# structuredSCA: Variable selection algorithm with a predefined component... In RegularizedSCA: Regularized Simultaneous Component Based Data Integration

## Description

Variable selection algorithm when the common/distinctive structure is known a priori. The common component can also be sparse, which is to be estimated by Lasso. The distinctive components are not sparse in the sense that the entire variables in a component (belonging to a certain block) are either all zeros or non-zeros.

## Usage

 `1` ```structuredSCA(DATA, Jk, R, Target, Position, LASSO, MaxIter, NRSTARTS) ```

## Arguments

 `DATA` A matrix, which contains the concatenated data with the same subjects from multiple blocks. `Jk` A vector containing number of variables in the concatinated data matrix. `R` Number of components (R>=2). `Target` A matrix containing 0's and 1's. Its number of columns equals to R, and its number of rows equals to the number of blocks to be integrated. Thus, if the element in `Position` Indicate on which component(s) the Lasso Penalty is imposed. If unspecified, the algorithm assume that the Lasso penalty is imposed on the common component(s) only. If there is no common component, then Lasso penalty is applied to all components. `LASSO` A Lasso tuning parameter. `MaxIter` The maximum rounds of iterations. It should be a positive integer. The default value is 400. `NRSTARTS` Multi-start procedure: The number of multi-starts. The default value is 20.

## Value

 `Pmatrix` The best estimated component loading matrix (i.e., P), if multi-starts >= 2. `Tmatrix` The best estimated component score matrix (i.e., T), if multi-starts >= 2. `Lossvec` A list of vectors containing the loss in each iteration for each multi-start.

## References

Gu, Z., & Van Deun, K. (2016). A variable selection method for simultaneous component based data integration. Chemometrics and Intelligent Laboratory Systems, 158, 187-199.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## Not run: DATA1 <- matrix(rnorm(50), nrow=5) DATA2 <- matrix(rnorm(100), nrow=5) DATA <- cbind(DATA1, DATA2) Jk <- c(10, 20) R <- 5 Target <- matrix(c(1,1,1,0,1,0,0,1,0,1), 2, 5) LASSO <- 0.2 MaxIter <- 400 NRSTARTS <- 5 structuredSCA(DATA, Jk, R, Target, LASSO = LASSO) ## End(Not run) ```

### Example output

```\$Pmatrix
[,1]        [,2]       [,3]        [,4]        [,5]
[1,] -0.62837412  0.16111847 -0.1913325  0.00000000  0.00000000
[2,]  0.61114893 -0.72676901  1.2199844  0.00000000  0.00000000
[3,] -0.46160491  0.95263612  0.8755834  0.00000000  0.00000000
[4,] -1.33588867  1.70067085 -0.6526561  0.00000000  0.00000000
[5,] -1.67455319  1.25707160 -0.9296565  0.00000000  0.00000000
[6,]  1.26527458 -0.14906662  1.3602231  0.00000000  0.00000000
[7,]  0.66199705 -1.68879106 -0.5882185  0.00000000  0.00000000
[8,]  1.80909798  1.55118187 -0.1439346  0.00000000  0.00000000
[9,] -0.85464507 -0.04371752  1.2575632  0.00000000  0.00000000
[10,]  0.61197864  2.49209521  0.6270205  0.00000000  0.00000000
[11,] -0.81129184  0.00000000  0.0000000 -1.29067378 -0.81024044
[12,]  0.81352616  0.00000000  0.0000000 -0.59009787 -0.52095229
[13,]  0.22943834  0.00000000  0.0000000  0.76158917 -0.98839709
[14,]  0.00000000  0.00000000  0.0000000  0.08938401  1.28169500
[15,]  0.34926700  0.00000000  0.0000000 -0.36384221 -0.05373306
[16,] -1.93992417  0.00000000  0.0000000  0.28317643  0.20307041
[17,]  0.20354490  0.00000000  0.0000000 -0.08436195  0.49652017
[18,]  0.04044184  0.00000000  0.0000000 -1.50930533 -1.14449606
[19,]  0.00000000  0.00000000  0.0000000  2.31997561  1.08242381
[20,]  2.53122005  0.00000000  0.0000000 -3.09530429  2.54432788
[21,]  0.46552299  0.00000000  0.0000000  0.69015720 -2.17330636
[22,]  0.06941531  0.00000000  0.0000000  1.27512706 -0.46596127
[23,] -1.03569436  0.00000000  0.0000000 -0.49896554 -1.40873786
[24,]  1.28287147  0.00000000  0.0000000  0.02814201 -1.37808039
[25,] -2.45982014  0.00000000  0.0000000  2.19467114 -0.28392989
[26,]  0.62952858  0.00000000  0.0000000  0.39144345 -0.20675334
[27,] -1.20963656  0.00000000  0.0000000 -0.56159427  1.27742658
[28,]  1.39061299  0.00000000  0.0000000  2.16998876 -1.32867071
[29,] -1.61444294  0.00000000  0.0000000  0.11309668 -0.27093942
[30,] -0.59629572  0.00000000  0.0000000 -1.57153202 -0.14691360

\$Tmatrix
[,1]         [,2]       [,3]        [,4]       [,5]
[1,] -0.79774415 -0.277683843  0.1747727  0.18083315  0.4724932
[2,] -0.09582747 -0.581008594  0.4910430 -0.17760979 -0.6169098
[3,] -0.49028021  0.092288688 -0.6137894 -0.49758317 -0.3560642
[4,] -0.14762795  0.008728919 -0.2894577  0.82934356 -0.4544594
[5,]  0.30373124 -0.759425639 -0.5175019  0.01882521  0.2507139

\$Lossvec
\$Lossvec[[1]]
[1] 185.04228  37.71835  28.66345  27.34610  26.91206  26.70835  26.59489
[8]  26.52232  26.47263  26.43920  26.41572  26.39844  26.38534  26.37533
[15]  26.36761  26.36164  26.35701  26.35340  26.35059  26.34840  26.34668
[22]  26.34534  26.34429  26.34346  26.34282  26.34231  26.34191  26.34160
[29]  26.34136  26.34116  26.34101  26.34089  26.34080  26.34073  26.34067
[36]  26.34063  26.34059  26.34056  26.34054  26.34052  26.34051  26.34050
[43]  26.34049  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047
[50]  26.34047  26.34047  26.34047  26.34046  26.34046  26.34046  26.34046
[57]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046

\$Lossvec[[2]]
[1] 162.22122  55.35531  44.13077  35.76077  31.16308  29.00202  27.99720
[8]  27.46989  27.18277  27.00373  26.87269  26.76969  26.68598  26.61729
[15]  26.56222  26.51820  26.48300  26.45487  26.43242  26.41451  26.40023
[22]  26.38885  26.37979  26.37258  26.36684  26.36227  26.35859  26.35553
[29]  26.35297  26.35084  26.34906  26.34758  26.34634  26.34531  26.34445
[36]  26.34373  26.34313  26.34263  26.34222  26.34187  26.34158  26.34134
[43]  26.34114  26.34097  26.34084  26.34074  26.34067  26.34062  26.34058
[50]  26.34055  26.34053  26.34052  26.34050  26.34049  26.34049  26.34048
[57]  26.34048  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[3]]
[1] 174.16747  53.30167  45.56198  40.64792  37.74008  36.38679  35.67913
[8]  35.33169  35.16976  35.06139  34.96381  34.84990  34.70767  34.51758
[15]  34.26613  33.93890  33.52805  33.02631  32.42022  31.75121  31.03206
[22]  30.30829  29.65124  29.17704  28.76795  28.39018  28.05017  27.75129
[29]  27.49405  27.27652  27.09427  26.94321  26.81930  26.71853  26.63715
[36]  26.57178  26.51950  26.47823  26.44641  26.42196  26.40334  26.38913
[43]  26.37823  26.36983  26.36334  26.35831  26.35440  26.35136  26.34899
[50]  26.34714  26.34569  26.34456  26.34368  26.34298  26.34244  26.34201
[57]  26.34168  26.34142  26.34121  26.34105  26.34092  26.34082  26.34075
[64]  26.34069  26.34064  26.34060  26.34057  26.34055  26.34053  26.34051
[71]  26.34050  26.34049  26.34049  26.34048  26.34048  26.34047  26.34047
[78]  26.34047  26.34047  26.34047  26.34047  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[99]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[106]  26.34046  26.34046

\$Lossvec[[4]]
[1] 199.82504  51.37579  41.03186  33.64096  31.06841  29.94887  29.17088
[8]  28.53115  27.98387  27.53106  27.16659  26.90108  26.72725  26.61870
[15]  26.54455  26.49239  26.45489  26.42743  26.40701  26.39164  26.37982
[22]  26.37092  26.36414  26.35892  26.35488  26.35174  26.34929  26.34738
[29]  26.34589  26.34472  26.34380  26.34308  26.34252  26.34208  26.34173
[36]  26.34146  26.34124  26.34108  26.34094  26.34084  26.34076  26.34070
[43]  26.34065  26.34061  26.34058  26.34055  26.34053  26.34052  26.34050
[50]  26.34050  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047
[57]  26.34047  26.34047  26.34047  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046

\$Lossvec[[5]]
[1] 168.71849  51.91164  38.32061  32.46004  30.22488  29.22373  28.62178
[8]  28.19607  27.85891  27.57845  27.34583  27.15268  26.99259  26.86051
[15]  26.75196  26.66302  26.59147  26.53628  26.49353  26.46019  26.43418
[22]  26.41385  26.39796  26.38552  26.37579  26.36816  26.36219  26.35751
[29]  26.35384  26.35096  26.34870  26.34693  26.34554  26.34445  26.34359
[36]  26.34292  26.34239  26.34198  26.34165  26.34140  26.34120  26.34104
[43]  26.34091  26.34082  26.34074  26.34068  26.34063  26.34060  26.34057
[50]  26.34055  26.34053  26.34051  26.34050  26.34049  26.34049  26.34048
[57]  26.34048  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[6]]
[1] 171.82058  49.95745  40.06440  34.08207  31.63844  30.51083  29.77260
[8]  29.23188  28.80353  28.42189  28.07886  27.77455  27.50833  27.27842
[15]  27.08205  26.91629  26.77795  26.66315  26.56825  26.49535  26.44236
[22]  26.40396  26.38054  26.36683  26.35855  26.35339  26.34988  26.34740
[29]  26.34562  26.34433  26.34338  26.34269  26.34216  26.34177  26.34147
[36]  26.34124  26.34107  26.34093  26.34083  26.34075  26.34069  26.34064
[43]  26.34060  26.34057  26.34055  26.34053  26.34051  26.34050  26.34049
[50]  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047  26.34047
[57]  26.34047  26.34047  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[7]]
[1] 183.68734  48.21070  35.80549  33.57958  32.77393  32.17515  31.59056
[8]  30.96789  30.31669  29.65764  29.01439  28.57936  28.22939  27.91860
[15]  27.64763  27.41509  27.21761  27.05106  26.91172  26.79595  26.70129
[22]  26.62534  26.56472  26.51683  26.47909  26.44938  26.42601  26.40763
[29]  26.39320  26.38186  26.37296  26.36597  26.36049  26.35618  26.35280
[36]  26.35015  26.34807  26.34643  26.34515  26.34414  26.34335  26.34273
[43]  26.34224  26.34186  26.34156  26.34132  26.34114  26.34099  26.34088
[50]  26.34079  26.34072  26.34066  26.34062  26.34059  26.34056  26.34054
[57]  26.34052  26.34051  26.34050  26.34049  26.34048  26.34048  26.34048
[64]  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[8]]
[1] 170.07541  51.98368  38.73438  34.27681  33.04386  32.23812  31.47297
[8]  30.74711  30.07116  29.40181  28.82727  28.37786  27.98264  27.62993
[15]  27.32557  27.07306  26.88365  26.75367  26.66434  26.59538  26.54058
[22]  26.49720  26.46297  26.43603  26.41489  26.39832  26.38558  26.37587
[29]  26.36847  26.36282  26.35844  26.35493  26.35212  26.34987  26.34805
[36]  26.34658  26.34539  26.34443  26.34365  26.34301  26.34250  26.34208
[43]  26.34173  26.34145  26.34123  26.34106  26.34093  26.34082  26.34075
[50]  26.34068  26.34064  26.34060  26.34057  26.34055  26.34053  26.34051
[57]  26.34050  26.34049  26.34049  26.34048  26.34048  26.34047  26.34047
[64]  26.34047  26.34047  26.34047  26.34047  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046

\$Lossvec[[9]]
[1] 162.25785  62.64676  52.30359  42.05917  36.17553  33.83138  32.72765
[8]  31.97242  31.28664  30.59758  29.91918  29.28676  28.70772  28.23908
[15]  27.88345  27.60080  27.38385  27.21524  27.07394  26.95204  26.84649
[22]  26.75560  26.67773  26.61191  26.55848  26.51552  26.48105  26.45344
[29]  26.43136  26.41372  26.39965  26.38843  26.37948  26.37235  26.36668
[36]  26.36215  26.35850  26.35545  26.35291  26.35079  26.34902  26.34754
[43]  26.34631  26.34528  26.34442  26.34371  26.34312  26.34262  26.34221
[50]  26.34186  26.34157  26.34133  26.34114  26.34097  26.34083  26.34074
[57]  26.34067  26.34062  26.34058  26.34055  26.34053  26.34051  26.34050
[64]  26.34049  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047
[71]  26.34047  26.34047  26.34047  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[99]  26.34046

\$Lossvec[[10]]
[1] 157.94381  36.35463  32.44776  31.39485  30.60895  29.88220  29.31558
[8]  28.88457  28.49161  28.13939  27.82872  27.55846  27.32615  27.12838
[15]  26.96173  26.82708  26.71956  26.63371  26.56514  26.51066  26.47039
[22]  26.44066  26.41817  26.40088  26.38753  26.37718  26.36915  26.36289
[29]  26.35802  26.35421  26.35123  26.34891  26.34708  26.34566  26.34454
[36]  26.34366  26.34297  26.34243  26.34201  26.34168  26.34142  26.34121
[43]  26.34105  26.34092  26.34082  26.34075  26.34069  26.34064  26.34060
[50]  26.34057  26.34055  26.34053  26.34051  26.34050  26.34049  26.34049
[57]  26.34048  26.34048  26.34047  26.34047  26.34047  26.34047  26.34047
[64]  26.34047  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[11]]
[1] 172.19121  40.40836  33.51606  30.85168  29.61576  28.97576  28.51308
[8]  28.13902  27.82437  27.55795  27.33416  27.14817  26.99372  26.86619
[15]  26.76153  26.67610  26.60667  26.55044  26.50540  26.46995  26.44210
[22]  26.42022  26.40305  26.38957  26.37899  26.37069  26.36418  26.35908
[29]  26.35507  26.35193  26.34946  26.34752  26.34601  26.34481  26.34388
[36]  26.34314  26.34257  26.34212  26.34176  26.34148  26.34126  26.34109
[43]  26.34096  26.34085  26.34077  26.34070  26.34065  26.34061  26.34058
[50]  26.34055  26.34053  26.34052  26.34051  26.34050  26.34049  26.34048
[57]  26.34048  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[12]]
[1] 194.21632  49.54159  40.48212  37.09443  35.70646  34.99028  34.47574
[8]  34.01948  33.54990  32.99836  32.35558  31.63248  30.98119  30.33669
[15]  29.84717  29.40295  28.98083  28.58834  28.23173  27.91409  27.63575
[22]  27.39504  27.18903  27.01415  26.86664  26.74366  26.64680  26.57137
[29]  26.51243  26.46634  26.43377  26.41092  26.39442  26.38204  26.37262
[36]  26.36540  26.35985  26.35556  26.35224  26.34967  26.34766  26.34610
[43]  26.34487  26.34392  26.34317  26.34259  26.34213  26.34177  26.34149
[50]  26.34127  26.34109  26.34096  26.34085  26.34077  26.34070  26.34065
[57]  26.34061  26.34058  26.34055  26.34053  26.34052  26.34051  26.34050
[64]  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047  26.34047
[71]  26.34047  26.34047  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[13]]
[1] 163.41900  46.80129  41.26575  34.34573  29.77168  27.89460  27.16714
[8]  26.86326  26.70556  26.60745  26.53870  26.48875  26.45336  26.42749
[15]  26.40791  26.39290  26.38132  26.37235  26.36538  26.35996  26.35572
[22]  26.35242  26.34983  26.34781  26.34622  26.34498  26.34401  26.34325
[29]  26.34265  26.34218  26.34181  26.34152  26.34129  26.34111  26.34097
[36]  26.34086  26.34078  26.34071  26.34066  26.34061  26.34058  26.34056
[43]  26.34054  26.34052  26.34051  26.34050  26.34049  26.34048  26.34048
[50]  26.34048  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046
[57]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[14]]
[1] 183.91958  55.92291  48.44515  41.65379  36.08677  33.79788  32.88245
[8]  32.21321  31.51527  30.78677  30.10019  29.51340  28.99372  28.51982
[15]  28.11167  27.78738  27.52033  27.29374  27.10428  26.94796  26.82039
[22]  26.71721  26.63434  26.56815  26.51551  26.47432  26.44280  26.41871
[29]  26.40032  26.38627  26.37553  26.36732  26.36104  26.35624  26.35255
[36]  26.34973  26.34756  26.34589  26.34461  26.34362  26.34285  26.34226
[43]  26.34180  26.34144  26.34117  26.34095  26.34078  26.34066  26.34059
[50]  26.34054  26.34051  26.34049  26.34048  26.34048  26.34047  26.34047
[57]  26.34047  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046

\$Lossvec[[15]]
[1] 186.70809  45.39263  36.90134  35.33815  34.86737  34.64070  34.43840
[8]  34.18834  33.85291  33.40344  32.84315  32.22503  31.58006  30.93584
[15]  30.33820  29.75842  29.18950  28.64507  28.14262  27.74074  27.45771
[22]  27.24255  27.06500  26.91944  26.80100  26.70520  26.62847  26.56798
[29]  26.52041  26.48302  26.45363  26.43053  26.41236  26.39806  26.38681
[36]  26.37784  26.37066  26.36490  26.36027  26.35653  26.35351  26.35106
[43]  26.34908  26.34747  26.34615  26.34508  26.34420  26.34349  26.34290
[50]  26.34241  26.34202  26.34169  26.34142  26.34120  26.34102  26.34087
[57]  26.34074  26.34066  26.34060  26.34056  26.34053  26.34051  26.34050
[64]  26.34049  26.34048  26.34048  26.34047  26.34047  26.34047  26.34047
[71]  26.34047  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[92]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[16]]
[1] 163.75756  35.54858  29.45801  27.88854  27.26073  26.93831  26.73186
[8]  26.58710  26.48995  26.43256  26.39931  26.37939  26.36708  26.35947
[15]  26.35445  26.35090  26.34833  26.34644  26.34504  26.34398  26.34318
[22]  26.34257  26.34210  26.34174  26.34146  26.34124  26.34107  26.34094
[29]  26.34083  26.34075  26.34069  26.34064  26.34060  26.34057  26.34055
[36]  26.34053  26.34052  26.34050  26.34049  26.34049  26.34048  26.34048
[43]  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046
[50]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[57]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[17]]
[1] 154.88837  49.18437  47.64068  46.16876  44.17429  40.92519  36.81575
[8]  33.07035  30.33398  28.59472  27.69101  27.22462  26.94540  26.76885
[15]  26.65054  26.56709  26.50884  26.46788  26.43811  26.41583  26.39890
[22]  26.38592  26.37591  26.36814  26.36211  26.35740  26.35373  26.35086
[29]  26.34861  26.34685  26.34548  26.34440  26.34355  26.34289  26.34236
[36]  26.34196  26.34163  26.34138  26.34118  26.34103  26.34091  26.34081
[43]  26.34074  26.34068  26.34063  26.34059  26.34057  26.34054  26.34053
[50]  26.34051  26.34050  26.34049  26.34049  26.34048  26.34048  26.34047
[57]  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046

\$Lossvec[[18]]
[1] 198.90282  47.62911  35.20028  30.85713  29.47504  28.81338  28.37612
[8]  28.02243  27.72208  27.46665  27.25141  27.07135  26.92254  26.80088
[15]  26.70229  26.62295  26.55946  26.50888  26.46909  26.43858  26.41527
[22]  26.39748  26.38390  26.37354  26.36565  26.35965  26.35515  26.35175
[29]  26.34918  26.34721  26.34570  26.34454  26.34364  26.34294  26.34240
[36]  26.34198  26.34165  26.34139  26.34119  26.34103  26.34091  26.34081
[43]  26.34074  26.34068  26.34063  26.34060  26.34057  26.34054  26.34053
[50]  26.34051  26.34050  26.34049  26.34049  26.34048  26.34048  26.34047
[57]  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046

\$Lossvec[[19]]
[1] 187.77340  48.26416  44.55243  41.50280  38.49557  36.21705  34.69631
[8]  33.56005  32.60097  31.78231  31.20340  30.71395  30.23810  29.78840
[15]  29.36001  28.94869  28.56016  28.20258  27.88097  27.59731  27.35119
[22]  27.14050  26.96210  26.81237  26.68789  26.58564  26.50432  26.44546
[29]  26.40339  26.37765  26.36323  26.35502  26.35024  26.34727  26.34527
[36]  26.34390  26.34295  26.34228  26.34181  26.34146  26.34122  26.34103
[43]  26.34090  26.34080  26.34072  26.34066  26.34062  26.34058  26.34055
[50]  26.34053  26.34052  26.34051  26.34050  26.34049  26.34048  26.34048
[57]  26.34047  26.34047  26.34047  26.34047  26.34047  26.34047  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[71]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[78]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[85]  26.34046  26.34046  26.34046  26.34046

\$Lossvec[[20]]
[1] 192.38312  62.08342  43.23105  36.95647  34.01656  32.34669  31.25614
[8]  30.39018  29.72266  29.15926  28.65792  28.21264  27.85300  27.57402
[15]  27.34061  27.14433  26.98167  26.84847  26.74041  26.65340  26.58376
[22]  26.52825  26.48467  26.45120  26.42554  26.40587  26.39079  26.37921
[29]  26.37033  26.36351  26.35826  26.35421  26.35109  26.34868  26.34682
[36]  26.34537  26.34425  26.34337  26.34269  26.34216  26.34174  26.34141
[43]  26.34115  26.34095  26.34079  26.34067  26.34059  26.34054  26.34051
[50]  26.34049  26.34048  26.34047  26.34047  26.34047  26.34046  26.34046
[57]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
[64]  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046  26.34046
```

RegularizedSCA documentation built on May 2, 2019, 8:24 a.m.