Fit and validate Maximum Entropy Models based on Ensemble of Small of Model approach
Source:R/esm_max.R
esm_max.RdThis function constructs Maxent Models using the Ensemble of Small Model (ESM) approach (Breiner et al., 2015, 2018).
Usage
esm_max(
data,
response,
predictors,
partition,
thr = NULL,
background = NULL,
clamp = TRUE,
classes = "default",
pred_type = "cloglog",
regmult = 2.5
)Arguments
- data
data.frame. Database with the response (0,1) and predictors values.
- response
character. Column name with species absence-presence data (0,1)
- predictors
character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). This function can only construct models with continuous variables, and does not allow categorical variables Usage predictors = c("aet", "cwd", "tmin").
- partition
character. Column name with training and validation partition groups.
- thr
character. Threshold used to get binary suitability values (i.e. 0,1). It is useful for threshold-dependent performance metrics. It is possible to use more than one threshold type. It is necessary to provide a vector for this argument. The following threshold criteria are available:
equal_sens_spec: Threshold at which the sensitivity and specificity are equal.
max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).
max_jaccard: The threshold at which Jaccard is the highest.
max_sorensen: The threshold at which Sorensen is highest.
max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.
sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If no sensitivity value is specified, the default is 0.9
If the user wants to include more than one threshold type, it is necessary concatenate threshold types, e.g., thr=c('max_sens_spec', 'max_jaccard'), or thr=c('max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('max_sens_spec', 'sensitivity'). Function will use all thresholds if no threshold is specified.
- background
data.frame. Database with response column only with 0 and predictors variables. All column names must be consistent with data. Default NULL
- clamp
logical. It is set with TRUE, predictors and features are restricted to the range seen during model training.
- classes
character. A single feature of any combinations of them. Features are symbolized by letters: l (linear), q (quadratic), h (hinge), p (product), and t (threshold). Usage classes = "lpq". Default "default" (see details).
- pred_type
character. Type of response required available "link", "exponential", "cloglog" and "logistic". Default "cloglog"
- regmult
numeric. A constant to adjust regularization. Because ESM are used for modeling species with few records default value is 2.5
Value
A list object with:
esm_model: A list with "maxnet" class object from maxnet package for each bivariate model. This object can be used for predicting ensembles of small models with
sdm_predictfunction.predictors: A tibble with variables use for modeling.
performance: Performance metrics (see
sdm_eval). Those threshold dependent metric are calculated based on the threshold specified in the argument.performance_part: Performance metric for each replica and partition (see
sdm_eval).
Details
This method consists of creating bivariate models with all the pair-wise combinations of predictors and perform an ensemble based on the average of suitability weighted by Somers' D metric (D = 2 x (AUC -0.5)). ESM is recommended for modeling species with few occurrences. This function does not allow categorical variables because the use of these types of variables could be problematic when using with few occurrences. For further detail see Breiner et al. (2015, 2018). This function use a default regularization multiplier equal to 2.5 (see Breiner et al., 2018)
When the argument “classes” is set as default MaxEnt will use different features combination depending of the number of presences (np) with the follow rule: if np < 10 classes = "l", if np between 10 and 15 classes = "lq", if np between 15 and 80 classes = "lqh", and if np >= 80 classes = "lqph"
When presence-absence (or presence-pseudo-absence) data are used in data argument in addition to background points, the function will fit models with presences and background points and validate with presences and absences. This procedure makes maxent comparable to other presences-absences models (e.g., random forest, support vector machine). If only presences and background points data are used, function will fit and validate model with presences and background data. If only presence-absences are used in data argument and without background, function will fit model with the specified data (not recommended).
References
Breiner, F. T., Guisan, A., Bergamini, A., & Nobis, M. P. (2015). Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6(10), 1210-218. https://doi.org/10.1111/2041-210X.12403
Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018). Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution, 9(4), 802-808. https://doi.org/10.1111/2041-210X.12957
Examples
# \donttest{
data("abies")
data("backg")
require(dplyr)
# Using k-fold partition method
set.seed(10)
abies2 <- abies %>%
na.omit() %>%
group_by(pr_ab) %>%
dplyr::slice_sample(n = 10) %>%
group_by()
abies2 <- part_random(
data = abies2,
pr_ab = "pr_ab",
method = c(method = "rep_kfold", folds = 3, replicates = 2)
)
abies2
#> # A tibble: 20 × 15
#> id pr_ab x y aet cwd tmin ppt_djf ppt_jja pH awc
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 12040 0 -308909. 384248. 573. 332. 4.84 521. 48.8 5.63 0.108
#> 2 10361 0 -254286. 417158. 260. 469. 2.93 151. 15.1 6.20 0.0950
#> 3 9402 0 -286979. 386206. 587. 376. 6.45 333. 15.7 5.5 0.160
#> 4 9815 0 -291849. 445595. 443. 455. 4.39 332. 19.1 6 0.0700
#> 5 10524 0 -256658. 184438. 355. 568. 5.87 303. 10.6 5.20 0.0800
#> 6 8860 0 121343. -164170. 354. 733. 3.97 182. 9.83 0 0
#> 7 6431 0 107903. -122968. 461. 578. 4.87 161. 7.66 5.90 0.0900
#> 8 11730 0 -333903. 431238. 561. 364. 6.73 387. 25.2 5.80 0.130
#> 9 808 0 -150163. 357180. 339. 564. 2.64 220. 15.3 6.40 0.100
#> 10 11054 0 -293663. 340981. 477. 396. 3.89 332. 26.4 4.60 0.0634
#> 11 2960 1 -49273. 181752. 512. 275. 0.920 319. 17.3 5.92 0.0900
#> 12 3065 1 126907. -198892. 322. 544. 0.700 203. 10.6 5.60 0.110
#> 13 5527 1 116751. -181089. 261. 537. 0.363 178. 7.43 0 0
#> 14 4035 1 -31777. 115940. 394. 440. 2.07 298. 11.2 6.01 0.0769
#> 15 4081 1 -5158. 90159. 301. 502. 0.703 203. 14.6 6.11 0.0633
#> 16 3087 1 102151. -143976. 299. 425. -2.08 205. 13.4 3.88 0.110
#> 17 3495 1 -19586. 89803. 438. 419. 2.13 189. 15.2 6.19 0.0959
#> 18 4441 1 49405. -60502. 362. 582. 2.42 218. 7.84 5.64 0.0786
#> 19 301 1 -132516. 270845. 367. 196. -2.56 422. 26.3 6.70 0.0300
#> 20 3162 1 59905. -53634. 319. 626. 1.99 212. 4.50 4.51 0.0396
#> # ℹ 4 more variables: depth <dbl>, landform <fct>, .part1 <int>, .part2 <int>
set.seed(10)
backg2 <- backg %>%
na.omit() %>%
group_by(pr_ab) %>%
dplyr::slice_sample(n = 100) %>%
group_by()
backg2 <- part_random(
data = backg2,
pr_ab = "pr_ab",
method = c(method = "rep_kfold", folds = 3, replicates = 2)
)
backg2
#> # A tibble: 100 × 15
#> pr_ab x y aet cwd tmin ppt_djf ppt_jja pH awc
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 -23361. 129722. 448. 257. 0.683 285. 19.6 0.0230 0.000356
#> 2 0 54669. -311728. 149. 1328. 10.9 24.7 0.710 8.20 0.0900
#> 3 0 -162141. -59278. 332. 863. 9.56 110. 1.92 6.34 0.138
#> 4 0 185349. -426208. 380. 1105. 11.6 105. 3.04 0.588 0.00995
#> 5 0 125949. -180238. 220. 303. -4.75 216. 9.95 0 0
#> 6 0 -27411. -378148. 322. 1047. 8.13 87.8 0.717 7.73 0.141
#> 7 0 17409. -99508. 340. 1003. 8.15 93.7 1.49 6.5 0.0708
#> 8 0 -216411. 432662. 259. 657. 4.44 110. 16.3 5.37 0.0704
#> 9 0 311709. -456718. 271. 877. 6.34 83.0 8.45 0 0
#> 10 0 -211821. 419972. 369. 633. 3.36 73.2 16.6 6.12 0.158
#> # ℹ 90 more rows
#> # ℹ 5 more variables: depth <dbl>, percent_clay <dbl>, landform <fct>,
#> # .part1 <int>, .part2 <int>
# Without threshold specification and with kfold
esm_max_t1 <- esm_max(
data = abies2,
response = "pr_ab",
predictors = c("aet", "cwd", "tmin", "ppt_djf", "ppt_jja", "pH", "awc", "depth"),
partition = ".part",
thr = NULL,
background = backg2,
clamp = TRUE,
classes = "default",
pred_type = "cloglog",
regmult = 1
)
#>
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esm_max_t1$esm_model # bivariate model
#> $`0.141203703703704`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 97880
#> 2 0 0.00 93460
#> 3 0 0.00 89230
#> 4 0 0.00 85190
#> 5 0 0.00 81340
#> 6 0 0.00 77660
#> 7 0 0.00 74150
#> 8 0 0.00 70790
#> 9 0 0.00 67590
#> 10 0 0.00 64540
#> 11 0 0.00 61620
#> 12 0 0.00 58830
#> 13 0 0.00 56170
#> 14 0 0.00 53630
#> 15 0 0.00 51200
#> 16 0 0.00 48890
#> 17 0 0.00 46680
#> 18 0 0.00 44570
#> 19 0 0.00 42550
#> 20 0 0.00 40630
#> 21 0 0.00 38790
#> 22 0 0.00 37030
#> 23 0 0.00 35360
#> 24 0 0.00 33760
#> 25 0 0.00 32230
#> 26 0 0.00 30770
#> 27 0 0.00 29380
#> 28 0 0.00 28050
#> 29 0 0.00 26780
#> 30 0 0.00 25570
#> 31 0 0.00 24420
#> 32 0 0.00 23310
#> 33 0 0.00 22260
#> 34 0 0.00 21250
#> 35 0 0.00 20290
#> 36 0 0.00 19370
#> 37 0 0.00 18500
#> 38 0 0.00 17660
#> 39 0 0.00 16860
#> 40 0 0.00 16100
#> 41 0 0.00 15370
#> 42 0 0.00 14680
#> 43 0 0.00 14010
#> 44 0 0.00 13380
#> 45 0 0.00 12770
#> 46 0 0.00 12200
#> 47 0 0.00 11640
#> 48 0 0.00 11120
#> 49 0 0.00 10610
#> 50 0 0.00 10130
#> 51 0 0.00 9676
#> 52 0 0.00 9238
#> 53 0 0.00 8820
#> 54 0 0.00 8421
#> 55 0 0.00 8040
#> 56 0 0.00 7677
#> 57 0 0.00 7330
#> 58 0 0.00 6998
#> 59 0 0.00 6682
#> 60 0 0.00 6379
#> 61 0 0.00 6091
#> 62 0 0.00 5815
#> 63 0 0.00 5552
#> 64 0 0.00 5301
#> 65 0 0.00 5061
#> 66 0 0.00 4833
#> 67 0 0.00 4614
#> 68 0 0.00 4405
#> 69 0 0.00 4206
#> 70 0 0.00 4016
#> 71 0 0.00 3834
#> 72 0 0.00 3661
#> 73 0 0.00 3495
#> 74 0 0.00 3337
#> 75 0 0.00 3186
#> 76 0 0.00 3042
#> 77 0 0.00 2904
#> 78 0 0.00 2773
#> 79 0 0.00 2648
#> 80 0 0.00 2528
#> 81 0 0.00 2414
#> 82 0 0.00 2304
#> 83 0 0.00 2200
#> 84 0 0.00 2101
#> 85 0 0.00 2006
#> 86 0 0.00 1915
#> 87 0 0.00 1828
#> 88 0 0.00 1746
#> 89 0 0.00 1667
#> 90 0 0.00 1591
#> 91 0 0.00 1519
#> 92 0 0.00 1451
#> 93 0 0.00 1385
#> 94 0 0.00 1322
#> 95 0 0.00 1263
#> 96 0 0.00 1205
#> 97 0 0.00 1151
#> 98 0 0.00 1099
#> 99 0 0.00 1049
#> 100 0 0.00 1002
#> 101 0 0.00 956
#> 102 0 0.00 913
#> 103 0 0.00 872
#> 104 0 0.00 832
#> 105 0 0.00 795
#> 106 0 0.00 759
#> 107 0 0.00 724
#> 108 0 0.00 692
#> 109 0 0.00 660
#> 110 0 0.00 631
#> 111 0 0.00 602
#> 112 0 0.00 575
#> 113 0 0.00 549
#> 114 0 0.00 524
#> 115 0 0.00 500
#> 116 0 0.00 478
#> 117 0 0.00 456
#> 118 0 0.00 436
#> 119 0 0.00 416
#> 120 0 0.00 397
#> 121 0 0.00 379
#> 122 0 0.00 362
#> 123 0 0.00 346
#> 124 0 0.00 330
#> 125 0 0.00 315
#> 126 0 0.00 301
#> 127 0 0.00 287
#> 128 0 0.00 274
#> 129 0 0.00 262
#> 130 0 0.00 250
#> 131 0 0.00 239
#> 132 0 0.00 228
#> 133 0 0.00 218
#> 134 0 0.00 208
#> 135 0 0.00 198
#> 136 0 0.00 189
#> 137 0 0.00 181
#> 138 0 0.00 173
#> 139 0 0.00 165
#> 140 1 0.10 157
#> 141 1 0.77 150
#> 142 1 1.39 143
#> 143 1 1.97 137
#> 144 1 2.52 131
#> 145 1 3.03 125
#> 146 1 3.51 119
#> 147 1 3.97 114
#> 148 1 4.39 109
#> 149 1 4.80 104
#> 150 1 5.18 99
#> 151 1 5.54 95
#> 152 1 5.87 90
#> 153 1 6.20 86
#> 154 1 6.50 82
#> 155 1 6.79 79
#> 156 1 7.06 75
#> 157 1 7.32 72
#> 158 1 7.56 68
#> 159 1 7.79 65
#> 160 1 8.01 62
#> 161 1 8.21 60
#> 162 1 8.41 57
#> 163 1 8.59 54
#> 164 1 8.77 52
#> 165 1 8.93 49
#> 166 1 9.09 47
#> 167 1 9.24 45
#> 168 1 9.38 43
#> 169 1 9.51 41
#> 170 1 9.64 39
#> 171 1 9.75 37
#> 172 1 9.86 36
#> 173 1 9.97 34
#> 174 1 10.06 33
#> 175 1 10.16 31
#> 176 1 10.24 30
#> 177 1 10.33 28
#> 178 1 10.40 27
#> 179 1 10.47 26
#> 180 1 10.54 25
#> 181 1 10.61 24
#> 182 1 10.66 23
#> 183 1 10.72 22
#> 184 1 10.77 21
#> 185 1 10.82 20
#> 186 1 10.86 19
#> 187 1 10.91 18
#> 188 1 10.95 17
#> 189 1 10.98 16
#> 190 1 11.02 16
#> 191 1 11.05 15
#> 192 2 11.11 14
#> 193 2 11.18 14
#> 194 2 11.24 13
#> 195 2 11.29 12
#> 196 2 11.34 12
#> 197 2 11.39 11
#> 198 2 11.43 11
#> 199 2 11.47 10
#> 200 2 11.51 10
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 33480
#> 2 0 0.00 31970
#> 3 0 0.00 30520
#> 4 0 0.00 29140
#> 5 0 0.00 27820
#> 6 0 0.00 26570
#> 7 0 0.00 25360
#> 8 0 0.00 24220
#> 9 0 0.00 23120
#> 10 0 0.00 22080
#> 11 0 0.00 21080
#> 12 0 0.00 20120
#> 13 0 0.00 19210
#> 14 0 0.00 18350
#> 15 0 0.00 17520
#> 16 0 0.00 16720
#> 17 0 0.00 15970
#> 18 0 0.00 15240
#> 19 0 0.00 14560
#> 20 0 0.00 13900
#> 21 0 0.00 13270
#> 22 0 0.00 12670
#> 23 0 0.00 12100
#> 24 0 0.00 11550
#> 25 0 0.00 11030
#> 26 0 0.00 10530
#> 27 0 0.00 10050
#> 28 0 0.00 9597
#> 29 0 0.00 9163
#> 30 0 0.00 8748
#> 31 0 0.00 8353
#> 32 0 0.00 7975
#> 33 0 0.00 7614
#> 34 0 0.00 7270
#> 35 0 0.00 6941
#> 36 0 0.00 6627
#> 37 0 0.00 6327
#> 38 0 0.00 6041
#> 39 0 0.00 5768
#> 40 0 0.00 5507
#> 41 0 0.00 5258
#> 42 0 0.00 5020
#> 43 0 0.00 4793
#> 44 0 0.00 4576
#> 45 0 0.00 4369
#> 46 0 0.00 4172
#> 47 0 0.00 3983
#> 48 0 0.00 3803
#> 49 0 0.00 3631
#> 50 0 0.00 3467
#> 51 0 0.00 3310
#> 52 0 0.00 3160
#> 53 0 0.00 3017
#> 54 0 0.00 2881
#> 55 0 0.00 2750
#> 56 0 0.00 2626
#> 57 0 0.00 2507
#> 58 0 0.00 2394
#> 59 0 0.00 2286
#> 60 0 0.00 2182
#> 61 0 0.00 2084
#> 62 0 0.00 1989
#> 63 0 0.00 1899
#> 64 0 0.00 1813
#> 65 0 0.00 1731
#> 66 0 0.00 1653
#> 67 0 0.00 1578
#> 68 0 0.00 1507
#> 69 0 0.00 1439
#> 70 0 0.00 1374
#> 71 0 0.00 1312
#> 72 0 0.00 1252
#> 73 0 0.00 1196
#> 74 0 0.00 1142
#> 75 0 0.00 1090
#> 76 0 0.00 1041
#> 77 0 0.00 994
#> 78 0 0.00 949
#> 79 0 0.00 906
#> 80 0 0.00 865
#> 81 0 0.00 826
#> 82 0 0.00 788
#> 83 0 0.00 753
#> 84 0 0.00 719
#> 85 0 0.00 686
#> 86 0 0.00 655
#> 87 0 0.00 625
#> 88 0 0.00 597
#> 89 0 0.00 570
#> 90 0 0.00 544
#> 91 0 0.00 520
#> 92 0 0.00 496
#> 93 0 0.00 474
#> 94 0 0.00 452
#> 95 0 0.00 432
#> 96 0 0.00 412
#> 97 0 0.00 394
#> 98 0 0.00 376
#> 99 0 0.00 359
#> 100 0 0.00 343
#> 101 0 0.00 327
#> 102 0 0.00 312
#> 103 0 0.00 298
#> 104 0 0.00 285
#> 105 1 0.55 272
#> 106 1 1.35 260
#> 107 1 2.09 248
#> 108 1 2.78 237
#> 109 1 3.42 226
#> 110 1 4.02 216
#> 111 1 4.59 206
#> 112 1 5.12 197
#> 113 1 5.62 188
#> 114 1 6.10 179
#> 115 1 6.54 171
#> 116 1 6.96 163
#> 117 1 7.36 156
#> 118 1 7.73 149
#> 119 1 8.09 142
#> 120 1 8.43 136
#> 121 1 8.76 130
#> 122 1 9.07 124
#> 123 1 9.36 118
#> 124 1 9.64 113
#> 125 1 9.91 108
#> 126 1 10.17 103
#> 127 1 10.41 98
#> 128 1 10.65 94
#> 129 1 10.87 90
#> 130 1 11.08 85
#> 131 1 11.29 82
#> 132 1 11.49 78
#> 133 1 11.68 74
#> 134 1 11.86 71
#> 135 1 12.03 68
#> 136 1 12.20 65
#> 137 1 12.36 62
#> 138 1 12.51 59
#> 139 1 12.66 56
#> 140 1 12.80 54
#> 141 1 12.94 51
#> 142 1 13.07 49
#> 143 1 13.20 47
#> 144 1 13.32 45
#> 145 1 13.44 43
#> 146 1 13.55 41
#> 147 1 13.66 39
#> 148 1 13.76 37
#> 149 1 13.86 35
#> 150 1 13.96 34
#> 151 1 14.05 32
#> 152 1 14.14 31
#> 153 1 14.22 29
#> 154 1 14.31 28
#> 155 1 14.38 27
#> 156 1 14.46 26
#> 157 1 14.53 24
#> 158 1 14.60 23
#> 159 1 14.66 22
#> 160 1 14.73 21
#> 161 1 14.79 20
#> 162 1 14.85 19
#> 163 1 14.90 19
#> 164 1 14.95 18
#> 165 1 15.00 17
#> 166 1 15.05 16
#> 167 1 15.09 15
#> 168 1 15.14 15
#> 169 1 15.18 14
#> 170 1 15.22 13
#> 171 1 15.25 13
#> 172 1 15.29 12
#> 173 1 15.32 12
#> 174 1 15.35 11
#> 175 1 15.38 11
#> 176 1 15.41 10
#> 177 1 15.43 10
#> 178 1 15.46 9
#> 179 1 15.48 9
#> 180 1 15.50 8
#> 181 2 15.59 8
#> 182 2 15.82 8
#> 183 2 16.02 7
#> 184 2 16.21 7
#> 185 2 16.38 7
#> 186 2 16.53 6
#> 187 2 16.66 6
#> 188 2 16.79 6
#> 189 2 16.90 6
#> 190 2 17.00 5
#> 191 2 17.09 5
#> 192 2 17.17 5
#> 193 2 17.25 5
#> 194 2 17.31 4
#> 195 2 17.37 4
#> 196 2 17.43 4
#> 197 2 17.48 4
#> 198 2 17.53 4
#> 199 2 17.57 4
#> 200 2 17.61 3
#>
#> $`0.0694444444444444`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 33490
#> 2 0 0.00 31980
#> 3 0 0.00 30530
#> 4 0 0.00 29150
#> 5 0 0.00 27830
#> 6 0 0.00 26570
#> 7 0 0.00 25370
#> 8 0 0.00 24220
#> 9 0 0.00 23130
#> 10 0 0.00 22080
#> 11 0 0.00 21080
#> 12 0 0.00 20130
#> 13 0 0.00 19220
#> 14 0 0.00 18350
#> 15 0 0.00 17520
#> 16 0 0.00 16730
#> 17 0 0.00 15970
#> 18 0 0.00 15250
#> 19 0 0.00 14560
#> 20 0 0.00 13900
#> 21 0 0.00 13270
#> 22 0 0.00 12670
#> 23 0 0.00 12100
#> 24 0 0.00 11550
#> 25 0 0.00 11030
#> 26 0 0.00 10530
#> 27 0 0.00 10050
#> 28 0 0.00 9598
#> 29 0 0.00 9164
#> 30 0 0.00 8750
#> 31 0 0.00 8354
#> 32 0 0.00 7976
#> 33 0 0.00 7616
#> 34 0 0.00 7271
#> 35 0 0.00 6942
#> 36 0 0.00 6628
#> 37 0 0.00 6328
#> 38 0 0.00 6042
#> 39 0 0.00 5769
#> 40 0 0.00 5508
#> 41 0 0.00 5259
#> 42 0 0.00 5021
#> 43 0 0.00 4794
#> 44 0 0.00 4577
#> 45 0 0.00 4370
#> 46 0 0.00 4172
#> 47 0 0.00 3984
#> 48 0 0.00 3804
#> 49 0 0.00 3632
#> 50 0 0.00 3467
#> 51 0 0.00 3310
#> 52 0 0.00 3161
#> 53 0 0.00 3018
#> 54 0 0.00 2881
#> 55 0 0.00 2751
#> 56 0 0.00 2627
#> 57 0 0.00 2508
#> 58 0 0.00 2394
#> 59 0 0.00 2286
#> 60 0 0.00 2183
#> 61 0 0.00 2084
#> 62 0 0.00 1990
#> 63 0 0.00 1900
#> 64 0 0.00 1814
#> 65 0 0.00 1732
#> 66 0 0.00 1653
#> 67 0 0.00 1579
#> 68 0 0.00 1507
#> 69 0 0.00 1439
#> 70 0 0.00 1374
#> 71 0 0.00 1312
#> 72 0 0.00 1253
#> 73 0 0.00 1196
#> 74 0 0.00 1142
#> 75 0 0.00 1090
#> 76 0 0.00 1041
#> 77 0 0.00 994
#> 78 0 0.00 949
#> 79 0 0.00 906
#> 80 0 0.00 865
#> 81 0 0.00 826
#> 82 0 0.00 788
#> 83 0 0.00 753
#> 84 0 0.00 719
#> 85 0 0.00 686
#> 86 0 0.00 655
#> 87 0 0.00 626
#> 88 0 0.00 597
#> 89 0 0.00 570
#> 90 0 0.00 544
#> 91 0 0.00 520
#> 92 0 0.00 496
#> 93 0 0.00 474
#> 94 0 0.00 452
#> 95 0 0.00 432
#> 96 0 0.00 412
#> 97 0 0.00 394
#> 98 0 0.00 376
#> 99 0 0.00 359
#> 100 0 0.00 343
#> 101 0 0.00 327
#> 102 0 0.00 312
#> 103 0 0.00 298
#> 104 0 0.00 285
#> 105 0 0.00 272
#> 106 0 0.00 260
#> 107 0 0.00 248
#> 108 0 0.00 237
#> 109 0 0.00 226
#> 110 0 0.00 216
#> 111 0 0.00 206
#> 112 0 0.00 197
#> 113 0 0.00 188
#> 114 0 0.00 179
#> 115 0 0.00 171
#> 116 0 0.00 163
#> 117 0 0.00 156
#> 118 0 0.00 149
#> 119 0 0.00 142
#> 120 0 0.00 136
#> 121 0 0.00 130
#> 122 0 0.00 124
#> 123 0 0.00 118
#> 124 0 0.00 113
#> 125 0 0.00 108
#> 126 0 0.00 103
#> 127 0 0.00 98
#> 128 0 0.00 94
#> 129 0 0.00 90
#> 130 0 0.00 86
#> 131 0 0.00 82
#> 132 0 0.00 78
#> 133 0 0.00 74
#> 134 0 0.00 71
#> 135 0 0.00 68
#> 136 0 0.00 65
#> 137 0 0.00 62
#> 138 0 0.00 59
#> 139 0 0.00 56
#> 140 0 0.00 54
#> 141 0 0.00 51
#> 142 0 0.00 49
#> 143 0 0.00 47
#> 144 0 0.00 45
#> 145 0 0.00 43
#> 146 0 0.00 41
#> 147 0 0.00 39
#> 148 0 0.00 37
#> 149 0 0.00 35
#> 150 0 0.00 34
#> 151 0 0.00 32
#> 152 0 0.00 31
#> 153 0 0.00 29
#> 154 0 0.00 28
#> 155 0 0.00 27
#> 156 0 0.00 26
#> 157 0 0.00 24
#> 158 0 0.00 23
#> 159 0 0.00 22
#> 160 0 0.00 21
#> 161 0 0.00 20
#> 162 0 0.00 19
#> 163 0 0.00 19
#> 164 0 0.00 18
#> 165 0 0.00 17
#> 166 0 0.00 16
#> 167 0 0.00 15
#> 168 0 0.00 15
#> 169 0 0.00 14
#> 170 0 0.00 13
#> 171 0 0.00 13
#> 172 0 0.00 12
#> 173 0 0.00 12
#> 174 0 0.00 11
#> 175 0 0.00 11
#> 176 0 0.00 10
#> 177 1 0.09 10
#> 178 1 0.27 9
#> 179 1 0.43 9
#> 180 1 0.57 8
#> 181 1 0.70 8
#> 182 1 0.82 8
#> 183 1 0.92 7
#> 184 1 1.02 7
#> 185 1 1.10 7
#> 186 1 1.18 6
#> 187 1 1.25 6
#> 188 1 1.31 6
#> 189 1 1.37 6
#> 190 1 1.42 5
#> 191 1 1.47 5
#> 192 1 1.51 5
#> 193 1 1.55 5
#> 194 1 1.59 4
#> 195 1 1.62 4
#> 196 1 1.65 4
#> 197 1 1.67 4
#> 198 1 1.70 4
#> 199 1 1.72 4
#> 200 1 1.74 3
#>
#> $`0.251157407407407`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 33480
#> 2 0 0.00 31970
#> 3 0 0.00 30520
#> 4 0 0.00 29140
#> 5 0 0.00 27820
#> 6 0 0.00 26560
#> 7 0 0.00 25360
#> 8 0 0.00 24220
#> 9 0 0.00 23120
#> 10 0 0.00 22070
#> 11 0 0.00 21080
#> 12 0 0.00 20120
#> 13 0 0.00 19210
#> 14 0 0.00 18340
#> 15 0 0.00 17510
#> 16 0 0.00 16720
#> 17 0 0.00 15970
#> 18 0 0.00 15240
#> 19 0 0.00 14550
#> 20 0 0.00 13900
#> 21 0 0.00 13270
#> 22 0 0.00 12670
#> 23 0 0.00 12090
#> 24 0 0.00 11550
#> 25 0 0.00 11030
#> 26 0 0.00 10530
#> 27 0 0.00 10050
#> 28 0 0.00 9596
#> 29 0 0.00 9162
#> 30 0 0.00 8748
#> 31 0 0.00 8352
#> 32 0 0.00 7974
#> 33 0 0.00 7614
#> 34 0 0.00 7269
#> 35 0 0.00 6940
#> 36 0 0.00 6626
#> 37 0 0.00 6327
#> 38 0 0.00 6041
#> 39 0 0.00 5767
#> 40 0 0.00 5507
#> 41 0 0.00 5258
#> 42 0 0.00 5020
#> 43 0 0.00 4793
#> 44 0 0.00 4576
#> 45 0 0.00 4369
#> 46 0 0.00 4171
#> 47 0 0.00 3983
#> 48 0 0.00 3803
#> 49 0 0.00 3631
#> 50 0 0.00 3466
#> 51 0 0.00 3310
#> 52 0 0.00 3160
#> 53 0 0.00 3017
#> 54 0 0.00 2881
#> 55 0 0.00 2750
#> 56 0 0.00 2626
#> 57 0 0.00 2507
#> 58 0 0.00 2394
#> 59 0 0.00 2285
#> 60 0 0.00 2182
#> 61 0 0.00 2083
#> 62 0 0.00 1989
#> 63 0 0.00 1899
#> 64 0 0.00 1813
#> 65 0 0.00 1731
#> 66 0 0.00 1653
#> 67 0 0.00 1578
#> 68 0 0.00 1507
#> 69 0 0.00 1439
#> 70 0 0.00 1374
#> 71 0 0.00 1311
#> 72 0 0.00 1252
#> 73 0 0.00 1196
#> 74 0 0.00 1141
#> 75 0 0.00 1090
#> 76 0 0.00 1041
#> 77 0 0.00 994
#> 78 0 0.00 949
#> 79 0 0.00 906
#> 80 0 0.00 865
#> 81 0 0.00 826
#> 82 0 0.00 788
#> 83 0 0.00 753
#> 84 0 0.00 719
#> 85 0 0.00 686
#> 86 0 0.00 655
#> 87 0 0.00 625
#> 88 0 0.00 597
#> 89 0 0.00 570
#> 90 0 0.00 544
#> 91 0 0.00 520
#> 92 0 0.00 496
#> 93 0 0.00 474
#> 94 0 0.00 452
#> 95 0 0.00 432
#> 96 0 0.00 412
#> 97 0 0.00 394
#> 98 0 0.00 376
#> 99 0 0.00 359
#> 100 0 0.00 343
#> 101 0 0.00 327
#> 102 0 0.00 312
#> 103 0 0.00 298
#> 104 0 0.00 285
#> 105 0 0.00 272
#> 106 0 0.00 260
#> 107 0 0.00 248
#> 108 0 0.00 237
#> 109 0 0.00 226
#> 110 0 0.00 216
#> 111 0 0.00 206
#> 112 0 0.00 197
#> 113 0 0.00 188
#> 114 0 0.00 179
#> 115 0 0.00 171
#> 116 0 0.00 163
#> 117 0 0.00 156
#> 118 0 0.00 149
#> 119 0 0.00 142
#> 120 0 0.00 136
#> 121 0 0.00 130
#> 122 0 0.00 124
#> 123 0 0.00 118
#> 124 0 0.00 113
#> 125 0 0.00 108
#> 126 0 0.00 103
#> 127 0 0.00 98
#> 128 0 0.00 94
#> 129 0 0.00 90
#> 130 0 0.00 85
#> 131 0 0.00 82
#> 132 0 0.00 78
#> 133 0 0.00 74
#> 134 0 0.00 71
#> 135 0 0.00 68
#> 136 0 0.00 65
#> 137 0 0.00 62
#> 138 0 0.00 59
#> 139 0 0.00 56
#> 140 0 0.00 54
#> 141 0 0.00 51
#> 142 0 0.00 49
#> 143 0 0.00 47
#> 144 0 0.00 45
#> 145 0 0.00 43
#> 146 0 0.00 41
#> 147 0 0.00 39
#> 148 0 0.00 37
#> 149 0 0.00 35
#> 150 0 0.00 34
#> 151 0 0.00 32
#> 152 0 0.00 31
#> 153 0 0.00 29
#> 154 0 0.00 28
#> 155 0 0.00 27
#> 156 0 0.00 26
#> 157 0 0.00 24
#> 158 0 0.00 23
#> 159 0 0.00 22
#> 160 0 0.00 21
#> 161 1 0.13 20
#> 162 1 0.33 19
#> 163 1 0.53 19
#> 164 1 0.72 18
#> 165 1 0.91 17
#> 166 1 1.10 16
#> 167 1 1.28 15
#> 168 1 1.45 15
#> 169 1 1.62 14
#> 170 1 1.77 13
#> 171 1 1.92 13
#> 172 1 2.06 12
#> 173 1 2.20 12
#> 174 1 2.32 11
#> 175 1 2.44 11
#> 176 1 2.55 10
#> 177 1 2.65 10
#> 178 1 2.75 9
#> 179 1 2.83 9
#> 180 1 2.92 8
#> 181 1 2.99 8
#> 182 1 3.07 8
#> 183 1 3.13 7
#> 184 1 3.19 7
#> 185 1 3.25 7
#> 186 1 3.30 6
#> 187 1 3.35 6
#> 188 1 3.40 6
#> 189 1 3.44 6
#> 190 1 3.48 5
#> 191 1 3.52 5
#> 192 1 3.55 5
#> 193 1 3.58 5
#> 194 1 3.61 4
#> 195 1 3.63 4
#> 196 1 3.66 4
#> 197 1 3.68 4
#> 198 1 3.70 4
#> 199 1 3.72 4
#> 200 1 3.74 3
#>
#> $`0.497685185185185`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 42180
#> 2 0 0.00 40270
#> 3 0 0.00 38450
#> 4 0 0.00 36710
#> 5 0 0.00 35050
#> 6 0 0.00 33460
#> 7 0 0.00 31950
#> 8 0 0.00 30510
#> 9 0 0.00 29130
#> 10 0 0.00 27810
#> 11 0 0.00 26550
#> 12 0 0.00 25350
#> 13 0 0.00 24200
#> 14 0 0.00 23110
#> 15 0 0.00 22060
#> 16 0 0.00 21070
#> 17 0 0.00 20110
#> 18 0 0.00 19200
#> 19 0 0.00 18330
#> 20 0 0.00 17510
#> 21 0 0.00 16710
#> 22 0 0.00 15960
#> 23 0 0.00 15240
#> 24 0 0.00 14550
#> 25 0 0.00 13890
#> 26 0 0.00 13260
#> 27 0 0.00 12660
#> 28 0 0.00 12090
#> 29 0 0.00 11540
#> 30 0 0.00 11020
#> 31 0 0.00 10520
#> 32 0 0.00 10050
#> 33 0 0.00 9591
#> 34 0 0.00 9157
#> 35 0 0.00 8743
#> 36 0 0.00 8348
#> 37 0 0.00 7970
#> 38 0 0.00 7610
#> 39 0 0.00 7265
#> 40 0 0.00 6937
#> 41 0 0.00 6623
#> 42 0 0.00 6324
#> 43 0 0.00 6038
#> 44 0 0.00 5765
#> 45 0 0.00 5504
#> 46 0 0.00 5255
#> 47 0 0.00 5017
#> 48 0 0.00 4790
#> 49 0 0.00 4574
#> 50 0 0.00 4367
#> 51 0 0.00 4169
#> 52 0 0.00 3981
#> 53 0 0.00 3801
#> 54 0 0.00 3629
#> 55 0 0.00 3465
#> 56 0 0.00 3308
#> 57 0 0.00 3158
#> 58 0 0.00 3015
#> 59 0 0.00 2879
#> 60 0 0.00 2749
#> 61 0 0.00 2625
#> 62 0 0.00 2506
#> 63 0 0.00 2393
#> 64 0 0.00 2284
#> 65 0 0.00 2181
#> 66 0 0.00 2082
#> 67 0 0.00 1988
#> 68 0 0.00 1898
#> 69 0 0.00 1812
#> 70 0 0.00 1730
#> 71 0 0.00 1652
#> 72 0 0.00 1577
#> 73 0 0.00 1506
#> 74 0 0.00 1438
#> 75 0 0.00 1373
#> 76 0 0.00 1311
#> 77 0 0.00 1252
#> 78 0 0.00 1195
#> 79 0 0.00 1141
#> 80 0 0.00 1089
#> 81 0 0.00 1040
#> 82 0 0.00 993
#> 83 0 0.00 948
#> 84 0 0.00 905
#> 85 0 0.00 864
#> 86 0 0.00 825
#> 87 0 0.00 788
#> 88 0 0.00 752
#> 89 0 0.00 718
#> 90 0 0.00 686
#> 91 0 0.00 655
#> 92 0 0.00 625
#> 93 0 0.00 597
#> 94 0 0.00 570
#> 95 0 0.00 544
#> 96 0 0.00 519
#> 97 0 0.00 496
#> 98 0 0.00 474
#> 99 0 0.00 452
#> 100 0 0.00 432
#> 101 0 0.00 412
#> 102 0 0.00 394
#> 103 0 0.00 376
#> 104 0 0.00 359
#> 105 0 0.00 342
#> 106 0 0.00 327
#> 107 0 0.00 312
#> 108 0 0.00 298
#> 109 0 0.00 285
#> 110 0 0.00 272
#> 111 0 0.00 259
#> 112 0 0.00 248
#> 113 0 0.00 236
#> 114 0 0.00 226
#> 115 0 0.00 216
#> 116 0 0.00 206
#> 117 0 0.00 196
#> 118 0 0.00 188
#> 119 0 0.00 179
#> 120 0 0.00 171
#> 121 0 0.00 163
#> 122 0 0.00 156
#> 123 0 0.00 149
#> 124 0 0.00 142
#> 125 0 0.00 136
#> 126 0 0.00 130
#> 127 0 0.00 124
#> 128 0 0.00 118
#> 129 0 0.00 113
#> 130 0 0.00 108
#> 131 0 0.00 103
#> 132 0 0.00 98
#> 133 0 0.00 94
#> 134 0 0.00 89
#> 135 0 0.00 85
#> 136 0 0.00 82
#> 137 0 0.00 78
#> 138 0 0.00 74
#> 139 0 0.00 71
#> 140 0 0.00 68
#> 141 0 0.00 65
#> 142 0 0.00 62
#> 143 0 0.00 59
#> 144 0 0.00 56
#> 145 0 0.00 54
#> 146 0 0.00 51
#> 147 0 0.00 49
#> 148 0 0.00 47
#> 149 0 0.00 45
#> 150 0 0.00 43
#> 151 0 0.00 41
#> 152 0 0.00 39
#> 153 0 0.00 37
#> 154 0 0.00 35
#> 155 0 0.00 34
#> 156 0 0.00 32
#> 157 0 0.00 31
#> 158 0 0.00 29
#> 159 0 0.00 28
#> 160 0 0.00 27
#> 161 0 0.00 26
#> 162 0 0.00 24
#> 163 0 0.00 23
#> 164 0 0.00 22
#> 165 0 0.00 21
#> 166 0 0.00 20
#> 167 0 0.00 19
#> 168 0 0.00 19
#> 169 0 0.00 18
#> 170 0 0.00 17
#> 171 0 0.00 16
#> 172 0 0.00 15
#> 173 0 0.00 15
#> 174 0 0.00 14
#> 175 0 0.00 13
#> 176 0 0.00 13
#> 177 0 0.00 12
#> 178 0 0.00 12
#> 179 0 0.00 11
#> 180 0 0.00 11
#> 181 0 0.00 10
#> 182 0 0.00 10
#> 183 1 0.13 9
#> 184 1 0.27 9
#> 185 1 0.40 8
#> 186 1 0.52 8
#> 187 1 0.63 8
#> 188 1 0.73 7
#> 189 1 0.82 7
#> 190 1 0.90 7
#> 191 1 0.97 6
#> 192 1 1.04 6
#> 193 1 1.10 6
#> 194 1 1.16 6
#> 195 1 1.21 5
#> 196 1 1.26 5
#> 197 1 1.30 5
#> 198 1 1.34 5
#> 199 1 1.38 4
#> 200 1 1.41 4
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 64400
#> 2 0 0.00 61490
#> 3 0 0.00 58710
#> 4 0 0.00 56050
#> 5 0 0.00 53520
#> 6 0 0.00 51100
#> 7 0 0.00 48790
#> 8 0 0.00 46580
#> 9 0 0.00 44470
#> 10 0 0.00 42460
#> 11 0 0.00 40540
#> 12 0 0.00 38710
#> 13 0 0.00 36960
#> 14 0 0.00 35290
#> 15 0 0.00 33690
#> 16 0 0.00 32170
#> 17 0 0.00 30710
#> 18 0 0.00 29320
#> 19 0 0.00 28000
#> 20 0 0.00 26730
#> 21 0 0.00 25520
#> 22 0 0.00 24370
#> 23 0 0.00 23260
#> 24 0 0.00 22210
#> 25 0 0.00 21210
#> 26 0 0.00 20250
#> 27 0 0.00 19330
#> 28 0 0.00 18460
#> 29 0 0.00 17620
#> 30 0 0.00 16830
#> 31 0 0.00 16070
#> 32 0 0.00 15340
#> 33 0 0.00 14650
#> 34 0 0.00 13980
#> 35 0 0.00 13350
#> 36 0 0.00 12750
#> 37 0 0.00 12170
#> 38 0 0.00 11620
#> 39 0 0.00 11090
#> 40 0 0.00 10590
#> 41 0 0.00 10110
#> 42 0 0.00 9656
#> 43 0 0.00 9219
#> 44 0 0.00 8802
#> 45 0 0.00 8404
#> 46 0 0.00 8024
#> 47 0 0.00 7661
#> 48 0 0.00 7315
#> 49 0 0.00 6984
#> 50 0 0.00 6668
#> 51 0 0.00 6366
#> 52 0 0.00 6078
#> 53 0 0.00 5803
#> 54 0 0.00 5541
#> 55 0 0.00 5290
#> 56 0 0.00 5051
#> 57 0 0.00 4823
#> 58 0 0.00 4604
#> 59 0 0.00 4396
#> 60 0 0.00 4197
#> 61 0 0.00 4008
#> 62 0 0.00 3826
#> 63 0 0.00 3653
#> 64 0 0.00 3488
#> 65 0 0.00 3330
#> 66 0 0.00 3180
#> 67 0 0.00 3036
#> 68 0 0.00 2899
#> 69 0 0.00 2767
#> 70 0 0.00 2642
#> 71 0 0.00 2523
#> 72 0 0.00 2409
#> 73 0 0.00 2300
#> 74 0 0.00 2196
#> 75 0 0.00 2096
#> 76 0 0.00 2002
#> 77 0 0.00 1911
#> 78 0 0.00 1825
#> 79 0 0.00 1742
#> 80 0 0.00 1663
#> 81 0 0.00 1588
#> 82 0 0.00 1516
#> 83 0 0.00 1448
#> 84 0 0.00 1382
#> 85 0 0.00 1320
#> 86 0 0.00 1260
#> 87 0 0.00 1203
#> 88 0 0.00 1149
#> 89 0 0.00 1097
#> 90 0 0.00 1047
#> 91 0 0.00 1000
#> 92 0 0.00 954
#> 93 0 0.00 911
#> 94 0 0.00 870
#> 95 0 0.00 831
#> 96 0 0.00 793
#> 97 0 0.00 757
#> 98 0 0.00 723
#> 99 0 0.00 690
#> 100 0 0.00 659
#> 101 0 0.00 629
#> 102 0 0.00 601
#> 103 0 0.00 574
#> 104 0 0.00 548
#> 105 1 0.55 523
#> 106 1 1.35 499
#> 107 1 2.09 477
#> 108 1 2.78 455
#> 109 1 3.42 435
#> 110 1 4.02 415
#> 111 1 4.59 396
#> 112 1 5.12 378
#> 113 1 5.62 361
#> 114 1 6.10 345
#> 115 1 6.54 329
#> 116 1 6.96 314
#> 117 1 7.36 300
#> 118 1 7.73 286
#> 119 1 8.09 274
#> 120 1 8.43 261
#> 121 1 8.76 249
#> 122 1 9.07 238
#> 123 1 9.36 227
#> 124 1 9.64 217
#> 125 1 9.91 207
#> 126 1 10.17 198
#> 127 1 10.41 189
#> 128 1 10.65 180
#> 129 1 10.87 172
#> 130 1 11.08 164
#> 131 1 11.29 157
#> 132 1 11.49 150
#> 133 1 11.68 143
#> 134 1 11.86 137
#> 135 1 12.03 130
#> 136 1 12.20 124
#> 137 1 12.36 119
#> 138 1 12.51 114
#> 139 1 12.66 108
#> 140 1 12.80 104
#> 141 1 12.94 99
#> 142 1 13.07 94
#> 143 1 13.20 90
#> 144 1 13.32 86
#> 145 1 13.44 82
#> 146 1 13.55 78
#> 147 1 13.66 75
#> 148 1 13.76 71
#> 149 1 13.86 68
#> 150 1 13.96 65
#> 151 1 14.05 62
#> 152 1 14.14 59
#> 153 1 14.22 57
#> 154 1 14.31 54
#> 155 1 14.38 52
#> 156 1 14.46 49
#> 157 1 14.53 47
#> 158 1 14.60 45
#> 159 1 14.66 43
#> 160 1 14.73 41
#> 161 1 14.79 39
#> 162 1 14.85 37
#> 163 1 14.90 36
#> 164 1 14.95 34
#> 165 1 15.00 33
#> 166 1 15.05 31
#> 167 1 15.09 30
#> 168 1 15.14 28
#> 169 1 15.18 27
#> 170 1 15.22 26
#> 171 1 15.25 25
#> 172 1 15.29 24
#> 173 1 15.32 22
#> 174 1 15.35 21
#> 175 1 15.38 20
#> 176 1 15.41 20
#> 177 1 15.43 19
#> 178 1 15.46 18
#> 179 1 15.48 17
#> 180 1 15.50 16
#> 181 2 15.54 16
#> 182 2 15.63 15
#> 183 2 15.71 14
#> 184 2 15.79 14
#> 185 2 15.87 13
#> 186 2 15.93 12
#> 187 2 16.00 12
#> 188 2 16.06 11
#> 189 2 16.12 11
#> 190 2 16.17 10
#> 191 2 16.22 10
#> 192 2 16.26 9
#> 193 2 16.31 9
#> 194 2 16.35 9
#> 195 2 16.38 8
#> 196 2 16.42 8
#> 197 2 16.45 7
#> 198 2 16.48 7
#> 199 2 16.51 7
#> 200 2 16.53 6
#>
#> $`0.0578703703703702`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 90880
#> 2 0 0.00 86770
#> 3 0 0.00 82840
#> 4 0 0.00 79100
#> 5 0 0.00 75520
#> 6 0 0.00 72100
#> 7 0 0.00 68840
#> 8 0 0.00 65730
#> 9 0 0.00 62760
#> 10 0 0.00 59920
#> 11 0 0.00 57210
#> 12 0 0.00 54620
#> 13 0 0.00 52150
#> 14 0 0.00 49790
#> 15 0 0.00 47540
#> 16 0 0.00 45390
#> 17 0 0.00 43340
#> 18 0 0.00 41380
#> 19 0 0.00 39500
#> 20 0 0.00 37720
#> 21 0 0.00 36010
#> 22 0 0.00 34380
#> 23 0 0.00 32830
#> 24 0 0.00 31340
#> 25 0 0.00 29930
#> 26 0 0.00 28570
#> 27 0 0.00 27280
#> 28 0 0.00 26050
#> 29 0 0.00 24870
#> 30 0 0.00 23740
#> 31 0 0.00 22670
#> 32 0 0.00 21640
#> 33 0 0.00 20670
#> 34 0 0.00 19730
#> 35 0 0.00 18840
#> 36 0 0.00 17990
#> 37 0 0.00 17170
#> 38 0 0.00 16400
#> 39 0 0.00 15650
#> 40 0 0.00 14950
#> 41 0 0.00 14270
#> 42 0 0.00 13620
#> 43 0 0.00 13010
#> 44 0 0.00 12420
#> 45 0 0.00 11860
#> 46 0 0.00 11320
#> 47 0 0.00 10810
#> 48 0 0.00 10320
#> 49 0 0.00 9854
#> 50 0 0.00 9409
#> 51 0 0.00 8983
#> 52 0 0.00 8577
#> 53 0 0.00 8189
#> 54 0 0.00 7819
#> 55 0 0.00 7465
#> 56 0 0.00 7127
#> 57 0 0.00 6805
#> 58 0 0.00 6497
#> 59 0 0.00 6203
#> 60 0 0.00 5923
#> 61 0 0.00 5655
#> 62 0 0.00 5399
#> 63 0 0.00 5155
#> 64 0 0.00 4922
#> 65 0 0.00 4699
#> 66 0 0.00 4487
#> 67 0 0.00 4284
#> 68 0 0.00 4090
#> 69 0 0.00 3905
#> 70 0 0.00 3728
#> 71 0 0.00 3560
#> 72 0 0.00 3399
#> 73 0 0.00 3245
#> 74 0 0.00 3098
#> 75 0 0.00 2958
#> 76 0 0.00 2824
#> 77 0 0.00 2697
#> 78 0 0.00 2575
#> 79 0 0.00 2458
#> 80 0 0.00 2347
#> 81 0 0.00 2241
#> 82 0 0.00 2140
#> 83 0 0.00 2043
#> 84 0 0.00 1950
#> 85 0 0.00 1862
#> 86 0 0.00 1778
#> 87 0 0.00 1698
#> 88 0 0.00 1621
#> 89 0 0.00 1547
#> 90 0 0.00 1477
#> 91 0 0.00 1411
#> 92 0 0.00 1347
#> 93 0 0.00 1286
#> 94 0 0.00 1228
#> 95 0 0.00 1172
#> 96 0 0.00 1119
#> 97 0 0.00 1069
#> 98 0 0.00 1020
#> 99 0 0.00 974
#> 100 0 0.00 930
#> 101 0 0.00 888
#> 102 0 0.00 848
#> 103 0 0.00 810
#> 104 0 0.00 773
#> 105 0 0.00 738
#> 106 0 0.00 704
#> 107 0 0.00 673
#> 108 0 0.00 642
#> 109 0 0.00 613
#> 110 0 0.00 586
#> 111 0 0.00 559
#> 112 0 0.00 534
#> 113 0 0.00 510
#> 114 0 0.00 486
#> 115 0 0.00 464
#> 116 0 0.00 444
#> 117 0 0.00 423
#> 118 0 0.00 404
#> 119 0 0.00 386
#> 120 0 0.00 368
#> 121 0 0.00 352
#> 122 0 0.00 336
#> 123 0 0.00 321
#> 124 0 0.00 306
#> 125 0 0.00 292
#> 126 0 0.00 279
#> 127 0 0.00 267
#> 128 0 0.00 254
#> 129 0 0.00 243
#> 130 0 0.00 232
#> 131 0 0.00 222
#> 132 0 0.00 212
#> 133 0 0.00 202
#> 134 0 0.00 193
#> 135 0 0.00 184
#> 136 0 0.00 176
#> 137 0 0.00 168
#> 138 0 0.00 160
#> 139 0 0.00 153
#> 140 1 0.10 146
#> 141 1 0.77 139
#> 142 1 1.39 133
#> 143 1 1.97 127
#> 144 1 2.52 121
#> 145 1 3.03 116
#> 146 1 3.51 111
#> 147 1 3.97 106
#> 148 1 4.39 101
#> 149 1 4.80 96
#> 150 1 5.18 92
#> 151 1 5.54 88
#> 152 1 5.87 84
#> 153 1 6.20 80
#> 154 1 6.50 76
#> 155 1 6.79 73
#> 156 1 7.06 70
#> 157 1 7.32 66
#> 158 1 7.56 63
#> 159 1 7.79 61
#> 160 1 8.01 58
#> 161 1 8.21 55
#> 162 1 8.41 53
#> 163 1 8.59 50
#> 164 1 8.77 48
#> 165 1 8.93 46
#> 166 1 9.09 44
#> 167 1 9.24 42
#> 168 1 9.38 40
#> 169 1 9.51 38
#> 170 1 9.64 36
#> 171 1 9.75 35
#> 172 1 9.86 33
#> 173 1 9.97 32
#> 174 1 10.06 30
#> 175 1 10.16 29
#> 176 1 10.24 28
#> 177 1 10.33 26
#> 178 1 10.40 25
#> 179 1 10.47 24
#> 180 1 10.54 23
#> 181 1 10.61 22
#> 182 1 10.66 21
#> 183 1 10.72 20
#> 184 1 10.77 19
#> 185 1 10.82 18
#> 186 1 10.86 17
#> 187 1 10.91 17
#> 188 1 10.95 16
#> 189 1 10.98 15
#> 190 1 11.02 14
#> 191 1 11.05 14
#> 192 1 11.08 13
#> 193 1 11.10 13
#> 194 1 11.13 12
#> 195 1 11.15 11
#> 196 1 11.17 11
#> 197 1 11.19 10
#> 198 1 11.21 10
#> 199 1 11.23 10
#> 200 1 11.25 9
#>
#> $`0.0578703703703702`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 64520
#> 2 0 0.00 61600
#> 3 0 0.00 58810
#> 4 0 0.00 56150
#> 5 0 0.00 53610
#> 6 0 0.00 51190
#> 7 0 0.00 48870
#> 8 0 0.00 46660
#> 9 0 0.00 44550
#> 10 0 0.00 42540
#> 11 0 0.00 40610
#> 12 0 0.00 38780
#> 13 0 0.00 37020
#> 14 0 0.00 35350
#> 15 0 0.00 33750
#> 16 0 0.00 32220
#> 17 0 0.00 30770
#> 18 0 0.00 29370
#> 19 0 0.00 28050
#> 20 0 0.00 26780
#> 21 0 0.00 25570
#> 22 0 0.00 24410
#> 23 0 0.00 23310
#> 24 0 0.00 22250
#> 25 0 0.00 21250
#> 26 0 0.00 20280
#> 27 0 0.00 19370
#> 28 0 0.00 18490
#> 29 0 0.00 17650
#> 30 0 0.00 16860
#> 31 0 0.00 16090
#> 32 0 0.00 15370
#> 33 0 0.00 14670
#> 34 0 0.00 14010
#> 35 0 0.00 13370
#> 36 0 0.00 12770
#> 37 0 0.00 12190
#> 38 0 0.00 11640
#> 39 0 0.00 11110
#> 40 0 0.00 10610
#> 41 0 0.00 10130
#> 42 0 0.00 9673
#> 43 0 0.00 9235
#> 44 0 0.00 8818
#> 45 0 0.00 8419
#> 46 0 0.00 8038
#> 47 0 0.00 7674
#> 48 0 0.00 7327
#> 49 0 0.00 6996
#> 50 0 0.00 6680
#> 51 0 0.00 6377
#> 52 0 0.00 6089
#> 53 0 0.00 5814
#> 54 0 0.00 5551
#> 55 0 0.00 5300
#> 56 0 0.00 5060
#> 57 0 0.00 4831
#> 58 0 0.00 4613
#> 59 0 0.00 4404
#> 60 0 0.00 4205
#> 61 0 0.00 4015
#> 62 0 0.00 3833
#> 63 0 0.00 3660
#> 64 0 0.00 3494
#> 65 0 0.00 3336
#> 66 0 0.00 3185
#> 67 0 0.00 3041
#> 68 0 0.00 2904
#> 69 0 0.00 2772
#> 70 0 0.00 2647
#> 71 0 0.00 2527
#> 72 0 0.00 2413
#> 73 0 0.00 2304
#> 74 0 0.00 2200
#> 75 0 0.00 2100
#> 76 0 0.00 2005
#> 77 0 0.00 1914
#> 78 0 0.00 1828
#> 79 0 0.00 1745
#> 80 0 0.00 1666
#> 81 0 0.00 1591
#> 82 0 0.00 1519
#> 83 0 0.00 1450
#> 84 0 0.00 1385
#> 85 0 0.00 1322
#> 86 0 0.00 1262
#> 87 0 0.00 1205
#> 88 0 0.00 1151
#> 89 0 0.00 1099
#> 90 0 0.00 1049
#> 91 0 0.00 1001
#> 92 0 0.00 956
#> 93 0 0.00 913
#> 94 0 0.00 872
#> 95 0 0.00 832
#> 96 0 0.00 795
#> 97 0 0.00 759
#> 98 0 0.00 724
#> 99 0 0.00 692
#> 100 0 0.00 660
#> 101 0 0.00 630
#> 102 0 0.00 602
#> 103 0 0.00 575
#> 104 0 0.00 549
#> 105 0 0.00 524
#> 106 0 0.00 500
#> 107 0 0.00 478
#> 108 0 0.00 456
#> 109 0 0.00 435
#> 110 0 0.00 416
#> 111 0 0.00 397
#> 112 0 0.00 379
#> 113 0 0.00 362
#> 114 0 0.00 345
#> 115 0 0.00 330
#> 116 0 0.00 315
#> 117 0 0.00 301
#> 118 0 0.00 287
#> 119 0 0.00 274
#> 120 0 0.00 262
#> 121 0 0.00 250
#> 122 0 0.00 238
#> 123 0 0.00 228
#> 124 0 0.00 217
#> 125 0 0.00 208
#> 126 0 0.00 198
#> 127 0 0.00 189
#> 128 0 0.00 181
#> 129 0 0.00 172
#> 130 0 0.00 165
#> 131 0 0.00 157
#> 132 0 0.00 150
#> 133 0 0.00 143
#> 134 0 0.00 137
#> 135 0 0.00 131
#> 136 0 0.00 125
#> 137 0 0.00 119
#> 138 0 0.00 114
#> 139 0 0.00 109
#> 140 1 0.10 104
#> 141 1 0.77 99
#> 142 1 1.39 95
#> 143 1 1.97 90
#> 144 1 2.52 86
#> 145 1 3.03 82
#> 146 1 3.51 79
#> 147 1 3.97 75
#> 148 1 4.39 72
#> 149 1 4.80 68
#> 150 1 5.18 65
#> 151 1 5.54 62
#> 152 1 5.87 60
#> 153 1 6.20 57
#> 154 1 6.50 54
#> 155 1 6.79 52
#> 156 1 7.06 49
#> 157 1 7.32 47
#> 158 1 7.56 45
#> 159 1 7.79 43
#> 160 1 8.01 41
#> 161 1 8.21 39
#> 162 1 8.41 37
#> 163 1 8.59 36
#> 164 1 8.77 34
#> 165 1 8.93 33
#> 166 1 9.09 31
#> 167 1 9.24 30
#> 168 1 9.38 28
#> 169 1 9.51 27
#> 170 1 9.64 26
#> 171 1 9.75 25
#> 172 1 9.86 24
#> 173 1 9.97 23
#> 174 1 10.06 21
#> 175 1 10.16 21
#> 176 1 10.24 20
#> 177 1 10.33 19
#> 178 1 10.40 18
#> 179 1 10.47 17
#> 180 1 10.54 16
#> 181 1 10.61 16
#> 182 1 10.66 15
#> 183 1 10.72 14
#> 184 1 10.77 14
#> 185 1 10.82 13
#> 186 1 10.86 12
#> 187 1 10.91 12
#> 188 1 10.95 11
#> 189 1 10.98 11
#> 190 1 11.02 10
#> 191 1 11.05 10
#> 192 1 11.08 9
#> 193 1 11.10 9
#> 194 1 11.13 9
#> 195 1 11.15 8
#> 196 1 11.17 8
#> 197 1 11.19 7
#> 198 1 11.21 7
#> 199 1 11.23 7
#> 200 1 11.25 6
#>
#> $`0.0208333333333333`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 64410
#> 2 0 0.00 61500
#> 3 0 0.00 58720
#> 4 0 0.00 56060
#> 5 0 0.00 53520
#> 6 0 0.00 51100
#> 7 0 0.00 48790
#> 8 0 0.00 46590
#> 9 0 0.00 44480
#> 10 0 0.00 42470
#> 11 0 0.00 40550
#> 12 0 0.00 38710
#> 13 0 0.00 36960
#> 14 0 0.00 35290
#> 15 0 0.00 33690
#> 16 0 0.00 32170
#> 17 0 0.00 30710
#> 18 0 0.00 29330
#> 19 0 0.00 28000
#> 20 0 0.00 26730
#> 21 0 0.00 25520
#> 22 0 0.00 24370
#> 23 0 0.00 23270
#> 24 0 0.00 22210
#> 25 0 0.00 21210
#> 26 0 0.00 20250
#> 27 0 0.00 19330
#> 28 0 0.00 18460
#> 29 0 0.00 17630
#> 30 0 0.00 16830
#> 31 0 0.00 16070
#> 32 0 0.00 15340
#> 33 0 0.00 14650
#> 34 0 0.00 13980
#> 35 0 0.00 13350
#> 36 0 0.00 12750
#> 37 0 0.00 12170
#> 38 0 0.00 11620
#> 39 0 0.00 11100
#> 40 0 0.00 10590
#> 41 0 0.00 10110
#> 42 0 0.00 9657
#> 43 0 0.00 9220
#> 44 0 0.00 8803
#> 45 0 0.00 8405
#> 46 0 0.00 8025
#> 47 0 0.00 7662
#> 48 0 0.00 7315
#> 49 0 0.00 6984
#> 50 0 0.00 6669
#> 51 0 0.00 6367
#> 52 0 0.00 6079
#> 53 0 0.00 5804
#> 54 0 0.00 5542
#> 55 0 0.00 5291
#> 56 0 0.00 5052
#> 57 0 0.00 4823
#> 58 0 0.00 4605
#> 59 0 0.00 4397
#> 60 0 0.00 4198
#> 61 0 0.00 4008
#> 62 0 0.00 3827
#> 63 0 0.00 3654
#> 64 0 0.00 3488
#> 65 0 0.00 3331
#> 66 0 0.00 3180
#> 67 0 0.00 3036
#> 68 0 0.00 2899
#> 69 0 0.00 2768
#> 70 0 0.00 2643
#> 71 0 0.00 2523
#> 72 0 0.00 2409
#> 73 0 0.00 2300
#> 74 0 0.00 2196
#> 75 0 0.00 2097
#> 76 0 0.00 2002
#> 77 0 0.00 1911
#> 78 0 0.00 1825
#> 79 0 0.00 1742
#> 80 0 0.00 1663
#> 81 0 0.00 1588
#> 82 0 0.00 1516
#> 83 0 0.00 1448
#> 84 0 0.00 1382
#> 85 0 0.00 1320
#> 86 0 0.00 1260
#> 87 0 0.00 1203
#> 88 0 0.00 1149
#> 89 0 0.00 1097
#> 90 0 0.00 1047
#> 91 0 0.00 1000
#> 92 0 0.00 955
#> 93 0 0.00 911
#> 94 0 0.00 870
#> 95 0 0.00 831
#> 96 0 0.00 793
#> 97 0 0.00 757
#> 98 0 0.00 723
#> 99 0 0.00 690
#> 100 0 0.00 659
#> 101 0 0.00 629
#> 102 0 0.00 601
#> 103 0 0.00 574
#> 104 0 0.00 548
#> 105 0 0.00 523
#> 106 0 0.00 499
#> 107 0 0.00 477
#> 108 0 0.00 455
#> 109 0 0.00 435
#> 110 0 0.00 415
#> 111 0 0.00 396
#> 112 0 0.00 378
#> 113 0 0.00 361
#> 114 0 0.00 345
#> 115 0 0.00 329
#> 116 0 0.00 314
#> 117 0 0.00 300
#> 118 0 0.00 286
#> 119 0 0.00 274
#> 120 0 0.00 261
#> 121 0 0.00 249
#> 122 0 0.00 238
#> 123 0 0.00 227
#> 124 0 0.00 217
#> 125 0 0.00 207
#> 126 0 0.00 198
#> 127 0 0.00 189
#> 128 0 0.00 180
#> 129 0 0.00 172
#> 130 0 0.00 164
#> 131 0 0.00 157
#> 132 0 0.00 150
#> 133 0 0.00 143
#> 134 0 0.00 137
#> 135 0 0.00 130
#> 136 0 0.00 125
#> 137 0 0.00 119
#> 138 0 0.00 114
#> 139 0 0.00 108
#> 140 1 0.10 104
#> 141 1 0.77 99
#> 142 1 1.39 94
#> 143 1 1.97 90
#> 144 1 2.52 86
#> 145 1 3.03 82
#> 146 1 3.51 78
#> 147 1 3.97 75
#> 148 1 4.39 71
#> 149 1 4.80 68
#> 150 1 5.18 65
#> 151 1 5.54 62
#> 152 1 5.87 59
#> 153 1 6.20 57
#> 154 1 6.50 54
#> 155 1 6.79 52
#> 156 1 7.06 49
#> 157 1 7.32 47
#> 158 1 7.56 45
#> 159 1 7.79 43
#> 160 1 8.01 41
#> 161 1 8.21 39
#> 162 1 8.41 37
#> 163 1 8.59 36
#> 164 1 8.77 34
#> 165 1 8.93 33
#> 166 1 9.09 31
#> 167 1 9.24 30
#> 168 1 9.38 28
#> 169 1 9.51 27
#> 170 1 9.64 26
#> 171 1 9.75 25
#> 172 1 9.86 24
#> 173 1 9.97 22
#> 174 1 10.06 21
#> 175 1 10.16 20
#> 176 1 10.24 20
#> 177 1 10.33 19
#> 178 1 10.40 18
#> 179 1 10.47 17
#> 180 1 10.54 16
#> 181 1 10.61 16
#> 182 1 10.66 15
#> 183 1 10.72 14
#> 184 1 10.77 14
#> 185 1 10.82 13
#> 186 1 10.86 12
#> 187 1 10.91 12
#> 188 1 10.95 11
#> 189 1 10.98 11
#> 190 1 11.02 10
#> 191 1 11.05 10
#> 192 1 11.08 9
#> 193 1 11.10 9
#> 194 1 11.13 9
#> 195 1 11.15 8
#> 196 1 11.17 8
#> 197 1 11.19 7
#> 198 1 11.21 7
#> 199 1 11.23 7
#> 200 1 11.25 6
#>
#> $`0.141203703703704`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 64400
#> 2 0 0.00 61490
#> 3 0 0.00 58710
#> 4 0 0.00 56050
#> 5 0 0.00 53520
#> 6 0 0.00 51100
#> 7 0 0.00 48790
#> 8 0 0.00 46580
#> 9 0 0.00 44470
#> 10 0 0.00 42460
#> 11 0 0.00 40540
#> 12 0 0.00 38710
#> 13 0 0.00 36960
#> 14 0 0.00 35280
#> 15 0 0.00 33690
#> 16 0 0.00 32170
#> 17 0 0.00 30710
#> 18 0 0.00 29320
#> 19 0 0.00 28000
#> 20 0 0.00 26730
#> 21 0 0.00 25520
#> 22 0 0.00 24370
#> 23 0 0.00 23260
#> 24 0 0.00 22210
#> 25 0 0.00 21210
#> 26 0 0.00 20250
#> 27 0 0.00 19330
#> 28 0 0.00 18460
#> 29 0 0.00 17620
#> 30 0 0.00 16830
#> 31 0 0.00 16060
#> 32 0 0.00 15340
#> 33 0 0.00 14640
#> 34 0 0.00 13980
#> 35 0 0.00 13350
#> 36 0 0.00 12750
#> 37 0 0.00 12170
#> 38 0 0.00 11620
#> 39 0 0.00 11090
#> 40 0 0.00 10590
#> 41 0 0.00 10110
#> 42 0 0.00 9655
#> 43 0 0.00 9219
#> 44 0 0.00 8802
#> 45 0 0.00 8404
#> 46 0 0.00 8024
#> 47 0 0.00 7661
#> 48 0 0.00 7314
#> 49 0 0.00 6983
#> 50 0 0.00 6668
#> 51 0 0.00 6366
#> 52 0 0.00 6078
#> 53 0 0.00 5803
#> 54 0 0.00 5541
#> 55 0 0.00 5290
#> 56 0 0.00 5051
#> 57 0 0.00 4822
#> 58 0 0.00 4604
#> 59 0 0.00 4396
#> 60 0 0.00 4197
#> 61 0 0.00 4007
#> 62 0 0.00 3826
#> 63 0 0.00 3653
#> 64 0 0.00 3488
#> 65 0 0.00 3330
#> 66 0 0.00 3180
#> 67 0 0.00 3036
#> 68 0 0.00 2898
#> 69 0 0.00 2767
#> 70 0 0.00 2642
#> 71 0 0.00 2523
#> 72 0 0.00 2409
#> 73 0 0.00 2300
#> 74 0 0.00 2196
#> 75 0 0.00 2096
#> 76 0 0.00 2002
#> 77 0 0.00 1911
#> 78 0 0.00 1825
#> 79 0 0.00 1742
#> 80 0 0.00 1663
#> 81 0 0.00 1588
#> 82 0 0.00 1516
#> 83 0 0.00 1448
#> 84 0 0.00 1382
#> 85 0 0.00 1320
#> 86 0 0.00 1260
#> 87 0 0.00 1203
#> 88 0 0.00 1149
#> 89 0 0.00 1097
#> 90 0 0.00 1047
#> 91 0 0.00 1000
#> 92 0 0.00 954
#> 93 0 0.00 911
#> 94 0 0.00 870
#> 95 0 0.00 831
#> 96 0 0.00 793
#> 97 0 0.00 757
#> 98 0 0.00 723
#> 99 0 0.00 690
#> 100 0 0.00 659
#> 101 0 0.00 629
#> 102 0 0.00 601
#> 103 0 0.00 574
#> 104 0 0.00 548
#> 105 0 0.00 523
#> 106 0 0.00 499
#> 107 0 0.00 477
#> 108 0 0.00 455
#> 109 0 0.00 435
#> 110 0 0.00 415
#> 111 0 0.00 396
#> 112 0 0.00 378
#> 113 0 0.00 361
#> 114 0 0.00 345
#> 115 0 0.00 329
#> 116 0 0.00 314
#> 117 0 0.00 300
#> 118 0 0.00 286
#> 119 0 0.00 274
#> 120 0 0.00 261
#> 121 0 0.00 249
#> 122 0 0.00 238
#> 123 0 0.00 227
#> 124 0 0.00 217
#> 125 0 0.00 207
#> 126 0 0.00 198
#> 127 0 0.00 189
#> 128 0 0.00 180
#> 129 0 0.00 172
#> 130 0 0.00 164
#> 131 0 0.00 157
#> 132 0 0.00 150
#> 133 0 0.00 143
#> 134 0 0.00 137
#> 135 0 0.00 130
#> 136 0 0.00 124
#> 137 0 0.00 119
#> 138 0 0.00 114
#> 139 0 0.00 108
#> 140 1 0.10 104
#> 141 1 0.77 99
#> 142 1 1.39 94
#> 143 1 1.97 90
#> 144 1 2.52 86
#> 145 1 3.03 82
#> 146 1 3.51 78
#> 147 1 3.97 75
#> 148 1 4.39 71
#> 149 1 4.80 68
#> 150 1 5.18 65
#> 151 1 5.54 62
#> 152 1 5.87 59
#> 153 1 6.20 57
#> 154 1 6.50 54
#> 155 1 6.79 52
#> 156 1 7.06 49
#> 157 1 7.32 47
#> 158 1 7.56 45
#> 159 1 7.79 43
#> 160 1 8.01 41
#> 161 1 8.21 39
#> 162 1 8.41 37
#> 163 1 8.59 36
#> 164 1 8.77 34
#> 165 1 8.93 33
#> 166 1 9.09 31
#> 167 1 9.24 30
#> 168 1 9.38 28
#> 169 1 9.51 27
#> 170 1 9.64 26
#> 171 1 9.75 25
#> 172 1 9.86 24
#> 173 1 9.97 22
#> 174 1 10.06 21
#> 175 1 10.16 20
#> 176 1 10.24 20
#> 177 1 10.33 19
#> 178 1 10.40 18
#> 179 1 10.47 17
#> 180 2 10.55 16
#> 181 2 10.64 16
#> 182 2 10.73 15
#> 183 2 10.81 14
#> 184 2 10.88 14
#> 185 2 10.95 13
#> 186 2 11.01 12
#> 187 2 11.08 12
#> 188 2 11.13 11
#> 189 2 11.18 11
#> 190 2 11.23 10
#> 191 2 11.28 10
#> 192 2 11.32 9
#> 193 2 11.36 9
#> 194 2 11.40 9
#> 195 2 11.43 8
#> 196 2 11.46 8
#> 197 2 11.49 7
#> 198 2 11.52 7
#> 199 2 11.54 7
#> 200 2 11.57 6
#>
#> $`0.141203703703704`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 73100
#> 2 0 0.00 69790
#> 3 0 0.00 66640
#> 4 0 0.00 63620
#> 5 0 0.00 60740
#> 6 0 0.00 58000
#> 7 0 0.00 55370
#> 8 0 0.00 52870
#> 9 0 0.00 50480
#> 10 0 0.00 48190
#> 11 0 0.00 46020
#> 12 0 0.00 43930
#> 13 0 0.00 41950
#> 14 0 0.00 40050
#> 15 0 0.00 38240
#> 16 0 0.00 36510
#> 17 0 0.00 34860
#> 18 0 0.00 33280
#> 19 0 0.00 31780
#> 20 0 0.00 30340
#> 21 0 0.00 28970
#> 22 0 0.00 27660
#> 23 0 0.00 26410
#> 24 0 0.00 25210
#> 25 0 0.00 24070
#> 26 0 0.00 22980
#> 27 0 0.00 21940
#> 28 0 0.00 20950
#> 29 0 0.00 20000
#> 30 0 0.00 19100
#> 31 0 0.00 18230
#> 32 0 0.00 17410
#> 33 0 0.00 16620
#> 34 0 0.00 15870
#> 35 0 0.00 15150
#> 36 0 0.00 14470
#> 37 0 0.00 13810
#> 38 0 0.00 13190
#> 39 0 0.00 12590
#> 40 0 0.00 12020
#> 41 0 0.00 11480
#> 42 0 0.00 10960
#> 43 0 0.00 10460
#> 44 0 0.00 9990
#> 45 0 0.00 9539
#> 46 0 0.00 9107
#> 47 0 0.00 8695
#> 48 0 0.00 8302
#> 49 0 0.00 7926
#> 50 0 0.00 7568
#> 51 0 0.00 7226
#> 52 0 0.00 6899
#> 53 0 0.00 6587
#> 54 0 0.00 6289
#> 55 0 0.00 6005
#> 56 0 0.00 5733
#> 57 0 0.00 5474
#> 58 0 0.00 5226
#> 59 0 0.00 4990
#> 60 0 0.00 4764
#> 61 0 0.00 4549
#> 62 0 0.00 4343
#> 63 0 0.00 4146
#> 64 0 0.00 3959
#> 65 0 0.00 3780
#> 66 0 0.00 3609
#> 67 0 0.00 3446
#> 68 0 0.00 3290
#> 69 0 0.00 3141
#> 70 0 0.00 2999
#> 71 0 0.00 2863
#> 72 0 0.00 2734
#> 73 0 0.00 2610
#> 74 0 0.00 2492
#> 75 0 0.00 2379
#> 76 0 0.00 2272
#> 77 0 0.00 2169
#> 78 0 0.00 2071
#> 79 0 0.00 1977
#> 80 0 0.00 1888
#> 81 0 0.00 1802
#> 82 0 0.00 1721
#> 83 0 0.00 1643
#> 84 0 0.00 1569
#> 85 0 0.00 1498
#> 86 0 0.00 1430
#> 87 0 0.00 1365
#> 88 0 0.00 1304
#> 89 0 0.00 1245
#> 90 0 0.00 1188
#> 91 0 0.00 1135
#> 92 0 0.00 1083
#> 93 0 0.00 1034
#> 94 0 0.00 988
#> 95 0 0.00 943
#> 96 0 0.00 900
#> 97 0 0.00 860
#> 98 0 0.00 821
#> 99 0 0.00 784
#> 100 0 0.00 748
#> 101 0 0.00 714
#> 102 0 0.00 682
#> 103 0 0.00 651
#> 104 0 0.00 622
#> 105 0 0.00 594
#> 106 0 0.00 567
#> 107 0 0.00 541
#> 108 0 0.00 517
#> 109 0 0.00 493
#> 110 0 0.00 471
#> 111 0 0.00 450
#> 112 0 0.00 429
#> 113 0 0.00 410
#> 114 0 0.00 391
#> 115 0 0.00 374
#> 116 0 0.00 357
#> 117 0 0.00 341
#> 118 0 0.00 325
#> 119 0 0.00 310
#> 120 0 0.00 296
#> 121 0 0.00 283
#> 122 0 0.00 270
#> 123 0 0.00 258
#> 124 0 0.00 246
#> 125 0 0.00 235
#> 126 0 0.00 225
#> 127 0 0.00 214
#> 128 0 0.00 205
#> 129 0 0.00 196
#> 130 0 0.00 187
#> 131 0 0.00 178
#> 132 0 0.00 170
#> 133 0 0.00 162
#> 134 0 0.00 155
#> 135 0 0.00 148
#> 136 0 0.00 141
#> 137 0 0.00 135
#> 138 0 0.00 129
#> 139 0 0.00 123
#> 140 1 0.10 118
#> 141 1 0.77 112
#> 142 1 1.39 107
#> 143 1 1.97 102
#> 144 1 2.52 98
#> 145 1 3.03 93
#> 146 1 3.51 89
#> 147 1 3.97 85
#> 148 1 4.39 81
#> 149 1 4.80 77
#> 150 1 5.18 74
#> 151 1 5.54 71
#> 152 1 5.87 67
#> 153 1 6.20 64
#> 154 1 6.50 61
#> 155 1 6.79 59
#> 156 1 7.06 56
#> 157 1 7.32 53
#> 158 1 7.56 51
#> 159 1 7.79 49
#> 160 1 8.01 47
#> 161 1 8.21 44
#> 162 1 8.41 42
#> 163 1 8.59 41
#> 164 1 8.77 39
#> 165 1 8.93 37
#> 166 1 9.09 35
#> 167 1 9.24 34
#> 168 1 9.38 32
#> 169 1 9.51 31
#> 170 1 9.64 29
#> 171 1 9.75 28
#> 172 1 9.86 27
#> 173 1 9.97 26
#> 174 1 10.06 24
#> 175 1 10.16 23
#> 176 1 10.24 22
#> 177 1 10.33 21
#> 178 1 10.40 20
#> 179 1 10.47 19
#> 180 1 10.54 18
#> 181 1 10.61 18
#> 182 1 10.66 17
#> 183 1 10.72 16
#> 184 1 10.77 15
#> 185 1 10.82 15
#> 186 1 10.86 14
#> 187 1 10.91 13
#> 188 1 10.95 13
#> 189 2 11.05 12
#> 190 2 11.16 12
#> 191 2 11.25 11
#> 192 2 11.34 11
#> 193 2 11.42 10
#> 194 2 11.50 10
#> 195 2 11.57 9
#> 196 2 11.63 9
#> 197 2 11.69 8
#> 198 2 11.74 8
#> 199 2 11.79 8
#> 200 2 11.83 7
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 26480.0
#> 2 0 0.00 25280.0
#> 3 0 0.00 24140.0
#> 4 0 0.00 23050.0
#> 5 0 0.00 22000.0
#> 6 0 0.00 21010.0
#> 7 0 0.00 20060.0
#> 8 0 0.00 19150.0
#> 9 0 0.00 18280.0
#> 10 0 0.00 17460.0
#> 11 0 0.00 16670.0
#> 12 0 0.00 15910.0
#> 13 0 0.00 15190.0
#> 14 0 0.00 14510.0
#> 15 0 0.00 13850.0
#> 16 0 0.00 13220.0
#> 17 0 0.00 12630.0
#> 18 0 0.00 12060.0
#> 19 0 0.00 11510.0
#> 20 0 0.00 10990.0
#> 21 0 0.00 10490.0
#> 22 0 0.00 10020.0
#> 23 0 0.00 9565.0
#> 24 0 0.00 9132.0
#> 25 0 0.00 8719.0
#> 26 0 0.00 8325.0
#> 27 0 0.00 7948.0
#> 28 0 0.00 7589.0
#> 29 0 0.00 7246.0
#> 30 0 0.00 6918.0
#> 31 0 0.00 6605.0
#> 32 0 0.00 6306.0
#> 33 0 0.00 6021.0
#> 34 0 0.00 5749.0
#> 35 0 0.00 5489.0
#> 36 0 0.00 5240.0
#> 37 0 0.00 5003.0
#> 38 0 0.00 4777.0
#> 39 0 0.00 4561.0
#> 40 0 0.00 4355.0
#> 41 0 0.00 4158.0
#> 42 0 0.00 3970.0
#> 43 0 0.00 3790.0
#> 44 0 0.00 3619.0
#> 45 0 0.00 3455.0
#> 46 0 0.00 3299.0
#> 47 0 0.00 3150.0
#> 48 0 0.00 3007.0
#> 49 0 0.00 2871.0
#> 50 0 0.00 2741.0
#> 51 0 0.00 2617.0
#> 52 0 0.00 2499.0
#> 53 0 0.00 2386.0
#> 54 0 0.00 2278.0
#> 55 0 0.00 2175.0
#> 56 0 0.00 2077.0
#> 57 0 0.00 1983.0
#> 58 0 0.00 1893.0
#> 59 0 0.00 1807.0
#> 60 0 0.00 1726.0
#> 61 0 0.00 1648.0
#> 62 0 0.00 1573.0
#> 63 0 0.00 1502.0
#> 64 0 0.00 1434.0
#> 65 0 0.00 1369.0
#> 66 0 0.00 1307.0
#> 67 0 0.00 1248.0
#> 68 0 0.00 1192.0
#> 69 0 0.00 1138.0
#> 70 0 0.00 1086.0
#> 71 0 0.00 1037.0
#> 72 0 0.00 990.3
#> 73 0 0.00 945.5
#> 74 0 0.00 902.7
#> 75 0 0.00 861.9
#> 76 0 0.00 822.9
#> 77 0 0.00 785.7
#> 78 0 0.00 750.2
#> 79 0 0.00 716.2
#> 80 0 0.00 683.8
#> 81 0 0.00 652.9
#> 82 0 0.00 623.4
#> 83 0 0.00 595.2
#> 84 0 0.00 568.3
#> 85 0 0.00 542.6
#> 86 0 0.00 518.0
#> 87 0 0.00 494.6
#> 88 0 0.00 472.2
#> 89 0 0.00 450.9
#> 90 0 0.00 430.5
#> 91 0 0.00 411.0
#> 92 0 0.00 392.4
#> 93 0 0.00 374.7
#> 94 0 0.00 357.7
#> 95 0 0.00 341.5
#> 96 0 0.00 326.1
#> 97 0 0.00 311.3
#> 98 0 0.00 297.3
#> 99 0 0.00 283.8
#> 100 0 0.00 271.0
#> 101 0 0.00 258.7
#> 102 0 0.00 247.0
#> 103 0 0.00 235.9
#> 104 0 0.00 225.2
#> 105 1 0.55 215.0
#> 106 1 1.35 205.3
#> 107 1 2.09 196.0
#> 108 1 2.78 187.1
#> 109 1 3.42 178.7
#> 110 1 4.02 170.6
#> 111 1 4.59 162.9
#> 112 1 5.12 155.5
#> 113 1 5.62 148.5
#> 114 1 6.10 141.8
#> 115 1 6.54 135.3
#> 116 1 6.96 129.2
#> 117 1 7.36 123.4
#> 118 1 7.73 117.8
#> 119 1 8.09 112.5
#> 120 1 8.43 107.4
#> 121 1 8.76 102.5
#> 122 1 9.07 97.9
#> 123 1 9.36 93.5
#> 124 1 9.64 89.2
#> 125 1 9.91 85.2
#> 126 1 10.17 81.3
#> 127 1 10.41 77.7
#> 128 1 10.65 74.2
#> 129 1 10.87 70.8
#> 130 1 11.08 67.6
#> 131 1 11.29 64.5
#> 132 1 11.49 61.6
#> 133 1 11.68 58.8
#> 134 1 11.86 56.2
#> 135 1 12.03 53.6
#> 136 1 12.20 51.2
#> 137 1 12.36 48.9
#> 138 1 12.51 46.7
#> 139 1 12.66 44.6
#> 140 1 12.80 42.5
#> 141 1 12.94 40.6
#> 142 1 13.07 38.8
#> 143 1 13.20 37.0
#> 144 1 13.32 35.4
#> 145 1 13.44 33.8
#> 146 1 13.55 32.2
#> 147 1 13.66 30.8
#> 148 1 13.76 29.4
#> 149 1 13.86 28.1
#> 150 1 13.96 26.8
#> 151 1 14.05 25.6
#> 152 1 14.14 24.4
#> 153 1 14.22 23.3
#> 154 1 14.31 22.3
#> 155 1 14.38 21.2
#> 156 1 14.46 20.3
#> 157 1 14.53 19.4
#> 158 1 14.60 18.5
#> 159 1 14.66 17.7
#> 160 1 14.73 16.9
#> 161 1 14.79 16.1
#> 162 1 14.85 15.4
#> 163 1 14.90 14.7
#> 164 1 14.95 14.0
#> 165 1 15.00 13.4
#> 166 1 15.05 12.8
#> 167 1 15.09 12.2
#> 168 1 15.14 11.6
#> 169 1 15.18 11.1
#> 170 1 15.22 10.6
#> 171 1 15.25 10.1
#> 172 1 15.29 9.7
#> 173 1 15.32 9.2
#> 174 1 15.35 8.8
#> 175 1 15.38 8.4
#> 176 2 15.48 8.0
#> 177 2 15.82 7.7
#> 178 2 16.13 7.3
#> 179 2 16.41 7.0
#> 180 2 16.67 6.7
#> 181 2 16.90 6.4
#> 182 2 17.11 6.1
#> 183 2 17.30 5.8
#> 184 2 17.47 5.6
#> 185 2 17.63 5.3
#> 186 2 17.78 5.1
#> 187 2 17.91 4.8
#> 188 2 18.03 4.6
#> 189 2 18.14 4.4
#> 190 2 18.24 4.2
#> 191 2 18.33 4.0
#> 192 2 18.41 3.8
#> 193 2 18.49 3.7
#> 194 2 18.56 3.5
#> 195 2 18.63 3.3
#> 196 2 18.69 3.2
#> 197 2 18.74 3.0
#> 198 2 18.79 2.9
#> 199 2 18.83 2.8
#> 200 2 18.88 2.6
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 117.600
#> 2 0 0.00 112.200
#> 3 0 0.00 107.200
#> 4 0 0.00 102.300
#> 5 0 0.00 97.690
#> 6 0 0.00 93.270
#> 7 0 0.00 89.050
#> 8 0 0.00 85.020
#> 9 0 0.00 81.180
#> 10 0 0.00 77.510
#> 11 0 0.00 74.000
#> 12 0 0.00 70.650
#> 13 0 0.00 67.460
#> 14 0 0.00 64.410
#> 15 0 0.00 61.490
#> 16 0 0.00 58.710
#> 17 0 0.00 56.060
#> 18 0 0.00 53.520
#> 19 0 0.00 51.100
#> 20 0 0.00 48.790
#> 21 0 0.00 46.580
#> 22 0 0.00 44.480
#> 23 0 0.00 42.470
#> 24 0 0.00 40.540
#> 25 0 0.00 38.710
#> 26 0 0.00 36.960
#> 27 0 0.00 35.290
#> 28 0 0.00 33.690
#> 29 0 0.00 32.170
#> 30 0 0.00 30.710
#> 31 0 0.00 29.320
#> 32 0 0.00 28.000
#> 33 0 0.00 26.730
#> 34 0 0.00 25.520
#> 35 0 0.00 24.370
#> 36 0 0.00 23.270
#> 37 0 0.00 22.210
#> 38 0 0.00 21.210
#> 39 0 0.00 20.250
#> 40 0 0.00 19.330
#> 41 0 0.00 18.460
#> 42 0 0.00 17.620
#> 43 0 0.00 16.830
#> 44 0 0.00 16.070
#> 45 0 0.00 15.340
#> 46 0 0.00 14.650
#> 47 0 0.00 13.980
#> 48 0 0.00 13.350
#> 49 0 0.00 12.750
#> 50 0 0.00 12.170
#> 51 0 0.00 11.620
#> 52 0 0.00 11.090
#> 53 0 0.00 10.590
#> 54 0 0.00 10.110
#> 55 0 0.00 9.656
#> 56 0 0.00 9.220
#> 57 0 0.00 8.803
#> 58 0 0.00 8.405
#> 59 0 0.00 8.024
#> 60 0 0.00 7.662
#> 61 0 0.00 7.315
#> 62 0 0.00 6.984
#> 63 0 0.00 6.668
#> 64 0 0.00 6.367
#> 65 0 0.00 6.079
#> 66 0 0.00 5.804
#> 67 0 0.00 5.541
#> 68 0 0.00 5.291
#> 69 0 0.00 5.051
#> 70 0 0.00 4.823
#> 71 0 0.00 4.605
#> 72 0 0.00 4.397
#> 73 0 0.00 4.198
#> 74 0 0.00 4.008
#> 75 0 0.00 3.827
#> 76 0 0.00 3.653
#> 77 0 0.00 3.488
#> 78 0 0.00 3.330
#> 79 0 0.00 3.180
#> 80 0 0.00 3.036
#> 81 0 0.00 2.899
#> 82 0 0.00 2.768
#> 83 0 0.00 2.642
#> 84 0 0.00 2.523
#> 85 0 0.00 2.409
#> 86 0 0.00 2.300
#> 87 0 0.00 2.196
#> 88 0 0.00 2.097
#> 89 0 0.00 2.002
#> 90 0 0.00 1.911
#> 91 0 0.00 1.825
#> 92 0 0.00 1.742
#> 93 0 0.00 1.663
#> 94 0 0.00 1.588
#> 95 0 0.00 1.516
#> 96 0 0.00 1.448
#> 97 0 0.00 1.382
#> 98 0 0.00 1.320
#> 99 0 0.00 1.260
#> 100 0 0.00 1.203
#> 101 0 0.00 1.149
#> 102 0 0.00 1.097
#> 103 0 0.00 1.047
#> 104 0 0.00 1.000
#> 105 1 0.55 0.955
#> 106 1 1.35 0.911
#> 107 1 2.09 0.870
#> 108 1 2.78 0.831
#> 109 1 3.42 0.793
#> 110 1 4.02 0.757
#> 111 1 4.59 0.723
#> 112 1 5.12 0.690
#> 113 1 5.62 0.659
#> 114 1 6.10 0.629
#> 115 1 6.54 0.601
#> 116 1 6.96 0.574
#> 117 1 7.36 0.548
#> 118 1 7.73 0.523
#> 119 1 8.09 0.499
#> 120 1 8.43 0.477
#> 121 1 8.76 0.455
#> 122 1 9.07 0.435
#> 123 1 9.36 0.415
#> 124 1 9.64 0.396
#> 125 1 9.91 0.378
#> 126 1 10.17 0.361
#> 127 1 10.41 0.345
#> 128 1 10.65 0.329
#> 129 1 10.87 0.314
#> 130 1 11.08 0.300
#> 131 1 11.29 0.286
#> 132 1 11.49 0.274
#> 133 1 11.68 0.261
#> 134 1 11.86 0.249
#> 135 1 12.03 0.238
#> 136 1 12.20 0.227
#> 137 1 12.36 0.217
#> 138 1 12.51 0.207
#> 139 1 12.66 0.198
#> 140 1 12.80 0.189
#> 141 1 12.94 0.180
#> 142 1 13.07 0.172
#> 143 1 13.20 0.164
#> 144 1 13.32 0.157
#> 145 1 13.44 0.150
#> 146 1 13.55 0.143
#> 147 1 13.66 0.137
#> 148 1 13.76 0.130
#> 149 1 13.86 0.125
#> 150 1 13.96 0.119
#> 151 1 14.05 0.114
#> 152 1 14.14 0.108
#> 153 1 14.22 0.104
#> 154 1 14.31 0.099
#> 155 1 14.38 0.094
#> 156 1 14.46 0.090
#> 157 1 14.53 0.086
#> 158 1 14.60 0.082
#> 159 1 14.66 0.078
#> 160 1 14.73 0.075
#> 161 1 14.79 0.071
#> 162 1 14.85 0.068
#> 163 1 14.90 0.065
#> 164 1 14.95 0.062
#> 165 1 15.00 0.059
#> 166 1 15.05 0.057
#> 167 1 15.09 0.054
#> 168 1 15.14 0.052
#> 169 1 15.18 0.049
#> 170 1 15.22 0.047
#> 171 1 15.25 0.045
#> 172 1 15.29 0.043
#> 173 1 15.32 0.041
#> 174 1 15.35 0.039
#> 175 1 15.38 0.037
#> 176 1 15.41 0.036
#> 177 1 15.43 0.034
#> 178 1 15.46 0.033
#> 179 1 15.48 0.031
#> 180 1 15.50 0.030
#> 181 1 15.52 0.028
#> 182 1 15.54 0.027
#> 183 1 15.56 0.026
#> 184 1 15.58 0.025
#> 185 1 15.59 0.024
#> 186 1 15.61 0.022
#> 187 1 15.62 0.021
#> 188 1 15.63 0.020
#> 189 1 15.65 0.020
#> 190 1 15.66 0.019
#> 191 1 15.67 0.018
#> 192 1 15.68 0.017
#> 193 1 15.69 0.016
#> 194 1 15.70 0.016
#> 195 1 15.70 0.015
#> 196 1 15.71 0.014
#> 197 1 15.72 0.014
#> 198 1 15.73 0.013
#> 199 1 15.73 0.012
#> 200 1 15.74 0.012
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 11.2200
#> 2 0 0.00 10.7100
#> 3 0 0.00 10.2300
#> 4 0 0.00 9.7630
#> 5 0 0.00 9.3210
#> 6 0 0.00 8.8990
#> 7 0 0.00 8.4970
#> 8 0 0.00 8.1130
#> 9 0 0.00 7.7460
#> 10 0 0.00 7.3950
#> 11 0 0.00 7.0610
#> 12 0 0.00 6.7420
#> 13 0 0.00 6.4370
#> 14 0 0.00 6.1460
#> 15 0 0.00 5.8680
#> 16 0 0.00 5.6020
#> 17 0 0.00 5.3490
#> 18 0 0.00 5.1070
#> 19 0 0.00 4.8760
#> 20 0 0.00 4.6550
#> 21 0 0.00 4.4450
#> 22 0 0.00 4.2440
#> 23 0 0.00 4.0520
#> 24 0 0.00 3.8690
#> 25 0 0.00 3.6940
#> 26 0 0.00 3.5270
#> 27 0 0.00 3.3670
#> 28 0 0.00 3.2150
#> 29 0 0.00 3.0690
#> 30 0 0.00 2.9310
#> 31 0 0.00 2.7980
#> 32 0 0.00 2.6710
#> 33 0 0.00 2.5510
#> 34 0 0.00 2.4350
#> 35 0 0.00 2.3250
#> 36 0 0.00 2.2200
#> 37 0 0.00 2.1200
#> 38 0 0.00 2.0240
#> 39 0 0.00 1.9320
#> 40 0 0.00 1.8450
#> 41 0 0.00 1.7610
#> 42 0 0.00 1.6820
#> 43 0 0.00 1.6060
#> 44 0 0.00 1.5330
#> 45 0 0.00 1.4640
#> 46 0 0.00 1.3970
#> 47 0 0.00 1.3340
#> 48 0 0.00 1.2740
#> 49 0 0.00 1.2160
#> 50 0 0.00 1.1610
#> 51 0 0.00 1.1090
#> 52 0 0.00 1.0590
#> 53 0 0.00 1.0110
#> 54 0 0.00 0.9650
#> 55 0 0.00 0.9214
#> 56 0 0.00 0.8797
#> 57 0 0.00 0.8399
#> 58 0 0.00 0.8019
#> 59 0 0.00 0.7657
#> 60 0 0.00 0.7310
#> 61 0 0.00 0.6980
#> 62 0 0.00 0.6664
#> 63 0 0.00 0.6363
#> 64 0 0.00 0.6075
#> 65 0 0.00 0.5800
#> 66 0 0.00 0.5538
#> 67 0 0.00 0.5287
#> 68 0 0.00 0.5048
#> 69 0 0.00 0.4820
#> 70 0 0.00 0.4602
#> 71 0 0.00 0.4394
#> 72 0 0.00 0.4195
#> 73 0 0.00 0.4005
#> 74 0 0.00 0.3824
#> 75 0 0.00 0.3651
#> 76 0 0.00 0.3486
#> 77 0 0.00 0.3328
#> 78 0 0.00 0.3178
#> 79 0 0.00 0.3034
#> 80 0 0.00 0.2897
#> 81 0 0.00 0.2766
#> 82 0 0.00 0.2641
#> 83 0 0.00 0.2521
#> 84 0 0.00 0.2407
#> 85 0 0.00 0.2298
#> 86 0 0.00 0.2194
#> 87 0 0.00 0.2095
#> 88 0 0.00 0.2000
#> 89 0 0.00 0.1910
#> 90 0 0.00 0.1824
#> 91 0 0.00 0.1741
#> 92 0 0.00 0.1662
#> 93 0 0.00 0.1587
#> 94 0 0.00 0.1515
#> 95 0 0.00 0.1447
#> 96 0 0.00 0.1381
#> 97 0 0.00 0.1319
#> 98 0 0.00 0.1259
#> 99 0 0.00 0.1202
#> 100 0 0.00 0.1148
#> 101 0 0.00 0.1096
#> 102 0 0.00 0.1046
#> 103 0 0.00 0.0999
#> 104 0 0.00 0.0954
#> 105 1 0.55 0.0911
#> 106 1 1.35 0.0870
#> 107 1 2.09 0.0830
#> 108 1 2.78 0.0793
#> 109 1 3.42 0.0757
#> 110 1 4.02 0.0723
#> 111 1 4.59 0.0690
#> 112 1 5.12 0.0659
#> 113 1 5.62 0.0629
#> 114 1 6.10 0.0600
#> 115 1 6.54 0.0573
#> 116 1 6.96 0.0547
#> 117 1 7.36 0.0523
#> 118 1 7.73 0.0499
#> 119 1 8.09 0.0476
#> 120 1 8.43 0.0455
#> 121 1 8.76 0.0434
#> 122 1 9.07 0.0415
#> 123 1 9.36 0.0396
#> 124 1 9.64 0.0378
#> 125 1 9.91 0.0361
#> 126 1 10.17 0.0345
#> 127 1 10.41 0.0329
#> 128 1 10.65 0.0314
#> 129 1 10.87 0.0300
#> 130 1 11.08 0.0286
#> 131 1 11.29 0.0273
#> 132 1 11.49 0.0261
#> 133 1 11.68 0.0249
#> 134 1 11.86 0.0238
#> 135 1 12.03 0.0227
#> 136 1 12.20 0.0217
#> 137 1 12.36 0.0207
#> 138 1 12.51 0.0198
#> 139 1 12.66 0.0189
#> 140 1 12.80 0.0180
#> 141 1 12.94 0.0172
#> 142 1 13.07 0.0164
#> 143 1 13.20 0.0157
#> 144 1 13.32 0.0150
#> 145 1 13.44 0.0143
#> 146 1 13.55 0.0137
#> 147 1 13.66 0.0130
#> 148 1 13.76 0.0124
#> 149 1 13.86 0.0119
#> 150 1 13.96 0.0114
#> 151 1 14.05 0.0108
#> 152 1 14.14 0.0103
#> 153 1 14.22 0.0099
#> 154 1 14.31 0.0094
#> 155 1 14.38 0.0090
#> 156 1 14.46 0.0086
#> 157 1 14.53 0.0082
#> 158 1 14.60 0.0078
#> 159 1 14.66 0.0075
#> 160 1 14.73 0.0071
#> 161 1 14.79 0.0068
#> 162 1 14.85 0.0065
#> 163 1 14.90 0.0062
#> 164 1 14.95 0.0059
#> 165 1 15.00 0.0057
#> 166 1 15.05 0.0054
#> 167 1 15.09 0.0052
#> 168 1 15.14 0.0049
#> 169 1 15.18 0.0047
#> 170 1 15.22 0.0045
#> 171 1 15.25 0.0043
#> 172 1 15.29 0.0041
#> 173 1 15.32 0.0039
#> 174 1 15.35 0.0037
#> 175 1 15.38 0.0036
#> 176 1 15.41 0.0034
#> 177 1 15.43 0.0033
#> 178 1 15.46 0.0031
#> 179 1 15.48 0.0030
#> 180 1 15.50 0.0028
#> 181 1 15.52 0.0027
#> 182 1 15.54 0.0026
#> 183 1 15.56 0.0025
#> 184 1 15.58 0.0024
#> 185 1 15.59 0.0022
#> 186 1 15.61 0.0021
#> 187 1 15.62 0.0020
#> 188 1 15.63 0.0020
#> 189 1 15.65 0.0019
#> 190 1 15.66 0.0018
#> 191 1 15.67 0.0017
#> 192 1 15.68 0.0016
#> 193 1 15.69 0.0016
#> 194 1 15.70 0.0015
#> 195 1 15.70 0.0014
#> 196 1 15.71 0.0014
#> 197 1 15.72 0.0013
#> 198 1 15.73 0.0012
#> 199 1 15.73 0.0012
#> 200 1 15.74 0.0011
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 2.41100
#> 2 0 0.00 2.30200
#> 3 0 0.00 2.19800
#> 4 0 0.00 2.09900
#> 5 0 0.00 2.00400
#> 6 0 0.00 1.91300
#> 7 0 0.00 1.82700
#> 8 0 0.00 1.74400
#> 9 0 0.00 1.66500
#> 10 0 0.00 1.59000
#> 11 0 0.00 1.51800
#> 12 0 0.00 1.44900
#> 13 0 0.00 1.38400
#> 14 0 0.00 1.32100
#> 15 0 0.00 1.26100
#> 16 0 0.00 1.20400
#> 17 0 0.00 1.15000
#> 18 0 0.00 1.09800
#> 19 0 0.00 1.04800
#> 20 0 0.00 1.00100
#> 21 0 0.00 0.95550
#> 22 0 0.00 0.91230
#> 23 0 0.00 0.87100
#> 24 0 0.00 0.83160
#> 25 0 0.00 0.79400
#> 26 0 0.00 0.75810
#> 27 0 0.00 0.72380
#> 28 0 0.00 0.69110
#> 29 0 0.00 0.65980
#> 30 0 0.00 0.63000
#> 31 0 0.00 0.60150
#> 32 0 0.00 0.57430
#> 33 0 0.00 0.54830
#> 34 0 0.00 0.52350
#> 35 0 0.00 0.49980
#> 36 0 0.00 0.47720
#> 37 0 0.00 0.45560
#> 38 0 0.00 0.43500
#> 39 0 0.00 0.41530
#> 40 0 0.00 0.39660
#> 41 0 0.00 0.37860
#> 42 0 0.00 0.36150
#> 43 0 0.00 0.34510
#> 44 0 0.00 0.32950
#> 45 0 0.00 0.31460
#> 46 0 0.00 0.30040
#> 47 0 0.00 0.28680
#> 48 0 0.00 0.27380
#> 49 0 0.00 0.26150
#> 50 0 0.00 0.24960
#> 51 0 0.00 0.23830
#> 52 0 0.00 0.22760
#> 53 0 0.00 0.21730
#> 54 0 0.00 0.20740
#> 55 0 0.00 0.19810
#> 56 0 0.00 0.18910
#> 57 0 0.00 0.18060
#> 58 0 0.00 0.17240
#> 59 0 0.00 0.16460
#> 60 0 0.00 0.15710
#> 61 0 0.00 0.15000
#> 62 0 0.00 0.14330
#> 63 0 0.00 0.13680
#> 64 0 0.00 0.13060
#> 65 0 0.00 0.12470
#> 66 0 0.00 0.11900
#> 67 0 0.00 0.11370
#> 68 0 0.00 0.10850
#> 69 0 0.00 0.10360
#> 70 0 0.00 0.09892
#> 71 0 0.00 0.09445
#> 72 0 0.00 0.09018
#> 73 0 0.00 0.08610
#> 74 0 0.00 0.08220
#> 75 0 0.00 0.07849
#> 76 0 0.00 0.07494
#> 77 0 0.00 0.07155
#> 78 0 0.00 0.06831
#> 79 0 0.00 0.06522
#> 80 0 0.00 0.06227
#> 81 0 0.00 0.05946
#> 82 0 0.00 0.05677
#> 83 0 0.00 0.05420
#> 84 0 0.00 0.05175
#> 85 0 0.00 0.04941
#> 86 0 0.00 0.04717
#> 87 0 0.00 0.04504
#> 88 0 0.00 0.04300
#> 89 0 0.00 0.04106
#> 90 0 0.00 0.03920
#> 91 0 0.00 0.03743
#> 92 0 0.00 0.03573
#> 93 0 0.00 0.03412
#> 94 0 0.00 0.03257
#> 95 0 0.00 0.03110
#> 96 0 0.00 0.02969
#> 97 0 0.00 0.02835
#> 98 0 0.00 0.02707
#> 99 0 0.00 0.02585
#> 100 0 0.00 0.02468
#> 101 0 0.00 0.02356
#> 102 0 0.00 0.02249
#> 103 0 0.00 0.02148
#> 104 0 0.00 0.02051
#> 105 1 0.55 0.01958
#> 106 1 1.35 0.01869
#> 107 1 2.09 0.01785
#> 108 1 2.78 0.01704
#> 109 1 3.42 0.01627
#> 110 1 4.02 0.01553
#> 111 1 4.59 0.01483
#> 112 1 5.12 0.01416
#> 113 1 5.62 0.01352
#> 114 1 6.10 0.01291
#> 115 1 6.54 0.01232
#> 116 1 6.96 0.01177
#> 117 1 7.36 0.01123
#> 118 1 7.73 0.01073
#> 119 1 8.09 0.01024
#> 120 1 8.43 0.00978
#> 121 1 8.76 0.00934
#> 122 1 9.07 0.00891
#> 123 1 9.36 0.00851
#> 124 1 9.64 0.00813
#> 125 1 9.91 0.00776
#> 126 1 10.17 0.00741
#> 127 1 10.41 0.00707
#> 128 1 10.65 0.00675
#> 129 1 10.87 0.00645
#> 130 1 11.08 0.00616
#> 131 1 11.29 0.00588
#> 132 1 11.49 0.00561
#> 133 1 11.68 0.00536
#> 134 1 11.86 0.00511
#> 135 1 12.03 0.00488
#> 136 1 12.20 0.00466
#> 137 1 12.36 0.00445
#> 138 1 12.51 0.00425
#> 139 1 12.66 0.00406
#> 140 1 12.80 0.00387
#> 141 1 12.94 0.00370
#> 142 1 13.07 0.00353
#> 143 1 13.20 0.00337
#> 144 1 13.32 0.00322
#> 145 1 13.44 0.00307
#> 146 1 13.55 0.00294
#> 147 1 13.66 0.00280
#> 148 1 13.76 0.00268
#> 149 1 13.86 0.00255
#> 150 1 13.96 0.00244
#> 151 1 14.05 0.00233
#> 152 1 14.14 0.00222
#> 153 1 14.22 0.00212
#> 154 1 14.31 0.00203
#> 155 1 14.38 0.00194
#> 156 1 14.46 0.00185
#> 157 1 14.53 0.00176
#> 158 1 14.60 0.00168
#> 159 1 14.66 0.00161
#> 160 1 14.73 0.00154
#> 161 1 14.79 0.00147
#> 162 1 14.85 0.00140
#> 163 1 14.90 0.00134
#> 164 1 14.95 0.00128
#> 165 1 15.00 0.00122
#> 166 1 15.05 0.00116
#> 167 1 15.09 0.00111
#> 168 1 15.14 0.00106
#> 169 1 15.18 0.00101
#> 170 1 15.22 0.00097
#> 171 1 15.25 0.00092
#> 172 1 15.29 0.00088
#> 173 1 15.32 0.00084
#> 174 1 15.35 0.00080
#> 175 1 15.38 0.00077
#> 176 1 15.41 0.00073
#> 177 1 15.43 0.00070
#> 178 1 15.46 0.00067
#> 179 1 15.48 0.00064
#> 180 1 15.50 0.00061
#> 181 1 15.52 0.00058
#> 182 1 15.54 0.00055
#> 183 1 15.56 0.00053
#> 184 1 15.58 0.00051
#> 185 1 15.59 0.00048
#> 186 1 15.61 0.00046
#> 187 1 15.62 0.00044
#> 188 1 15.63 0.00042
#> 189 1 15.65 0.00040
#> 190 1 15.66 0.00038
#> 191 1 15.67 0.00037
#> 192 1 15.68 0.00035
#> 193 1 15.69 0.00033
#> 194 1 15.70 0.00032
#> 195 1 15.70 0.00030
#> 196 1 15.71 0.00029
#> 197 1 15.72 0.00028
#> 198 1 15.73 0.00026
#> 199 1 15.73 0.00025
#> 200 1 15.74 0.00024
#>
#> $`1`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 8699.0
#> 2 0 0.00 8306.0
#> 3 0 0.00 7930.0
#> 4 0 0.00 7571.0
#> 5 0 0.00 7229.0
#> 6 0 0.00 6902.0
#> 7 0 0.00 6590.0
#> 8 0 0.00 6292.0
#> 9 0 0.00 6007.0
#> 10 0 0.00 5735.0
#> 11 0 0.00 5476.0
#> 12 0 0.00 5228.0
#> 13 0 0.00 4992.0
#> 14 0 0.00 4766.0
#> 15 0 0.00 4551.0
#> 16 0 0.00 4345.0
#> 17 0 0.00 4148.0
#> 18 0 0.00 3961.0
#> 19 0 0.00 3781.0
#> 20 0 0.00 3610.0
#> 21 0 0.00 3447.0
#> 22 0 0.00 3291.0
#> 23 0 0.00 3142.0
#> 24 0 0.00 3000.0
#> 25 0 0.00 2865.0
#> 26 0 0.00 2735.0
#> 27 0 0.00 2611.0
#> 28 0 0.00 2493.0
#> 29 0 0.00 2380.0
#> 30 0 0.00 2273.0
#> 31 0 0.00 2170.0
#> 32 0 0.00 2072.0
#> 33 0 0.00 1978.0
#> 34 0 0.00 1889.0
#> 35 0 0.00 1803.0
#> 36 0 0.00 1722.0
#> 37 0 0.00 1644.0
#> 38 0 0.00 1569.0
#> 39 0 0.00 1498.0
#> 40 0 0.00 1431.0
#> 41 0 0.00 1366.0
#> 42 0 0.00 1304.0
#> 43 0 0.00 1245.0
#> 44 0 0.00 1189.0
#> 45 0 0.00 1135.0
#> 46 0 0.00 1084.0
#> 47 0 0.00 1035.0
#> 48 0 0.00 988.0
#> 49 0 0.00 943.3
#> 50 0 0.00 900.6
#> 51 0 0.00 859.9
#> 52 0 0.00 821.0
#> 53 0 0.00 783.9
#> 54 0 0.00 748.4
#> 55 0 0.00 714.6
#> 56 0 0.00 682.2
#> 57 0 0.00 651.4
#> 58 0 0.00 621.9
#> 59 0 0.00 593.8
#> 60 0 0.00 566.9
#> 61 0 0.00 541.3
#> 62 0 0.00 516.8
#> 63 0 0.00 493.4
#> 64 0 0.00 471.1
#> 65 0 0.00 449.8
#> 66 0 0.00 429.5
#> 67 0 0.00 410.1
#> 68 0 0.00 391.5
#> 69 0 0.00 373.8
#> 70 0 0.00 356.9
#> 71 0 0.00 340.7
#> 72 0 0.00 325.3
#> 73 0 0.00 310.6
#> 74 0 0.00 296.6
#> 75 0 0.00 283.2
#> 76 0 0.00 270.4
#> 77 0 0.00 258.1
#> 78 0 0.00 246.5
#> 79 0 0.00 235.3
#> 80 0 0.00 224.7
#> 81 0 0.00 214.5
#> 82 0 0.00 204.8
#> 83 0 0.00 195.5
#> 84 0 0.00 186.7
#> 85 0 0.00 178.2
#> 86 0 0.00 170.2
#> 87 0 0.00 162.5
#> 88 0 0.00 155.1
#> 89 0 0.00 148.1
#> 90 0 0.00 141.4
#> 91 0 0.00 135.0
#> 92 0 0.00 128.9
#> 93 0 0.00 123.1
#> 94 0 0.00 117.5
#> 95 0 0.00 112.2
#> 96 0 0.00 107.1
#> 97 0 0.00 102.3
#> 98 0 0.00 97.7
#> 99 0 0.00 93.2
#> 100 0 0.00 89.0
#> 101 0 0.00 85.0
#> 102 0 0.00 81.2
#> 103 0 0.00 77.5
#> 104 0 0.00 74.0
#> 105 1 0.55 70.6
#> 106 1 1.35 67.4
#> 107 1 2.09 64.4
#> 108 1 2.78 61.5
#> 109 1 3.42 58.7
#> 110 1 4.02 56.0
#> 111 1 4.59 53.5
#> 112 1 5.12 51.1
#> 113 1 5.62 48.8
#> 114 1 6.10 46.6
#> 115 1 6.54 44.5
#> 116 1 6.96 42.5
#> 117 1 7.36 40.5
#> 118 1 7.73 38.7
#> 119 1 8.09 37.0
#> 120 1 8.43 35.3
#> 121 1 8.76 33.7
#> 122 1 9.07 32.2
#> 123 1 9.36 30.7
#> 124 1 9.64 29.3
#> 125 1 9.91 28.0
#> 126 1 10.17 26.7
#> 127 1 10.41 25.5
#> 128 1 10.65 24.4
#> 129 1 10.87 23.3
#> 130 1 11.08 22.2
#> 131 1 11.29 21.2
#> 132 1 11.49 20.2
#> 133 1 11.68 19.3
#> 134 1 11.86 18.4
#> 135 1 12.03 17.6
#> 136 1 12.20 16.8
#> 137 1 12.36 16.1
#> 138 1 12.51 15.3
#> 139 1 12.66 14.6
#> 140 1 12.80 14.0
#> 141 1 12.94 13.3
#> 142 1 13.07 12.7
#> 143 1 13.20 12.2
#> 144 1 13.32 11.6
#> 145 1 13.44 11.1
#> 146 1 13.55 10.6
#> 147 1 13.66 10.1
#> 148 1 13.76 9.7
#> 149 1 13.86 9.2
#> 150 1 13.96 8.8
#> 151 1 14.05 8.4
#> 152 1 14.14 8.0
#> 153 1 14.22 7.7
#> 154 1 14.31 7.3
#> 155 1 14.38 7.0
#> 156 1 14.46 6.7
#> 157 1 14.53 6.4
#> 158 1 14.60 6.1
#> 159 1 14.66 5.8
#> 160 1 14.73 5.5
#> 161 1 14.79 5.3
#> 162 1 14.85 5.0
#> 163 1 14.90 4.8
#> 164 1 14.95 4.6
#> 165 1 15.00 4.4
#> 166 1 15.05 4.2
#> 167 1 15.09 4.0
#> 168 1 15.14 3.8
#> 169 1 15.18 3.7
#> 170 1 15.22 3.5
#> 171 1 15.25 3.3
#> 172 1 15.29 3.2
#> 173 1 15.32 3.0
#> 174 1 15.35 2.9
#> 175 1 15.38 2.8
#> 176 1 15.41 2.6
#> 177 1 15.43 2.5
#> 178 1 15.46 2.4
#> 179 1 15.48 2.3
#> 180 1 15.50 2.2
#> 181 1 15.52 2.1
#> 182 2 15.69 2.0
#> 183 2 15.91 1.9
#> 184 2 16.10 1.8
#> 185 2 16.27 1.7
#> 186 2 16.43 1.7
#> 187 2 16.57 1.6
#> 188 2 16.70 1.5
#> 189 2 16.81 1.4
#> 190 2 16.92 1.4
#> 191 2 17.02 1.3
#> 192 2 17.10 1.3
#> 193 2 17.18 1.2
#> 194 2 17.26 1.1
#> 195 2 17.32 1.1
#> 196 2 17.38 1.0
#> 197 2 17.44 1.0
#> 198 2 17.49 1.0
#> 199 2 17.54 0.9
#> 200 2 17.58 0.9
#>
#> $`0.230324074074074`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 8.9330
#> 2 0 0.00 8.5290
#> 3 0 0.00 8.1440
#> 4 0 0.00 7.7750
#> 5 0 0.00 7.4240
#> 6 0 0.00 7.0880
#> 7 0 0.00 6.7670
#> 8 0 0.00 6.4610
#> 9 0 0.00 6.1690
#> 10 0 0.00 5.8900
#> 11 0 0.00 5.6240
#> 12 0 0.00 5.3690
#> 13 0 0.00 5.1260
#> 14 0 0.00 4.8950
#> 15 0 0.00 4.6730
#> 16 0 0.00 4.4620
#> 17 0 0.00 4.2600
#> 18 0 0.00 4.0670
#> 19 0 0.00 3.8830
#> 20 0 0.00 3.7080
#> 21 0 0.00 3.5400
#> 22 0 0.00 3.3800
#> 23 0 0.00 3.2270
#> 24 0 0.00 3.0810
#> 25 0 0.00 2.9420
#> 26 0 0.00 2.8090
#> 27 0 0.00 2.6820
#> 28 0 0.00 2.5600
#> 29 0 0.00 2.4450
#> 30 0 0.00 2.3340
#> 31 0 0.00 2.2280
#> 32 0 0.00 2.1280
#> 33 0 0.00 2.0310
#> 34 0 0.00 1.9400
#> 35 0 0.00 1.8520
#> 36 0 0.00 1.7680
#> 37 0 0.00 1.6880
#> 38 0 0.00 1.6120
#> 39 0 0.00 1.5390
#> 40 0 0.00 1.4690
#> 41 0 0.00 1.4030
#> 42 0 0.00 1.3390
#> 43 0 0.00 1.2790
#> 44 0 0.00 1.2210
#> 45 0 0.00 1.1660
#> 46 0 0.00 1.1130
#> 47 0 0.00 1.0630
#> 48 0 0.00 1.0150
#> 49 0 0.00 0.9687
#> 50 0 0.00 0.9249
#> 51 0 0.00 0.8831
#> 52 0 0.00 0.8431
#> 53 0 0.00 0.8050
#> 54 0 0.00 0.7686
#> 55 0 0.00 0.7338
#> 56 0 0.00 0.7006
#> 57 0 0.00 0.6689
#> 58 0 0.00 0.6387
#> 59 0 0.00 0.6098
#> 60 0 0.00 0.5822
#> 61 0 0.00 0.5559
#> 62 0 0.00 0.5307
#> 63 0 0.00 0.5067
#> 64 0 0.00 0.4838
#> 65 0 0.00 0.4619
#> 66 0 0.00 0.4410
#> 67 0 0.00 0.4211
#> 68 0 0.00 0.4021
#> 69 0 0.00 0.3839
#> 70 0 0.00 0.3665
#> 71 0 0.00 0.3499
#> 72 0 0.00 0.3341
#> 73 0 0.00 0.3190
#> 74 0 0.00 0.3046
#> 75 0 0.00 0.2908
#> 76 0 0.00 0.2776
#> 77 0 0.00 0.2651
#> 78 0 0.00 0.2531
#> 79 0 0.00 0.2416
#> 80 0 0.00 0.2307
#> 81 0 0.00 0.2203
#> 82 0 0.00 0.2103
#> 83 0 0.00 0.2008
#> 84 0 0.00 0.1917
#> 85 0 0.00 0.1831
#> 86 0 0.00 0.1748
#> 87 0 0.00 0.1669
#> 88 0 0.00 0.1593
#> 89 0 0.00 0.1521
#> 90 0 0.00 0.1452
#> 91 0 0.00 0.1387
#> 92 0 0.00 0.1324
#> 93 0 0.00 0.1264
#> 94 0 0.00 0.1207
#> 95 0 0.00 0.1152
#> 96 0 0.00 0.1100
#> 97 0 0.00 0.1050
#> 98 0 0.00 0.1003
#> 99 0 0.00 0.0958
#> 100 0 0.00 0.0914
#> 101 0 0.00 0.0873
#> 102 0 0.00 0.0833
#> 103 0 0.00 0.0796
#> 104 0 0.00 0.0760
#> 105 0 0.00 0.0725
#> 106 0 0.00 0.0693
#> 107 0 0.00 0.0661
#> 108 0 0.00 0.0631
#> 109 0 0.00 0.0603
#> 110 0 0.00 0.0575
#> 111 0 0.00 0.0550
#> 112 0 0.00 0.0525
#> 113 0 0.00 0.0501
#> 114 0 0.00 0.0478
#> 115 0 0.00 0.0457
#> 116 0 0.00 0.0436
#> 117 0 0.00 0.0416
#> 118 0 0.00 0.0397
#> 119 0 0.00 0.0379
#> 120 0 0.00 0.0362
#> 121 0 0.00 0.0346
#> 122 0 0.00 0.0330
#> 123 0 0.00 0.0315
#> 124 0 0.00 0.0301
#> 125 0 0.00 0.0287
#> 126 0 0.00 0.0274
#> 127 0 0.00 0.0262
#> 128 0 0.00 0.0250
#> 129 0 0.00 0.0239
#> 130 0 0.00 0.0228
#> 131 0 0.00 0.0218
#> 132 0 0.00 0.0208
#> 133 0 0.00 0.0198
#> 134 0 0.00 0.0190
#> 135 0 0.00 0.0181
#> 136 0 0.00 0.0173
#> 137 0 0.00 0.0165
#> 138 0 0.00 0.0158
#> 139 0 0.00 0.0150
#> 140 0 0.00 0.0144
#> 141 0 0.00 0.0137
#> 142 0 0.00 0.0131
#> 143 0 0.00 0.0125
#> 144 0 0.00 0.0119
#> 145 0 0.00 0.0114
#> 146 0 0.00 0.0109
#> 147 0 0.00 0.0104
#> 148 0 0.00 0.0099
#> 149 0 0.00 0.0095
#> 150 0 0.00 0.0090
#> 151 0 0.00 0.0086
#> 152 0 0.00 0.0082
#> 153 0 0.00 0.0079
#> 154 0 0.00 0.0075
#> 155 0 0.00 0.0072
#> 156 0 0.00 0.0068
#> 157 0 0.00 0.0065
#> 158 0 0.00 0.0062
#> 159 0 0.00 0.0060
#> 160 0 0.00 0.0057
#> 161 1 0.21 0.0054
#> 162 1 0.42 0.0052
#> 163 1 0.62 0.0050
#> 164 1 0.83 0.0047
#> 165 1 1.03 0.0045
#> 166 1 1.22 0.0043
#> 167 1 1.40 0.0041
#> 168 1 1.58 0.0039
#> 169 1 1.75 0.0038
#> 170 1 1.91 0.0036
#> 171 1 2.07 0.0034
#> 172 1 2.21 0.0033
#> 173 1 2.35 0.0031
#> 174 1 2.48 0.0030
#> 175 1 2.60 0.0028
#> 176 1 2.71 0.0027
#> 177 1 2.82 0.0026
#> 178 1 2.92 0.0025
#> 179 1 3.01 0.0024
#> 180 1 3.09 0.0023
#> 181 1 3.17 0.0022
#> 182 1 3.24 0.0021
#> 183 1 3.31 0.0020
#> 184 1 3.38 0.0019
#> 185 1 3.44 0.0018
#> 186 1 3.49 0.0017
#> 187 1 3.54 0.0016
#> 188 1 3.59 0.0016
#> 189 1 3.63 0.0015
#> 190 1 3.67 0.0014
#> 191 1 3.71 0.0014
#> 192 1 3.74 0.0013
#> 193 1 3.77 0.0012
#> 194 1 3.80 0.0012
#> 195 1 3.83 0.0011
#> 196 1 3.85 0.0011
#> 197 1 3.88 0.0010
#> 198 1 3.90 0.0010
#> 199 1 3.92 0.0009
#> 200 1 3.94 0.0009
#>
#> $`0.293981481481481`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 8785.0
#> 2 0 0.00 8388.0
#> 3 0 0.00 8009.0
#> 4 0 0.00 7646.0
#> 5 0 0.00 7300.0
#> 6 0 0.00 6970.0
#> 7 0 0.00 6655.0
#> 8 0 0.00 6354.0
#> 9 0 0.00 6067.0
#> 10 0 0.00 5792.0
#> 11 0 0.00 5530.0
#> 12 0 0.00 5280.0
#> 13 0 0.00 5041.0
#> 14 0 0.00 4813.0
#> 15 0 0.00 4596.0
#> 16 0 0.00 4388.0
#> 17 0 0.00 4189.0
#> 18 0 0.00 4000.0
#> 19 0 0.00 3819.0
#> 20 0 0.00 3646.0
#> 21 0 0.00 3481.0
#> 22 0 0.00 3324.0
#> 23 0 0.00 3174.0
#> 24 0 0.00 3030.0
#> 25 0 0.00 2893.0
#> 26 0 0.00 2762.0
#> 27 0 0.00 2637.0
#> 28 0 0.00 2518.0
#> 29 0 0.00 2404.0
#> 30 0 0.00 2295.0
#> 31 0 0.00 2191.0
#> 32 0 0.00 2092.0
#> 33 0 0.00 1998.0
#> 34 0 0.00 1907.0
#> 35 0 0.00 1821.0
#> 36 0 0.00 1739.0
#> 37 0 0.00 1660.0
#> 38 0 0.00 1585.0
#> 39 0 0.00 1513.0
#> 40 0 0.00 1445.0
#> 41 0 0.00 1380.0
#> 42 0 0.00 1317.0
#> 43 0 0.00 1258.0
#> 44 0 0.00 1201.0
#> 45 0 0.00 1146.0
#> 46 0 0.00 1095.0
#> 47 0 0.00 1045.0
#> 48 0 0.00 997.8
#> 49 0 0.00 952.6
#> 50 0 0.00 909.6
#> 51 0 0.00 868.4
#> 52 0 0.00 829.1
#> 53 0 0.00 791.6
#> 54 0 0.00 755.8
#> 55 0 0.00 721.7
#> 56 0 0.00 689.0
#> 57 0 0.00 657.8
#> 58 0 0.00 628.1
#> 59 0 0.00 599.7
#> 60 0 0.00 572.6
#> 61 0 0.00 546.7
#> 62 0 0.00 521.9
#> 63 0 0.00 498.3
#> 64 0 0.00 475.8
#> 65 0 0.00 454.3
#> 66 0 0.00 433.7
#> 67 0 0.00 414.1
#> 68 0 0.00 395.4
#> 69 0 0.00 377.5
#> 70 0 0.00 360.4
#> 71 0 0.00 344.1
#> 72 0 0.00 328.6
#> 73 0 0.00 313.7
#> 74 0 0.00 299.5
#> 75 0 0.00 286.0
#> 76 0 0.00 273.0
#> 77 0 0.00 260.7
#> 78 0 0.00 248.9
#> 79 0 0.00 237.6
#> 80 0 0.00 226.9
#> 81 0 0.00 216.6
#> 82 0 0.00 206.8
#> 83 0 0.00 197.5
#> 84 0 0.00 188.5
#> 85 0 0.00 180.0
#> 86 0 0.00 171.9
#> 87 0 0.00 164.1
#> 88 0 0.00 156.7
#> 89 0 0.00 149.6
#> 90 0 0.00 142.8
#> 91 0 0.00 136.4
#> 92 0 0.00 130.2
#> 93 0 0.00 124.3
#> 94 0 0.00 118.7
#> 95 0 0.00 113.3
#> 96 0 0.00 108.2
#> 97 0 0.00 103.3
#> 98 0 0.00 98.6
#> 99 0 0.00 94.2
#> 100 0 0.00 89.9
#> 101 0 0.00 85.8
#> 102 0 0.00 82.0
#> 103 0 0.00 78.2
#> 104 0 0.00 74.7
#> 105 0 0.00 71.3
#> 106 0 0.00 68.1
#> 107 0 0.00 65.0
#> 108 0 0.00 62.1
#> 109 0 0.00 59.3
#> 110 0 0.00 56.6
#> 111 0 0.00 54.0
#> 112 0 0.00 51.6
#> 113 0 0.00 49.3
#> 114 0 0.00 47.0
#> 115 0 0.00 44.9
#> 116 0 0.00 42.9
#> 117 0 0.00 40.9
#> 118 0 0.00 39.1
#> 119 0 0.00 37.3
#> 120 0 0.00 35.6
#> 121 0 0.00 34.0
#> 122 0 0.00 32.5
#> 123 0 0.00 31.0
#> 124 0 0.00 29.6
#> 125 0 0.00 28.3
#> 126 0 0.00 27.0
#> 127 0 0.00 25.8
#> 128 0 0.00 24.6
#> 129 0 0.00 23.5
#> 130 0 0.00 22.4
#> 131 0 0.00 21.4
#> 132 0 0.00 20.4
#> 133 0 0.00 19.5
#> 134 0 0.00 18.6
#> 135 0 0.00 17.8
#> 136 0 0.00 17.0
#> 137 0 0.00 16.2
#> 138 0 0.00 15.5
#> 139 0 0.00 14.8
#> 140 0 0.00 14.1
#> 141 0 0.00 13.5
#> 142 0 0.00 12.9
#> 143 0 0.00 12.3
#> 144 0 0.00 11.7
#> 145 0 0.00 11.2
#> 146 0 0.00 10.7
#> 147 0 0.00 10.2
#> 148 0 0.00 9.7
#> 149 0 0.00 9.3
#> 150 0 0.00 8.9
#> 151 0 0.00 8.5
#> 152 0 0.00 8.1
#> 153 0 0.00 7.7
#> 154 0 0.00 7.4
#> 155 0 0.00 7.1
#> 156 0 0.00 6.7
#> 157 0 0.00 6.4
#> 158 0 0.00 6.1
#> 159 0 0.00 5.9
#> 160 0 0.00 5.6
#> 161 0 0.00 5.3
#> 162 0 0.00 5.1
#> 163 0 0.00 4.9
#> 164 0 0.00 4.6
#> 165 0 0.00 4.4
#> 166 0 0.00 4.2
#> 167 0 0.00 4.0
#> 168 0 0.00 3.9
#> 169 0 0.00 3.7
#> 170 0 0.00 3.5
#> 171 0 0.00 3.4
#> 172 0 0.00 3.2
#> 173 0 0.00 3.1
#> 174 0 0.00 2.9
#> 175 0 0.00 2.8
#> 176 1 0.05 2.7
#> 177 1 0.26 2.5
#> 178 1 0.44 2.4
#> 179 1 0.60 2.3
#> 180 1 0.75 2.2
#> 181 1 0.88 2.1
#> 182 2 1.28 2.0
#> 183 2 1.66 1.9
#> 184 2 1.99 1.8
#> 185 2 2.28 1.8
#> 186 2 2.54 1.7
#> 187 2 2.78 1.6
#> 188 2 2.99 1.5
#> 189 2 3.17 1.5
#> 190 2 3.34 1.4
#> 191 2 3.49 1.3
#> 192 2 3.63 1.3
#> 193 2 3.75 1.2
#> 194 2 3.86 1.2
#> 195 2 3.96 1.1
#> 196 2 4.05 1.1
#> 197 2 4.13 1.0
#> 198 2 4.20 1.0
#> 199 2 4.27 0.9
#> 200 2 4.33 0.9
#>
#> $`0.377314814814815`
#>
#> Call: glmnet::glmnet(x = mm, y = as.factor(p), family = "binomial", weights = weights, lambda = 10^(seq(4, 0, length.out = 200)) * sum(reg)/length(reg) * sum(p)/sum(weights), standardize = F, penalty.factor = reg)
#>
#> Df %Dev Lambda
#> 1 0 0.00 8776.0
#> 2 0 0.00 8379.0
#> 3 0 0.00 8000.0
#> 4 0 0.00 7639.0
#> 5 0 0.00 7293.0
#> 6 0 0.00 6963.0
#> 7 0 0.00 6648.0
#> 8 0 0.00 6348.0
#> 9 0 0.00 6061.0
#> 10 0 0.00 5786.0
#> 11 0 0.00 5525.0
#> 12 0 0.00 5275.0
#> 13 0 0.00 5036.0
#> 14 0 0.00 4808.0
#> 15 0 0.00 4591.0
#> 16 0 0.00 4383.0
#> 17 0 0.00 4185.0
#> 18 0 0.00 3996.0
#> 19 0 0.00 3815.0
#> 20 0 0.00 3643.0
#> 21 0 0.00 3478.0
#> 22 0 0.00 3321.0
#> 23 0 0.00 3170.0
#> 24 0 0.00 3027.0
#> 25 0 0.00 2890.0
#> 26 0 0.00 2759.0
#> 27 0 0.00 2635.0
#> 28 0 0.00 2515.0
#> 29 0 0.00 2402.0
#> 30 0 0.00 2293.0
#> 31 0 0.00 2189.0
#> 32 0 0.00 2090.0
#> 33 0 0.00 1996.0
#> 34 0 0.00 1905.0
#> 35 0 0.00 1819.0
#> 36 0 0.00 1737.0
#> 37 0 0.00 1658.0
#> 38 0 0.00 1583.0
#> 39 0 0.00 1512.0
#> 40 0 0.00 1443.0
#> 41 0 0.00 1378.0
#> 42 0 0.00 1316.0
#> 43 0 0.00 1256.0
#> 44 0 0.00 1199.0
#> 45 0 0.00 1145.0
#> 46 0 0.00 1093.0
#> 47 0 0.00 1044.0
#> 48 0 0.00 996.8
#> 49 0 0.00 951.7
#> 50 0 0.00 908.6
#> 51 0 0.00 867.5
#> 52 0 0.00 828.3
#> 53 0 0.00 790.8
#> 54 0 0.00 755.1
#> 55 0 0.00 720.9
#> 56 0 0.00 688.3
#> 57 0 0.00 657.2
#> 58 0 0.00 627.5
#> 59 0 0.00 599.1
#> 60 0 0.00 572.0
#> 61 0 0.00 546.1
#> 62 0 0.00 521.4
#> 63 0 0.00 497.8
#> 64 0 0.00 475.3
#> 65 0 0.00 453.8
#> 66 0 0.00 433.3
#> 67 0 0.00 413.7
#> 68 0 0.00 395.0
#> 69 0 0.00 377.1
#> 70 0 0.00 360.1
#> 71 0 0.00 343.8
#> 72 0 0.00 328.2
#> 73 0 0.00 313.4
#> 74 0 0.00 299.2
#> 75 0 0.00 285.7
#> 76 0 0.00 272.8
#> 77 0 0.00 260.4
#> 78 0 0.00 248.6
#> 79 0 0.00 237.4
#> 80 0 0.00 226.7
#> 81 0 0.00 216.4
#> 82 0 0.00 206.6
#> 83 0 0.00 197.3
#> 84 0 0.00 188.4
#> 85 0 0.00 179.8
#> 86 0 0.00 171.7
#> 87 0 0.00 163.9
#> 88 0 0.00 156.5
#> 89 0 0.00 149.4
#> 90 0 0.00 142.7
#> 91 0 0.00 136.2
#> 92 0 0.00 130.1
#> 93 0 0.00 124.2
#> 94 0 0.00 118.6
#> 95 0 0.00 113.2
#> 96 0 0.00 108.1
#> 97 0 0.00 103.2
#> 98 0 0.00 98.5
#> 99 0 0.00 94.1
#> 100 0 0.00 89.8
#> 101 0 0.00 85.8
#> 102 0 0.00 81.9
#> 103 0 0.00 78.2
#> 104 0 0.00 74.6
#> 105 0 0.00 71.3
#> 106 0 0.00 68.0
#> 107 0 0.00 65.0
#> 108 0 0.00 62.0
#> 109 0 0.00 59.2
#> 110 0 0.00 56.5
#> 111 0 0.00 54.0
#> 112 0 0.00 51.5
#> 113 0 0.00 49.2
#> 114 0 0.00 47.0
#> 115 0 0.00 44.9
#> 116 0 0.00 42.8
#> 117 0 0.00 40.9
#> 118 0 0.00 39.0
#> 119 0 0.00 37.3
#> 120 0 0.00 35.6
#> 121 0 0.00 34.0
#> 122 0 0.00 32.5
#> 123 0 0.00 31.0
#> 124 0 0.00 29.6
#> 125 0 0.00 28.2
#> 126 0 0.00 27.0
#> 127 0 0.00 25.7
#> 128 0 0.00 24.6
#> 129 0 0.00 23.5
#> 130 0 0.00 22.4
#> 131 0 0.00 21.4
#> 132 0 0.00 20.4
#> 133 0 0.00 19.5
#> 134 0 0.00 18.6
#> 135 0 0.00 17.8
#> 136 0 0.00 17.0
#> 137 0 0.00 16.2
#> 138 0 0.00 15.5
#> 139 0 0.00 14.8
#> 140 0 0.00 14.1
#> 141 0 0.00 13.5
#> 142 0 0.00 12.9
#> 143 0 0.00 12.3
#> 144 0 0.00 11.7
#> 145 0 0.00 11.2
#> 146 0 0.00 10.7
#> 147 0 0.00 10.2
#> 148 0 0.00 9.7
#> 149 0 0.00 9.3
#> 150 0 0.00 8.9
#> 151 0 0.00 8.5
#> 152 0 0.00 8.1
#> 153 0 0.00 7.7
#> 154 0 0.00 7.4
#> 155 0 0.00 7.0
#> 156 0 0.00 6.7
#> 157 0 0.00 6.4
#> 158 0 0.00 6.1
#> 159 0 0.00 5.9
#> 160 0 0.00 5.6
#> 161 1 0.21 5.3
#> 162 1 0.42 5.1
#> 163 1 0.62 4.9
#> 164 1 0.83 4.6
#> 165 1 1.03 4.4
#> 166 1 1.22 4.2
#> 167 1 1.40 4.0
#> 168 1 1.58 3.9
#> 169 1 1.75 3.7
#> 170 1 1.91 3.5
#> 171 1 2.07 3.4
#> 172 1 2.21 3.2
#> 173 1 2.35 3.1
#> 174 1 2.48 2.9
#> 175 1 2.60 2.8
#> 176 1 2.71 2.7
#> 177 1 2.82 2.5
#> 178 2 2.95 2.4
#> 179 2 3.38 2.3
#> 180 2 3.76 2.2
#> 181 2 4.11 2.1
#> 182 2 4.42 2.0
#> 183 2 4.71 1.9
#> 184 2 4.96 1.8
#> 185 2 5.19 1.8
#> 186 2 5.40 1.7
#> 187 2 5.59 1.6
#> 188 2 5.76 1.5
#> 189 2 5.92 1.5
#> 190 2 6.06 1.4
#> 191 2 6.18 1.3
#> 192 2 6.30 1.3
#> 193 2 6.41 1.2
#> 194 2 6.50 1.2
#> 195 2 6.59 1.1
#> 196 2 6.67 1.1
#> 197 2 6.74 1.0
#> 198 2 6.81 1.0
#> 199 2 6.87 0.9
#> 200 2 6.92 0.9
#>
esm_max_t1$predictors
#> # A tibble: 1 × 8
#> c1 c2 c3 c4 c5 c6 c7 c8
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 aet cwd tmin ppt_djf ppt_jja pH awc depth
esm_max_t1$performance
#> # A tibble: 7 × 33
#> model threshold thr_value n_presences n_absences TPR_mean TPR_sd TNR_mean
#> <chr> <chr> <dbl> <int> <int> <dbl> <dbl> <dbl>
#> 1 esm_max equal_sens_… 0.555 10 10 1 0 1
#> 2 esm_max lpt 0.551 10 10 1 0 1
#> 3 esm_max max_fpb 0.551 10 10 1 0 1
#> 4 esm_max max_jaccard 0.551 10 10 1 0 1
#> 5 esm_max max_sens_sp… 0.581 10 10 1 0 1
#> 6 esm_max max_sorensen 0.551 10 10 1 0 1
#> 7 esm_max sensitivity 0.592 10 10 1 0 1
#> # ℹ 25 more variables: TNR_sd <dbl>, W_TPR_TNR_mean <dbl>, W_TPR_TNR_sd <dbl>,
#> # SORENSEN_mean <dbl>, SORENSEN_sd <dbl>, JACCARD_mean <dbl>,
#> # JACCARD_sd <dbl>, FPB_mean <dbl>, FPB_sd <dbl>, OR_mean <dbl>, OR_sd <dbl>,
#> # TSS_mean <dbl>, TSS_sd <dbl>, KAPPA_mean <dbl>, KAPPA_sd <dbl>,
#> # MCC_mean <dbl>, MCC_sd <dbl>, AUC_mean <dbl>, AUC_sd <dbl>,
#> # BOYCE_mean <dbl>, BOYCE_sd <dbl>, CRPS_mean <dbl>, CRPS_sd <dbl>,
#> # IMAE_mean <dbl>, IMAE_sd <dbl>
esm_max_t1$performance_part
#> # A tibble: 42 × 21
#> model replicates part threshold thr_value n_presences n_absences TPR TNR
#> <chr> <chr> <chr> <chr> <dbl> <int> <int> <dbl> <dbl>
#> 1 esm_… .part1 1 max_sore… 0.551 4 4 1 1
#> 2 esm_… .part1 1 max_jacc… 0.551 4 4 1 1
#> 3 esm_… .part1 1 max_fpb 0.551 4 4 1 1
#> 4 esm_… .part1 1 max_sens… 0.551 4 4 1 1
#> 5 esm_… .part1 1 equal_se… 0.551 4 4 1 1
#> 6 esm_… .part1 1 lpt 0.551 4 4 1 1
#> 7 esm_… .part1 1 sensitiv… 0.551 4 4 1 1
#> 8 esm_… .part1 2 max_sore… 0.715 3 3 1 1
#> 9 esm_… .part1 2 max_jacc… 0.715 3 3 1 1
#> 10 esm_… .part1 2 max_fpb 0.715 3 3 1 1
#> # ℹ 32 more rows
#> # ℹ 12 more variables: W_TPR_TNR <dbl>, SORENSEN <dbl>, JACCARD <dbl>,
#> # FPB <dbl>, OR <dbl>, TSS <dbl>, KAPPA <dbl>, MCC <dbl>, AUC <dbl>,
#> # BOYCE <dbl>, CRPS <dbl>, IMAE <dbl>
# }