Convert glmnet::cv.glmnet to data.frame
# S3 method for class 'cv.glmnet'
fortify(model, data = NULL, ...)glmnet::cv.glmnet instance
original dataset, if needed
other arguments passed to methods
data.frame
if (requireNamespace("survival", quietly = TRUE)) {
fortify(glmnet::cv.glmnet(data.matrix(Orange[-3]), data.matrix(Orange[3])))
}
#> lambda cvm cvup cvlo nz
#> s0 3.94663432 3293.0859 3962.8333 2623.3386 0
#> s1 3.85360057 2982.9485 3661.4468 2304.4502 1
#> s2 3.76056683 2598.8231 3210.3739 1987.2723 1
#> s3 3.66753309 2262.3848 2803.0016 1721.7681 1
#> s4 3.57449935 1982.8804 2462.9182 1502.8427 1
#> s5 3.48146561 1750.6601 2178.7878 1322.5325 1
#> s6 3.38843187 1557.7111 1941.2068 1174.2155 1
#> s7 3.29539813 1397.3797 1742.3742 1052.3852 1
#> s8 3.20236439 1264.1405 1575.8186 952.4623 1
#> s9 3.10933064 1153.4052 1436.1706 870.6398 1
#> s10 3.01629690 1063.1462 1320.7658 805.5265 1
#> s11 2.92326316 989.0301 1225.0084 753.0518 1
#> s12 2.83022942 914.8232 1134.0252 695.6212 2
#> s13 2.73719568 826.2000 1030.4926 621.9073 2
#> s14 2.64416194 734.9843 917.1229 552.8457 2
#> s15 2.55112820 655.4973 816.1289 494.8658 2
#> s16 2.45809446 589.3228 731.6837 446.9619 2
#> s17 2.36506072 534.4494 661.4583 407.4405 2
#> s18 2.27202697 488.9524 603.0512 374.8535 2
#> s19 2.17899323 451.2344 554.4695 347.9993 2
#> s20 2.08595949 419.9700 514.0587 325.8812 2
#> s21 1.99292575 394.0589 480.4450 307.6729 2
#> s22 1.89989201 372.5884 452.4869 292.6899 2
#> s23 1.80685827 354.8007 429.2357 280.3657 2
#> s24 1.71382453 340.0673 409.9026 270.2320 2
#> s25 1.62079079 327.8666 393.8310 261.9021 2
#> s26 1.52775705 317.7657 380.4744 255.0570 2
#> s27 1.43472330 309.4057 369.3776 249.4338 2
#> s28 1.34168956 302.4886 360.1613 244.8160 2
#> s29 1.24865582 296.7675 352.5095 241.0255 2
#> s30 1.15562208 292.0373 346.1589 237.9157 2
#> s31 1.06258834 288.1280 340.8899 235.3661 2
#> s32 0.96955460 284.8988 336.5200 233.2776 2
#> s33 0.87652086 282.2326 332.8969 231.5683 2
#> s34 0.78348712 280.0326 329.8939 230.1713 2
#> s35 0.69045338 278.2183 327.4058 229.0309 2
#> s36 0.59741963 276.7234 325.3449 228.1018 2
#> s37 0.50438589 275.4925 323.6386 227.3463 2
#> s38 0.41135215 274.4798 322.2262 226.7335 2
#> s39 0.31831841 273.6477 321.0576 226.2377 2
#> s40 0.22528467 272.9645 320.0910 225.8379 2
#> s41 0.13225093 272.4043 319.2918 225.5168 2
#> s42 0.03921719 271.9456 318.6313 225.2600 2
#> s43 -0.05381655 271.5707 318.0857 225.0557 2
#> s44 -0.14685029 271.2648 317.6353 224.8943 2
#> s45 -0.23988404 271.0156 317.2635 224.7677 2
#> s46 -0.33291778 270.8132 316.9570 224.6694 2
#> s47 -0.42595152 270.6491 316.7044 224.5939 2
#> s48 -0.51898526 270.5166 316.4963 224.5369 2
#> s49 -0.61201900 270.4100 316.3252 224.4948 2
#> s50 -0.70505274 270.3244 316.1845 224.4644 2
#> s51 -0.79808648 270.2562 316.0689 224.4436 2
#> s52 -0.89112022 270.2021 315.9741 224.4301 2
#> s53 -0.98415396 270.1595 315.8965 224.4225 2
#> s54 -1.07718771 270.1262 315.8330 224.4194 2
#> s55 -1.17022145 270.1005 315.7812 224.4199 2
#> s56 -1.26325519 270.0621 315.7153 224.4090 2
#> s57 -1.35628893 270.0149 315.6490 224.3809 2