Extract Diagnostics from a big_pls_cox_gd Model
Source:R/big_pls_cox_gd_diagnostics.R
gd_diagnostics.RdExtract Diagnostics from a big_pls_cox_gd Model
Arguments
- object
A model returned by
big_pls_cox_gd().
Examples
library(bigmemory)
set.seed(1)
n <- 50
p <- 10
X <- bigmemory::as.big.matrix(matrix(rnorm(n * p), n, p))
time <- rexp(n, rate = 0.1)
status <- rbinom(n, 1, 0.7)
fit <- big_pls_cox_gd(X, time, status, ncomp = 3, max_iter = 200)
str(fit)
#> List of 16
#> $ coefficients : num [1:3] 0.593 0.165 0.314
#> $ loglik : num -104
#> $ iterations : int 200
#> $ converged : logi FALSE
#> $ scores : num [1:50, 1:3] 0.0513 1.555 -1.1934 0.7405 -1.9847 ...
#> $ loadings : num [1:10, 1:3] 0.3689 -0.0474 0.5673 0.1476 0.4647 ...
#> $ weights : num [1:10, 1:3] 0.5334 -0.1685 0.4853 0.0962 0.3655 ...
#> $ center : num [1:10] 0.1004 0.1173 -0.1525 0.0769 -0.0313 ...
#> $ scale : num [1:10] 0.831 0.969 0.9 1.009 1.095 ...
#> $ keepX : int [1:3] 0 0 0
#> $ time : num [1:50] 0.6162 3.6972 12.2628 0.271 0.0901 ...
#> $ status : num [1:50] 1 1 0 1 0 1 1 1 1 1 ...
#> $ loglik_trace : num [1:200, 1] -106 -106 -105 -104 -104 ...
#> $ step_trace : num [1:200, 1] 0.05 0.05 0.025 0.025 0.025 0.05 0.025 0.025 0.025 0.05 ...
#> $ gradnorm_trace: num [1:200, 1] 10.87 11.592 3.16 0.662 0.166 ...
#> $ cox_fit :List of 19
#> ..$ coefficients : Named num [1:3] 0.376 -0.323 -0.199
#> .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
#> ..$ var : num [1:3, 1:3] 0.03725 -0.00601 0.00358 -0.00601 0.0223 ...
#> ..$ loglik : num [1:2] -110 -106
#> ..$ score : num 7.83
#> ..$ iter : int 4
#> ..$ linear.predictors: num [1:50] -0.399 0.338 -1.056 1.072 -0.37 ...
#> ..$ residuals : Named num [1:50] 0.938 0.4757 -0.2709 0.899 -0.0117 ...
#> .. ..- attr(*, "names")= chr [1:50] "1" "2" "3" "4" ...
#> ..$ means : Named num [1:3] 4.44e-18 1.02e-16 8.88e-18
#> .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
#> ..$ method : chr "efron"
#> ..$ n : int 50
#> ..$ nevent : num 37
#> ..$ terms :Classes 'terms', 'formula' language survival::Surv(time, status) ~ comp1 + comp2 + comp3
#> .. .. ..- attr(*, "variables")= language list(survival::Surv(time, status), comp1, comp2, comp3)
#> .. .. ..- attr(*, "factors")= int [1:4, 1:3] 0 1 0 0 0 0 1 0 0 0 ...
#> .. .. .. ..- attr(*, "dimnames")=List of 2
#> .. .. .. .. ..$ : chr [1:4] "survival::Surv(time, status)" "comp1" "comp2" "comp3"
#> .. .. .. .. ..$ : chr [1:3] "comp1" "comp2" "comp3"
#> .. .. ..- attr(*, "term.labels")= chr [1:3] "comp1" "comp2" "comp3"
#> .. .. ..- attr(*, "specials")=Dotted pair list of 5
#> .. .. .. ..$ strata : NULL
#> .. .. .. ..$ tt : NULL
#> .. .. .. ..$ frailty: NULL
#> .. .. .. ..$ ridge : NULL
#> .. .. .. ..$ pspline: NULL
#> .. .. ..- attr(*, "order")= int [1:3] 1 1 1
#> .. .. ..- attr(*, "intercept")= num 1
#> .. .. ..- attr(*, "response")= int 1
#> .. .. ..- attr(*, ".Environment")=<environment: 0x16a965fd0>
#> .. .. ..- attr(*, "predvars")= language list(survival::Surv(time, status), comp1, comp2, comp3)
#> .. .. ..- attr(*, "dataClasses")= Named chr [1:4] "nmatrix.2" "numeric" "numeric" "numeric"
#> .. .. .. ..- attr(*, "names")= chr [1:4] "survival::Surv(time, status)" "comp1" "comp2" "comp3"
#> ..$ assign :List of 3
#> .. ..$ comp1: int 1
#> .. ..$ comp2: int 2
#> .. ..$ comp3: int 3
#> ..$ wald.test : num 7.52
#> ..$ concordance : Named num [1:7] 589 313 0 0 0 ...
#> .. ..- attr(*, "names")= chr [1:7] "concordant" "discordant" "tied.x" "tied.y" ...
#> ..$ y : 'Surv' num [1:50, 1:2] 0.6162 3.6972 12.2628+ 0.2710 0.0901+ 1.5697 6.7861 3.0889 0.5886 2.0551 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:50] "1" "2" "3" "4" ...
#> .. .. ..$ : chr [1:2] "time" "status"
#> .. ..- attr(*, "type")= chr "right"
#> ..$ timefix : logi TRUE
#> ..$ formula :Class 'formula' language survival::Surv(time, status) ~ comp1 + comp2 + comp3
#> .. .. ..- attr(*, ".Environment")=<environment: 0x16a965fd0>
#> ..$ call : language survival::coxph(formula = survival::Surv(time, status) ~ ., data = scores_df, ties = "efron", x = FALSE)
#> ..- attr(*, "class")= chr "coxph"
#> - attr(*, "class")= chr "big_pls_cox_gd"
head(fit$scores)
#> [,1] [,2] [,3]
#> [1,] 0.05129539 0.7550799 0.8793004
#> [2,] 1.55497806 0.3715325 0.6353775
#> [3,] -1.19340964 0.4907382 2.2626350
#> [4,] 0.74046728 -2.1053220 -0.5759915
#> [5,] -1.98468237 -0.8043244 -0.5814998
#> [6,] 0.42019957 1.5593684 0.5762548
gd_diagnostics(fit)
#> $iterations
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200
#>
#> $loglik
#> [,1]
#> [1,] -106.2192
#> [2,] -105.8233
#> [3,] -104.5723
#> [4,] -104.4657
#> [5,] -104.4608
#> [6,] -104.4608
#> [7,] -104.4604
#> [8,] -104.4604
#> [9,] -104.4604
#> [10,] -104.4604
#> [11,] -104.4604
#> [12,] -104.4604
#> [13,] -104.4604
#> [14,] -104.4604
#> [15,] -104.4604
#> [16,] -104.4604
#> [17,] -104.4604
#> [18,] -104.4604
#> [19,] -104.4604
#> [20,] -104.4604
#> [21,] -104.4604
#> [22,] -104.4604
#> [23,] -104.4604
#> [24,] -104.4604
#> [25,] -104.4604
#> [26,] -104.4604
#> [27,] -104.4604
#> [28,] -104.4604
#> [29,] -104.4604
#> [30,] -104.4604
#> [31,] -104.4604
#> [32,] -104.4604
#> [33,] -104.4604
#> [34,] -104.4604
#> [35,] -104.4604
#> [36,] -104.4604
#> [37,] -104.4604
#> [38,] -104.4604
#> [39,] -104.4604
#> [40,] -104.4604
#> [41,] -104.4604
#> [42,] -104.4604
#> [43,] -104.4604
#> [44,] -104.4604
#> [45,] -104.4604
#> [46,] -104.4604
#> [47,] -104.4604
#> [48,] -104.4604
#> [49,] -104.4604
#> [50,] -104.4604
#> [51,] -104.4604
#> [52,] -104.4604
#> [53,] -104.4604
#> [54,] -104.4604
#> [55,] -104.4604
#> [56,] -104.4604
#> [57,] -104.4604
#> [58,] -104.4604
#> [59,] -104.4604
#> [60,] -104.4604
#> [61,] -104.4604
#> [62,] -104.4604
#> [63,] -104.4604
#> [64,] -104.4604
#> [65,] -104.4604
#> [66,] -104.4604
#> [67,] -104.4604
#> [68,] -104.4604
#> [69,] -104.4604
#> [70,] -104.4604
#> [71,] -104.4604
#> [72,] -104.4604
#> [73,] -104.4604
#> [74,] -104.4604
#> [75,] -104.4604
#> [76,] -104.4604
#> [77,] -104.4604
#> [78,] -104.4604
#> [79,] -104.4604
#> [80,] -104.4604
#> [81,] -104.4604
#> [82,] -104.4604
#> [83,] -104.4604
#> [84,] -104.4604
#> [85,] -104.4604
#> [86,] -104.4604
#> [87,] -104.4604
#> [88,] -104.4604
#> [89,] -104.4604
#> [90,] -104.4604
#> [91,] -104.4604
#> [92,] -104.4604
#> [93,] -104.4604
#> [94,] -104.4604
#> [95,] -104.4604
#> [96,] -104.4604
#> [97,] -104.4604
#> [98,] -104.4604
#> [99,] -104.4604
#> [100,] -104.4604
#> [101,] -104.4604
#> [102,] -104.4604
#> [103,] -104.4604
#> [104,] -104.4604
#> [105,] -104.4604
#> [106,] -104.4604
#> [107,] -104.4604
#> [108,] -104.4604
#> [109,] -104.4604
#> [110,] -104.4604
#> [111,] -104.4604
#> [112,] -104.4604
#> [113,] -104.4604
#> [114,] -104.4604
#> [115,] -104.4604
#> [116,] -104.4604
#> [117,] -104.4604
#> [118,] -104.4604
#> [119,] -104.4604
#> [120,] -104.4604
#> [121,] -104.4604
#> [122,] -104.4604
#> [123,] -104.4604
#> [124,] -104.4604
#> [125,] -104.4604
#> [126,] -104.4604
#> [127,] -104.4604
#> [128,] -104.4604
#> [129,] -104.4604
#> [130,] -104.4604
#> [131,] -104.4604
#> [132,] -104.4604
#> [133,] -104.4604
#> [134,] -104.4604
#> [135,] -104.4604
#> [136,] -104.4604
#> [137,] -104.4604
#> [138,] -104.4604
#> [139,] -104.4604
#> [140,] -104.4604
#> [141,] -104.4604
#> [142,] -104.4604
#> [143,] -104.4604
#> [144,] -104.4604
#> [145,] -104.4604
#> [146,] -104.4604
#> [147,] -104.4604
#> [148,] -104.4604
#> [149,] -104.4604
#> [150,] -104.4604
#> [151,] -104.4604
#> [152,] -104.4604
#> [153,] -104.4604
#> [154,] -104.4604
#> [155,] -104.4604
#> [156,] -104.4604
#> [157,] -104.4604
#> [158,] -104.4604
#> [159,] -104.4604
#> [160,] -104.4604
#> [161,] -104.4604
#> [162,] -104.4604
#> [163,] -104.4604
#> [164,] -104.4604
#> [165,] -104.4604
#> [166,] -104.4604
#> [167,] -104.4604
#> [168,] -104.4604
#> [169,] -104.4604
#> [170,] -104.4604
#> [171,] -104.4604
#> [172,] -104.4604
#> [173,] -104.4604
#> [174,] -104.4604
#> [175,] -104.4604
#> [176,] -104.4604
#> [177,] -104.4604
#> [178,] -104.4604
#> [179,] -104.4604
#> [180,] -104.4604
#> [181,] -104.4604
#> [182,] -104.4604
#> [183,] -104.4604
#> [184,] -104.4604
#> [185,] -104.4604
#> [186,] -104.4604
#> [187,] -104.4604
#> [188,] -104.4604
#> [189,] -104.4604
#> [190,] -104.4604
#> [191,] -104.4604
#> [192,] -104.4604
#> [193,] -104.4604
#> [194,] -104.4604
#> [195,] -104.4604
#> [196,] -104.4604
#> [197,] -104.4604
#> [198,] -104.4604
#> [199,] -104.4604
#> [200,] -104.4604
#>
#> $step_sizes
#> [,1]
#> [1,] 0.050
#> [2,] 0.050
#> [3,] 0.025
#> [4,] 0.025
#> [5,] 0.025
#> [6,] 0.050
#> [7,] 0.025
#> [8,] 0.025
#> [9,] 0.025
#> [10,] 0.050
#> [11,] 0.025
#> [12,] 0.025
#> [13,] 0.050
#> [14,] 0.025
#> [15,] 0.050
#> [16,] 0.050
#> [17,] 0.050
#> [18,] 0.050
#> [19,] 0.025
#> [20,] 0.050
#> [21,] 0.050
#> [22,] 0.050
#> [23,] 0.050
#> [24,] 0.025
#> [25,] 0.050
#> [26,] 0.050
#> [27,] 0.050
#> [28,] 0.050
#> [29,] 0.050
#> [30,] 0.025
#> [31,] 0.050
#> [32,] 0.050
#> [33,] 0.050
#> [34,] 0.050
#> [35,] 0.025
#> [36,] 0.050
#> [37,] 0.050
#> [38,] 0.050
#> [39,] 0.050
#> [40,] 0.050
#> [41,] 0.025
#> [42,] 0.050
#> [43,] 0.050
#> [44,] 0.050
#> [45,] 0.050
#> [46,] 0.025
#> [47,] 0.050
#> [48,] 0.050
#> [49,] 0.050
#> [50,] 0.050
#> [51,] 0.050
#> [52,] 0.025
#> [53,] 0.050
#> [54,] 0.050
#> [55,] 0.050
#> [56,] 0.050
#> [57,] 0.025
#> [58,] 0.050
#> [59,] 0.050
#> [60,] 0.050
#> [61,] 0.050
#> [62,] 0.050
#> [63,] 0.025
#> [64,] 0.050
#> [65,] 0.050
#> [66,] 0.050
#> [67,] 0.050
#> [68,] 0.025
#> [69,] 0.050
#> [70,] 0.050
#> [71,] 0.050
#> [72,] 0.050
#> [73,] 0.050
#> [74,] 0.025
#> [75,] 0.050
#> [76,] 0.050
#> [77,] 0.050
#> [78,] 0.050
#> [79,] 0.025
#> [80,] 0.050
#> [81,] 0.050
#> [82,] 0.050
#> [83,] 0.050
#> [84,] 0.050
#> [85,] 0.025
#> [86,] 0.050
#> [87,] 0.050
#> [88,] 0.050
#> [89,] 0.050
#> [90,] 0.025
#> [91,] 0.050
#> [92,] 0.050
#> [93,] 0.050
#> [94,] 0.050
#> [95,] 0.050
#> [96,] 0.025
#> [97,] 0.050
#> [98,] 0.050
#> [99,] 0.050
#> [100,] 0.050
#> [101,] 0.025
#> [102,] 0.050
#> [103,] 0.050
#> [104,] 0.050
#> [105,] 0.050
#> [106,] 0.050
#> [107,] 0.025
#> [108,] 0.050
#> [109,] 0.050
#> [110,] 0.050
#> [111,] 0.050
#> [112,] 0.025
#> [113,] 0.050
#> [114,] 0.050
#> [115,] 0.050
#> [116,] 0.050
#> [117,] 0.050
#> [118,] 0.025
#> [119,] 0.050
#> [120,] 0.050
#> [121,] 0.050
#> [122,] 0.050
#> [123,] 0.050
#> [124,] 0.025
#> [125,] 0.050
#> [126,] 0.050
#> [127,] 0.050
#> [128,] 0.050
#> [129,] 0.025
#> [130,] 0.050
#> [131,] 0.050
#> [132,] 0.050
#> [133,] 0.050
#> [134,] 0.050
#> [135,] 0.025
#> [136,] 0.050
#> [137,] 0.050
#> [138,] 0.050
#> [139,] 0.050
#> [140,] 0.025
#> [141,] 0.050
#> [142,] 0.050
#> [143,] 0.050
#> [144,] 0.050
#> [145,] 0.050
#> [146,] 0.025
#> [147,] 0.050
#> [148,] 0.050
#> [149,] 0.050
#> [150,] 0.050
#> [151,] 0.025
#> [152,] 0.050
#> [153,] 0.050
#> [154,] 0.050
#> [155,] 0.050
#> [156,] 0.050
#> [157,] 0.025
#> [158,] 0.050
#> [159,] 0.050
#> [160,] 0.050
#> [161,] 0.050
#> [162,] 0.025
#> [163,] 0.050
#> [164,] 0.050
#> [165,] 0.050
#> [166,] 0.050
#> [167,] 0.050
#> [168,] 0.025
#> [169,] 0.050
#> [170,] 0.050
#> [171,] 0.050
#> [172,] 0.050
#> [173,] 0.025
#> [174,] 0.050
#> [175,] 0.050
#> [176,] 0.050
#> [177,] 0.050
#> [178,] 0.050
#> [179,] 0.025
#> [180,] 0.050
#> [181,] 0.050
#> [182,] 0.050
#> [183,] 0.050
#> [184,] 0.025
#> [185,] 0.050
#> [186,] 0.050
#> [187,] 0.050
#> [188,] 0.050
#> [189,] 0.050
#> [190,] 0.025
#> [191,] 0.050
#> [192,] 0.050
#> [193,] 0.050
#> [194,] 0.050
#> [195,] 0.025
#> [196,] 0.050
#> [197,] 0.050
#> [198,] 0.050
#> [199,] 0.050
#> [200,] 0.050
#>
#> $gradient_norm
#> [,1]
#> [1,] 1.086983e+01
#> [2,] 1.159163e+01
#> [3,] 3.159702e+00
#> [4,] 6.622136e-01
#> [5,] 1.656123e-01
#> [6,] 1.832664e-01
#> [7,] 3.898439e-02
#> [8,] 8.539566e-03
#> [9,] 2.156550e-03
#> [10,] 2.342108e-03
#> [11,] 4.949827e-04
#> [12,] 1.091977e-04
#> [13,] 1.430079e-04
#> [14,] 2.990906e-05
#> [15,] 4.209353e-05
#> [16,] 5.960417e-05
#> [17,] 8.443400e-05
#> [18,] 1.196120e-04
#> [19,] 2.491752e-05
#> [20,] 3.529909e-05
#> [21,] 5.000601e-05
#> [22,] 7.084042e-05
#> [23,] 1.003552e-04
#> [24,] 2.090583e-05
#> [25,] 2.961599e-05
#> [26,] 4.195514e-05
#> [27,] 5.943522e-05
#> [28,] 8.419821e-05
#> [29,] 1.192783e-04
#> [30,] 2.484786e-05
#> [31,] 3.520041e-05
#> [32,] 4.986624e-05
#> [33,] 7.064239e-05
#> [34,] 1.000747e-04
#> [35,] 2.084751e-05
#> [36,] 2.953337e-05
#> [37,] 4.183809e-05
#> [38,] 5.926943e-05
#> [39,] 8.396330e-05
#> [40,] 1.189456e-04
#> [41,] 2.477870e-05
#> [42,] 3.510245e-05
#> [43,] 4.972745e-05
#> [44,] 7.044580e-05
#> [45,] 9.979616e-05
#> [46,] 2.078938e-05
#> [47,] 2.945101e-05
#> [48,] 4.172142e-05
#> [49,] 5.910414e-05
#> [50,] 8.372918e-05
#> [51,] 1.186139e-04
#> [52,] 2.470944e-05
#> [53,] 3.500432e-05
#> [54,] 4.958846e-05
#> [55,] 7.024887e-05
#> [56,] 9.951724e-05
#> [57,] 2.073138e-05
#> [58,] 2.936886e-05
#> [59,] 4.160503e-05
#> [60,] 5.893927e-05
#> [61,] 8.349558e-05
#> [62,] 1.182830e-04
#> [63,] 2.464067e-05
#> [64,] 3.490691e-05
#> [65,] 4.945044e-05
#> [66,] 7.005338e-05
#> [67,] 9.924024e-05
#> [68,] 2.067357e-05
#> [69,] 2.928696e-05
#> [70,] 4.148901e-05
#> [71,] 5.877490e-05
#> [72,] 8.326276e-05
#> [73,] 1.179531e-04
#> [74,] 2.457180e-05
#> [75,] 3.480933e-05
#> [76,] 4.931222e-05
#> [77,] 6.985755e-05
#> [78,] 9.896288e-05
#> [79,] 2.061589e-05
#> [80,] 2.920526e-05
#> [81,] 4.137327e-05
#> [82,] 5.861095e-05
#> [83,] 8.303046e-05
#> [84,] 1.176241e-04
#> [85,] 2.450341e-05
#> [86,] 3.471246e-05
#> [87,] 4.917498e-05
#> [88,] 6.966315e-05
#> [89,] 9.868742e-05
#> [90,] 2.055840e-05
#> [91,] 2.912381e-05
#> [92,] 4.125790e-05
#> [93,] 5.844749e-05
#> [94,] 8.279895e-05
#> [95,] 1.172961e-04
#> [96,] 2.443492e-05
#> [97,] 3.461543e-05
#> [98,] 4.903753e-05
#> [99,] 6.946841e-05
#> [100,] 9.841160e-05
#> [101,] 2.050105e-05
#> [102,] 2.904257e-05
#> [103,] 4.114280e-05
#> [104,] 5.828445e-05
#> [105,] 8.256794e-05
#> [106,] 1.169689e-04
#> [107,] 2.436691e-05
#> [108,] 3.451909e-05
#> [109,] 4.890105e-05
#> [110,] 6.927508e-05
#> [111,] 9.813768e-05
#> [112,] 2.044388e-05
#> [113,] 2.896158e-05
#> [114,] 4.102807e-05
#> [115,] 5.812191e-05
#> [116,] 8.233771e-05
#> [117,] 1.166427e-04
#> [118,] 2.429880e-05
#> [119,] 3.442260e-05
#> [120,] 4.876437e-05
#> [121,] 6.908143e-05
#> [122,] 9.786340e-05
#> [123,] 1.386369e-04
#> [124,] 2.888059e-05
#> [125,] 4.091334e-05
#> [126,] 5.795939e-05
#> [127,] 8.210744e-05
#> [128,] 1.163166e-04
#> [129,] 2.423101e-05
#> [130,] 3.432657e-05
#> [131,] 4.862832e-05
#> [132,] 6.888873e-05
#> [133,] 9.759035e-05
#> [134,] 1.382502e-04
#> [135,] 2.880024e-05
#> [136,] 4.079952e-05
#> [137,] 5.779814e-05
#> [138,] 8.187904e-05
#> [139,] 1.159929e-04
#> [140,] 2.416345e-05
#> [141,] 3.423085e-05
#> [142,] 4.849272e-05
#> [143,] 6.869661e-05
#> [144,] 9.731825e-05
#> [145,] 1.378647e-04
#> [146,] 2.871971e-05
#> [147,] 4.068543e-05
#> [148,] 5.763653e-05
#> [149,] 8.165006e-05
#> [150,] 1.156686e-04
#> [151,] 2.409603e-05
#> [152,] 3.413536e-05
#> [153,] 4.835743e-05
#> [154,] 6.850498e-05
#> [155,] 9.704672e-05
#> [156,] 1.374801e-04
#> [157,] 2.863981e-05
#> [158,] 4.057224e-05
#> [159,] 5.747617e-05
#> [160,] 8.142293e-05
#> [161,] 1.153468e-04
#> [162,] 2.402884e-05
#> [163,] 3.404016e-05
#> [164,] 4.822259e-05
#> [165,] 6.831393e-05
#> [166,] 9.677613e-05
#> [167,] 1.370967e-04
#> [168,] 2.855973e-05
#> [169,] 4.045879e-05
#> [170,] 5.731546e-05
#> [171,] 8.119523e-05
#> [172,] 1.150243e-04
#> [173,] 2.396181e-05
#> [174,] 3.394520e-05
#> [175,] 4.808805e-05
#> [176,] 6.812337e-05
#> [177,] 9.650612e-05
#> [178,] 1.367143e-04
#> [179,] 2.848027e-05
#> [180,] 4.034623e-05
#> [181,] 5.715599e-05
#> [182,] 8.096936e-05
#> [183,] 1.147042e-04
#> [184,] 2.389499e-05
#> [185,] 3.385054e-05
#> [186,] 4.795397e-05
#> [187,] 6.793339e-05
#> [188,] 9.623704e-05
#> [189,] 1.363330e-04
#> [190,] 2.840064e-05
#> [191,] 4.023341e-05
#> [192,] 5.699618e-05
#> [193,] 8.074293e-05
#> [194,] 1.143835e-04
#> [195,] 2.382833e-05
#> [196,] 3.375611e-05
#> [197,] 4.782018e-05
#> [198,] 6.774389e-05
#> [199,] 9.596853e-05
#> [200,] 1.359527e-04
#>
#> $coef_trace
#> NULL
#>
#> $eta_trace
#> NULL
#>