Description Usage Arguments Value Author(s) References See Also Examples
Performs selection and supervised grouping of predictor variables in large (microarray gene expression) datasets, with an option for simultaneous classification. Works in a greedy forward strategy and optimizes the binomial log-likelihood, based on estimated conditional probabilities from penalized logistic regression analysis.
1 2 |
x |
Numeric matrix of explanatory variables (p variables in columns, n cases in rows). For example, these can be microarray gene expression data which should be grouped. |
y |
Numeric vector of length n containing the class labels of the individuals. These labels have to be coded by 0 and 1. |
u |
Numeric matrix of additional (clinical) explanatory variables (m variables in columns, n cases in rows) that are used in the (penalized logistic regression) prediction model, but neither grouped nor averaged. For example, these can be 'traditional' clinical variables. |
noc |
Integer, the number of clusters that should be searched for on the data. |
lambda |
Real, defaults to 1/32. Rescaled penalty parameter that should be in [0,1]. |
flip |
Character string, describing a method how the |
standardize |
Logical, defaults to |
trace |
Integer >= 0; when positive, the output of the internal
loops is provided; |
pelora
returns an object of class "pelora". The functions
print
and summary
are used to obtain an overview of the
variables (genes) that have been selected and the groups that have
been formed. The function plot
yields a two-dimensional
projection into the space of the first two group centroids that
pelora
found. The generic function fitted
returns
the fitted values, these are the cluster representatives. coef
returns the penalized logistic regression coefficients θ_j
for each of the predictors. Finally, predict
is used for
classifying test data with Pelora's internal penalized logistic
regression classifier on the basis of the (gene) groups that have been
found.
An object of class "pelora" is a list containing:
genes |
A list of length |
values |
A numerical matrix with dimension n \times \code{noc}, containing the fitted values, i.e. the group centroids \tilde{x}_j. |
y |
Numeric vector of length n containing the class labels of the individuals. These labels are coded by 0 and 1. |
steps |
Numerical vector of length |
lambda |
The rescaled penalty parameter. |
noc |
The number of clusters that has been searched for on the data. |
px |
The number of columns (genes) in the |
flip |
The method that has been chosen for sign-flipping the
|
var.type |
A factor with |
crit |
A list of length |
signs |
Numerical vector of length p, saying whether the ith variable (gene) should be sign-flipped (-1) or not (+1). |
samp.names |
The names of the samples (rows) in the
|
gene.names |
The names of the variables (columns) in the
|
call |
The function call. |
Marcel Dettling, dettling@stat.math.ethz.ch
Marcel Dettling (2003) Finding Predictive Gene Groups from Microarray Data, see https://stat.ethz.ch/~dettling/supervised.html
Marcel Dettling and Peter Bühlmann (2002). Supervised Clustering of Genes. Genome Biology, 3(12): research0069.1-0069.15, doi: 10.1186/gb-2002-3-12-research0069.
Marcel Dettling and Peter Bühlmann (2004). Finding Predictive Gene Groups from Microarray Data. Journal of Multivariate Analysis 90, 106–131, doi: 10.1016/j.jmva.2004.02.012
wilma
for another supervised clustering technique.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## Working with a "real" microarray dataset
data(leukemia, package="supclust")
## Generating random test data: 3 observations and 250 variables (genes)
set.seed(724)
xN <- matrix(rnorm(750), nrow = 3, ncol = 250)
## Fitting Pelora
fit <- pelora(leukemia.x, leukemia.y, noc = 3)
## Working with the output
fit
summary(fit)
plot(fit)
fitted(fit)
coef(fit)
## Fitted values and class probabilities for the training data
predict(fit, type = "cla")
predict(fit, type = "prob")
## Predicting fitted values and class labels for the random test data
predict(fit, newdata = xN)
predict(fit, newdata = xN, type = "cla", noc = c(1,2,3))
predict(fit, newdata = xN, type = "pro", noc = c(1,3))
## Fitting Pelora such that the first 70 variables (genes) are not grouped
fit <- pelora(leukemia.x[, -(1:70)], leukemia.y, leukemia.x[,1:70])
## Working with the output
fit
summary(fit)
plot(fit)
fitted(fit)
coef(fit)
## Fitted values and class probabilities for the training data
predict(fit, type = "cla")
predict(fit, type = "prob")
## Predicting fitted values and class labels for the random test data
predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70])
predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70], "cla", noc = 1:10)
predict(fit, newdata = xN[, -(1:70)], newclin = xN[, 1:70], type = "pro")
|
...................
Cluster 1 terminated
.............
Cluster 2 terminated
..............
Cluster 3 terminated
Pelora called with lambda = 0.03125, 3 clusters fitted
Cluster 1 : Contains 18 genes, final criterion 8.967
Cluster 2 : Contains 11 genes, final criterion 6.157
Cluster 3 : Contains 13 genes, final criterion 4.870
Pelora called with lambda = 0.03125, 3 clusters fitted
Cluster 1 : Contains 18 genes, final criterion 8.967
Entry 1 : Gene 69
Entry 2 : Gene 174
Entry 3 : Gene 126 (flipped)
Entry 4 : Gene 183
Entry 5 : Gene 161 (flipped)
Entry 6 : Gene 160
Entry 7 : Gene 100
Entry 8 : Gene 225
Entry 9 : Gene 148
Entry 10 : Gene 188 (flipped)
Entry 11 : Gene 7 (flipped)
Entry 12 : Gene 211
Entry 13 : Gene 99
Entry 14 : Gene 105
Entry 15 : Gene 215
Entry 16 : Gene 106
Entry 17 : Gene 59
Entry 18 : Gene 185
Cluster 2 : Contains 11 genes, final criterion 6.157
Entry 1 : Gene 174
Entry 2 : Gene 126 (flipped)
Entry 3 : Gene 183
Entry 4 : Gene 160
Entry 5 : Gene 219
Entry 6 : Gene 75
Entry 7 : Gene 82
Entry 8 : Gene 96
Entry 9 : Gene 30 (flipped)
Entry 10 : Gene 224
Entry 11 : Gene 16 (flipped)
Cluster 3 : Contains 13 genes, final criterion 4.870
Entry 1 : Gene 69
Entry 2 : Gene 183
Entry 3 : Gene 126 (flipped)
Entry 4 : Gene 160
Entry 5 : Gene 208
Entry 6 : Gene 114
Entry 7 : Gene 174
Entry 8 : Gene 94
Entry 9 : Gene 53
Entry 10 : Gene 73
Entry 11 : Gene 172 (flipped)
Entry 12 : Gene 120
Entry 13 : Gene 66 (flipped)
Predictor 1 Predictor 2 Predictor 3
1 -0.3067501 -0.2965628 -0.2807912
2 -0.2374449 -0.3131413 -0.2380897
3 -0.4191425 -0.3808118 -0.2312147
4 -0.3244100 -0.4359319 -0.3629393
5 -0.3349981 -0.2282353 -0.2410168
6 -0.3680656 -0.4578520 -0.1462987
7 -0.2662159 -0.2216154 -0.2543470
8 -0.2709801 -0.6160297 -0.2720166
9 -0.3497270 -0.2566270 -0.3303750
10 -0.2484176 -0.2943709 -0.2994681
11 -0.4637102 -0.3178805 -0.2984117
12 -0.3235507 -0.2917100 -0.3461716
13 -0.3366444 -0.3897017 -0.4069550
14 -0.2931148 -0.3388245 -0.3624948
15 -0.4169962 -0.2032941 -0.3515948
16 -0.4391570 -0.3784187 -0.2363739
17 -0.3128851 -0.3411971 -0.2828232
18 -0.2615623 -0.3756845 -0.2058427
19 -0.2831775 -0.2943629 -0.2397261
20 -0.3336864 -0.5069509 -0.2640107
21 -0.3837958 -0.3845756 -0.2993117
22 -0.3142992 -0.4328201 -0.1530615
23 -0.1996516 -0.4057742 -0.1693670
24 -0.2665744 -0.4606513 -0.1923565
25 -0.3142851 -0.3554785 -0.2537461
26 -0.3204541 -0.3454162 -0.1195990
27 -0.4312616 -0.2115664 -0.2837958
28 0.7438087 0.9335735 0.5623016
29 0.7871312 0.9655664 0.5585013
30 0.7860592 0.9352526 0.6519974
31 0.9007759 0.8123499 0.5657786
32 0.8102071 0.7373147 0.5826707
33 0.8063673 0.8564196 0.7031533
34 0.7725103 0.8871276 0.6922753
35 0.7453184 0.7902142 0.7821098
36 0.8641912 0.8211966 0.6916977
37 0.7685894 0.9798044 0.6990970
38 0.8359995 0.8166661 0.6326162
Intercept Predictor 1 Predictor 2 Predictor 3
-1.393629 1.797641 1.636808 2.152874
3 Predictors
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
3 Predictors
1 0.04587036
2 0.05490925
3 0.03667876
4 0.03012823
5 0.05273539
6 0.04230364
7 0.05827671
8 0.03004090
9 0.04094897
10 0.04895124
11 0.03261054
12 0.03924127
13 0.02895065
14 0.03712830
15 0.03794509
16 0.03518360
17 0.04215062
18 0.05108146
19 0.05212408
20 0.03255774
21 0.03365325
22 0.04758007
23 0.05834136
24 0.04560974
25 0.04365777
26 0.05772796
27 0.04204126
28 0.93596308
29 0.94290124
30 0.95044840
31 0.94121875
32 0.92581351
33 0.95127097
34 0.94966962
35 0.94899101
36 0.95226392
37 0.95676209
38 0.94309939
Predictor 1 Predictor 2 Predictor 3
1 -0.4320335 -0.27838607 -0.02386751
2 -0.1053294 -0.01931307 -0.09584530
3 -0.1268362 -0.08248432 -0.14098159
1 Predictors 2 Predictors 3 Predictors
1 0 0 0
2 0 0 0
3 0 0 0
1 Predictors 3 Predictors
1 0.0386497 0.06432507
2 0.1397782 0.13932249
3 0.1290847 0.11302959
.
Cluster 1 terminated
......................
Cluster 2 terminated
.................
Cluster 3 terminated
.............
Cluster 4 terminated
...........
Cluster 5 terminated
.................
Cluster 6 terminated
......
Cluster 7 terminated
.
Cluster 8 terminated
.
Cluster 9 terminated
............................
Cluster 10 terminated
Pelora called with lambda = 0.03125,
7 clusters and 3 clinical variables fitted
Predictor 1 : Clinical variable 69, final criterion 12.146
Predictor 2 : Cluster with 20 genes, final criterion 6.805
Predictor 3 : Cluster with 16 genes, final criterion 5.154
Predictor 4 : Cluster with 12 genes, final criterion 4.285
Predictor 5 : Cluster with 10 genes, final criterion 3.739
Predictor 6 : Cluster with 16 genes, final criterion 3.348
Predictor 7 : Cluster with 5 genes, final criterion 3.077
Predictor 8 : Clinical variable 66, final criterion 2.959
Predictor 9 : Clinical variable 31, final criterion 2.867
Predictor 10 : Cluster with 26 genes, final criterion 2.662
Pelora called with lambda = 0.03125,
7 clusters and 3 clinical variables fitted
Predictor 1 : Clinical variable 69, final criterion 12.146
Predictor 2 : Cluster with 20 genes, final criterion 6.805
Entry 1 : Gene 104
Entry 2 : Gene 56 (flipped)
Entry 3 : Gene 113
Entry 4 : Gene 149
Entry 5 : Gene 44
Entry 6 : Gene 49
Entry 7 : Gene 12
Entry 8 : Gene 36
Entry 9 : Gene 27
Entry 10 : Gene 122
Entry 11 : Gene 147 (flipped)
Entry 12 : Gene 90
Entry 13 : Gene 152
Entry 14 : Gene 76
Entry 15 : Gene 138
Entry 16 : Gene 129
Entry 17 : Gene 26
Entry 18 : Gene 128
Entry 19 : Gene 170
Entry 20 : Gene 109 (flipped)
Predictor 3 : Cluster with 16 genes, final criterion 5.154
Entry 1 : Gene 104
Entry 2 : Gene 113
Entry 3 : Gene 56 (flipped)
Entry 4 : Gene 90
Entry 5 : Gene 5
Entry 6 : Gene 149
Entry 7 : Gene 12
Entry 8 : Gene 26
Entry 9 : Gene 76
Entry 10 : Gene 131
Entry 11 : Gene 122
Entry 12 : Gene 155
Entry 13 : Gene 100 (flipped)
Entry 14 : Gene 30
Entry 15 : Gene 62
Entry 16 : Gene 10 (flipped)
Predictor 4 : Cluster with 12 genes, final criterion 4.285
Entry 1 : Gene 104
Entry 2 : Gene 113
Entry 3 : Gene 56 (flipped)
Entry 4 : Gene 90
Entry 5 : Gene 5
Entry 6 : Gene 126
Entry 7 : Gene 155
Entry 8 : Gene 16
Entry 9 : Gene 158
Entry 10 : Gene 84
Entry 11 : Gene 161 (flipped)
Entry 12 : Gene 3
Predictor 5 : Cluster with 10 genes, final criterion 3.739
Entry 1 : Gene 104
Entry 2 : Gene 91 (flipped)
Entry 3 : Gene 113
Entry 4 : Gene 30
Entry 5 : Gene 132
Entry 6 : Gene 155
Entry 7 : Gene 154
Entry 8 : Gene 78
Entry 9 : Gene 161 (flipped)
Entry 10 : Gene 9
Predictor 6 : Cluster with 16 genes, final criterion 3.348
Entry 1 : Gene 104
Entry 2 : Gene 91 (flipped)
Entry 3 : Gene 113
Entry 4 : Gene 30
Entry 5 : Gene 132
Entry 6 : Gene 155
Entry 7 : Gene 154
Entry 8 : Gene 5
Entry 9 : Gene 119
Entry 10 : Gene 24
Entry 11 : Gene 151 (flipped)
Entry 12 : Gene 149
Entry 13 : Gene 140 (flipped)
Entry 14 : Gene 10 (flipped)
Entry 15 : Gene 71
Entry 16 : Gene 50
Predictor 7 : Cluster with 5 genes, final criterion 3.077
Entry 1 : Gene 104
Entry 2 : Gene 91 (flipped)
Entry 3 : Gene 113
Entry 4 : Gene 30
Entry 5 : Gene 132
Predictor 8 : Clinical variable 66, final criterion 2.959
Predictor 9 : Clinical variable 31, final criterion 2.867
Predictor 10 : Cluster with 26 genes, final criterion 2.662
Entry 1 : Gene 149
Entry 2 : Gene 12
Entry 3 : Gene 113
Entry 4 : Gene 26
Entry 5 : Gene 65
Entry 6 : Gene 121
Entry 7 : Gene 161 (flipped)
Entry 8 : Gene 132
Entry 9 : Gene 44
Entry 10 : Gene 170
Entry 11 : Gene 137
Entry 12 : Gene 133 (flipped)
Entry 13 : Gene 120
Entry 14 : Gene 30
Entry 15 : Gene 58
Entry 16 : Gene 145
Entry 17 : Gene 122
Entry 18 : Gene 138
Entry 19 : Gene 100 (flipped)
Entry 20 : Gene 76
Entry 21 : Gene 177
Entry 22 : Gene 63 (flipped)
Entry 23 : Gene 128
Entry 24 : Gene 178
Entry 25 : Gene 10 (flipped)
Entry 26 : Gene 147
Predictor 1 Predictor 2 Predictor 3 Predictor 4 Predictor 5 Predictor 6
1 -0.4060399 -0.1988190 -0.1764151 -0.5276747 -0.43261774 -0.2709890
2 0.9430274 -0.3024245 -0.3223401 -0.5329070 -0.30186209 -0.2857035
3 -0.6145205 -0.3365585 -0.3011438 -0.3601139 -0.42086686 -0.3006743
4 -1.3627031 -0.2594442 -0.2981212 -0.2832939 -0.38733884 -0.3447153
5 -1.3734152 -0.2692426 -0.2108856 -0.1628854 -0.25294441 -0.2564958
6 1.0657803 -0.3924563 -0.4329788 -0.5385678 -0.37081949 -0.2798448
7 -1.3374205 -0.2294537 -0.2833225 -0.2630923 -0.08457562 -0.2446500
8 -1.3354139 -0.2312732 -0.2094792 -0.3029268 -0.24043921 -0.1731558
9 -0.4067449 -0.3452204 -0.3282927 -0.4073596 -0.46628899 -0.1789397
10 0.2760965 -0.2621182 -0.3549939 -0.3327177 -0.44048202 -0.1903839
11 -1.2437437 -0.2228681 -0.2852932 -0.3878044 -0.58752197 -0.2115417
12 -0.9485866 -0.1954846 -0.2497780 -0.2519208 -0.40750018 -0.2963952
13 -1.3183164 -0.2929590 -0.1526353 -0.3588182 -0.49039866 -0.3520898
14 0.5652176 -0.4275906 -0.3407247 -0.2437327 -0.32728358 -0.3864132
15 -1.3396861 -0.2195409 -0.2474477 -0.3421138 -0.35555360 -0.2834904
16 -1.3573863 -0.3362329 -0.2724343 -0.3233672 -0.22479880 -0.2014709
17 -0.4053540 -0.2170572 -0.3454366 -0.3534689 -0.38016032 -0.1608026
18 -1.1799757 -0.2721089 -0.2945062 -0.3373299 -0.18761222 -0.3230994
19 -1.2076066 -0.2042550 -0.2828239 -0.1467543 -0.15759320 -0.2677612
20 -0.9575222 -0.3129403 -0.2710245 -0.3661953 -0.45554503 -0.2277328
21 -1.0457692 -0.2260243 -0.2695839 -0.1713213 -0.41803519 -0.2698210
22 -1.1115955 -0.1787175 -0.3100426 -0.2658555 -0.32307801 -0.2112505
23 0.1934126 -0.3300765 -0.4028228 -0.2141802 -0.33808943 -0.2949899
24 -1.4346453 -0.2309166 -0.3560609 -0.1628456 -0.29616359 -0.3641453
25 -1.2171742 -0.1839816 -0.2260751 -0.2935019 -0.39610751 -0.3254803
26 -1.2463141 -0.2101915 -0.1701988 -0.3030649 -0.46707433 -0.2582409
27 -1.2163623 -0.3313818 -0.2114892 -0.2873244 -0.40834397 -0.3068746
28 0.7410577 0.8644460 0.8123971 0.8030237 0.95358870 0.5416967
29 2.8713446 0.5091642 0.6323301 0.7137462 0.90306498 0.6968565
30 2.8404211 0.6085351 0.7190366 0.7471776 0.83533181 0.7134020
31 2.5610426 0.6083006 0.6731309 0.6143815 0.92147667 0.6234324
32 2.8598801 0.5794000 0.5498537 0.6949743 0.81219055 0.7228671
33 1.6246713 0.7242354 0.7266119 0.8285891 0.88759536 0.6110106
34 1.1566060 0.7456103 0.7304296 0.9371997 0.98673366 0.5910254
35 1.3758300 0.7578748 0.6902259 0.8781558 0.81929405 0.7543096
36 2.8477323 0.5502931 0.6184029 0.9061539 0.71769130 0.6763093
37 2.8649981 0.5469703 0.7333408 0.6684482 0.98778671 0.6531476
38 1.2206500 0.7245081 0.7205910 0.7292881 0.79434106 0.6830947
Predictor 7 Predictor 8 Predictor 9 Predictor 10
1 -0.3377947 -0.54172399 -0.98103854 -0.24262700
2 -0.2598511 -1.32647301 0.21200654 -0.33026179
3 -0.6078839 -0.44441588 -0.79209596 -0.21531241
4 -0.1707361 -0.38894378 -1.36270310 -0.22026996
5 -0.4517779 -0.11559041 -0.26099616 -0.37148180
6 -0.4129790 -1.19283327 -0.55588074 -0.17041470
7 -0.4831630 -1.33742052 -1.33742052 -0.09763985
8 -0.2067421 -0.50767464 -1.16396233 -0.27696790
9 -0.6862245 -1.05639670 -1.22369368 -0.06686727
10 -0.3417866 -0.71879040 -0.27461021 -0.21466096
11 -0.7233215 -0.94330213 -1.24374365 -0.14290683
12 -0.5365463 0.00202941 0.06379095 -0.34423180
13 -0.4393363 -0.94953049 -1.31831643 -0.10417821
14 -0.5653535 -1.01678710 -0.27144455 -0.31414727
15 -0.2118393 -1.33968610 -1.12421926 -0.10603075
16 -0.3355517 -0.69925752 -1.09044682 -0.19459049
17 -0.4749806 -0.63182986 -1.51021939 -0.22101496
18 -0.3598141 -0.85163418 -1.17997570 -0.15392574
19 -0.5286960 -0.79177347 -1.20760661 -0.22561805
20 -0.3552274 -1.06203925 -1.53441337 -0.12800886
21 -0.8458994 -0.74633774 -0.83562242 -0.17875482
22 -0.1830584 -0.90968516 -1.11159548 -0.23687896
23 -0.4502238 -1.11368825 -1.18711813 -0.19449288
24 -0.3956765 -1.41634053 -0.90685483 -0.04129460
25 -0.5487348 -0.56720728 -1.21717418 -0.14357893
26 -0.3180278 -1.01980964 -1.24631406 -0.17155724
27 -0.3441807 -1.21636235 -0.92405624 -0.12719614
28 0.9471456 -0.35455408 -0.52250494 0.74819296
29 0.7868549 0.52584356 1.48838553 0.50876676
30 1.2425456 0.72995414 2.49675387 0.25958430
31 1.0185054 1.06700435 0.71014772 0.42509865
32 1.1924235 0.40793212 -0.30247047 0.53876303
33 1.2133198 -0.16927634 0.53393296 0.54281280
34 1.0705221 1.10391475 0.48085144 0.41491610
35 0.8002570 0.76088132 0.84562749 0.43643592
36 1.1476762 0.52069147 2.17426590 0.35226695
37 1.1229132 -0.11080491 1.80504585 0.44747837
38 1.0332438 -0.39202249 1.13339969 0.56059432
Intercept Predictor 1 Predictor 2 Predictor 3 Predictor 4 Predictor 5
-1.1716983 0.1842654 0.8385458 0.7976833 0.6847451 0.6067519
Predictor 6 Predictor 7 Predictor 8 Predictor 9 Predictor 10
0.8211166 0.4847442 0.2955851 0.1878370 1.2569333
10 Predictors
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
10 Predictors
1 0.03866462
2 0.03974743
3 0.03224375
4 0.03486537
5 0.04463813
6 0.03406801
7 0.03653106
8 0.04346592
9 0.03030718
10 0.04857966
11 0.02524001
12 0.04508957
13 0.02969606
14 0.03226090
15 0.03469224
16 0.03685924
17 0.03601400
18 0.03544747
19 0.03887285
20 0.02999918
21 0.03312515
22 0.03956643
23 0.03412293
24 0.03433607
25 0.03619673
26 0.03483489
27 0.03233529
28 0.95720621
29 0.96622035
30 0.97474460
31 0.96496611
32 0.95933805
33 0.96241723
34 0.96869354
35 0.96463227
36 0.97012698
37 0.96790089
38 0.95607335
Predictor 1 Predictor 2 Predictor 3 Predictor 4 Predictor 5 Predictor 6
1 0.4595198 -0.24995809 -0.13411424 -0.4149285 -0.6214466 -0.07272322
2 0.3588264 -0.01687939 0.13076628 -0.2711565 0.0143638 0.41795265
3 -1.4099869 -0.14563161 -0.06807276 0.2943940 -0.1881499 -0.01274870
Predictor 7 Predictor 8 Predictor 9 Predictor 10
1 -0.5565468 0.8467976 -0.2796180 -0.187531434
2 0.2207801 -0.2882260 2.0823453 0.001349072
3 0.1095344 0.7402682 0.2787512 -0.112053556
1 Predictors 2 Predictors 3 Predictors 4 Predictors 5 Predictors 6 Predictors
1 0 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
7 Predictors 8 Predictors 9 Predictors 10 Predictors
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
10 Predictors
1 0.08074821
2 0.39304527
3 0.20627144
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