comb_GLMNET.RdComputes forecast combination weights using GLMNET Regression (OLS) regression.
comb_GLMNET(x, custom_error = NULL)An object of class 'foreccomb'. Contains training set (actual values + matrix of model forecasts) and optionally a test set.
Returns an object of class ForecastComb::foreccomb_res with the following components:
Returns the best-fit forecast combination method.
Returns the individual input models that were used for the forecast combinations.
Returns the combination weights obtained by applying the combination method to the training set.
Returns the intercept of the linear regression.
Returns the fitted values of the combination method for the training set.
Returns range of summary measures of the forecast accuracy for the training set.
Returns forecasts produced by the combination method for the test set. Only returned if input included a forecast matrix for the test set.
Returns range of summary measures of the forecast accuracy for the test set. Only returned if input included a forecast matrix and a vector of actual values for the test set.
Returns the data forwarded to the method.
The function integrates the GLMNET Regression forecast combination implementation of the ForecastCombinations package into ForecastComb.
The results are stored in an object of class 'ForecastComb::foreccomb_res', for which separate plot and summary functions are provided.
Forecast_comb,
foreccomb,
plot.ForecastComb::foreccomb_res,
summary.ForecastComb::foreccomb_res,
accuracy
obs <- rnorm(100)
preds <- matrix(rnorm(1000, 1), 100, 10)
train_o<-obs[1:80]
train_p<-preds[1:80,]
test_o<-obs[81:100]
test_p<-preds[81:100,]
data<-ForecastComb::foreccomb(train_o, train_p, test_o, test_p)
#> Registered S3 methods overwritten by 'ForecastComb':
#> method from
#> plot.foreccomb_res ahead
#> predict.foreccomb_res ahead
#> print.foreccomb_res_summary ahead
#> summary.foreccomb_res ahead
ahead::comb_GLMNET(data)
#> $Method
#> [1] "GLMNET Regression Regression"
#>
#> $Models
#> [1] "Series 1" "Series 2" "Series 3" "Series 4" "Series 5" "Series 6"
#> [7] "Series 7" "Series 8" "Series 9" "Series 10"
#>
#> $Fitted
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> [1] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [7] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [13] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [19] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [25] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [31] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [37] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [43] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [49] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [55] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [61] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [67] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [73] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [79] -0.1117191 -0.1117191
#>
#> $Accuracy_Train
#> ME RMSE MAE MPE MAPE
#> Test set 8.152523e-18 0.9850397 0.8206731 -1.722997e-14 734.586
#>
#> $Input_Data
#> $Input_Data$Actual_Train
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> [1] 0.21246298 0.34654149 0.39365925 1.47734294 0.78351580 1.30828925
#> [7] -1.23421588 -1.94728232 -0.52346389 0.45521213 -1.29385864 -0.13065036
#> [13] -1.90498277 1.03092252 -0.41481013 1.07306729 -0.45833007 0.52027485
#> [19] 0.04826655 -0.97844830 1.80817185 1.28166798 -0.98606533 0.51245947
#> [25] -0.90940181 -1.26700565 -1.22679206 0.02029513 -1.01276743 -0.35192885
#> [31] -0.76421131 -0.23535934 -0.08883041 -0.99738217 0.74651348 0.31164607
#> [37] -0.13175415 1.46999303 0.85676897 0.77175650 -0.53605677 -0.55875190
#> [43] -0.87708205 0.28935714 -0.07299547 -1.25676105 -0.56292608 -0.22946602
#> [49] -0.88345122 -1.69699786 0.12526704 -0.53319322 1.31590195 1.71816602
#> [55] -0.56764815 -0.01546457 -0.68217052 1.97025092 0.42827299 -1.80160435
#> [61] 1.04800029 0.50220940 -0.21879221 -1.07714866 0.76300604 1.43507860
#> [67] -0.36209006 -1.51169957 -0.41839249 -0.97105126 1.06908434 -1.20466020
#> [73] 0.04351017 -1.37623561 0.82465795 1.54098071 -1.17290347 -1.96460923
#> [79] 0.25631701 -0.28672556
#>
#> $Input_Data$Forecasts_Train
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> Series 1 Series 2 Series 3 Series 4 Series 5 Series 6
#> 1 -0.51224649 -0.45001047 -0.04356552 0.49672575 1.342560929 0.2492455
#> 2 0.14814473 -0.13973760 1.68982356 2.16069663 0.585023352 3.2096813
#> 3 0.42116313 -0.22086192 0.38283045 0.60330473 0.995723574 2.0879933
#> 4 2.03205051 0.60917693 0.34857490 1.33375854 0.696594835 2.4126182
#> 5 0.92876173 0.26736926 0.09022575 0.69213004 1.115802475 1.3819462
#> 6 3.04869307 0.46667814 -0.47023098 0.67510049 -0.168167855 1.3798269
#> 7 -0.01670488 0.77739539 1.57705557 0.24686180 1.422663130 0.1313116
#> 8 1.19229407 0.42456934 -0.13961902 2.44035834 1.643716141 1.1473217
#> 9 3.83560789 0.14381731 0.62390798 0.33472402 0.274511877 1.3820358
#> 10 2.94256044 0.90928543 0.05242335 1.43965059 1.395673155 -0.6108412
#> 11 2.00662228 0.41871639 -0.21915767 1.01330597 1.631793097 1.9098183
#> 12 -0.05893329 0.58376612 0.31378307 -0.28071021 1.294720804 1.6208598
#> 13 1.87258096 0.63975141 0.41333250 3.34476027 2.149954872 0.5742539
#> 14 1.31942950 0.41168193 -1.43781667 -0.01120514 1.191696444 -0.2641612
#> 15 -1.42745569 0.38535309 0.43032424 2.34857465 -0.577006210 -0.3302695
#> 16 0.68097355 0.08607968 1.01535604 3.92565860 1.042551502 0.7973448
#> 17 -0.54929210 3.39886338 0.96615881 1.41079006 0.909058278 1.8142556
#> 18 0.26903532 0.63863997 0.47705673 0.90237294 -0.052101051 0.4475389
#> 19 -0.59096489 0.82739111 0.16045587 1.46501919 0.559512999 0.5807107
#> 20 1.82751821 1.97747728 -1.31002416 0.39021709 1.504383799 2.7818016
#> 21 -0.30409444 2.11577397 -0.31194987 -0.25575659 0.277187872 1.2074286
#> 22 0.76423056 -0.40926319 2.02014971 0.18586756 0.671832400 0.5648789
#> 23 -0.46327023 1.31880313 0.10677493 2.19776858 1.024232743 -0.2410406
#> 24 0.82644537 0.03593709 0.74705950 0.71658948 2.076289908 0.2175615
#> 25 2.49880853 0.16122984 1.76182756 -0.15979461 1.698898737 1.8615229
#> 26 1.77395286 0.24876900 1.55341466 1.29237882 0.006720057 2.2411506
#> 27 1.88703392 1.07596251 0.94970087 2.25341448 0.468120975 2.9744071
#> 28 1.80868715 -0.72699460 -0.08043250 -0.57670050 0.403469569 0.7127342
#> 29 0.71406128 0.37437843 1.45171502 2.20283294 -0.944111080 -0.4423329
#> 30 0.50719857 3.96043557 0.22430368 2.90066688 0.122209729 0.5007546
#> 31 0.78745499 2.74338931 3.82509354 0.75670216 1.523595920 -0.6317171
#> 32 2.13824044 0.26162004 2.40620074 1.16933969 1.825201055 0.9777294
#> 33 0.12591135 -0.02482892 2.98256826 0.97612728 -0.316131496 1.2851729
#> 34 0.66262513 1.37844030 2.74654722 1.89161902 1.664840362 2.1255083
#> 35 0.95746703 1.66636889 0.92091355 2.15866270 1.018087791 -0.0436378
#> 36 1.83593021 1.51711481 0.03097920 1.10636697 1.318381403 2.8453819
#> 37 0.42166429 1.54798361 0.48859001 0.61440349 1.093942984 2.1036600
#> 38 2.60748334 0.55658815 1.57352288 -0.38283928 0.913020535 -0.5784783
#> 39 -0.44537763 1.35422746 2.16659217 2.01950238 0.927497339 0.2199252
#> 40 -2.10980677 -0.67440983 1.35864979 0.65165778 0.634377381 -1.1616889
#> 41 -0.02792231 1.02039476 1.39661174 0.51686901 1.316231629 0.8831710
#> 42 2.20162463 4.08873241 2.50387667 0.52400042 0.941787020 -0.3954883
#> 43 0.61120890 0.65401508 0.80915630 0.97833324 -0.833536750 1.6868389
#> 44 1.40713129 0.86971144 1.69736083 3.66848160 -0.253171507 1.8546134
#> 45 0.43506876 -0.29878593 0.93056425 0.65650617 2.300710302 0.6608462
#> 46 3.11252635 0.63837911 0.59845364 0.30094140 1.214454547 2.2317328
#> 47 0.98258419 0.84476315 1.56909694 3.09784693 2.460530774 -0.2449140
#> 48 -0.51533987 2.73615505 0.99833890 1.92780288 1.347533884 0.5277143
#> 49 1.16338170 1.70453907 -1.02503484 1.73945658 -0.433631732 0.5907109
#> 50 0.76983392 2.65794680 -1.55430294 1.02250781 2.027774838 2.6780763
#> 51 -0.40979524 0.78997336 1.68389429 -0.75173271 1.540976733 1.4424340
#> 52 1.80953679 1.46116922 1.87989210 0.86840735 1.797279028 0.8689834
#> 53 0.59782884 0.63721382 1.41579092 -0.09235797 0.916971700 0.9957288
#> 54 1.94697832 0.09576814 0.15830173 1.98862535 0.281072454 1.2991383
#> 55 -0.40579690 1.69824676 0.66247189 0.91479221 -0.957200924 1.6059357
#> 56 0.55531492 0.61687922 1.05125017 1.17692228 0.481208446 1.3023292
#> 57 0.12598998 0.39096982 0.47040466 0.74115120 2.465877355 -0.0382106
#> 58 -0.15048134 1.71117562 1.07097509 1.28114802 1.833655053 1.0553410
#> 59 0.66754210 1.22792817 3.13065557 2.04042540 2.462956363 2.8339819
#> 60 -0.21583264 -0.01856450 1.23656450 0.27879727 0.207064875 0.9775166
#> 61 1.43357904 1.98220029 0.44150324 -0.79417752 2.131229790 1.7001937
#> 62 3.13069262 1.12397608 -0.21197306 0.16399455 0.378987821 0.3021621
#> 63 0.68319517 1.46362652 1.52183245 1.63723724 0.960678951 0.5280580
#> 64 1.11416687 -0.31074769 1.23143860 1.41895388 1.258464201 1.8987766
#> 65 2.21020731 2.26253768 -0.78612947 0.98557603 0.724862751 0.4468699
#> 66 2.16259285 1.65767369 0.48160400 1.11889294 0.677505053 1.9215916
#> 67 1.59844782 0.01272479 1.54234252 0.48803958 2.025800420 0.8587595
#> 68 2.06611816 0.91532957 -0.05236183 2.38681306 1.447539521 0.4213179
#> 69 1.19023111 -0.39491782 2.07930898 0.44763516 1.608121466 1.4486015
#> 70 -0.38259156 -0.43063410 -0.18871693 0.45736696 1.852043700 -0.1758736
#> 71 0.84524063 -0.11834815 2.80312720 0.13296658 -1.056098198 1.9357264
#> 72 1.29021470 0.11247585 2.48328669 1.53909984 1.090880899 1.4386815
#> 73 1.57515604 0.79982783 2.16271231 1.25422663 -0.757503495 1.7147743
#> 74 1.04664277 0.24716669 0.65289625 1.53735052 1.187252226 -0.1225274
#> 75 0.78898272 2.59866017 1.09260415 -1.30108159 1.315960755 2.6014269
#> 76 -0.60101254 1.71181570 1.72949898 1.17933120 0.693265698 2.3862434
#> 77 1.06245486 1.21842761 0.67362644 0.73108285 1.685010922 -1.1961236
#> 78 1.56749571 -0.06248774 0.39340724 -1.02425130 1.055290209 1.1946924
#> 79 -0.48691479 0.41800826 1.50648186 0.31245124 1.128377607 1.1531974
#> 80 1.08417137 2.19576815 3.22508406 0.06058075 1.420187430 0.7934068
#> Series 7 Series 8 Series 9 Series 10
#> 1 0.534359711 0.74887140 0.64574652 1.37583877
#> 2 1.371595568 2.47629322 -0.24925701 1.18046433
#> 3 -0.441438064 0.80158270 1.88718009 1.80332034
#> 4 1.008632285 1.52850293 1.86668223 -0.35926430
#> 5 0.259740507 -0.95646632 0.09794211 1.46548216
#> 6 0.389195859 -0.25078512 -0.78418046 1.70874956
#> 7 1.280357020 -0.26062242 -0.16608485 -1.00703705
#> 8 0.327951391 0.33925115 0.04657371 1.59477064
#> 9 1.517053713 1.86498688 1.14684347 0.40758604
#> 10 0.991379599 -0.18112342 0.56246376 0.47963096
#> 11 1.021423230 2.19889063 0.71695426 1.80648613
#> 12 0.018164608 0.65444163 0.83360122 0.98052640
#> 13 0.139422095 2.56768853 -0.60576800 1.86986134
#> 14 -0.538718146 0.87430709 1.00075437 0.51726970
#> 15 -0.052349224 1.36422736 0.05070988 2.47587353
#> 16 1.382667603 0.55790668 -0.09003089 2.37762385
#> 17 1.101454248 1.27283355 1.48150253 0.12618098
#> 18 0.170870826 2.15943489 -0.08080197 0.99608920
#> 19 1.724917369 0.85920274 1.13293460 -0.23419222
#> 20 1.359129923 2.68359758 1.27218816 3.14462344
#> 21 0.700195965 1.44084795 -2.07589617 1.69508233
#> 22 1.279544354 3.06475591 2.10154030 1.31512170
#> 23 -0.209346327 2.72700367 1.77477927 -1.24898746
#> 24 -1.057795950 1.16803017 1.58597557 2.76054532
#> 25 -0.292499229 -0.51710571 2.34136160 1.73887327
#> 26 0.760523698 0.61671879 2.10740958 0.96725450
#> 27 0.661594327 1.14215636 2.51533419 1.10704267
#> 28 2.232646859 1.04128671 0.59165601 -1.29676974
#> 29 1.440331160 2.44206310 0.84074769 0.25927842
#> 30 0.684750407 2.04126708 1.69573114 1.30380583
#> 31 -1.300531576 2.02122596 0.80822946 0.80993112
#> 32 2.976385773 0.91095263 1.29556760 1.58412320
#> 33 1.500590831 0.65332199 0.98785289 1.99008659
#> 34 1.404122773 2.48396032 1.80188504 1.01949267
#> 35 0.524800828 1.45825602 0.66321085 2.85306627
#> 36 0.636179629 0.87476161 2.42695779 0.26315595
#> 37 2.195751511 1.28988906 -0.08615995 0.34269923
#> 38 -0.268129170 1.70093990 1.13890944 0.04705632
#> 39 0.045722993 -0.95971392 -1.81916410 1.48908031
#> 40 0.228396281 -0.55364809 0.33179865 1.13193926
#> 41 0.687065879 1.34295259 1.34606255 2.37530412
#> 42 1.089041596 2.18933870 2.44764296 0.48789165
#> 43 1.734033739 0.81383923 1.86118161 0.13340987
#> 44 0.708164413 1.52141987 0.51995361 2.43640337
#> 45 2.444023898 3.02275798 1.69754353 -0.07223254
#> 46 0.210728104 1.34521234 -0.41117217 0.78563105
#> 47 0.320065208 2.02728689 -0.90164159 1.94952539
#> 48 0.009636034 2.12041789 1.81355612 1.22988595
#> 49 1.819377971 1.54446368 2.85103405 3.02495768
#> 50 -0.033395062 2.38702327 1.34559175 0.19901856
#> 51 0.672423876 2.02977629 0.41420119 1.04139377
#> 52 0.097538780 -0.11982175 0.47375606 1.49967296
#> 53 1.158148010 1.56407313 2.16872676 0.39667389
#> 54 1.553856358 1.03879134 1.85567685 1.84445523
#> 55 -0.844843975 1.33528302 0.14291118 2.56586898
#> 56 1.218139750 0.99876620 1.18136005 -1.03862401
#> 57 0.421953087 0.59012602 1.26702064 0.12510429
#> 58 3.342368705 1.16990820 1.83690933 1.63603292
#> 59 0.303235944 2.03293303 1.38583408 1.30552568
#> 60 1.507077064 1.38209644 1.18668288 1.80020114
#> 61 1.971718081 1.20554709 1.12827341 1.77163883
#> 62 0.704109529 2.73418927 0.49565900 -0.08137332
#> 63 2.194146112 1.69154016 -1.51837926 0.82416586
#> 64 2.339869106 0.84938961 0.65899934 0.21906926
#> 65 1.272863046 2.01305264 1.99752775 0.79881519
#> 66 0.049069937 1.14524107 0.86911021 1.09320597
#> 67 0.004454819 -0.37299377 1.17644299 0.64982987
#> 68 1.718113139 3.84010622 0.38278262 0.37053627
#> 69 1.363283467 0.91010819 -0.06164738 1.75330348
#> 70 -0.008814099 1.23566493 -0.49907385 0.20440954
#> 71 1.711324686 0.08029131 1.13859542 -0.16266154
#> 72 1.500176042 -0.30282799 2.04868627 1.41499266
#> 73 1.117285273 -0.46876571 1.70568032 1.57744578
#> 74 0.603528940 2.28714201 0.51927861 0.53989841
#> 75 1.123615066 3.26651575 1.92081024 2.60172926
#> 76 2.153517176 2.16707695 3.47491405 0.96899095
#> 77 1.766724722 2.05378115 -0.05763120 0.05441693
#> 78 1.440432366 2.30349739 0.93678985 0.97606516
#> 79 2.289475857 0.52777302 0.93118401 1.40147659
#> 80 -0.097664625 0.46232917 1.29727867 2.82405475
#>
#> $Input_Data$Actual_Test
#> [1] -0.71176453 -0.66882473 0.26514018 0.35736087 0.55969324 -0.01791227
#> [7] 0.09512255 0.72057255 -0.71998649 1.95075783 1.41910457 0.30752598
#> [13] -0.20342352 0.31717526 -1.97646603 2.04850319 1.73158497 0.95569161
#> [19] 0.36688851 -0.34733207
#>
#> $Input_Data$Forecasts_Test
#> Series 1 Series 2 Series 3 Series 4 Series 5 Series 6
#> [1,] 1.98970524 -0.06972209 -0.17519993 -0.08963597 1.22618483 1.5403178
#> [2,] 1.66048361 1.22527583 1.35372425 1.16801798 0.38702843 1.5205716
#> [3,] 0.28245723 1.34256765 0.34182397 0.53509625 1.07642826 1.0031732
#> [4,] 1.93228135 2.02601541 1.12347825 1.02674694 1.99041518 2.1090700
#> [5,] 0.08904543 0.29750028 -0.52106977 0.36232223 0.98951065 1.3469187
#> [6,] 1.36706793 2.56054273 -1.27246299 1.80267154 1.06257711 0.9342006
#> [7,] 3.47968498 -0.51718665 1.23956303 0.92918661 0.30635499 2.6519710
#> [8,] 0.07663246 0.48109044 0.83246791 2.54976272 2.23440153 1.0185490
#> [9,] 1.09983687 1.56688617 -0.20368886 0.84867498 0.03988159 2.7567787
#> [10,] 1.15594362 0.84174747 0.32226386 1.42581574 1.44727570 0.4626972
#> [11,] 0.06586154 -0.02944939 1.15600750 1.37561822 0.11327062 2.9681009
#> [12,] 0.69742196 1.07340678 0.88186745 1.17956681 0.17182912 0.7194141
#> [13,] 2.37278458 1.10256911 0.92147587 -0.10224850 2.36715898 0.7558930
#> [14,] 0.81636799 1.53859621 1.37124201 -0.17638867 2.15828150 1.9460339
#> [15,] 0.06002841 2.20379306 1.11591549 1.49210755 2.86901934 1.0844945
#> [16,] 0.53668684 -1.12019934 2.63506201 1.75204176 0.70924834 1.1935694
#> [17,] 0.66510522 0.92435555 1.83352894 0.60180039 0.79545659 2.7160679
#> [18,] -0.90602233 1.51026127 0.08054463 1.03499972 0.73362708 2.5395535
#> [19,] 0.68598503 1.85971785 1.52776827 -0.49063801 0.58653191 0.1254105
#> [20,] 0.54961692 1.19632347 1.42148372 1.37872081 0.84802381 2.6125470
#> Series 7 Series 8 Series 9 Series 10
#> [1,] -0.08757916 0.4012063 1.41012752 1.82241453
#> [2,] 1.90335746 1.8022300 0.08178475 -0.01909239
#> [3,] 1.79836738 3.3541273 0.60705341 0.14660848
#> [4,] -0.09816279 -0.4829956 0.81605490 0.50704747
#> [5,] 2.68509112 -0.4986725 0.86204635 0.85613497
#> [6,] 2.23086575 1.3069169 0.61660671 2.47550628
#> [7,] 1.19627115 0.4889441 2.45131269 1.77218197
#> [8,] -0.17320187 0.6827512 0.72990977 0.11548316
#> [9,] -0.89307008 0.6477640 1.33531778 0.33230064
#> [10,] 0.29767395 -0.1863128 3.20890880 1.94706955
#> [11,] 1.89857609 0.3088158 0.73655855 0.83200103
#> [12,] 0.08074218 0.1690462 -0.63592871 0.92244004
#> [13,] 0.78792222 1.0439330 0.13271685 0.99419208
#> [14,] 2.18802367 2.5170329 1.86139163 0.77281665
#> [15,] -0.70798314 1.2062636 0.75068746 1.06544539
#> [16,] 2.48165453 1.3915295 0.37799969 -0.45246111
#> [17,] 1.14801899 0.3124118 1.17968231 -0.82559605
#> [18,] 0.80737181 0.7450269 3.19665927 1.46048753
#> [19,] 2.22461045 2.4529380 0.03733679 0.29764980
#> [20,] 0.57535708 0.1300686 0.75063812 1.21451091
#>
#>
#> $Predict
#> function (object, newpreds)
#> {
#> return(drop(predict(object$lin_model, newpreds, s = "lambda.min")))
#> }
#> <bytecode: 0x7f995ffd9320>
#> <environment: namespace:ahead>
#>
#> $Intercept
#> [1] -0.1117191
#>
#> $Weights
#> [1] 0 0 0 0 0 0 0 0 0 0
#>
#> $Forecasts_Test
#> [1] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [7] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [13] -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191 -0.1117191
#> [19] -0.1117191 -0.1117191
#>
#> $Accuracy_Test
#> ME RMSE MAE MPE MAPE
#> Test set -0.4341897 1.061331 0.829938 388.644 742.879
#>
#> $lin_model
#>
#> Call: glmnet::cv.glmnet(x = as.matrix(prediction_matrix), y = as.numeric(observed_vector))
#>
#> Measure: Mean-Squared Error
#>
#> Lambda Index Measure SE Nonzero
#> min 0.1553 1 1.055 0.1147 0
#> 1se 0.1553 1 1.055 0.1147 0
#>
#> attr(,"class")
#> [1] "foreccomb_res" "comb_GLMNET"