Parametric and non-parametric implementation of
bootstrap estimates for MOBSTER fits. This computation is parallel
and uses ?easypar.
Usage
mobster_bootstrap(
x,
n.resamples = 100,
bootstrap = "nonparametric",
cores.ratio = 0.8,
cache = NULL,
save_data = NULL,
...
)Examples
# Random small dataset
dataset = random_dataset(N = 200, seed = 123, Beta_variance_scaling = 100)
x = mobster_fit(dataset$data, auto_setup = 'FAST')
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:30.220264 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.4s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 52% [C1] and 48% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 102, 52%] with mean = 0.58.
#> ● Beta C2 [n = 98, 48%] with mean = 0.11.
#> ℹ Score(s): NLL = -108.12; ICL = -182.58 (-182.58), H = 1.88 (1.88). Fit
#> converged by MM in 70 steps.
# Just 5 resamples of a nonparametric bootstrap run, disabling the parallel engine
options(easypar.parallel = FALSE)
boot_results = mobster_bootstrap(x$best, n.resamples = 5, auto_setup = 'FAST')
#> [ MOBSTER bootstrap ~ 5 resamples from nonparametric bootstrap ]
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 52% [C1] and 48% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 102, 52%] with mean = 0.58.
#> ● Beta C2 [n = 98, 48%] with mean = 0.11.
#> ℹ Score(s): NLL = -108.12; ICL = -182.58 (-182.58), H = 1.88 (1.88). Fit
#> converged by MM in 70 steps.
#>
#> ℹ Creating nonparametric bootstrap resamples
#> ✔ Creating nonparametric bootstrap resamples ... done
#>
#>
#> ── Running fits ─────────────────────────────────── Might take some time ... ──
#> [easypar] 2025-11-21 08:50:33.717127 - Overriding parallel execution setup [TRUE] with global option : FALSE
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:33.762079 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.9s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 50% [C1] and 50% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 98, 50%] with mean = 0.59.
#> ● Beta C2 [n = 102, 50%] with mean = 0.11.
#> ℹ Score(s): NLL = -101.04; ICL = -168.07 (-168.07), H = 2.23 (2.23). Fit
#> interrupted by MM in 100 steps.
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:37.739139 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.3s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 51% [C1] and 49% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 101, 51%] with mean = 0.59.
#> ● Beta C2 [n = 99, 49%] with mean = 0.11.
#> ℹ Score(s): NLL = -115.21; ICL = -197.42 (-197.42), H = 1.21 (1.21). Fit
#> converged by MM in 62 steps.
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:41.090482 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.4s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 50% [C2] and 50% [C1], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 99, 50%] with mean = 0.59.
#> ● Beta C2 [n = 101, 50%] with mean = 0.12.
#> ℹ Score(s): NLL = -108.28; ICL = -182.9 (-182.9), H = 1.87 (1.87). Fit
#> converged by MM in 70 steps.
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:44.554851 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.6s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 57% [C1] and 43% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 113, 57%] with mean = 0.59.
#> ● Beta C2 [n = 87, 43%] with mean = 0.11.
#> ℹ Score(s): NLL = -115.77; ICL = -199.22 (-199.22), H = 0.52 (0.52). Fit
#> converged by MM in 25 steps.
#> [ MOBSTER fit ]
#>
#> ✔ Loaded input data, n = 200.
#> ❯ n = 200. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#>
#> [easypar] 2025-11-21 08:50:48.279657 - Overriding parallel execution setup [FALSE] with global option : FALSE
#>
#>
#> ℹ MOBSTER fits completed in 3.7s.
#>
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 55% [C1] and 45% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 109, 55%] with mean = 0.59.
#> ● Beta C2 [n = 91, 45%] with mean = 0.13.
#> ℹ Score(s): NLL = -102.08; ICL = -171.04 (-171.04), H = 1.34 (1.34). Fit
#> converged by MM in 29 steps.
# The resample data is available in a list
print(boot_results$resamples[[1]])
#> [[1]]
#> id VAF original.id
#> 1 1 0.52355677 12
#> 2 2 0.84646937 3
#> 3 3 0.05393297 137
#> 4 4 0.43401627 14
#> 5 5 0.08026471 141
#> 6 6 0.08394884 192
#> 7 7 0.04428176 148
#> 8 8 0.08810669 106
#> 9 9 0.04328793 119
#> 10 10 0.47680339 16
#> 11 11 0.74474824 80
#> 12 12 0.46164409 62
#> 13 13 0.03345377 98
#> 14 14 0.06208767 123
#> 15 15 0.72430352 60
#> 16 16 0.04140095 105
#> 17 17 0.53245520 32
#> 18 18 0.63063057 25
#> 19 19 0.60339029 36
#> 20 20 0.59300401 166
#> 21 21 0.05393297 137
#> 22 22 0.14138817 133
#> 23 23 0.16810276 145
#> 24 24 0.02120704 127
#> 25 25 0.16810276 145
#> 26 26 0.16570645 165
#> 27 27 0.55147229 58
#> 28 28 0.08810669 106
#> 29 29 0.67301958 195
#> 30 30 0.71187995 13
#> 31 31 0.62264994 67
#> 32 32 0.11033293 177
#> 33 33 0.54932251 74
#> 34 34 0.71166697 56
#> 35 35 0.05714139 107
#> 36 36 0.30370976 121
#> 37 37 0.51231087 91
#> 38 38 0.14894925 116
#> 39 39 0.65434252 34
#> 40 40 0.08293964 172
#> 41 41 0.09575708 163
#> 42 42 0.17316129 109
#> 43 43 0.06036449 179
#> 44 44 0.64917596 55
#> 45 45 0.64982215 23
#> 46 46 0.52103494 95
#> 47 47 0.14648825 154
#> 48 48 0.08318635 183
#> 49 49 0.10144527 135
#> 50 50 0.13620251 143
#> 51 51 0.58490241 46
#> 52 52 0.20522784 140
#> 53 53 0.58780098 90
#> 54 54 0.20998868 124
#> 55 55 0.16312791 99
#> 56 56 0.50179798 5
#> 57 57 0.05714139 107
#> 58 58 0.53701189 68
#> 59 59 0.30370976 121
#> 60 60 0.65404812 11
#> 61 61 0.48903433 49
#> 62 62 0.62264994 67
#> 63 63 0.54932251 74
#> 64 64 0.71007760 15
#> 65 65 0.53021870 7
#> 66 66 0.62684659 42
#> 67 67 0.58780098 90
#> 68 68 0.64917596 55
#> 69 69 0.09192788 132
#> 70 70 0.09192788 132
#> 71 71 0.17316129 109
#> 72 72 0.53245520 32
#> 73 73 0.69144367 57
#> 74 74 0.09462104 193
#> 75 75 0.25434749 176
#> 76 76 0.51683769 86
#> 77 77 0.64917596 55
#> 78 78 0.16810276 145
#> 79 79 0.84646937 3
#> 80 80 0.57785081 18
#> 81 81 0.15346219 197
#> 82 82 0.10144527 135
#> 83 83 0.52103494 95
#> 84 84 0.04428176 148
#> 85 85 0.20522784 140
#> 86 86 0.03345377 98
#> 87 87 0.53701189 68
#> 88 88 0.09575708 163
#> 89 89 0.02233042 134
#> 90 90 0.07211605 120
#> 91 91 0.64982215 23
#> 92 92 0.02979764 111
#> 93 93 0.53701189 68
#> 94 94 0.12286390 187
#> 95 95 0.46040413 30
#> 96 96 0.14806814 178
#> 97 97 0.57466625 31
#> 98 98 0.16312791 99
#> 99 99 0.18577837 153
#> 100 100 0.25434749 176
#> 101 101 0.63063057 25
#> 102 102 0.27925367 156
#> 103 103 0.51106345 76
#> 104 104 0.47006049 92
#> 105 105 0.08415006 100
#> 106 106 0.69142420 8
#> 107 107 0.52259202 78
#> 108 108 0.17617266 146
#> 109 109 0.04428176 148
#> 110 110 0.63865136 85
#> 111 111 0.74760698 21
#> 112 112 0.66034681 47
#> 113 113 0.65404812 11
#> 114 114 0.63495385 51
#> 115 115 0.54101481 71
#> 116 116 0.08293964 172
#> 117 117 0.07584066 152
#> 118 118 0.57319452 63
#> 119 119 0.46164409 62
#> 120 120 0.04140095 105
#> 121 121 0.08391171 110
#> 122 122 0.07533878 200
#> 123 123 0.71166697 56
#> 124 124 0.53788908 37
#> 125 125 0.65404812 11
#> 126 126 0.05393297 137
#> 127 127 0.11370181 164
#> 128 128 0.60126291 10
#> 129 129 0.11806906 199
#> 130 130 0.55147229 58
#> 131 131 0.68651983 77
#> 132 132 0.03345377 98
#> 133 133 0.49730321 82
#> 134 134 0.29749078 174
#> 135 135 0.12928620 129
#> 136 136 0.14806814 178
#> 137 137 0.04140095 105
#> 138 138 0.08318635 183
#> 139 139 0.49730321 82
#> 140 140 0.51796731 93
#> 141 141 0.10662581 189
#> 142 142 0.15346219 197
#> 143 143 0.71187995 13
#> 144 144 0.05902402 175
#> 145 145 0.05902402 175
#> 146 146 0.61440798 79
#> 147 147 0.12286390 187
#> 148 148 0.46164409 62
#> 149 149 0.05278961 161
#> 150 150 0.04328793 119
#> 151 151 0.23287406 190
#> 152 152 0.08668096 122
#> 153 153 0.15346219 197
#> 154 154 0.43401627 14
#> 155 155 0.67301958 195
#> 156 156 0.57466625 31
#> 157 157 0.08434598 136
#> 158 158 0.02979764 111
#> 159 159 0.29749078 174
#> 160 160 0.51683769 86
#> 161 161 0.72430352 60
#> 162 162 0.46164409 62
#> 163 163 0.53161319 66
#> 164 164 0.61513016 1
#> 165 165 0.07562712 113
#> 166 166 0.51683769 86
#> 167 167 0.67912870 38
#> 168 168 0.46627543 88
#> 169 169 0.71166697 56
#> 170 170 0.08810669 106
#> 171 171 0.13620251 143
#> 172 172 0.11612617 138
#> 173 173 0.49730321 82
#> 174 174 0.06208767 123
#> 175 175 0.70284180 94
#> 176 176 0.06208767 123
#> 177 177 0.42751004 81
#> 178 178 0.67862073 4
#> 179 179 0.08747705 168
#> 180 180 0.08668096 122
#> 181 181 0.65434252 34
#> 182 182 0.08415006 100
#> 183 183 0.67301958 195
#> 184 184 0.08391171 110
#> 185 185 0.57319452 63
#> 186 186 0.62264994 67
#> 187 187 0.58635506 59
#> 188 188 0.61645161 45
#> 189 189 0.06731439 185
#> 190 190 0.52103494 95
#> 191 191 0.20522784 140
#> 192 192 0.18409048 159
#> 193 193 0.45979987 40
#> 194 194 0.08434598 136
#> 195 195 0.44827642 24
#> 196 196 0.06010055 191
#> 197 197 0.05393297 137
#> 198 198 0.15942611 160
#> 199 199 0.71007760 15
#> 200 200 0.05310553 170
#>
# The best fits are returned
print(boot_results$fits)
#> $`1`
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 50% [C1] and 50% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 98, 50%] with mean = 0.59.
#> ● Beta C2 [n = 102, 50%] with mean = 0.11.
#> ℹ Score(s): NLL = -101.04; ICL = -168.07 (-168.07), H = 2.23 (2.23). Fit
#> interrupted by MM in 100 steps.
#>
#> $`2`
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 51% [C1] and 49% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 101, 51%] with mean = 0.59.
#> ● Beta C2 [n = 99, 49%] with mean = 0.11.
#> ℹ Score(s): NLL = -115.21; ICL = -197.42 (-197.42), H = 1.21 (1.21). Fit
#> converged by MM in 62 steps.
#>
#> $`3`
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 50% [C2] and 50% [C1], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 99, 50%] with mean = 0.59.
#> ● Beta C2 [n = 101, 50%] with mean = 0.12.
#> ℹ Score(s): NLL = -108.28; ICL = -182.9 (-182.9), H = 1.87 (1.87). Fit
#> converged by MM in 70 steps.
#>
#> $`4`
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 57% [C1] and 43% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 113, 57%] with mean = 0.59.
#> ● Beta C2 [n = 87, 43%] with mean = 0.11.
#> ℹ Score(s): NLL = -115.77; ICL = -199.22 (-199.22), H = 0.52 (0.52). Fit
#> converged by MM in 25 steps.
#>
#> $`5`
#> ── [ MOBSTER ] My MOBSTER model n = 200 with k = 2 Beta(s) without tail ────────
#> ● Clusters: π = 55% [C1] and 45% [C2], with π > 0.
#> ✖ No tail fit.
#>
#> ● Beta C1 [n = 109, 55%] with mean = 0.59.
#> ● Beta C2 [n = 91, 45%] with mean = 0.13.
#> ℹ Score(s): NLL = -102.08; ICL = -171.04 (-171.04), H = 1.34 (1.34). Fit
#> converged by MM in 29 steps.
#>