 0.4),
rbinom(50, 1, 0.4)
)
n_col <- 50
data <- list(
row_clusters %*% diag(c(5, 5, 5)) %*% t(col_clusters) +
abs(matrix(rnorm(100 * n_col), 100, n_col)),
row_clusters %*% diag(c(5, 5, 5)) %*% t(col_clusters) +
abs(0.01 * matrix(rnorm(100 * n_col), 100, n_col))
)
apply_resnmtf(data, k_max = 4)
#> $output_f
#> $output_f[[1]]
#> [,1] [,2] [,3]
#> row_1 1.124721e-03 2.124347e-02 3.489044e-03
#> row_2 1.490707e-03 1.443675e-03 8.267938e-03
#> row_3 1.080945e-03 1.950898e-03 8.150935e-03
#> row_4 1.551162e-02 1.125939e-06 1.727779e-02
#> row_5 1.698429e-02 1.442196e-03 2.576556e-03
#> row_6 1.696373e-02 4.796346e-09 6.518341e-03
#> row_7 1.544944e-02 2.180299e-02 3.291557e-03
#> row_8 1.578897e-02 2.049121e-02 1.018521e-02
#> row_9 1.759396e-02 2.032653e-02 1.316285e-03
#> row_10 1.514661e-02 8.739374e-04 1.603774e-02
#> row_11 1.434326e-12 4.780325e-09 1.474375e-02
#> row_12 1.560574e-02 9.004444e-04 1.360785e-02
#> row_13 1.703301e-02 1.246320e-16 6.133628e-03
#> row_14 1.614318e-02 5.918708e-04 1.518478e-02
#> row_15 8.933593e-04 1.429004e-04 1.714605e-02
#> row_16 1.640792e-02 2.393595e-03 1.259026e-02
#> row_17 1.578318e-02 5.883537e-06 1.486598e-02
#> row_18 1.600007e-02 2.175167e-02 8.398057e-03
#> row_19 1.751453e-02 5.218131e-10 2.778290e-03
#> row_20 1.644339e-02 1.972119e-02 1.847282e-18
#> row_21 1.580493e-03 2.161756e-02 2.522980e-03
#> row_22 1.566387e-02 2.360506e-04 1.551753e-02
#> row_23 1.545464e-02 1.979761e-02 1.274608e-02
#> row_24 9.482729e-04 2.216437e-02 1.100377e-02
#> row_25 6.359352e-04 1.494479e-03 9.096468e-03
#> row_26 1.571836e-02 2.062961e-02 1.186367e-02
#> row_27 9.071510e-04 2.220371e-02 1.555838e-02
#> row_28 1.568924e-04 2.328459e-02 1.409021e-02
#> row_29 4.594576e-06 3.066958e-03 1.575997e-02
#> row_30 1.673839e-02 4.876445e-05 4.655880e-03
#> row_31 1.593641e-02 2.785150e-04 1.356918e-02
#> row_32 1.680885e-02 2.178287e-02 1.604209e-08
#> row_33 1.687477e-02 3.535298e-14 6.028869e-03
#> row_34 1.649335e-02 1.608882e-07 1.389512e-02
#> row_35 1.582027e-02 2.042602e-02 9.015363e-03
#> row_36 1.226018e-03 2.132806e-02 5.821650e-03
#> row_37 2.591128e-04 4.138549e-04 1.975615e-02
#> row_38 1.645200e-02 9.939939e-04 1.549223e-02
#> row_39 1.088311e-03 2.098848e-02 1.593190e-02
#> row_40 1.673891e-02 5.885030e-06 7.662736e-03
#> row_41 1.515506e-02 2.395433e-02 7.840155e-03
#> row_42 7.334505e-04 2.142872e-02 3.332583e-03
#> row_43 1.572083e-02 5.644640e-04 1.350724e-02
#> row_44 8.796090e-07 2.171048e-02 1.528139e-02
#> row_45 1.685469e-02 2.203287e-02 1.004376e-02
#> row_46 9.581065e-04 2.236180e-02 1.581536e-02
#> row_47 1.587304e-02 2.143509e-02 1.180302e-02
#> row_48 1.708331e-02 3.654453e-08 3.798345e-03
#> row_49 1.536260e-02 3.276960e-06 1.488505e-02
#> row_50 2.157299e-03 1.768562e-03 6.723795e-03
#> row_51 1.601811e-02 3.329926e-06 1.638779e-02
#> row_52 7.096000e-04 2.227204e-02 1.182745e-02
#> row_53 1.920189e-03 3.761612e-05 6.169934e-03
#> row_54 5.399712e-04 2.185681e-02 1.482394e-02
#> row_55 1.658462e-02 6.138198e-11 5.260999e-03
#> row_56 1.133590e-03 2.189502e-02 3.201712e-03
#> row_57 1.391194e-03 4.842570e-04 1.820604e-02
#> row_58 1.731825e-02 1.479028e-09 3.583977e-03
#> row_59 1.705072e-02 2.060535e-02 2.211913e-03
#> row_60 1.695876e-02 1.409667e-04 1.760828e-02
#> row_61 1.564604e-02 2.041866e-02 1.168431e-02
#> row_62 1.629995e-02 2.131129e-04 1.760651e-02
#> row_63 1.563842e-02 2.066598e-02 1.166365e-02
#> row_64 1.562918e-02 4.697053e-04 1.598238e-02
#> row_65 1.643828e-02 2.194231e-02 1.389490e-03
#> row_66 1.001768e-03 2.355533e-02 3.277271e-03
#> row_67 1.171006e-03 2.218400e-02 5.071702e-03
#> row_68 1.638875e-02 3.535195e-04 1.231972e-02
#> row_69 1.507070e-02 8.818572e-06 1.810946e-02
#> row_70 3.718030e-06 3.517588e-03 1.479196e-02
#> row_71 1.357540e-03 6.269781e-05 1.031798e-02
#> row_72 1.638078e-02 9.506517e-04 1.330890e-02
#> row_73 6.662293e-04 7.201946e-04 8.664663e-03
#> row_74 1.552277e-02 1.448340e-04 1.595410e-02
#> row_75 1.628991e-02 1.382909e-03 1.502633e-02
#> row_76 1.297067e-03 2.157768e-02 3.476594e-03
#> row_77 2.662089e-05 2.135392e-02 1.560376e-02
#> row_78 1.621499e-02 8.147974e-04 1.379943e-02
#> row_79 1.694501e-02 2.003022e-02 2.372203e-03
#> row_80 8.949218e-06 2.370090e-02 1.272959e-02
#> row_81 1.728452e-02 5.843896e-06 3.444855e-03
#> row_82 9.920099e-04 1.203854e-03 6.638551e-03
#> row_83 1.674329e-02 1.891422e-07 1.572448e-02
#> row_84 9.703883e-04 2.156353e-02 5.668583e-03
#> row_85 1.105150e-03 1.387334e-03 1.846620e-02
#> row_86 7.385619e-04 1.376485e-04 1.869247e-02
#> row_87 1.582324e-02 2.057503e-02 1.248088e-02
#> row_88 1.283815e-03 1.012036e-03 1.417113e-02
#> row_89 1.636709e-02 2.151066e-02 1.079453e-02
#> row_90 1.778814e-03 3.107814e-10 1.045406e-02
#> row_91 2.087942e-03 2.053566e-02 4.578865e-03
#> row_92 1.664660e-02 2.063627e-02 1.682696e-07
#> row_93 1.671914e-02 1.953246e-02 1.258677e-02
#> row_94 1.488838e-03 3.011139e-04 8.678340e-03
#> row_95 1.774183e-02 2.070605e-02 2.296976e-04
#> row_96 1.606954e-02 2.239877e-02 6.766391e-12
#> row_97 2.500110e-04 2.454227e-03 1.411221e-02
#> row_98 1.693684e-02 1.170006e-05 3.596151e-03
#> row_99 1.365025e-05 2.353607e-02 1.166926e-02
#> row_100 1.697081e-02 2.002358e-02 1.866836e-06
#>
#> $output_f[[2]]
#> [,1] [,2] [,3]
#> row_101 4.271201e-12 2.151954e-02 5.714741e-03
#> row_102 3.349525e-33 5.213666e-51 7.244612e-03
#> row_103 8.458303e-27 4.444365e-45 9.203372e-03
#> row_104 1.689769e-02 4.729692e-04 1.302359e-02
#> row_105 1.621694e-02 1.357121e-20 6.697545e-03
#> row_106 1.708269e-02 4.642124e-15 2.995320e-03
#> row_107 1.687419e-02 2.164772e-02 7.224109e-04
#> row_108 1.706916e-02 2.249884e-02 1.080186e-02
#> row_109 1.686652e-02 2.183470e-02 4.586582e-10
#> row_110 1.680155e-02 8.862040e-10 1.780074e-02
#> row_111 2.921532e-29 2.414397e-33 9.125433e-03
#> row_112 1.667274e-02 6.586426e-07 1.556976e-02
#> row_113 1.705121e-02 8.025828e-18 2.598502e-03
#> row_114 1.688876e-02 3.047036e-06 1.543605e-02
#> row_115 1.130796e-06 2.522328e-04 1.681693e-02
#> row_116 1.693717e-02 2.060484e-05 1.478196e-02
#> row_117 1.708409e-02 7.057854e-05 1.309540e-02
#> row_118 1.714956e-02 2.251751e-02 1.239583e-02
#> row_119 1.686464e-02 3.400538e-16 1.893012e-03
#> row_120 1.689264e-02 2.170049e-02 1.855086e-03
#> row_121 1.816881e-10 2.136572e-02 6.359684e-03
#> row_122 1.717333e-02 5.383109e-05 1.301986e-02
#> row_123 1.724776e-02 2.252038e-02 1.043538e-02
#> row_124 4.509443e-10 2.191127e-02 1.603168e-02
#> row_125 1.749462e-27 1.181808e-47 9.418312e-03
#> row_126 1.739854e-02 2.272517e-02 1.085094e-02
#> row_127 1.234442e-04 2.253070e-02 1.385352e-02
#> row_128 1.305895e-04 2.251567e-02 1.181136e-02
#> row_129 9.793105e-14 5.658752e-10 2.072843e-02
#> row_130 1.651522e-02 9.520639e-18 2.847552e-03
#> row_131 1.666754e-02 1.758388e-08 1.679733e-02
#> row_132 1.660736e-02 2.218370e-02 1.545370e-03
#> row_133 1.662914e-02 2.376995e-18 4.397702e-03
#> row_134 1.712021e-02 9.720670e-05 1.233800e-02
#> row_135 1.666852e-02 2.264748e-02 1.247374e-02
#> row_136 2.026652e-04 2.191508e-02 2.053304e-03
#> row_137 9.136504e-08 2.604854e-04 1.497936e-02
#> row_138 1.696475e-02 1.493996e-08 1.570033e-02
#> row_139 3.100701e-05 2.206495e-02 1.716372e-02
#> row_140 1.694792e-02 5.152200e-20 2.727445e-03
#> row_141 1.720098e-02 2.253738e-02 1.057353e-02
#> row_142 1.132155e-07 2.167450e-02 2.747017e-03
#> row_143 1.719167e-02 1.872789e-08 1.468092e-02
#> row_144 3.056322e-05 2.250834e-02 1.305156e-02
#> row_145 1.698611e-02 2.257265e-02 1.280604e-02
#> row_146 9.185643e-06 2.225228e-02 1.766805e-02
#> row_147 1.695908e-02 2.292861e-02 1.051720e-02
#> row_148 1.673977e-02 6.665387e-15 2.806676e-03
#> row_149 1.714869e-02 1.744813e-05 1.315360e-02
#> row_150 1.834403e-32 2.022721e-48 1.131050e-02
#> row_151 1.702267e-02 5.346869e-06 1.583907e-02
#> row_152 6.290648e-08 2.240537e-02 1.481781e-02
#> row_153 7.913918e-27 7.724824e-45 8.314511e-03
#> row_154 3.768658e-06 2.192521e-02 1.770286e-02
#> row_155 1.682908e-02 9.379261e-22 6.984760e-03
#> row_156 1.239840e-05 2.166958e-02 3.937137e-03
#> row_157 5.765348e-12 1.043059e-05 1.844879e-02
#> row_158 1.693233e-02 1.714112e-18 3.963485e-03
#> row_159 1.684118e-02 2.164570e-02 1.601895e-03
#> row_160 1.705503e-02 5.524239e-05 1.511248e-02
#> row_161 1.690181e-02 2.274969e-02 1.050562e-02
#> row_162 1.723073e-02 4.364453e-04 1.200784e-02
#> row_163 1.709359e-02 2.260918e-02 1.029736e-02
#> row_164 1.715255e-02 2.492119e-04 1.305005e-02
#> row_165 1.690936e-02 2.195355e-02 4.869067e-09
#> row_166 5.703394e-12 2.187627e-02 3.236434e-03
#> row_167 5.478549e-07 2.178817e-02 2.303237e-03
#> row_168 1.703778e-02 2.762424e-05 1.323757e-02
#> row_169 1.689680e-02 2.238749e-09 1.817458e-02
#> row_170 1.392484e-07 3.723017e-04 1.843521e-02
#> row_171 5.474799e-25 4.170199e-45 7.018238e-03
#> row_172 1.675771e-02 6.439604e-09 1.708906e-02
#> row_173 3.899112e-20 1.887417e-41 8.198252e-03
#> row_174 1.710372e-02 5.902080e-06 1.580695e-02
#> row_175 1.652142e-02 7.309912e-05 1.555239e-02
#> row_176 9.782243e-09 2.160682e-02 2.689314e-03
#> row_177 4.291560e-06 2.237769e-02 1.605363e-02
#> row_178 1.720976e-02 7.750192e-05 1.411083e-02
#> row_179 1.706469e-02 2.225855e-02 6.007205e-05
#> row_180 1.139989e-04 2.244308e-02 1.433701e-02
#> row_181 1.681037e-02 2.054751e-17 6.084757e-03
#> row_182 1.705919e-39 2.520111e-39 6.490315e-03
#> row_183 1.666321e-02 4.509637e-08 1.551656e-02
#> row_184 5.895434e-09 2.182631e-02 3.162654e-03
#> row_185 4.316105e-13 1.921933e-07 1.984539e-02
#> row_186 8.891724e-10 6.514076e-09 2.053674e-02
#> row_187 1.687873e-02 2.261122e-02 1.131622e-02
#> row_188 6.076290e-08 1.126583e-07 1.817799e-02
#> row_189 1.725366e-02 2.273228e-02 1.122831e-02
#> row_190 1.062029e-20 3.528786e-43 5.850652e-03
#> row_191 1.338917e-12 2.169689e-02 5.086278e-03
#> row_192 1.657205e-02 2.192785e-02 3.117054e-08
#> row_193 1.705489e-02 2.274224e-02 9.991530e-03
#> row_194 7.041559e-35 3.820260e-50 6.648698e-03
#> row_195 1.700211e-02 2.202999e-02 1.709337e-03
#> row_196 1.715830e-02 2.177320e-02 7.719859e-04
#> row_197 3.981475e-15 3.386589e-11 1.988363e-02
#> row_198 1.687397e-02 1.183238e-16 1.870026e-03
#> row_199 1.313342e-06 2.251734e-02 1.432219e-02
#> row_200 1.699098e-02 2.169855e-02 9.830722e-11
#>
#>
#> $output_s
#> $output_s[[1]]
#> [,1] [,2] [,3]
#> [1,] 18.3954366 0.7736583 0.2014727
#> [2,] 0.2998802 12.2436280 1.4782212
#> [3,] 1.5790724 0.1077851 14.9743729
#>
#> $output_s[[2]]
#> [,1] [,2] [,3]
#> [1,] 19.60691571 0.02570607 0.03716244
#> [2,] 0.03526241 13.75571940 0.62528316
#> [3,] 0.26318403 0.01370505 15.61754944
#>
#>
#> $output_g
#> $output_g[[1]]
#> [,1] [,2] [,3]
#> col_1 0.0023855970 1.892947e-05 6.148891e-02
#> col_2 0.0014548160 6.733900e-02 3.334008e-03
#> col_3 0.0113788442 1.396161e-04 4.140390e-02
#> col_4 0.0463908870 1.887495e-05 4.521146e-03
#> col_5 0.0116924220 1.056446e-02 3.335867e-02
#> col_6 0.0159142635 1.545945e-02 2.425424e-02
#> col_7 0.0286352776 3.271214e-02 6.186171e-06
#> col_8 0.0472730045 1.342884e-03 2.008771e-03
#> col_9 0.0001494312 6.869569e-02 4.147271e-03
#> col_10 0.0006938184 3.249356e-02 3.607705e-02
#> col_11 0.0121844261 7.667378e-03 3.384919e-02
#> col_12 0.0285170799 3.334228e-02 1.467620e-05
#> col_13 0.0088886886 1.278832e-02 3.593447e-02
#> col_14 0.0123347735 3.488912e-03 4.023974e-02
#> col_15 0.0291860600 3.195304e-02 2.041613e-05
#> col_16 0.0473851726 4.456965e-04 2.684463e-03
#> col_17 0.0195842398 2.119776e-02 2.190885e-02
#> col_18 0.0272873755 4.078282e-05 3.015525e-02
#> col_19 0.0482415558 2.009937e-03 1.273214e-04
#> col_20 0.0013116590 6.515095e-02 5.986548e-03
#> col_21 0.0006885199 6.752003e-02 4.861925e-03
#> col_22 0.0284076740 3.303970e-02 2.939896e-06
#> col_23 0.0271495856 1.437048e-04 3.004596e-02
#> col_24 0.0088836407 5.435819e-03 4.259275e-02
#> col_25 0.0290897934 3.211466e-02 1.025982e-05
#> col_26 0.0022347780 3.276226e-02 3.418209e-02
#> col_27 0.0284166012 2.136308e-05 2.856317e-02
#> col_28 0.0001882670 6.547435e-02 6.861760e-03
#> col_29 0.0491142068 1.525450e-04 1.604323e-04
#> col_30 0.0489390142 1.963123e-03 2.431411e-07
#> col_31 0.0023928007 1.892164e-06 6.218255e-02
#> col_32 0.0014592904 3.216427e-02 3.564504e-02
#> col_33 0.0009885636 3.293112e-02 3.527345e-02
#> col_34 0.0270450709 5.761962e-05 3.078799e-02
#> col_35 0.0136858002 7.618344e-03 3.089977e-02
#> col_36 0.0201035244 2.156892e-02 2.105574e-02
#> col_37 0.0194065968 2.112788e-02 2.187710e-02
#> col_38 0.0013423301 3.282725e-02 3.491870e-02
#> col_39 0.0075046377 1.602772e-02 3.455745e-02
#> col_40 0.0201639147 7.895591e-03 2.478027e-02
#> col_41 0.0492595594 1.445066e-04 1.427592e-04
#> col_42 0.0503256986 3.356977e-06 2.309541e-06
#> col_43 0.0280273109 3.284240e-02 1.426440e-04
#> col_44 0.0131413221 1.171642e-02 3.282454e-02
#> col_45 0.0286134420 3.147587e-02 5.290590e-04
#> col_46 0.0273788385 3.571720e-05 3.018238e-02
#> col_47 0.0292791652 3.129057e-02 1.540034e-04
#> col_48 0.0061916432 9.298869e-03 4.010077e-02
#> col_49 0.0011877730 3.365215e-02 3.491567e-02
#> col_50 0.0285012447 3.182231e-02 2.252168e-04
#>
#> $output_g[[2]]
#> [,1] [,2] [,3]
#> col_51 4.176798e-03 1.142256e-06 6.341956e-02
#> col_52 2.348355e-05 7.261634e-02 1.332875e-05
#> col_53 6.092173e-04 8.620791e-03 4.743455e-02
#> col_54 5.071439e-02 2.108636e-08 1.030495e-07
#> col_55 9.210909e-03 6.615830e-03 4.041149e-02
#> col_56 8.771621e-03 6.685077e-03 3.712080e-02
#> col_57 2.866606e-02 3.136839e-02 1.758919e-05
#> col_58 5.070536e-02 2.224623e-08 1.091075e-07
#> col_59 2.846868e-05 7.256258e-02 1.626320e-05
#> col_60 2.310391e-03 3.236262e-02 3.496523e-02
#> col_61 1.471421e-02 1.209583e-02 2.681500e-02
#> col_62 2.865834e-02 3.137562e-02 1.761393e-05
#> col_63 5.424472e-03 1.429709e-02 3.706119e-02
#> col_64 1.057184e-02 1.135197e-02 3.206467e-02
#> col_65 2.866184e-02 3.137671e-02 1.729305e-05
#> col_66 5.070279e-02 2.299732e-08 1.183319e-07
#> col_67 2.022569e-02 2.036843e-02 2.191946e-02
#> col_68 2.818230e-02 9.001865e-06 3.059583e-02
#> col_69 5.070735e-02 2.368401e-08 1.097460e-07
#> col_70 2.515933e-05 7.262733e-02 1.255380e-05
#> col_71 2.916199e-05 7.257588e-02 1.471341e-05
#> col_72 2.865878e-02 3.137917e-02 1.754181e-05
#> col_73 2.817050e-02 9.305862e-06 3.060185e-02
#> col_74 9.727406e-03 6.512645e-03 3.869191e-02
#> col_75 2.866698e-02 3.136639e-02 1.794293e-05
#> col_76 2.307186e-03 3.234583e-02 3.498693e-02
#> col_77 2.817024e-02 9.062211e-06 3.060525e-02
#> col_78 2.708211e-05 7.259711e-02 1.343120e-05
#> col_79 5.070094e-02 2.456220e-08 1.156330e-07
#> col_80 5.071025e-02 2.331161e-08 1.112523e-07
#> col_81 4.172779e-03 1.028501e-06 6.343510e-02
#> col_82 2.294073e-03 3.236076e-02 3.499217e-02
#> col_83 2.298960e-03 3.235423e-02 3.498853e-02
#> col_84 2.817216e-02 8.908118e-06 3.060017e-02
#> col_85 1.215010e-02 3.184348e-03 3.645723e-02
#> col_86 2.023558e-02 2.037784e-02 2.189716e-02
#> col_87 2.023075e-02 2.037170e-02 2.191013e-02
#> col_88 2.296204e-03 3.235892e-02 3.498692e-02
#> col_89 1.158857e-02 1.145830e-02 3.368115e-02
#> col_90 1.253316e-02 6.839371e-03 3.615857e-02
#> col_91 5.070798e-02 2.334777e-08 1.060486e-07
#> col_92 5.070537e-02 2.712881e-08 1.100393e-07
#> col_93 2.865739e-02 3.137035e-02 1.827389e-05
#> col_94 7.728488e-03 3.320348e-03 4.285383e-02
#> col_95 2.866785e-02 3.136714e-02 1.706932e-05
#> col_96 2.816769e-02 9.318700e-06 3.060475e-02
#> col_97 2.865894e-02 3.137980e-02 1.762620e-05
#> col_98 9.508599e-03 8.404312e-03 3.550899e-02
#> col_99 2.303652e-03 3.233464e-02 3.500121e-02
#> col_100 2.866251e-02 3.136837e-02 1.826221e-05
#>
#>
#> $Error
#> [1] 0.1091507
#>
#> $All_Error
#> [1] 0.2544918 0.2444899 0.2393453 0.2384624 0.2377350 0.2364845 0.2339774
#> [8] 0.2292764 0.2232488 0.2177534 0.2130925 0.2089904 0.2051556 0.2014212
#> [15] 0.1977303 0.1940735 0.1904312 0.1867567 0.1830030 0.1791533 0.1752286
#> [22] 0.1712745 0.1673445 0.1634925 0.1597758 0.1562548 0.1529839 0.1499958
#> [29] 0.1472933 0.1448504 0.1426228 0.1405607 0.1386188 0.1367619 0.1349679
#> [36] 0.1332259 0.1315355 0.1299033 0.1283404 0.1268594 0.1254720 0.1241867
#> [43] 0.1230080 0.1219360 0.1209665 0.1200924 0.1193048 0.1185943 0.1179516
#> [50] 0.1173686 0.1168381 0.1163540 0.1159112 0.1155052 0.1151323 0.1147892
#> [57] 0.1144729 0.1141809 0.1139110 0.1136609 0.1134291 0.1132137 0.1130134
#> [64] 0.1128268 0.1126527 0.1124901 0.1123379 0.1121953 0.1120616 0.1119360
#> [71] 0.1118178 0.1117066 0.1116018 0.1115029 0.1114095 0.1113213 0.1112379
#> [78] 0.1111589 0.1110842 0.1110134 0.1109463 0.1108826 0.1108222 0.1107648
#> [85] 0.1107103 0.1106584 0.1106091 0.1105621 0.1105173 0.1104746 0.1104339
#> [92] 0.1103950 0.1103578 0.1103222 0.1102882 0.1102556 0.1102243 0.1101944
#> [99] 0.1101656 0.1101379 0.1101113 0.1100857 0.1100611 0.1100372 0.1100142
#> [106] 0.1099919 0.1099703 0.1099493 0.1099289 0.1099090 0.1098896 0.1098707
#> [113] 0.1098522 0.1098341 0.1098163 0.1097990 0.1097820 0.1097654 0.1097492
#> [120] 0.1097334 0.1097180 0.1097030 0.1096884 0.1096743 0.1096606 0.1096474
#> [127] 0.1096346 0.1096222 0.1096102 0.1095987 0.1095875 0.1095767 0.1095663
#> [134] 0.1095562 0.1095465 0.1095370 0.1095278 0.1095189 0.1095103 0.1095018
#> [141] 0.1094936 0.1094856 0.1094778 0.1094701 0.1094626 0.1094553 0.1094481
#> [148] 0.1094410 0.1094340 0.1094272 0.1094205 0.1094139 0.1094074 0.1094011
#> [155] 0.1093950 0.1093890 0.1093832 0.1093775 0.1093721 0.1093668 0.1093617
#> [162] 0.1093568 0.1093520 0.1093473 0.1093428 0.1093384 0.1093342 0.1093300
#> [169] 0.1093259 0.1093220 0.1093181 0.1093142 0.1093105 0.1093068 0.1093031
#> [176] 0.1092995 0.1092959 0.1092924 0.1092889 0.1092854 0.1092819 0.1092785
#> [183] 0.1092751 0.1092717 0.1092683 0.1092649 0.1092615 0.1092581 0.1092548
#> [190] 0.1092514 0.1092480 0.1092446 0.1092412 0.1092378 0.1092344 0.1092311
#> [197] 0.1092279 0.1092249 0.1092219 0.1092191 0.1092165 0.1092140 0.1092116
#> [204] 0.1092093 0.1092072 0.1092051 0.1092031 0.1092011 0.1091993 0.1091974
#> [211] 0.1091956 0.1091938 0.1091921 0.1091904 0.1091887 0.1091870 0.1091854
#> [218] 0.1091838 0.1091822 0.1091807 0.1091792 0.1091776 0.1091762 0.1091747
#> [225] 0.1091732 0.1091718 0.1091704 0.1091690 0.1091676 0.1091663 0.1091650
#> [232] 0.1091636 0.1091623 0.1091610 0.1091597 0.1091584 0.1091572 0.1091559
#> [239] 0.1091547 0.1091535 0.1091523 0.1091512 0.1091500 0.1091489 0.1091479
#> [246] 0.1091468 0.1091458
#>
#> $bisil
#> [1] 0.1954041
#>
#> $row_clusters
#> $row_clusters[[1]]
#> [,1] [,2] [,3]
#> row_1 0 1 0
#> row_2 0 0 0
#> row_3 0 0 0
#> row_4 1 0 1
#> row_5 1 0 0
#> row_6 1 0 0
#> row_7 1 1 0
#> row_8 1 1 1
#> row_9 1 1 0
#> row_10 1 0 1
#> row_11 0 0 1
#> row_12 1 0 1
#> row_13 1 0 0
#> row_14 1 0 1
#> row_15 0 0 1
#> row_16 1 0 1
#> row_17 1 0 1
#> row_18 1 1 0
#> row_19 1 0 0
#> row_20 1 1 0
#> row_21 0 1 0
#> row_22 1 0 1
#> row_23 1 1 1
#> row_24 0 1 1
#> row_25 0 0 0
#> row_26 1 1 1
#> row_27 0 1 1
#> row_28 0 1 1
#> row_29 0 0 1
#> row_30 1 0 0
#> row_31 1 0 1
#> row_32 1 1 0
#> row_33 1 0 0
#> row_34 1 0 1
#> row_35 1 1 0
#> row_36 0 1 0
#> row_37 0 0 1
#> row_38 1 0 1
#> row_39 0 1 1
#> row_40 1 0 0
#> row_41 1 1 0
#> row_42 0 1 0
#> row_43 1 0 1
#> row_44 0 1 1
#> row_45 1 1 1
#> row_46 0 1 1
#> row_47 1 1 1
#> row_48 1 0 0
#> row_49 1 0 1
#> row_50 0 0 0
#> row_51 1 0 1
#> row_52 0 1 1
#> row_53 0 0 0
#> row_54 0 1 1
#> row_55 1 0 0
#> row_56 0 1 0
#> row_57 0 0 1
#> row_58 1 0 0
#> row_59 1 1 0
#> row_60 1 0 1
#> row_61 1 1 1
#> row_62 1 0 1
#> row_63 1 1 1
#> row_64 1 0 1
#> row_65 1 1 0
#> row_66 0 1 0
#> row_67 0 1 0
#> row_68 1 0 1
#> row_69 1 0 1
#> row_70 0 0 1
#> row_71 0 0 1
#> row_72 1 0 1
#> row_73 0 0 0
#> row_74 1 0 1
#> row_75 1 0 1
#> row_76 0 1 0
#> row_77 0 1 1
#> row_78 1 0 1
#> row_79 1 1 0
#> row_80 0 1 1
#> row_81 1 0 0
#> row_82 0 0 0
#> row_83 1 0 1
#> row_84 0 1 0
#> row_85 0 0 1
#> row_86 0 0 1
#> row_87 1 1 1
#> row_88 0 0 1
#> row_89 1 1 1
#> row_90 0 0 1
#> row_91 0 1 0
#> row_92 1 1 0
#> row_93 1 1 1
#> row_94 0 0 0
#> row_95 1 1 0
#> row_96 1 1 0
#> row_97 0 0 1
#> row_98 1 0 0
#> row_99 0 1 1
#> row_100 1 1 0
#>
#> $row_clusters[[2]]
#> [,1] [,2] [,3]
#> row_101 0 1 0
#> row_102 0 0 0
#> row_103 0 0 0
#> row_104 1 0 1
#> row_105 1 0 0
#> row_106 1 0 0
#> row_107 1 1 0
#> row_108 1 1 1
#> row_109 1 1 0
#> row_110 1 0 1
#> row_111 0 0 0
#> row_112 1 0 1
#> row_113 1 0 0
#> row_114 1 0 1
#> row_115 0 0 1
#> row_116 1 0 1
#> row_117 1 0 1
#> row_118 1 1 1
#> row_119 1 0 0
#> row_120 1 1 0
#> row_121 0 1 0
#> row_122 1 0 1
#> row_123 1 1 1
#> row_124 0 1 1
#> row_125 0 0 0
#> row_126 1 1 1
#> row_127 0 1 1
#> row_128 0 1 1
#> row_129 0 0 1
#> row_130 1 0 0
#> row_131 1 0 1
#> row_132 1 1 0
#> row_133 1 0 0
#> row_134 1 0 1
#> row_135 1 1 1
#> row_136 0 1 0
#> row_137 0 0 1
#> row_138 1 0 1
#> row_139 0 1 1
#> row_140 1 0 0
#> row_141 1 1 1
#> row_142 0 1 0
#> row_143 1 0 1
#> row_144 0 1 1
#> row_145 1 1 1
#> row_146 0 1 1
#> row_147 1 1 1
#> row_148 1 0 0
#> row_149 1 0 1
#> row_150 0 0 1
#> row_151 1 0 1
#> row_152 0 1 1
#> row_153 0 0 0
#> row_154 0 1 1
#> row_155 1 0 0
#> row_156 0 1 0
#> row_157 0 0 1
#> row_158 1 0 0
#> row_159 1 1 0
#> row_160 1 0 1
#> row_161 1 1 1
#> row_162 1 0 1
#> row_163 1 1 1
#> row_164 1 0 1
#> row_165 1 1 0
#> row_166 0 1 0
#> row_167 0 1 0
#> row_168 1 0 1
#> row_169 1 0 1
#> row_170 0 0 1
#> row_171 0 0 0
#> row_172 1 0 1
#> row_173 0 0 0
#> row_174 1 0 1
#> row_175 1 0 1
#> row_176 0 1 0
#> row_177 0 1 1
#> row_178 1 0 1
#> row_179 1 1 0
#> row_180 0 1 1
#> row_181 1 0 0
#> row_182 0 0 0
#> row_183 1 0 1
#> row_184 0 1 0
#> row_185 0 0 1
#> row_186 0 0 1
#> row_187 1 1 1
#> row_188 0 0 1
#> row_189 1 1 1
#> row_190 0 0 0
#> row_191 0 1 0
#> row_192 1 1 0
#> row_193 1 1 0
#> row_194 0 0 0
#> row_195 1 1 0
#> row_196 1 1 0
#> row_197 0 0 1
#> row_198 1 0 0
#> row_199 0 1 1
#> row_200 1 1 0
#>
#>
#> $col_clusters
#> $col_clusters[[1]]
#> [,1] [,2] [,3]
#> col_1 0 0 1
#> col_2 0 1 0
#> col_3 0 0 1
#> col_4 1 0 0
#> col_5 0 0 1
#> col_6 0 0 1
#> col_7 1 1 0
#> col_8 1 0 0
#> col_9 0 1 0
#> col_10 0 1 1
#> col_11 0 0 1
#> col_12 1 1 0
#> col_13 0 0 1
#> col_14 0 0 1
#> col_15 1 1 0
#> col_16 1 0 0
#> col_17 0 1 1
#> col_18 1 0 1
#> col_19 1 0 0
#> col_20 0 1 0
#> col_21 0 1 0
#> col_22 1 1 0
#> col_23 1 0 1
#> col_24 0 0 1
#> col_25 1 1 0
#> col_26 0 1 1
#> col_27 1 0 1
#> col_28 0 1 0
#> col_29 1 0 0
#> col_30 1 0 0
#> col_31 0 0 1
#> col_32 0 1 1
#> col_33 0 1 1
#> col_34 1 0 1
#> col_35 0 0 1
#> col_36 1 1 1
#> col_37 0 1 1
#> col_38 0 1 1
#> col_39 0 0 1
#> col_40 1 0 1
#> col_41 1 0 0
#> col_42 1 0 0
#> col_43 1 1 0
#> col_44 0 0 1
#> col_45 1 1 0
#> col_46 1 0 1
#> col_47 1 1 0
#> col_48 0 0 1
#> col_49 0 1 1
#> col_50 1 1 0
#>
#> $col_clusters[[2]]
#> [,1] [,2] [,3]
#> col_51 0 0 1
#> col_52 0 1 0
#> col_53 0 0 1
#> col_54 1 0 0
#> col_55 0 0 1
#> col_56 0 0 1
#> col_57 1 1 0
#> col_58 1 0 0
#> col_59 0 1 0
#> col_60 0 1 1
#> col_61 0 0 1
#> col_62 1 1 0
#> col_63 0 0 1
#> col_64 0 0 1
#> col_65 1 1 0
#> col_66 1 0 0
#> col_67 1 1 1
#> col_68 1 0 1
#> col_69 1 0 0
#> col_70 0 1 0
#> col_71 0 1 0
#> col_72 1 1 0
#> col_73 1 0 1
#> col_74 0 0 1
#> col_75 1 1 0
#> col_76 0 1 1
#> col_77 1 0 1
#> col_78 0 1 0
#> col_79 1 0 0
#> col_80 1 0 0
#> col_81 0 0 1
#> col_82 0 1 1
#> col_83 0 1 1
#> col_84 1 0 1
#> col_85 0 0 1
#> col_86 1 1 1
#> col_87 1 1 1
#> col_88 0 1 1
#> col_89 0 0 1
#> col_90 0 0 1
#> col_91 1 0 0
#> col_92 1 0 0
#> col_93 1 1 0
#> col_94 0 0 1
#> col_95 1 1 0
#> col_96 1 0 1
#> col_97 1 1 0
#> col_98 0 0 1
#> col_99 0 1 1
#> col_100 1 1 0
#>
#>
#> $lambda
#> $lambda[[1]]
#> [1] 1.099969e-153 7.952029e-177 7.554946e-175
#>
#> $lambda[[2]]
#> [1] 7.049886e-144 5.161829e-181 6.270154e-194
#>
#>
#> $mu
#> $mu[[1]]
#> [1] 1.255809e-136 7.363321e-178 8.052771e-178
#>
#> $mu[[2]]
#> [1] 7.868284e-136 1.565183e-179 1.655582e-153
#>
#>