orderedLasso.cv: Cross-validation function for the ordered lasso

In orderedLasso: Ordered Lasso and Time-Lag Sparse Regression

Description

Uses cross-validation to estimate the regularization parameter for the ordered lasso model.

Usage

orderedLasso.cv(x, y, lamlist = NULL, minlam = NULL, maxlam = NULL,
  nlam = 50, flmin = 5e-04, strongly.ordered = FALSE, intercept = TRUE,
  standardize = TRUE, nfolds = 10, folds = NULL, niter = 500,
  iter.gg = 100, method = c("Solve.QP", "GG"), trace = FALSE,
  epsilon = 1e-05)

Arguments

x	A matrix of predictors, where the rows are the samples and the columns are the predictors
y	A vector of observations, where length(y) equals nrow(x)
lamlist	Optional vector of values of lambda (the regularization parameter)
minlam	Optional minimum value for lambda
maxlam	Optional maximum value for lambda
nlam	Number of values of lambda to be tried. Default nlam = 50.
flmin	Fraction of maxlam minlam= flmin*maxlam. If computation is slow, try increasing flmin to focus on the sparser part of the path. Default flmin = 1e-4.
strongly.ordered	An option which allows users to order the coefficients in absolute value.
intercept	True if there is an intercept in the model.
standardize	Standardize the data matrix.
nfolds	Number of cross-validation folds.
folds	(Optional) user-supplied cross-validation folds. If provided, nfolds is ignored.
niter	Number of iterations the ordered lasso takes to converge. Default nither = 500.
iter.gg	Number of iterations of generalized gradient method; default 100
method	Two options available, Solve.QP and Generalized Gradient. Details of two options can be seen in the orderedLasso description.
trace	Output option; trace=TRUE gives verbose output
epsilon	Error tolerance parameter for convergence criterion; default 1e-5

Examples

set.seed(3)
n = 50
b = c(4,3,1,0)
p = length(b)
x = matrix(rnorm(n*p),nrow = n)
sigma = 5
y = x %*% b + sigma * rnorm(n, 0, 1)
cvmodel = orderedLasso.cv(x,y, intercept = FALSE, trace = TRUE, 
          method = "Solve.QP", strongly.ordered = TRUE)
print(cvmodel)
plot(cvmodel)

Example output

Loading required package: Matrix
Fold 1 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 2 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 3 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 4 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 5 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 6 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 7 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 8 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 9 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Fold 10 :
lambda= 0.0555431880114968
lambda= 0.0648632585178277
lambda= 0.0757472240282587
lambda= 0.0884575039721481
lambda= 0.103300551397943
lambda= 0.120634241753858
lambda= 0.14087650149579
lambda= 0.164515384563759
lambda= 0.192120839677229
lambda= 0.22435845216639
lambda= 0.26200549166382
lambda= 0.305969652576716
lambda= 0.357310939184576
lambda= 0.417267223026156
lambda= 0.487284088780777
lambda= 0.569049688247448
lambda= 0.664535442773709
lambda= 0.776043575496029
lambda= 0.906262619424709
lambda= 1.05833223970905
lambda= 1.23591893298942
lambda= 1.44330442899635
lambda= 1.6854889258164
lambda= 1.96831164789345
lambda= 2.29859163349676
lambda= 2.68429214613239
lambda= 3.1347126739632
lambda= 3.6607131464672
lambda= 4.27497577434272
lambda= 4.99231082578924
lambda= 5.83001371162726
lambda= 6.80828198880995
lambda= 7.9507023365501
lambda= 9.28481924637094
lambda= 10.8427991375648
lambda= 12.662205910312
lambda= 14.7869066355452
lambda= 17.2681292183268
lambda= 20.165697535686
lambda= 23.5494738288839
lambda= 27.5010431271164
lambda= 32.1156803151964
lambda= 37.5046472724847
lambda= 43.7978754685737
lambda= 51.1470986948335
lambda= 59.7295114640004
lambda= 69.752041284182
lambda= 81.4563378145596
lambda= 95.124599194552
lambda= 111.086376022994
Call
orderedLasso.cv(x = x, y = y, strongly.ordered = TRUE, intercept = FALSE, 
    method = "Solve.QP", trace = TRUE)

            Lambda Mean CV Error       SE Mean CV Error Ordered SE Ordered
 [1,]   0.05554319      31.98255 5.803466              30.72322   5.211535
 [2,]   0.06486326      31.97538 5.802354              30.72229   5.211953
 [3,]   0.07574722      31.96702 5.801057              30.72120   5.212440
 [4,]   0.08845750      31.95725 5.799544              30.71993   5.213010
 [5,]   0.10330055      31.94586 5.797779              30.71845   5.213675
 [6,]   0.12063424      31.93256 5.795721              30.71673   5.214452
 [7,]   0.14087650      31.91705 5.793322              30.71472   5.215359
 [8,]   0.16451538      31.89896 5.790525              30.71237   5.216418
 [9,]   0.19212084      31.87786 5.787266              30.70963   5.217656
[10,]   0.22435845      31.85367 5.783356              30.70644   5.219101
[11,]   0.26200549      31.82565 5.778752              30.70271   5.220789
[12,]   0.30596965      31.79299 5.773394              30.69837   5.222761
[13,]   0.35731094      31.75496 5.767165              30.69332   5.225064
[14,]   0.41726722      31.71067 5.759926              30.68743   5.227754
[15,]   0.48728409      31.65785 5.751519              30.58307   5.230910
[16,]   0.56904969      31.59666 5.741596              30.55910   5.234692
[17,]   0.66453544      31.52631 5.730073              30.53208   5.239130
[18,]   0.77604358      31.45282 5.716422              30.50895   5.244173
[19,]   0.90626262      31.36703 5.700832              30.48220   5.250121
[20,]   1.05833224      31.26665 5.683182              30.45108   5.257006
[21,]   1.23591893      31.15190 5.663510              30.41429   5.264596
[22,]   1.44330443      31.07060 5.659683              30.37197   5.273587
[23,]   1.68548893      31.01076 5.660966              30.33596   5.283934
[24,]   1.96831165      30.96631 5.661648              30.31918   5.295460
[25,]   2.29859163      30.91508 5.662508              30.30004   5.308927
[26,]   2.68429215      30.85493 5.662778              30.27832   5.324661
[27,]   3.13471267      30.78379 5.661802              30.25381   5.343045
[28,]   3.66071315      30.70202 5.661054              30.22585   5.364672
[29,]   4.27497577      30.61968 5.657037              30.19484   5.389928
[30,]   4.99231083      30.53095 5.651452              30.16083   5.419412
[31,]   5.83001371      30.43124 5.645716              30.12415   5.453820
[32,]   6.80828199      30.31921 5.639456              30.09848   5.501909
[33,]   7.95070234      30.18287 5.626361              30.04249   5.539491
[34,]   9.28481925      30.03263 5.613456              29.98350   5.583302
[35,]  10.84279914      29.92556 5.634055              29.92556   5.634055
[36,]  12.66220591      29.87427 5.692295              29.87427   5.692295
[37,]  14.78690664      29.81740 5.756452              29.81740   5.756452
[38,]  17.26812922      29.76109 5.821068              29.76109   5.821068
[39,]  20.16569754      29.72410 5.897599              29.72410   5.897599
[40,]  23.54947383      29.72020 5.988670              29.72020   5.988670
[41,]  27.50104313      29.76978 6.098403              29.76978   6.098403
[42,]  32.11568032      29.93925 6.228961              29.93925   6.228961
[43,]  37.50464727      30.32258 6.371707              30.32258   6.371707
[44,]  43.79787547      30.89948 6.550550              30.89948   6.550550
[45,]  51.14709869      31.83099 6.789201              31.83099   6.789201
[46,]  59.72951146      33.14047 7.134676              33.14047   7.134676
[47,]  69.75204128      34.93032 7.646339              34.93032   7.646339
[48,]  81.45633781      37.37598 8.407704              37.37598   8.407704
[49,]  95.12459919      39.47084 8.639792              39.47084   8.639792
[50,] 111.08637602      41.13182 8.598040              41.13182   8.598040

lamhat= 23.54947 lamhat.1se= 81.45634 lamhat.ordered =  23.54947 
lamhat.ordered.1se =  81.45634
            Lambda Mean CV Error       SE Mean CV Error Ordered SE Ordered
 [1,]   0.05554319      31.98255 5.803466              30.72322   5.211535
 [2,]   0.06486326      31.97538 5.802354              30.72229   5.211953
 [3,]   0.07574722      31.96702 5.801057              30.72120   5.212440
 [4,]   0.08845750      31.95725 5.799544              30.71993   5.213010
 [5,]   0.10330055      31.94586 5.797779              30.71845   5.213675
 [6,]   0.12063424      31.93256 5.795721              30.71673   5.214452
 [7,]   0.14087650      31.91705 5.793322              30.71472   5.215359
 [8,]   0.16451538      31.89896 5.790525              30.71237   5.216418
 [9,]   0.19212084      31.87786 5.787266              30.70963   5.217656
[10,]   0.22435845      31.85367 5.783356              30.70644   5.219101
[11,]   0.26200549      31.82565 5.778752              30.70271   5.220789
[12,]   0.30596965      31.79299 5.773394              30.69837   5.222761
[13,]   0.35731094      31.75496 5.767165              30.69332   5.225064
[14,]   0.41726722      31.71067 5.759926              30.68743   5.227754
[15,]   0.48728409      31.65785 5.751519              30.58307   5.230910
[16,]   0.56904969      31.59666 5.741596              30.55910   5.234692
[17,]   0.66453544      31.52631 5.730073              30.53208   5.239130
[18,]   0.77604358      31.45282 5.716422              30.50895   5.244173
[19,]   0.90626262      31.36703 5.700832              30.48220   5.250121
[20,]   1.05833224      31.26665 5.683182              30.45108   5.257006
[21,]   1.23591893      31.15190 5.663510              30.41429   5.264596
[22,]   1.44330443      31.07060 5.659683              30.37197   5.273587
[23,]   1.68548893      31.01076 5.660966              30.33596   5.283934
[24,]   1.96831165      30.96631 5.661648              30.31918   5.295460
[25,]   2.29859163      30.91508 5.662508              30.30004   5.308927
[26,]   2.68429215      30.85493 5.662778              30.27832   5.324661
[27,]   3.13471267      30.78379 5.661802              30.25381   5.343045
[28,]   3.66071315      30.70202 5.661054              30.22585   5.364672
[29,]   4.27497577      30.61968 5.657037              30.19484   5.389928
[30,]   4.99231083      30.53095 5.651452              30.16083   5.419412
[31,]   5.83001371      30.43124 5.645716              30.12415   5.453820
[32,]   6.80828199      30.31921 5.639456              30.09848   5.501909
[33,]   7.95070234      30.18287 5.626361              30.04249   5.539491
[34,]   9.28481925      30.03263 5.613456              29.98350   5.583302
[35,]  10.84279914      29.92556 5.634055              29.92556   5.634055
[36,]  12.66220591      29.87427 5.692295              29.87427   5.692295
[37,]  14.78690664      29.81740 5.756452              29.81740   5.756452
[38,]  17.26812922      29.76109 5.821068              29.76109   5.821068
[39,]  20.16569754      29.72410 5.897599              29.72410   5.897599
[40,]  23.54947383      29.72020 5.988670              29.72020   5.988670
[41,]  27.50104313      29.76978 6.098403              29.76978   6.098403
[42,]  32.11568032      29.93925 6.228961              29.93925   6.228961
[43,]  37.50464727      30.32258 6.371707              30.32258   6.371707
[44,]  43.79787547      30.89948 6.550550              30.89948   6.550550
[45,]  51.14709869      31.83099 6.789201              31.83099   6.789201
[46,]  59.72951146      33.14047 7.134676              33.14047   7.134676
[47,]  69.75204128      34.93032 7.646339              34.93032   7.646339
[48,]  81.45633781      37.37598 8.407704              37.37598   8.407704
[49,]  95.12459919      39.47084 8.639792              39.47084   8.639792
[50,] 111.08637602      41.13182 8.598040              41.13182   8.598040

orderedLasso documentation built on May 2, 2019, 6:36 a.m.