Bagging.lasso: A Bagging Prediction Model Using LASSO Selection Algorithm.
In SparseLearner: Sparse Learning Algorithms Using a LASSO-Type Penalty for Coefficient Estimation and Model Prediction
Description
Usage
Arguments
Details
Value
References
Examples
View source: R/Bagging.lasso.R
Description
This function performs a bagging prediction for linear and logistic regression model using the LASSO selection algorithm.
Usage
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Arguments
x
input matrix. The dimension of the matrix is nobs x nvars; each row is a vector
of observations of the variables.
y
response variable. For family="gaussian", y is a vector of quantitative response.
For family="binomial" should be a factor with two levels '0' and '1' and the level of '1'
is the target class.
family
response type (see above).
M
the number of base-level models (LASSO linear or logistic regression models) to
obtain a final prediction. Note that it also corresponds to the number of bootstrap samples
to draw. Defaults to 100.
subspace.size
the number of random subspaces to construct an ensemble prediction
model. Defaults to 10.
predictor.subset
the subset of randomly selected predictors from the training set
to reduce the original p-dimensional feature space. Defaults to (9/10)*ncol(x) where
ncol(x) represents the the original p-dimensional feature space of input matrix x.
boot.scale
the scale of sample size in each bootstrap re-sampling, relative to the
original sample size. Defaults to 1.0, equaling to the original size of training samples.
kfold
the number of folds of cross validation - default is 10. Although kfold
can be as large as the sample size (leave-one-out CV), it is not recommended for large
datasets. Smallest value allowable is kfold=3.
predictor.importance
logical. Should the importance of each predictor in the bagging
LASSO model be evaluated? Defaults to TRUE. A permutation-based variable importance measure
estimated by the out-of-bag error rate is adapted for the bagging model.
trimmed
logical. Should a trimmed bagging strategy be performed? Defaults to FALSE.
Traditional bagging draws bootstrap samples from the training sample, applies the base-level
model to each bootstrap sample, and then averages over all obtained prediction rules. The
idea of trimmed bagging is to exclude the bootstrapped prediction rules that yield the highest
error rates and to aggregate over the remaining ones.
weighted
logical. Should a weighted rank aggregation procedure be performed? Defaults
to TRUE. This procedure uses a Monte Carlo cross-entropy algorithm combining the ranks of a
set of based-level model under consideration via a weighted aggregation that optimizes a
distance criterion to determine the best performance base-level model.
verbose
logical. Should the iterative process information of bagging model be presented?
Defaults to TRUE.
seed
the seed for random sampling, with the default value 0123.
Details
This bagging LASSO model Bagging.lasso generates an ensemble prediction based
on the L1-regularized linear or logistic regression models. The Bagging.lasso
function uses a Monte Carlo cross-entropy algorithm to combine the ranks of a
set of based-level LASSO regression model under consideration via a weighted
aggregation to determine the best base-level model. In the Bagging.lasso, the
glmnet algorithm is performed to fit LASSO model paths for linear and logistic
regression using coordinate descent. A random subspace method is employed to
improve the predictive performance. In addition, a strategy of trimmed bagging
can be defined to exclude the bootstrapped prediction rules that yield the
highest error rates and to aggregate over the remaining prediction rules.
Value
family
the response type.
M
the number of base-level models to obtain a bagging prediction.
predictor.subset
the subset of randomly selected predictors from the
training set to reduce the original p-dimensional feature space.
subspace.size
the number of random subspaces to construct an ensemble
prediction model.
validation.metric
the model validation measures.
boot.scale
the scale of sample size in each bootstrap re-sampling,
relative to the original sample size.
distance
the distance function used in the weighted aggregation
to define the similarity between each two sets of based-level model.
models.fitted
the base-level LASSO regression models fitted by the Bagging.lasso function.
models.trimmed
the trimmed base-level models fitted by the Bagging.lasso function if the
trimmed bagging strategy is performed.
y.true
the true values of reponse vector y.
conv.scores
the score matrix generated in the Monte Carlo cross-entropy algorithm according
to the validation measures defined.
importance
the importance socres of variables identified by the Bagging.lasso model.
References
[1] Guo, P., Zeng, F., Hu, X., Zhang, D., Zhu, S., Deng, Y., & Hao, Y. (2015). Improved Variable
Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B
Infection Relevant Factors in Community Residents. PLoS One, 27;10(7):e0134151.
[2] Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the royal
statistical society series B (statistical methodology), 73(3):273-282.
[3] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Examples
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# Example 1: Bagging LASSO linear regression model.
library(mlbench)
set.seed(0123)
mydata <- mlbench.threenorm(100, d=10)
x <- mydata$x
y <- mydata$classes
mydata <- as.data.frame(cbind(x, y))
colnames(mydata) <- c(paste("A", 1:10, sep=""), "y")
mydata$y <- ifelse(mydata$y==1, 0, 1)
# Split into training and testing data.
S1 <- as.vector(which(mydata$y==0))
S2 <- as.vector(which(mydata$y==1))
S3 <- sample(S1, ceiling(length(S1)*0.8), replace=FALSE)
S4 <- sample(S2, ceiling(length(S2)*0.8), replace=FALSE)
TrainInd <- c(S3, S4)
TestInd <- setdiff(1:length(mydata$y), TrainInd)
TrainXY <- mydata[TrainInd, ]
TestXY <- mydata[TestInd, ]
# Fit a bagging LASSO linear regression model, where the parameters
# of M in the following example is set as small values to reduce the
# running time, however the default value is proposed.
Bagging.fit <- Bagging.lasso(x=TrainXY[, -10], y=TrainXY[, 10],
family=c("gaussian"), M=2, predictor.subset=round((9/10)*ncol(x)),
predictor.importance=TRUE, trimmed=FALSE, weighted=TRUE, seed=0123)
# Print a 'bagging' object fitted by the Bagging.fit function.
Print.bagging(Bagging.fit)
# Make predictions from a bagging LASSO linear regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -10], y=NULL, trimmed=FALSE)
pred
# Generate the plot of variable importance.
Plot.importance(Bagging.fit)
# Example 2: Bagging LASSO logistic regression model.
library(mlbench)
set.seed(0123)
mydata <- mlbench.threenorm(100, d=10)
x <- mydata$x
y <- mydata$classes
mydata <- as.data.frame(cbind(x, y))
colnames(mydata) <- c(paste("A", 1:10, sep=""), "y")
mydata$y <- ifelse(mydata$y==1, 0, 1)
# Split into training and testing data.
S1 <- as.vector(which(mydata$y==0))
S2 <- as.vector(which(mydata$y==1))
S3 <- sample(S1, ceiling(length(S1)*0.8), replace=FALSE)
S4 <- sample(S2, ceiling(length(S2)*0.8), replace=FALSE)
TrainInd <- c(S3, S4)
TestInd <- setdiff(1:length(mydata$y), TrainInd)
TrainXY <- mydata[TrainInd, ]
TestXY <- mydata[TestInd, ]
# Fit a bagging LASSO logistic regression model, where the parameters
# of M in the following example is set as small values to reduce the
# running time, however the default value is proposed.
Bagging.fit <- Bagging.lasso(x=TrainXY[, -11], y=TrainXY[, 11],
family=c("binomial"), M=2, predictor.subset=round((9/10)*ncol(x)),
predictor.importance=TRUE, trimmed=FALSE, weighted=TRUE, seed=0123)
# Print a 'bagging' object fitted by the Bagging.fit function.
Print.bagging(Bagging.fit)
# Make predictions from a bagging LASSO logistic regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -11], y=NULL, trimmed=FALSE)
pred
# Generate the plot of variable importance.
Plot.importance(Bagging.fit)
Example output
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-12
Iter 1
Iter 2
$family
[1] "gaussian"
$M
[1] 2
$predictor.subset
[1] 9
$subspace.size
[1] 10
$validation.metric
[1] "rmse" "mae" "re" "smape"
$boot.scale
[1] 1
$distance
[1] "Spearman"
$models.fitted
$models.fitted[[1]]
$lambda
[1] 0.334996951 0.305236745 0.278120354 0.253412908 0.230900404 0.210387848
[7] 0.191697572 0.174667688 0.159150692 0.145012183 0.132129700 0.120391662
[13] 0.109696399 0.099951273 0.091071877 0.082981303 0.075609472 0.068892535
[19] 0.062772312 0.057195793 0.052114677 0.047484952 0.043266520 0.039422842
[25] 0.035920625 0.032729536 0.029821934 0.027172636 0.024758693 0.022559199
[31] 0.020555102 0.018729044 0.017065207 0.015549181 0.014167835 0.012909203
[37] 0.011762385 0.010717447 0.009765339 0.008897813 0.008107356 0.007387121
[43] 0.006730869 0.006132917 0.005588086 0.005091656 0.004639327 0.004227182
[49] 0.003851651 0.003509481 0.003197708 0.002913633 0.002654794 0.002418949
[55] 0.002204056 0.002008254 0.001829846 0.001667288 0.001519170 0.001384212
[61] 0.001261242 0.001149197 0.001047105
$cvm
[1] 1.170612 1.171834 1.167885 1.158435 1.148482 1.139699 1.131608 1.123252
[9] 1.115103 1.106609 1.097212 1.086507 1.077951 1.070438 1.063447 1.057730
[17] 1.053037 1.049194 1.046098 1.043674 1.042377 1.042595 1.043767 1.045995
[25] 1.048606 1.051590 1.054781 1.057938 1.061084 1.064116 1.066907 1.069368
[33] 1.071647 1.073573 1.075355 1.077017 1.078578 1.080044 1.081413 1.082683
[41] 1.083853 1.084938 1.085952 1.086871 1.087723 1.088511 1.089230 1.089902
[49] 1.090525 1.091127 1.091690 1.092116 1.092455 1.092752 1.093022 1.093276
[57] 1.093510 1.093738 1.093911 1.094093 1.094257 1.094407 1.094545
$cvsd
[1] 0.1485115 0.1478064 0.1468317 0.1447677 0.1425382 0.1404797 0.1383893
[8] 0.1357820 0.1337884 0.1324965 0.1314530 0.1307501 0.1306333 0.1302056
[15] 0.1293205 0.1287382 0.1283754 0.1281913 0.1281387 0.1281642 0.1280273
[22] 0.1277982 0.1276712 0.1275375 0.1274735 0.1274279 0.1274987 0.1276283
[29] 0.1277789 0.1279861 0.1281145 0.1280807 0.1280520 0.1280708 0.1280963
[36] 0.1281242 0.1281533 0.1281877 0.1282255 0.1282668 0.1283050 0.1283450
[43] 0.1283894 0.1284260 0.1284595 0.1284920 0.1285241 0.1285561 0.1285885
[50] 0.1286352 0.1286842 0.1287594 0.1288378 0.1289120 0.1289809 0.1290423
[57] 0.1290980 0.1291455 0.1291952 0.1292374 0.1292775 0.1293135 0.1293460
$cvup
[1] 1.319123 1.319641 1.314717 1.303202 1.291021 1.280179 1.269998 1.259034
[9] 1.248892 1.239105 1.228665 1.217257 1.208585 1.200644 1.192767 1.186468
[17] 1.181412 1.177385 1.174237 1.171838 1.170405 1.170394 1.171439 1.173532
[25] 1.176079 1.179018 1.182280 1.185566 1.188862 1.192102 1.195021 1.197449
[33] 1.199698 1.201644 1.203451 1.205141 1.206732 1.208232 1.209639 1.210950
[41] 1.212158 1.213283 1.214341 1.215297 1.216183 1.217003 1.217755 1.218458
[49] 1.219113 1.219762 1.220375 1.220876 1.221293 1.221664 1.222003 1.222318
[57] 1.222608 1.222883 1.223107 1.223330 1.223535 1.223721 1.223891
$cvlo
[1] 1.0221003 1.0240278 1.0210534 1.0136670 1.0059441 0.9992192 0.9932191
[8] 0.9874701 0.9813149 0.9741120 0.9657590 0.9557566 0.9473180 0.9402323
[15] 0.9341263 0.9289920 0.9246613 0.9210022 0.9179595 0.9155100 0.9143500
[22] 0.9147973 0.9160961 0.9184573 0.9211321 0.9241623 0.9272827 0.9303098
[29] 0.9333047 0.9361300 0.9387920 0.9412873 0.9435945 0.9455026 0.9472584
[36] 0.9488926 0.9504252 0.9518561 0.9531877 0.9544162 0.9555482 0.9565934
[43] 0.9575622 0.9584446 0.9592639 0.9600192 0.9607064 0.9613463 0.9619363
[50] 0.9624916 0.9630061 0.9633570 0.9636175 0.9638398 0.9640413 0.9642338
[57] 0.9644121 0.9645924 0.9647161 0.9648556 0.9649797 0.9650940 0.9651992
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 3 3 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
5 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62
9 9 9
$name
mse
"Mean-Squared Error"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = trainY, family = "gaussian")
Df %Dev Lambda
[1,] 0 0.00000 0.3350000
[2,] 3 0.01892 0.3052000
[3,] 3 0.05056 0.2781000
[4,] 3 0.07684 0.2534000
[5,] 3 0.09865 0.2309000
[6,] 3 0.11680 0.2104000
[7,] 3 0.13180 0.1917000
[8,] 4 0.14530 0.1747000
[9,] 4 0.15670 0.1592000
[10,] 5 0.17030 0.1450000
[11,] 5 0.18290 0.1321000
[12,] 5 0.19330 0.1204000
[13,] 5 0.20200 0.1097000
[14,] 5 0.20920 0.0999500
[15,] 5 0.21510 0.0910700
[16,] 5 0.22010 0.0829800
[17,] 5 0.22420 0.0756100
[18,] 5 0.22760 0.0688900
[19,] 5 0.23050 0.0627700
[20,] 5 0.23280 0.0572000
[21,] 5 0.23480 0.0521100
[22,] 6 0.23650 0.0474800
[23,] 6 0.23800 0.0432700
[24,] 6 0.23920 0.0394200
[25,] 6 0.24030 0.0359200
[26,] 6 0.24110 0.0327300
[27,] 7 0.24240 0.0298200
[28,] 7 0.24400 0.0271700
[29,] 7 0.24530 0.0247600
[30,] 7 0.24640 0.0225600
[31,] 7 0.24730 0.0205600
[32,] 7 0.24800 0.0187300
[33,] 7 0.24860 0.0170700
[34,] 7 0.24910 0.0155500
[35,] 7 0.24950 0.0141700
[36,] 7 0.24990 0.0129100
[37,] 8 0.25020 0.0117600
[38,] 8 0.25050 0.0107200
[39,] 8 0.25080 0.0097650
[40,] 8 0.25100 0.0088980
[41,] 9 0.25120 0.0081070
[42,] 9 0.25130 0.0073870
[43,] 9 0.25140 0.0067310
[44,] 9 0.25150 0.0061330
[45,] 9 0.25160 0.0055880
[46,] 9 0.25170 0.0050920
[47,] 9 0.25170 0.0046390
[48,] 9 0.25180 0.0042270
[49,] 9 0.25180 0.0038520
[50,] 9 0.25190 0.0035090
[51,] 9 0.25190 0.0031980
[52,] 9 0.25190 0.0029140
[53,] 9 0.25190 0.0026550
[54,] 9 0.25190 0.0024190
[55,] 9 0.25200 0.0022040
[56,] 9 0.25200 0.0020080
[57,] 9 0.25200 0.0018300
[58,] 9 0.25200 0.0016670
[59,] 9 0.25200 0.0015190
[60,] 9 0.25200 0.0013840
[61,] 9 0.25200 0.0012610
[62,] 9 0.25200 0.0011490
[63,] 9 0.25200 0.0010470
[64,] 9 0.25200 0.0009541
[65,] 9 0.25200 0.0008693
$lambda.min
[1] 0.05211468
$lambda.1se
[1] 0.2781204
attr(,"class")
[1] "cv.glmnet"
$models.fitted[[2]]
$lambda
[1] 0.2576272052 0.2347403143 0.2138866318 0.1948855329 0.1775724393
[6] 0.1617973932 0.1474237587 0.1343270383 0.1223937944 0.1115206671
[11] 0.1016134784 0.0925864170 0.0843612949 0.0768668700 0.0700382291
[16] 0.0638162259 0.0581469682 0.0529813518 0.0482746346 0.0439860491
[21] 0.0400784497 0.0365179907 0.0332738331 0.0303178774 0.0276245208
[26] 0.0251704345 0.0229343624 0.0208969369 0.0190405106 0.0173490041
[31] 0.0158077664 0.0144034480 0.0131238854 0.0119579956 0.0108956802
[36] 0.0099277380 0.0090457851 0.0082421825 0.0075099697 0.0068428047
[41] 0.0062349088 0.0056810167 0.0051763308 0.0047164799 0.0042974808
[46] 0.0039157044 0.0035678441 0.0032508866 0.0029620868 0.0026989432
[51] 0.0024591765 0.0022407100 0.0020416515 0.0018602768 0.0016950150
[56] 0.0015444345 0.0014072312 0.0012822167 0.0011683081 0.0010645188
[61] 0.0009699499 0.0008837822
$cvm
[1] 1.0193890 1.0206737 1.0187454 1.0128485 1.0063380 1.0000077 0.9941295
[8] 0.9879911 0.9816970 0.9762128 0.9721782 0.9699116 0.9688261 0.9686737
[15] 0.9697535 0.9727381 0.9767684 0.9809112 0.9854143 0.9900350 0.9947992
[22] 1.0004479 1.0064029 1.0126150 1.0186856 1.0244149 1.0297851 1.0345482
[29] 1.0388061 1.0428233 1.0463685 1.0495586 1.0524386 1.0550940 1.0575701
[36] 1.0598734 1.0619954 1.0639591 1.0657745 1.0674508 1.0689375 1.0702232
[43] 1.0714150 1.0725131 1.0735200 1.0744367 1.0752916 1.0760645 1.0767596
[50] 1.0774099 1.0780050 1.0785157 1.0789579 1.0793509 1.0797238 1.0800779
[57] 1.0803763 1.0806615 1.0809128 1.0811314 1.0813496 1.0815404
$cvsd
[1] 0.1169343 0.1173638 0.1173544 0.1184963 0.1192615 0.1190444 0.1183406
[8] 0.1173577 0.1162086 0.1151823 0.1142939 0.1134235 0.1127108 0.1120990
[15] 0.1115532 0.1109913 0.1104222 0.1100619 0.1099154 0.1099646 0.1102156
[22] 0.1110962 0.1120843 0.1131655 0.1142901 0.1154163 0.1165235 0.1174469
[29] 0.1182046 0.1188957 0.1195249 0.1201614 0.1207885 0.1213875 0.1219495
[36] 0.1224758 0.1229673 0.1234225 0.1238473 0.1242468 0.1246533 0.1250727
[43] 0.1254564 0.1258077 0.1261303 0.1264251 0.1266933 0.1269410 0.1271661
[50] 0.1273753 0.1275632 0.1277191 0.1278522 0.1279684 0.1280796 0.1281825
[57] 0.1282761 0.1283594 0.1284387 0.1285117 0.1285799 0.1286414
$cvup
[1] 1.136323 1.138037 1.136100 1.131345 1.125599 1.119052 1.112470 1.105349
[9] 1.097906 1.091395 1.086472 1.083335 1.081537 1.080773 1.081307 1.083729
[17] 1.087191 1.090973 1.095330 1.100000 1.105015 1.111544 1.118487 1.125781
[25] 1.132976 1.139831 1.146309 1.151995 1.157011 1.161719 1.165893 1.169720
[33] 1.173227 1.176481 1.179520 1.182349 1.184963 1.187382 1.189622 1.191698
[41] 1.193591 1.195296 1.196871 1.198321 1.199650 1.200862 1.201985 1.203006
[49] 1.203926 1.204785 1.205568 1.206235 1.206810 1.207319 1.207803 1.208260
[57] 1.208652 1.209021 1.209351 1.209643 1.209929 1.210182
$cvlo
[1] 0.9024546 0.9033099 0.9013909 0.8943522 0.8870765 0.8809633 0.8757889
[8] 0.8706334 0.8654883 0.8610305 0.8578843 0.8564881 0.8561153 0.8565747
[15] 0.8582003 0.8617468 0.8663461 0.8708493 0.8754989 0.8800704 0.8845836
[22] 0.8893517 0.8943186 0.8994495 0.9043955 0.9089986 0.9132616 0.9171012
[29] 0.9206015 0.9239276 0.9268436 0.9293973 0.9316500 0.9337065 0.9356206
[36] 0.9373976 0.9390280 0.9405366 0.9419272 0.9432040 0.9442842 0.9451505
[43] 0.9459586 0.9467054 0.9473897 0.9480116 0.9485984 0.9491234 0.9495936
[50] 0.9500346 0.9504418 0.9507966 0.9511057 0.9513826 0.9516442 0.9518954
[57] 0.9521002 0.9523021 0.9524741 0.9526196 0.9527697 0.9528990
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 2 2 3 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
6 6 6 6 6 7 8 8 8 8 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61
9 9
$name
mse
"Mean-Squared Error"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = trainY, family = "gaussian")
Df %Dev Lambda
[1,] 0 0.00000 0.2576000
[2,] 2 0.01339 0.2347000
[3,] 2 0.03147 0.2139000
[4,] 3 0.04921 0.1949000
[5,] 4 0.06632 0.1776000
[6,] 4 0.08253 0.1618000
[7,] 4 0.09599 0.1474000
[8,] 4 0.10720 0.1343000
[9,] 4 0.11640 0.1224000
[10,] 5 0.12440 0.1115000
[11,] 5 0.13130 0.1016000
[12,] 5 0.13710 0.0925900
[13,] 5 0.14190 0.0843600
[14,] 5 0.14580 0.0768700
[15,] 6 0.15060 0.0700400
[16,] 6 0.15470 0.0638200
[17,] 6 0.15810 0.0581500
[18,] 6 0.16090 0.0529800
[19,] 6 0.16320 0.0482700
[20,] 6 0.16520 0.0439900
[21,] 6 0.16680 0.0400800
[22,] 6 0.16810 0.0365200
[23,] 6 0.16920 0.0332700
[24,] 6 0.17010 0.0303200
[25,] 6 0.17090 0.0276200
[26,] 7 0.17210 0.0251700
[27,] 8 0.17360 0.0229300
[28,] 8 0.17500 0.0209000
[29,] 8 0.17610 0.0190400
[30,] 8 0.17710 0.0173500
[31,] 9 0.17780 0.0158100
[32,] 9 0.17850 0.0144000
[33,] 9 0.17910 0.0131200
[34,] 9 0.17960 0.0119600
[35,] 9 0.18000 0.0109000
[36,] 9 0.18030 0.0099280
[37,] 9 0.18060 0.0090460
[38,] 9 0.18080 0.0082420
[39,] 9 0.18100 0.0075100
[40,] 9 0.18120 0.0068430
[41,] 9 0.18130 0.0062350
[42,] 9 0.18140 0.0056810
[43,] 9 0.18150 0.0051760
[44,] 9 0.18160 0.0047160
[45,] 9 0.18160 0.0042970
[46,] 9 0.18170 0.0039160
[47,] 9 0.18170 0.0035680
[48,] 9 0.18180 0.0032510
[49,] 9 0.18180 0.0029620
[50,] 9 0.18180 0.0026990
[51,] 9 0.18190 0.0024590
[52,] 9 0.18190 0.0022410
[53,] 9 0.18190 0.0020420
[54,] 9 0.18190 0.0018600
[55,] 9 0.18190 0.0016950
[56,] 9 0.18190 0.0015440
[57,] 9 0.18190 0.0014070
[58,] 9 0.18190 0.0012820
[59,] 9 0.18190 0.0011680
[60,] 9 0.18190 0.0010650
[61,] 9 0.18190 0.0009699
[62,] 9 0.18190 0.0008838
[63,] 9 0.18190 0.0008053
[64,] 9 0.18190 0.0007337
[65,] 9 0.18190 0.0006685
$lambda.min
[1] 0.07686687
$lambda.1se
[1] 0.2576272
attr(,"class")
[1] "cv.glmnet"
$models.trimmed
list()
$y.true
[1] -0.83775479 -1.14851936 0.33924804 -1.43829554 -1.18967101 -0.41107042
[7] 0.25222901 1.59426588 -2.06595813 1.02968473 -0.95343363 1.01458714
[13] 0.28231774 -1.68147706 0.57755498 0.63981125 -0.71844715 -0.78385149
[19] 1.12153585 1.41082054 2.31689124 0.05806684 -0.83275425 -0.19727404
[25] 0.75305102 -0.88536477 1.04273063 0.09621294 0.56114745 -0.01265842
[31] 1.08066531 -0.73433879 -1.15332488 -2.51413424 -1.83666523 -0.37910482
[37] -0.71644425 0.72703906 0.15528332 0.26264430 -0.25781196 -1.84640000
[43] -0.60635531 -1.48151667 -0.20135660 -2.24649499 -0.48603835 -0.37209404
[49] 0.08876528 1.03353537 -1.21352291 0.01505783 -1.94896593 -0.66214950
[55] -0.33336863 -0.22916520 -1.26203427 -1.32576006 -0.91152770 -0.88164621
[61] 0.99116568 -1.07941484 -2.71094480 -2.07449018 -0.23715966 -0.79074956
[67] 0.35391033 0.91415362 -0.51173620 0.73567712 -1.28623536 0.32291011
[73] 1.45426190 -0.82834478 0.37822243 -0.38892281 -1.84089825 0.11562563
[79] 0.38670154 0.35751608
$conv.scores
$conv.scores$ranks
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "5" "10" "8" "7" "4" "3" "2" "1" "9" "6"
[2,] "5" "10" "8" "7" "4" "3" "2" "1" "9" "6"
[3,] "1" "9" "6" "3" "2" "4" "7" "8" "10" "5"
[4,] "5" "10" "4" "7" "8" "2" "3" "9" "6" "1"
$conv.scores$weights
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.9770869 0.9770843 0.9747954 0.9747930 0.9697949 0.9658363 0.9653150
[2,] 1.2499899 1.2499870 1.2460446 1.2460409 1.2393659 1.2319513 1.2314571
[3,] 0.7616848 0.7609513 0.7375416 0.7373028 0.7370965 0.7358816 0.7355996
[4,] 0.7672880 0.7672870 0.7666914 0.7659642 0.7659635 0.7627853 0.7625705
[,8] [,9] [,10]
[1,] 0.9618191 0.9606570 0.9594676
[2,] 1.2300032 1.2289072 1.2220636
[3,] 0.7355948 0.7346079 0.7346060
[4,] 0.7578440 0.7577510 0.7568872
$importance
[,1]
A1 0.12302406
A2 0.00000000
A3 0.06926666
A4 0.06271640
A5 0.13859420
A6 0.06824081
A7 0.04522673
A8 0.07795560
A9 0.00000000
y 0.23927699
attr(,"class")
[1] "bagging"
$y.new
[1] 0.04447873 -0.12871182 -0.23723027 -0.02300019 -0.03851816 -0.44884138
[7] -0.80601068 -0.71891720 0.03472918 0.05418729 -0.06476798 -0.17292143
[13] -0.61339668 -0.26723470 -0.28321384 -0.67176706 -0.51268009 -0.61530304
[19] -0.51708935 -0.81701762
$y.se
[1] 0.266523429 0.169630890 0.055204841 0.308323909 0.044965845 0.313516781
[7] 0.343833731 0.129416122 0.036851445 0.153187885 0.219717119 0.004629453
[13] 0.015876657 0.015483059 0.138680946 0.079378230 0.062891881 0.051941293
[19] 0.325012999 0.293268186
$predicted.matrix
[,1] [,2]
[1,] 0.311002160 -0.222044699
[2,] 0.040919067 -0.298342714
[3,] -0.292435110 -0.182025429
[4,] 0.285323717 -0.331324101
[5,] -0.083484002 0.006447688
[6,] -0.762358161 -0.135324600
[7,] -1.149844416 -0.462176953
[8,] -0.848333322 -0.589501079
[9,] -0.002122263 0.071580627
[10,] -0.099000596 0.207375173
[11,] -0.284485102 0.154949137
[12,] -0.168291977 -0.177550882
[13,] -0.597520023 -0.629273337
[14,] -0.282717763 -0.251751645
[15,] -0.421894781 -0.144532889
[16,] -0.592388826 -0.751145287
[17,] -0.575571975 -0.449788213
[18,] -0.667244329 -0.563361743
[19,] -0.842102350 -0.192076351
[20,] -1.110285808 -0.523749436
attr(,"class")
[1] "BaggingPrediction"
Iter 1
Iter 2
$family
[1] "binomial"
$M
[1] 2
$predictor.subset
[1] 9
$subspace.size
[1] 10
$validation.metric
[1] "accuracy" "sensitivity" "specificity" "auc" "kia"
$boot.scale
[1] 1
$distance
[1] "Spearman"
$models.fitted
$models.fitted[[1]]
$lambda
[1] 0.2317925169 0.2112007086 0.1924382197 0.1753425387 0.1597655908
[6] 0.1455724560 0.1326402002 0.1208568104 0.1101202245 0.1003374472
[11] 0.0914237450 0.0833019114 0.0759015992 0.0691587103 0.0630148411
[16] 0.0574167763 0.0523160282 0.0476684164 0.0434336857 0.0395751567
[21] 0.0360594089 0.0328559904 0.0299371548 0.0272776204 0.0248543518
[26] 0.0226463597 0.0206345195 0.0188014057 0.0171311406 0.0156092573
[31] 0.0142225739 0.0129590796 0.0118078307 0.0107588556 0.0098030685
[36] 0.0089321909 0.0081386797 0.0074156618 0.0067568749 0.0061566127
[41] 0.0056096762 0.0051113280 0.0046572517 0.0042435143 0.0038665322
[46] 0.0035230402 0.0032100630 0.0029248898 0.0026650507 0.0024282949
[51] 0.0022125719 0.0020160131 0.0018369161 0.0016737295 0.0015250400
[56] 0.0013895596 0.0012661150 0.0011536368 0.0010511509 0.0009577696
[61] 0.0008726839 0.0007951571 0.0007245175 0.0006601534 0.0006015072
$cvm
[1] 1.390173 1.367903 1.342192 1.320709 1.303998 1.286680 1.260045 1.227663
[9] 1.197318 1.173190 1.154497 1.140684 1.131308 1.122471 1.114412 1.106902
[17] 1.100074 1.093191 1.088555 1.084766 1.082259 1.081368 1.080179 1.079510
[25] 1.079812 1.080260 1.081292 1.083419 1.086438 1.090247 1.094154 1.098015
[33] 1.102337 1.107088 1.112051 1.116835 1.121787 1.126853 1.131967 1.137072
[41] 1.142120 1.147071 1.151894 1.156561 1.161054 1.165352 1.169441 1.173328
[49] 1.176984 1.180454 1.183721 1.186783 1.189647 1.192315 1.194777 1.197087
[57] 1.199213 1.201198 1.203039 1.204717 1.206281 1.207707 1.209035 1.210263
[65] 1.211390
$cvsd
[1] 0.01364905 0.01398154 0.01353940 0.01589334 0.01981455 0.02347824
[7] 0.02760413 0.03108016 0.03420529 0.03784397 0.04209536 0.04684658
[13] 0.05266356 0.05914513 0.06560504 0.07180801 0.07834203 0.08502745
[19] 0.09142801 0.09762097 0.10356497 0.10925687 0.11463930 0.11984261
[25] 0.12470843 0.12893245 0.13303690 0.13708268 0.14105121 0.14497771
[31] 0.14880315 0.15230877 0.15572848 0.15906025 0.16227773 0.16532385
[37] 0.16825828 0.17107872 0.17378063 0.17636080 0.17881741 0.18114930
[43] 0.18335678 0.18544105 0.18740365 0.18924620 0.19097283 0.19258829
[49] 0.19409078 0.19549721 0.19680761 0.19802528 0.19915447 0.20019774
[55] 0.20115984 0.20204958 0.20286787 0.20362746 0.20432956 0.20497083
[61] 0.20556062 0.20609455 0.20659363 0.20705534 0.20747840
$cvup
[1] 1.403822 1.381884 1.355731 1.336602 1.323812 1.310158 1.287649 1.258743
[9] 1.231523 1.211034 1.196592 1.187531 1.183972 1.181616 1.180017 1.178710
[17] 1.178416 1.178219 1.179983 1.182387 1.185824 1.190625 1.194818 1.199353
[25] 1.204521 1.209193 1.214329 1.220501 1.227489 1.235225 1.242957 1.250324
[33] 1.258066 1.266148 1.274328 1.282159 1.290046 1.297932 1.305748 1.313433
[41] 1.320937 1.328220 1.335251 1.342002 1.348458 1.354598 1.360414 1.365917
[49] 1.371075 1.375952 1.380529 1.384808 1.388801 1.392513 1.395937 1.399137
[57] 1.402081 1.404826 1.407369 1.409688 1.411842 1.413802 1.415629 1.417318
[65] 1.418868
$cvlo
[1] 1.3765243 1.3539213 1.3286522 1.3048155 1.2841830 1.2632013 1.2324406
[8] 1.1965829 1.1631129 1.1353460 1.1124013 1.0938379 1.0786444 1.0633256
[15] 1.0488068 1.0350944 1.0217322 1.0081639 0.9971273 0.9871455 0.9786940
[22] 0.9721111 0.9655396 0.9596674 0.9551040 0.9513277 0.9482551 0.9463361
[29] 0.9453867 0.9452695 0.9453508 0.9457060 0.9466089 0.9480277 0.9497729
[36] 0.9515116 0.9535290 0.9557743 0.9581863 0.9607113 0.9633024 0.9659219
[43] 0.9685371 0.9711203 0.9736508 0.9761054 0.9784679 0.9807400 0.9828936
[50] 0.9849572 0.9869136 0.9887573 0.9904921 0.9921174 0.9936171 0.9950374
[57] 0.9963455 0.9975708 0.9987099 0.9997466 1.0007208 1.0016128 1.0024416
[64] 1.0032073 1.0039113
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 1 1 1 2 2 3 3 3 3 3 4 5 5 5 6 6 7 8 8
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62 s63 s64
9 9 9 9 9
$name
deviance
"Binomial Deviance"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = as.factor(trainY), family = "binomial")
Df %Dev Lambda
[1,] 0 8.023e-16 0.2318000
[2,] 1 2.647e-02 0.2112000
[3,] 1 4.860e-02 0.1924000
[4,] 1 6.724e-02 0.1753000
[5,] 2 8.603e-02 0.1598000
[6,] 2 1.110e-01 0.1456000
[7,] 3 1.357e-01 0.1326000
[8,] 3 1.662e-01 0.1209000
[9,] 3 1.923e-01 0.1101000
[10,] 3 2.148e-01 0.1003000
[11,] 3 2.343e-01 0.0914200
[12,] 4 2.520e-01 0.0833000
[13,] 5 2.698e-01 0.0759000
[14,] 5 2.874e-01 0.0691600
[15,] 5 3.027e-01 0.0630100
[16,] 6 3.181e-01 0.0574200
[17,] 6 3.321e-01 0.0523200
[18,] 7 3.460e-01 0.0476700
[19,] 8 3.594e-01 0.0434300
[20,] 8 3.716e-01 0.0395800
[21,] 8 3.824e-01 0.0360600
[22,] 8 3.920e-01 0.0328600
[23,] 8 4.004e-01 0.0299400
[24,] 8 4.078e-01 0.0272800
[25,] 8 4.143e-01 0.0248500
[26,] 9 4.210e-01 0.0226500
[27,] 9 4.270e-01 0.0206300
[28,] 9 4.323e-01 0.0188000
[29,] 9 4.369e-01 0.0171300
[30,] 9 4.409e-01 0.0156100
[31,] 9 4.444e-01 0.0142200
[32,] 9 4.475e-01 0.0129600
[33,] 9 4.501e-01 0.0118100
[34,] 9 4.524e-01 0.0107600
[35,] 9 4.544e-01 0.0098030
[36,] 9 4.561e-01 0.0089320
[37,] 9 4.576e-01 0.0081390
[38,] 9 4.588e-01 0.0074160
[39,] 9 4.599e-01 0.0067570
[40,] 9 4.608e-01 0.0061570
[41,] 9 4.616e-01 0.0056100
[42,] 9 4.622e-01 0.0051110
[43,] 9 4.628e-01 0.0046570
[44,] 9 4.633e-01 0.0042440
[45,] 9 4.637e-01 0.0038670
[46,] 9 4.640e-01 0.0035230
[47,] 9 4.643e-01 0.0032100
[48,] 9 4.645e-01 0.0029250
[49,] 9 4.647e-01 0.0026650
[50,] 9 4.649e-01 0.0024280
[51,] 9 4.651e-01 0.0022130
[52,] 9 4.652e-01 0.0020160
[53,] 9 4.653e-01 0.0018370
[54,] 9 4.654e-01 0.0016740
[55,] 9 4.654e-01 0.0015250
[56,] 9 4.655e-01 0.0013900
[57,] 9 4.655e-01 0.0012660
[58,] 9 4.656e-01 0.0011540
[59,] 9 4.656e-01 0.0010510
[60,] 9 4.656e-01 0.0009578
[61,] 9 4.657e-01 0.0008727
[62,] 9 4.657e-01 0.0007952
[63,] 9 4.657e-01 0.0007245
[64,] 9 4.657e-01 0.0006602
[65,] 9 4.657e-01 0.0006015
[66,] 9 4.657e-01 0.0005481
$lambda.min
[1] 0.02727762
$lambda.1se
[1] 0.1101202
attr(,"class")
[1] "cv.glmnet"
$models.fitted[[2]]
$lambda
[1] 0.2245280966 0.2045816393 0.1864071703 0.1698472711 0.1547585076
[6] 0.1410101884 0.1284832319 0.1170691357 0.1066690363 0.0971928531
[11] 0.0885585079 0.0806912141 0.0735228291 0.0669912636 0.0610399444
[16] 0.0556173239 0.0506764342 0.0461744794 0.0420724659 0.0383348640
[21] 0.0349293004 0.0318262776 0.0289989188 0.0264227348 0.0240754118
[26] 0.0219366186 0.0199878299 0.0182121662 0.0165942475 0.0151200603
[31] 0.0137768358 0.0125529397 0.0114377711 0.0104216711 0.0094958385
[36] 0.0086522544 0.0078836120 0.0071832536 0.0065451132 0.0059636633
[41] 0.0054338679 0.0049511381 0.0045112926 0.0041105218 0.0037453544
[46] 0.0034126274 0.0031094590 0.0028332232 0.0025815275 0.0023521917
[51] 0.0021432295 0.0019528309 0.0017793468 0.0016212745 0.0014772449
[56] 0.0013460106 0.0012264347 0.0011174816 0.0010182076 0.0009277529
[61] 0.0008453339 0.0007702367 0.0007018110
$cvm
[1] 1.436664 1.423640 1.398594 1.377528 1.354445 1.328559 1.295393 1.261669
[9] 1.231858 1.206731 1.185966 1.167854 1.152180 1.137593 1.125069 1.112630
[17] 1.101179 1.088093 1.075992 1.066618 1.057952 1.051075 1.046205 1.043135
[25] 1.041544 1.040995 1.041384 1.042551 1.044292 1.046487 1.049111 1.052060
[33] 1.055096 1.058048 1.061124 1.064315 1.067570 1.070847 1.074098 1.077316
[41] 1.080466 1.083330 1.085997 1.088573 1.091049 1.093416 1.095671 1.097687
[49] 1.099398 1.101037 1.102592 1.104056 1.105434 1.106698 1.107886 1.109012
[57] 1.110061 1.111036 1.111940 1.112778 1.113512 1.114225 1.114870
$cvsd
[1] 0.02516253 0.02666794 0.02625097 0.03000431 0.03516634 0.04126805
[7] 0.04801390 0.05488220 0.06169901 0.06846521 0.07524027 0.08207557
[13] 0.08905053 0.09601892 0.10323588 0.11050499 0.11712924 0.12123882
[19] 0.12540364 0.12970617 0.13410626 0.13848489 0.14277816 0.14706867
[25] 0.15137164 0.15567526 0.15992841 0.16416856 0.16838316 0.17255072
[31] 0.17663619 0.18062496 0.18450766 0.18834344 0.19206159 0.19563984
[37] 0.19907234 0.20235475 0.20547509 0.20844955 0.21126974 0.21386270
[43] 0.21626923 0.21853578 0.22066576 0.22266305 0.22453187 0.22618606
[49] 0.22757133 0.22888084 0.23010467 0.23124629 0.23230883 0.23327641
[55] 0.23418586 0.23503541 0.23582198 0.23654890 0.23721987 0.23783853
[61] 0.23838269 0.23890589 0.23938272
$cvup
[1] 1.461826 1.450308 1.424845 1.407533 1.389612 1.369827 1.343407 1.316551
[9] 1.293557 1.275196 1.261207 1.249930 1.241230 1.233612 1.228305 1.223135
[17] 1.218309 1.209332 1.201396 1.196324 1.192058 1.189560 1.188983 1.190204
[25] 1.192916 1.196671 1.201313 1.206720 1.212675 1.219038 1.225747 1.232685
[33] 1.239604 1.246392 1.253186 1.259955 1.266642 1.273201 1.279573 1.285766
[41] 1.291735 1.297192 1.302266 1.307109 1.311714 1.316079 1.320203 1.323873
[49] 1.326969 1.329918 1.332697 1.335303 1.337743 1.339975 1.342072 1.344047
[57] 1.345883 1.347585 1.349160 1.350616 1.351895 1.353130 1.354252
$cvlo
[1] 1.4115010 1.3969717 1.3723426 1.3475241 1.3192791 1.2872912 1.2473788
[8] 1.2067869 1.1701591 1.1382657 1.1107262 1.0857786 1.0631293 1.0415745
[15] 1.0218331 1.0021252 0.9840502 0.9668547 0.9505887 0.9369114 0.9238453
[22] 0.9125904 0.9034264 0.8960662 0.8901723 0.8853201 0.8814561 0.8783828
[29] 0.8759088 0.8739362 0.8724747 0.8714346 0.8705888 0.8697047 0.8690629
[36] 0.8686748 0.8684972 0.8684918 0.8686232 0.8688664 0.8691960 0.8694670
[43] 0.8697277 0.8700376 0.8703830 0.8707525 0.8711389 0.8715005 0.8718262
[50] 0.8721565 0.8724873 0.8728102 0.8731254 0.8734217 0.8737002 0.8739763
[57] 0.8742389 0.8744870 0.8747204 0.8749393 0.8751295 0.8753186 0.8754868
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 1 1 1 4 4 4 4 4 4 6 6 6 6 7 7 8 8 8 8
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62
9 9 9
$name
deviance
"Binomial Deviance"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = as.factor(trainY), family = "binomial")
Df %Dev Lambda
[1,] 0 6.418e-16 0.2245000
[2,] 1 2.484e-02 0.2046000
[3,] 1 4.563e-02 0.1864000
[4,] 1 6.312e-02 0.1698000
[5,] 4 9.377e-02 0.1548000
[6,] 4 1.284e-01 0.1410000
[7,] 4 1.582e-01 0.1285000
[8,] 4 1.840e-01 0.1171000
[9,] 4 2.065e-01 0.1067000
[10,] 4 2.260e-01 0.0971900
[11,] 6 2.452e-01 0.0885600
[12,] 6 2.627e-01 0.0806900
[13,] 6 2.780e-01 0.0735200
[14,] 6 2.913e-01 0.0669900
[15,] 7 3.064e-01 0.0610400
[16,] 7 3.204e-01 0.0556200
[17,] 8 3.340e-01 0.0506800
[18,] 8 3.471e-01 0.0461700
[19,] 8 3.586e-01 0.0420700
[20,] 8 3.687e-01 0.0383300
[21,] 8 3.777e-01 0.0349300
[22,] 8 3.855e-01 0.0318300
[23,] 8 3.924e-01 0.0290000
[24,] 8 3.984e-01 0.0264200
[25,] 8 4.037e-01 0.0240800
[26,] 8 4.083e-01 0.0219400
[27,] 8 4.124e-01 0.0199900
[28,] 9 4.162e-01 0.0182100
[29,] 9 4.198e-01 0.0165900
[30,] 9 4.229e-01 0.0151200
[31,] 9 4.256e-01 0.0137800
[32,] 9 4.279e-01 0.0125500
[33,] 9 4.300e-01 0.0114400
[34,] 9 4.317e-01 0.0104200
[35,] 9 4.332e-01 0.0094960
[36,] 9 4.345e-01 0.0086520
[37,] 9 4.356e-01 0.0078840
[38,] 9 4.366e-01 0.0071830
[39,] 9 4.374e-01 0.0065450
[40,] 9 4.381e-01 0.0059640
[41,] 9 4.387e-01 0.0054340
[42,] 9 4.392e-01 0.0049510
[43,] 9 4.396e-01 0.0045110
[44,] 9 4.400e-01 0.0041110
[45,] 9 4.403e-01 0.0037450
[46,] 9 4.406e-01 0.0034130
[47,] 9 4.408e-01 0.0031090
[48,] 9 4.410e-01 0.0028330
[49,] 9 4.411e-01 0.0025820
[50,] 9 4.413e-01 0.0023520
[51,] 9 4.414e-01 0.0021430
[52,] 9 4.415e-01 0.0019530
[53,] 9 4.416e-01 0.0017790
[54,] 9 4.416e-01 0.0016210
[55,] 9 4.417e-01 0.0014770
[56,] 9 4.417e-01 0.0013460
[57,] 9 4.418e-01 0.0012260
[58,] 9 4.418e-01 0.0011170
[59,] 9 4.418e-01 0.0010180
[60,] 9 4.418e-01 0.0009278
[61,] 9 4.419e-01 0.0008453
[62,] 9 4.419e-01 0.0007702
[63,] 9 4.419e-01 0.0007018
[64,] 9 4.419e-01 0.0006395
[65,] 9 4.419e-01 0.0005827
$lambda.min
[1] 0.02193662
$lambda.1se
[1] 0.08855851
attr(,"class")
[1] "cv.glmnet"
$models.trimmed
list()
$y.true
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[39] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[77] 1 1 1 1
Levels: 0 1
$conv.scores
$conv.scores$ranks
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "3" "4" "5" "6" "7" "10" "1" "2" "9" "8"
[2,] "1" "2" "5" "6" "8" "3" "4" "7" "9" "10"
[3,] "3" "4" "7" "10" "5" "6" "9" "1" "2" "8"
[4,] "1" "2" "7" "3" "5" "6" "9" "4" "10" "8"
[5,] "5" "6" "3" "4" "7" "10" "1" "2" "9" "8"
$conv.scores$weights
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.7500000 0.7500000 0.7500000 0.7500000 0.7500000 0.7500000 0.7187500
[2,] 0.8666667 0.8666667 0.8666667 0.8666667 0.8666667 0.8000000 0.8000000
[3,] 0.7058824 0.7058824 0.7058824 0.7058824 0.6470588 0.6470588 0.6470588
[4,] 0.7882353 0.7705882 0.7686275 0.7588235 0.7588235 0.7588235 0.7549020
[5,] 0.5057915 0.5057915 0.5019455 0.5019455 0.5019455 0.5019455 0.4461538
[,8] [,9] [,10]
[1,] 0.7187500 0.7187500 0.6875000
[2,] 0.8000000 0.8000000 0.8000000
[3,] 0.5882353 0.5882353 0.5294118
[4,] 0.7470588 0.7372549 0.7156863
[5,] 0.4461538 0.4418605 0.3869732
$importance
[,1]
A1 0.2758553
A2 0.5464931
A3 0.8955025
A4 0.2191840
A5 0.2008697
A6 0.1540545
A7 0.5832975
A8 0.7176415
A9 0.1270706
A10 0.5371627
attr(,"class")
[1] "bagging"
$y.new
[1] 0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1
Levels: 0 1
$probabilities
[1] 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 0.5 0.0
[20] 1.0
$predicted.matrix
[,1] [,2]
[1,] 0 0
[2,] 0 0
[3,] 0 0
[4,] 1 1
[5,] 0 0
[6,] 0 0
[7,] 1 1
[8,] 0 0
[9,] 0 0
[10,] 0 0
[11,] 0 0
[12,] 1 1
[13,] 1 1
[14,] 1 1
[15,] 0 0
[16,] 1 1
[17,] 0 0
[18,] 0 1
[19,] 0 0
[20,] 1 1
attr(,"class")
[1] "BaggingPrediction"
SparseLearner documentation built on May 29, 2017, 9:18 p.m.
R Package Documentation
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Description Usage Arguments Details Value References Examples
View source: R/Bagging.lasso.R
Description
This function performs a bagging prediction for linear and logistic regression model using the LASSO selection algorithm.
Usage
1 2 3 4 |
Arguments
x |
input matrix. The dimension of the matrix is nobs x nvars; each row is a vector of observations of the variables. |
y |
response variable. For family="gaussian", y is a vector of quantitative response. For family="binomial" should be a factor with two levels '0' and '1' and the level of '1' is the target class. |
family |
response type (see above). |
M |
the number of base-level models (LASSO linear or logistic regression models) to obtain a final prediction. Note that it also corresponds to the number of bootstrap samples to draw. Defaults to 100. |
subspace.size |
the number of random subspaces to construct an ensemble prediction model. Defaults to 10. |
predictor.subset |
the subset of randomly selected predictors from the training set to reduce the original p-dimensional feature space. Defaults to (9/10)*ncol(x) where ncol(x) represents the the original p-dimensional feature space of input matrix x. |
boot.scale |
the scale of sample size in each bootstrap re-sampling, relative to the original sample size. Defaults to 1.0, equaling to the original size of training samples. |
kfold |
the number of folds of cross validation - default is 10. Although kfold can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is kfold=3. |
predictor.importance |
logical. Should the importance of each predictor in the bagging LASSO model be evaluated? Defaults to TRUE. A permutation-based variable importance measure estimated by the out-of-bag error rate is adapted for the bagging model. |
trimmed |
logical. Should a trimmed bagging strategy be performed? Defaults to FALSE. Traditional bagging draws bootstrap samples from the training sample, applies the base-level model to each bootstrap sample, and then averages over all obtained prediction rules. The idea of trimmed bagging is to exclude the bootstrapped prediction rules that yield the highest error rates and to aggregate over the remaining ones. |
weighted |
logical. Should a weighted rank aggregation procedure be performed? Defaults to TRUE. This procedure uses a Monte Carlo cross-entropy algorithm combining the ranks of a set of based-level model under consideration via a weighted aggregation that optimizes a distance criterion to determine the best performance base-level model. |
verbose |
logical. Should the iterative process information of bagging model be presented? Defaults to TRUE. |
seed |
the seed for random sampling, with the default value 0123. |
Details
This bagging LASSO model Bagging.lasso generates an ensemble prediction based on the L1-regularized linear or logistic regression models. The Bagging.lasso function uses a Monte Carlo cross-entropy algorithm to combine the ranks of a set of based-level LASSO regression model under consideration via a weighted aggregation to determine the best base-level model. In the Bagging.lasso, the glmnet algorithm is performed to fit LASSO model paths for linear and logistic regression using coordinate descent. A random subspace method is employed to improve the predictive performance. In addition, a strategy of trimmed bagging can be defined to exclude the bootstrapped prediction rules that yield the highest error rates and to aggregate over the remaining prediction rules.
Value
family |
the response type. |
M |
the number of base-level models to obtain a bagging prediction. |
predictor.subset |
the subset of randomly selected predictors from the training set to reduce the original p-dimensional feature space. |
subspace.size |
the number of random subspaces to construct an ensemble prediction model. |
validation.metric |
the model validation measures. |
boot.scale |
the scale of sample size in each bootstrap re-sampling, relative to the original sample size. |
distance |
the distance function used in the weighted aggregation to define the similarity between each two sets of based-level model. |
models.fitted |
the base-level LASSO regression models fitted by the Bagging.lasso function. |
models.trimmed |
the trimmed base-level models fitted by the Bagging.lasso function if the trimmed bagging strategy is performed. |
y.true |
the true values of reponse vector y. |
conv.scores |
the score matrix generated in the Monte Carlo cross-entropy algorithm according to the validation measures defined. |
importance |
the importance socres of variables identified by the Bagging.lasso model. |
References
[1] Guo, P., Zeng, F., Hu, X., Zhang, D., Zhu, S., Deng, Y., & Hao, Y. (2015). Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents. PLoS One, 27;10(7):e0134151.
[2] Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the royal statistical society series B (statistical methodology), 73(3):273-282.
[3] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Examples
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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # Example 1: Bagging LASSO linear regression model.
library(mlbench)
set.seed(0123)
mydata <- mlbench.threenorm(100, d=10)
x <- mydata$x
y <- mydata$classes
mydata <- as.data.frame(cbind(x, y))
colnames(mydata) <- c(paste("A", 1:10, sep=""), "y")
mydata$y <- ifelse(mydata$y==1, 0, 1)
# Split into training and testing data.
S1 <- as.vector(which(mydata$y==0))
S2 <- as.vector(which(mydata$y==1))
S3 <- sample(S1, ceiling(length(S1)*0.8), replace=FALSE)
S4 <- sample(S2, ceiling(length(S2)*0.8), replace=FALSE)
TrainInd <- c(S3, S4)
TestInd <- setdiff(1:length(mydata$y), TrainInd)
TrainXY <- mydata[TrainInd, ]
TestXY <- mydata[TestInd, ]
# Fit a bagging LASSO linear regression model, where the parameters
# of M in the following example is set as small values to reduce the
# running time, however the default value is proposed.
Bagging.fit <- Bagging.lasso(x=TrainXY[, -10], y=TrainXY[, 10],
family=c("gaussian"), M=2, predictor.subset=round((9/10)*ncol(x)),
predictor.importance=TRUE, trimmed=FALSE, weighted=TRUE, seed=0123)
# Print a 'bagging' object fitted by the Bagging.fit function.
Print.bagging(Bagging.fit)
# Make predictions from a bagging LASSO linear regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -10], y=NULL, trimmed=FALSE)
pred
# Generate the plot of variable importance.
Plot.importance(Bagging.fit)
# Example 2: Bagging LASSO logistic regression model.
library(mlbench)
set.seed(0123)
mydata <- mlbench.threenorm(100, d=10)
x <- mydata$x
y <- mydata$classes
mydata <- as.data.frame(cbind(x, y))
colnames(mydata) <- c(paste("A", 1:10, sep=""), "y")
mydata$y <- ifelse(mydata$y==1, 0, 1)
# Split into training and testing data.
S1 <- as.vector(which(mydata$y==0))
S2 <- as.vector(which(mydata$y==1))
S3 <- sample(S1, ceiling(length(S1)*0.8), replace=FALSE)
S4 <- sample(S2, ceiling(length(S2)*0.8), replace=FALSE)
TrainInd <- c(S3, S4)
TestInd <- setdiff(1:length(mydata$y), TrainInd)
TrainXY <- mydata[TrainInd, ]
TestXY <- mydata[TestInd, ]
# Fit a bagging LASSO logistic regression model, where the parameters
# of M in the following example is set as small values to reduce the
# running time, however the default value is proposed.
Bagging.fit <- Bagging.lasso(x=TrainXY[, -11], y=TrainXY[, 11],
family=c("binomial"), M=2, predictor.subset=round((9/10)*ncol(x)),
predictor.importance=TRUE, trimmed=FALSE, weighted=TRUE, seed=0123)
# Print a 'bagging' object fitted by the Bagging.fit function.
Print.bagging(Bagging.fit)
# Make predictions from a bagging LASSO logistic regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -11], y=NULL, trimmed=FALSE)
pred
# Generate the plot of variable importance.
Plot.importance(Bagging.fit)
|
Example output
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-12
Iter 1
Iter 2
$family
[1] "gaussian"
$M
[1] 2
$predictor.subset
[1] 9
$subspace.size
[1] 10
$validation.metric
[1] "rmse" "mae" "re" "smape"
$boot.scale
[1] 1
$distance
[1] "Spearman"
$models.fitted
$models.fitted[[1]]
$lambda
[1] 0.334996951 0.305236745 0.278120354 0.253412908 0.230900404 0.210387848
[7] 0.191697572 0.174667688 0.159150692 0.145012183 0.132129700 0.120391662
[13] 0.109696399 0.099951273 0.091071877 0.082981303 0.075609472 0.068892535
[19] 0.062772312 0.057195793 0.052114677 0.047484952 0.043266520 0.039422842
[25] 0.035920625 0.032729536 0.029821934 0.027172636 0.024758693 0.022559199
[31] 0.020555102 0.018729044 0.017065207 0.015549181 0.014167835 0.012909203
[37] 0.011762385 0.010717447 0.009765339 0.008897813 0.008107356 0.007387121
[43] 0.006730869 0.006132917 0.005588086 0.005091656 0.004639327 0.004227182
[49] 0.003851651 0.003509481 0.003197708 0.002913633 0.002654794 0.002418949
[55] 0.002204056 0.002008254 0.001829846 0.001667288 0.001519170 0.001384212
[61] 0.001261242 0.001149197 0.001047105
$cvm
[1] 1.170612 1.171834 1.167885 1.158435 1.148482 1.139699 1.131608 1.123252
[9] 1.115103 1.106609 1.097212 1.086507 1.077951 1.070438 1.063447 1.057730
[17] 1.053037 1.049194 1.046098 1.043674 1.042377 1.042595 1.043767 1.045995
[25] 1.048606 1.051590 1.054781 1.057938 1.061084 1.064116 1.066907 1.069368
[33] 1.071647 1.073573 1.075355 1.077017 1.078578 1.080044 1.081413 1.082683
[41] 1.083853 1.084938 1.085952 1.086871 1.087723 1.088511 1.089230 1.089902
[49] 1.090525 1.091127 1.091690 1.092116 1.092455 1.092752 1.093022 1.093276
[57] 1.093510 1.093738 1.093911 1.094093 1.094257 1.094407 1.094545
$cvsd
[1] 0.1485115 0.1478064 0.1468317 0.1447677 0.1425382 0.1404797 0.1383893
[8] 0.1357820 0.1337884 0.1324965 0.1314530 0.1307501 0.1306333 0.1302056
[15] 0.1293205 0.1287382 0.1283754 0.1281913 0.1281387 0.1281642 0.1280273
[22] 0.1277982 0.1276712 0.1275375 0.1274735 0.1274279 0.1274987 0.1276283
[29] 0.1277789 0.1279861 0.1281145 0.1280807 0.1280520 0.1280708 0.1280963
[36] 0.1281242 0.1281533 0.1281877 0.1282255 0.1282668 0.1283050 0.1283450
[43] 0.1283894 0.1284260 0.1284595 0.1284920 0.1285241 0.1285561 0.1285885
[50] 0.1286352 0.1286842 0.1287594 0.1288378 0.1289120 0.1289809 0.1290423
[57] 0.1290980 0.1291455 0.1291952 0.1292374 0.1292775 0.1293135 0.1293460
$cvup
[1] 1.319123 1.319641 1.314717 1.303202 1.291021 1.280179 1.269998 1.259034
[9] 1.248892 1.239105 1.228665 1.217257 1.208585 1.200644 1.192767 1.186468
[17] 1.181412 1.177385 1.174237 1.171838 1.170405 1.170394 1.171439 1.173532
[25] 1.176079 1.179018 1.182280 1.185566 1.188862 1.192102 1.195021 1.197449
[33] 1.199698 1.201644 1.203451 1.205141 1.206732 1.208232 1.209639 1.210950
[41] 1.212158 1.213283 1.214341 1.215297 1.216183 1.217003 1.217755 1.218458
[49] 1.219113 1.219762 1.220375 1.220876 1.221293 1.221664 1.222003 1.222318
[57] 1.222608 1.222883 1.223107 1.223330 1.223535 1.223721 1.223891
$cvlo
[1] 1.0221003 1.0240278 1.0210534 1.0136670 1.0059441 0.9992192 0.9932191
[8] 0.9874701 0.9813149 0.9741120 0.9657590 0.9557566 0.9473180 0.9402323
[15] 0.9341263 0.9289920 0.9246613 0.9210022 0.9179595 0.9155100 0.9143500
[22] 0.9147973 0.9160961 0.9184573 0.9211321 0.9241623 0.9272827 0.9303098
[29] 0.9333047 0.9361300 0.9387920 0.9412873 0.9435945 0.9455026 0.9472584
[36] 0.9488926 0.9504252 0.9518561 0.9531877 0.9544162 0.9555482 0.9565934
[43] 0.9575622 0.9584446 0.9592639 0.9600192 0.9607064 0.9613463 0.9619363
[50] 0.9624916 0.9630061 0.9633570 0.9636175 0.9638398 0.9640413 0.9642338
[57] 0.9644121 0.9645924 0.9647161 0.9648556 0.9649797 0.9650940 0.9651992
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 3 3 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
5 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8 8
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62
9 9 9
$name
mse
"Mean-Squared Error"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = trainY, family = "gaussian")
Df %Dev Lambda
[1,] 0 0.00000 0.3350000
[2,] 3 0.01892 0.3052000
[3,] 3 0.05056 0.2781000
[4,] 3 0.07684 0.2534000
[5,] 3 0.09865 0.2309000
[6,] 3 0.11680 0.2104000
[7,] 3 0.13180 0.1917000
[8,] 4 0.14530 0.1747000
[9,] 4 0.15670 0.1592000
[10,] 5 0.17030 0.1450000
[11,] 5 0.18290 0.1321000
[12,] 5 0.19330 0.1204000
[13,] 5 0.20200 0.1097000
[14,] 5 0.20920 0.0999500
[15,] 5 0.21510 0.0910700
[16,] 5 0.22010 0.0829800
[17,] 5 0.22420 0.0756100
[18,] 5 0.22760 0.0688900
[19,] 5 0.23050 0.0627700
[20,] 5 0.23280 0.0572000
[21,] 5 0.23480 0.0521100
[22,] 6 0.23650 0.0474800
[23,] 6 0.23800 0.0432700
[24,] 6 0.23920 0.0394200
[25,] 6 0.24030 0.0359200
[26,] 6 0.24110 0.0327300
[27,] 7 0.24240 0.0298200
[28,] 7 0.24400 0.0271700
[29,] 7 0.24530 0.0247600
[30,] 7 0.24640 0.0225600
[31,] 7 0.24730 0.0205600
[32,] 7 0.24800 0.0187300
[33,] 7 0.24860 0.0170700
[34,] 7 0.24910 0.0155500
[35,] 7 0.24950 0.0141700
[36,] 7 0.24990 0.0129100
[37,] 8 0.25020 0.0117600
[38,] 8 0.25050 0.0107200
[39,] 8 0.25080 0.0097650
[40,] 8 0.25100 0.0088980
[41,] 9 0.25120 0.0081070
[42,] 9 0.25130 0.0073870
[43,] 9 0.25140 0.0067310
[44,] 9 0.25150 0.0061330
[45,] 9 0.25160 0.0055880
[46,] 9 0.25170 0.0050920
[47,] 9 0.25170 0.0046390
[48,] 9 0.25180 0.0042270
[49,] 9 0.25180 0.0038520
[50,] 9 0.25190 0.0035090
[51,] 9 0.25190 0.0031980
[52,] 9 0.25190 0.0029140
[53,] 9 0.25190 0.0026550
[54,] 9 0.25190 0.0024190
[55,] 9 0.25200 0.0022040
[56,] 9 0.25200 0.0020080
[57,] 9 0.25200 0.0018300
[58,] 9 0.25200 0.0016670
[59,] 9 0.25200 0.0015190
[60,] 9 0.25200 0.0013840
[61,] 9 0.25200 0.0012610
[62,] 9 0.25200 0.0011490
[63,] 9 0.25200 0.0010470
[64,] 9 0.25200 0.0009541
[65,] 9 0.25200 0.0008693
$lambda.min
[1] 0.05211468
$lambda.1se
[1] 0.2781204
attr(,"class")
[1] "cv.glmnet"
$models.fitted[[2]]
$lambda
[1] 0.2576272052 0.2347403143 0.2138866318 0.1948855329 0.1775724393
[6] 0.1617973932 0.1474237587 0.1343270383 0.1223937944 0.1115206671
[11] 0.1016134784 0.0925864170 0.0843612949 0.0768668700 0.0700382291
[16] 0.0638162259 0.0581469682 0.0529813518 0.0482746346 0.0439860491
[21] 0.0400784497 0.0365179907 0.0332738331 0.0303178774 0.0276245208
[26] 0.0251704345 0.0229343624 0.0208969369 0.0190405106 0.0173490041
[31] 0.0158077664 0.0144034480 0.0131238854 0.0119579956 0.0108956802
[36] 0.0099277380 0.0090457851 0.0082421825 0.0075099697 0.0068428047
[41] 0.0062349088 0.0056810167 0.0051763308 0.0047164799 0.0042974808
[46] 0.0039157044 0.0035678441 0.0032508866 0.0029620868 0.0026989432
[51] 0.0024591765 0.0022407100 0.0020416515 0.0018602768 0.0016950150
[56] 0.0015444345 0.0014072312 0.0012822167 0.0011683081 0.0010645188
[61] 0.0009699499 0.0008837822
$cvm
[1] 1.0193890 1.0206737 1.0187454 1.0128485 1.0063380 1.0000077 0.9941295
[8] 0.9879911 0.9816970 0.9762128 0.9721782 0.9699116 0.9688261 0.9686737
[15] 0.9697535 0.9727381 0.9767684 0.9809112 0.9854143 0.9900350 0.9947992
[22] 1.0004479 1.0064029 1.0126150 1.0186856 1.0244149 1.0297851 1.0345482
[29] 1.0388061 1.0428233 1.0463685 1.0495586 1.0524386 1.0550940 1.0575701
[36] 1.0598734 1.0619954 1.0639591 1.0657745 1.0674508 1.0689375 1.0702232
[43] 1.0714150 1.0725131 1.0735200 1.0744367 1.0752916 1.0760645 1.0767596
[50] 1.0774099 1.0780050 1.0785157 1.0789579 1.0793509 1.0797238 1.0800779
[57] 1.0803763 1.0806615 1.0809128 1.0811314 1.0813496 1.0815404
$cvsd
[1] 0.1169343 0.1173638 0.1173544 0.1184963 0.1192615 0.1190444 0.1183406
[8] 0.1173577 0.1162086 0.1151823 0.1142939 0.1134235 0.1127108 0.1120990
[15] 0.1115532 0.1109913 0.1104222 0.1100619 0.1099154 0.1099646 0.1102156
[22] 0.1110962 0.1120843 0.1131655 0.1142901 0.1154163 0.1165235 0.1174469
[29] 0.1182046 0.1188957 0.1195249 0.1201614 0.1207885 0.1213875 0.1219495
[36] 0.1224758 0.1229673 0.1234225 0.1238473 0.1242468 0.1246533 0.1250727
[43] 0.1254564 0.1258077 0.1261303 0.1264251 0.1266933 0.1269410 0.1271661
[50] 0.1273753 0.1275632 0.1277191 0.1278522 0.1279684 0.1280796 0.1281825
[57] 0.1282761 0.1283594 0.1284387 0.1285117 0.1285799 0.1286414
$cvup
[1] 1.136323 1.138037 1.136100 1.131345 1.125599 1.119052 1.112470 1.105349
[9] 1.097906 1.091395 1.086472 1.083335 1.081537 1.080773 1.081307 1.083729
[17] 1.087191 1.090973 1.095330 1.100000 1.105015 1.111544 1.118487 1.125781
[25] 1.132976 1.139831 1.146309 1.151995 1.157011 1.161719 1.165893 1.169720
[33] 1.173227 1.176481 1.179520 1.182349 1.184963 1.187382 1.189622 1.191698
[41] 1.193591 1.195296 1.196871 1.198321 1.199650 1.200862 1.201985 1.203006
[49] 1.203926 1.204785 1.205568 1.206235 1.206810 1.207319 1.207803 1.208260
[57] 1.208652 1.209021 1.209351 1.209643 1.209929 1.210182
$cvlo
[1] 0.9024546 0.9033099 0.9013909 0.8943522 0.8870765 0.8809633 0.8757889
[8] 0.8706334 0.8654883 0.8610305 0.8578843 0.8564881 0.8561153 0.8565747
[15] 0.8582003 0.8617468 0.8663461 0.8708493 0.8754989 0.8800704 0.8845836
[22] 0.8893517 0.8943186 0.8994495 0.9043955 0.9089986 0.9132616 0.9171012
[29] 0.9206015 0.9239276 0.9268436 0.9293973 0.9316500 0.9337065 0.9356206
[36] 0.9373976 0.9390280 0.9405366 0.9419272 0.9432040 0.9442842 0.9451505
[43] 0.9459586 0.9467054 0.9473897 0.9480116 0.9485984 0.9491234 0.9495936
[50] 0.9500346 0.9504418 0.9507966 0.9511057 0.9513826 0.9516442 0.9518954
[57] 0.9521002 0.9523021 0.9524741 0.9526196 0.9527697 0.9528990
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 2 2 3 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
6 6 6 6 6 7 8 8 8 8 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61
9 9
$name
mse
"Mean-Squared Error"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = trainY, family = "gaussian")
Df %Dev Lambda
[1,] 0 0.00000 0.2576000
[2,] 2 0.01339 0.2347000
[3,] 2 0.03147 0.2139000
[4,] 3 0.04921 0.1949000
[5,] 4 0.06632 0.1776000
[6,] 4 0.08253 0.1618000
[7,] 4 0.09599 0.1474000
[8,] 4 0.10720 0.1343000
[9,] 4 0.11640 0.1224000
[10,] 5 0.12440 0.1115000
[11,] 5 0.13130 0.1016000
[12,] 5 0.13710 0.0925900
[13,] 5 0.14190 0.0843600
[14,] 5 0.14580 0.0768700
[15,] 6 0.15060 0.0700400
[16,] 6 0.15470 0.0638200
[17,] 6 0.15810 0.0581500
[18,] 6 0.16090 0.0529800
[19,] 6 0.16320 0.0482700
[20,] 6 0.16520 0.0439900
[21,] 6 0.16680 0.0400800
[22,] 6 0.16810 0.0365200
[23,] 6 0.16920 0.0332700
[24,] 6 0.17010 0.0303200
[25,] 6 0.17090 0.0276200
[26,] 7 0.17210 0.0251700
[27,] 8 0.17360 0.0229300
[28,] 8 0.17500 0.0209000
[29,] 8 0.17610 0.0190400
[30,] 8 0.17710 0.0173500
[31,] 9 0.17780 0.0158100
[32,] 9 0.17850 0.0144000
[33,] 9 0.17910 0.0131200
[34,] 9 0.17960 0.0119600
[35,] 9 0.18000 0.0109000
[36,] 9 0.18030 0.0099280
[37,] 9 0.18060 0.0090460
[38,] 9 0.18080 0.0082420
[39,] 9 0.18100 0.0075100
[40,] 9 0.18120 0.0068430
[41,] 9 0.18130 0.0062350
[42,] 9 0.18140 0.0056810
[43,] 9 0.18150 0.0051760
[44,] 9 0.18160 0.0047160
[45,] 9 0.18160 0.0042970
[46,] 9 0.18170 0.0039160
[47,] 9 0.18170 0.0035680
[48,] 9 0.18180 0.0032510
[49,] 9 0.18180 0.0029620
[50,] 9 0.18180 0.0026990
[51,] 9 0.18190 0.0024590
[52,] 9 0.18190 0.0022410
[53,] 9 0.18190 0.0020420
[54,] 9 0.18190 0.0018600
[55,] 9 0.18190 0.0016950
[56,] 9 0.18190 0.0015440
[57,] 9 0.18190 0.0014070
[58,] 9 0.18190 0.0012820
[59,] 9 0.18190 0.0011680
[60,] 9 0.18190 0.0010650
[61,] 9 0.18190 0.0009699
[62,] 9 0.18190 0.0008838
[63,] 9 0.18190 0.0008053
[64,] 9 0.18190 0.0007337
[65,] 9 0.18190 0.0006685
$lambda.min
[1] 0.07686687
$lambda.1se
[1] 0.2576272
attr(,"class")
[1] "cv.glmnet"
$models.trimmed
list()
$y.true
[1] -0.83775479 -1.14851936 0.33924804 -1.43829554 -1.18967101 -0.41107042
[7] 0.25222901 1.59426588 -2.06595813 1.02968473 -0.95343363 1.01458714
[13] 0.28231774 -1.68147706 0.57755498 0.63981125 -0.71844715 -0.78385149
[19] 1.12153585 1.41082054 2.31689124 0.05806684 -0.83275425 -0.19727404
[25] 0.75305102 -0.88536477 1.04273063 0.09621294 0.56114745 -0.01265842
[31] 1.08066531 -0.73433879 -1.15332488 -2.51413424 -1.83666523 -0.37910482
[37] -0.71644425 0.72703906 0.15528332 0.26264430 -0.25781196 -1.84640000
[43] -0.60635531 -1.48151667 -0.20135660 -2.24649499 -0.48603835 -0.37209404
[49] 0.08876528 1.03353537 -1.21352291 0.01505783 -1.94896593 -0.66214950
[55] -0.33336863 -0.22916520 -1.26203427 -1.32576006 -0.91152770 -0.88164621
[61] 0.99116568 -1.07941484 -2.71094480 -2.07449018 -0.23715966 -0.79074956
[67] 0.35391033 0.91415362 -0.51173620 0.73567712 -1.28623536 0.32291011
[73] 1.45426190 -0.82834478 0.37822243 -0.38892281 -1.84089825 0.11562563
[79] 0.38670154 0.35751608
$conv.scores
$conv.scores$ranks
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "5" "10" "8" "7" "4" "3" "2" "1" "9" "6"
[2,] "5" "10" "8" "7" "4" "3" "2" "1" "9" "6"
[3,] "1" "9" "6" "3" "2" "4" "7" "8" "10" "5"
[4,] "5" "10" "4" "7" "8" "2" "3" "9" "6" "1"
$conv.scores$weights
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.9770869 0.9770843 0.9747954 0.9747930 0.9697949 0.9658363 0.9653150
[2,] 1.2499899 1.2499870 1.2460446 1.2460409 1.2393659 1.2319513 1.2314571
[3,] 0.7616848 0.7609513 0.7375416 0.7373028 0.7370965 0.7358816 0.7355996
[4,] 0.7672880 0.7672870 0.7666914 0.7659642 0.7659635 0.7627853 0.7625705
[,8] [,9] [,10]
[1,] 0.9618191 0.9606570 0.9594676
[2,] 1.2300032 1.2289072 1.2220636
[3,] 0.7355948 0.7346079 0.7346060
[4,] 0.7578440 0.7577510 0.7568872
$importance
[,1]
A1 0.12302406
A2 0.00000000
A3 0.06926666
A4 0.06271640
A5 0.13859420
A6 0.06824081
A7 0.04522673
A8 0.07795560
A9 0.00000000
y 0.23927699
attr(,"class")
[1] "bagging"
$y.new
[1] 0.04447873 -0.12871182 -0.23723027 -0.02300019 -0.03851816 -0.44884138
[7] -0.80601068 -0.71891720 0.03472918 0.05418729 -0.06476798 -0.17292143
[13] -0.61339668 -0.26723470 -0.28321384 -0.67176706 -0.51268009 -0.61530304
[19] -0.51708935 -0.81701762
$y.se
[1] 0.266523429 0.169630890 0.055204841 0.308323909 0.044965845 0.313516781
[7] 0.343833731 0.129416122 0.036851445 0.153187885 0.219717119 0.004629453
[13] 0.015876657 0.015483059 0.138680946 0.079378230 0.062891881 0.051941293
[19] 0.325012999 0.293268186
$predicted.matrix
[,1] [,2]
[1,] 0.311002160 -0.222044699
[2,] 0.040919067 -0.298342714
[3,] -0.292435110 -0.182025429
[4,] 0.285323717 -0.331324101
[5,] -0.083484002 0.006447688
[6,] -0.762358161 -0.135324600
[7,] -1.149844416 -0.462176953
[8,] -0.848333322 -0.589501079
[9,] -0.002122263 0.071580627
[10,] -0.099000596 0.207375173
[11,] -0.284485102 0.154949137
[12,] -0.168291977 -0.177550882
[13,] -0.597520023 -0.629273337
[14,] -0.282717763 -0.251751645
[15,] -0.421894781 -0.144532889
[16,] -0.592388826 -0.751145287
[17,] -0.575571975 -0.449788213
[18,] -0.667244329 -0.563361743
[19,] -0.842102350 -0.192076351
[20,] -1.110285808 -0.523749436
attr(,"class")
[1] "BaggingPrediction"
Iter 1
Iter 2
$family
[1] "binomial"
$M
[1] 2
$predictor.subset
[1] 9
$subspace.size
[1] 10
$validation.metric
[1] "accuracy" "sensitivity" "specificity" "auc" "kia"
$boot.scale
[1] 1
$distance
[1] "Spearman"
$models.fitted
$models.fitted[[1]]
$lambda
[1] 0.2317925169 0.2112007086 0.1924382197 0.1753425387 0.1597655908
[6] 0.1455724560 0.1326402002 0.1208568104 0.1101202245 0.1003374472
[11] 0.0914237450 0.0833019114 0.0759015992 0.0691587103 0.0630148411
[16] 0.0574167763 0.0523160282 0.0476684164 0.0434336857 0.0395751567
[21] 0.0360594089 0.0328559904 0.0299371548 0.0272776204 0.0248543518
[26] 0.0226463597 0.0206345195 0.0188014057 0.0171311406 0.0156092573
[31] 0.0142225739 0.0129590796 0.0118078307 0.0107588556 0.0098030685
[36] 0.0089321909 0.0081386797 0.0074156618 0.0067568749 0.0061566127
[41] 0.0056096762 0.0051113280 0.0046572517 0.0042435143 0.0038665322
[46] 0.0035230402 0.0032100630 0.0029248898 0.0026650507 0.0024282949
[51] 0.0022125719 0.0020160131 0.0018369161 0.0016737295 0.0015250400
[56] 0.0013895596 0.0012661150 0.0011536368 0.0010511509 0.0009577696
[61] 0.0008726839 0.0007951571 0.0007245175 0.0006601534 0.0006015072
$cvm
[1] 1.390173 1.367903 1.342192 1.320709 1.303998 1.286680 1.260045 1.227663
[9] 1.197318 1.173190 1.154497 1.140684 1.131308 1.122471 1.114412 1.106902
[17] 1.100074 1.093191 1.088555 1.084766 1.082259 1.081368 1.080179 1.079510
[25] 1.079812 1.080260 1.081292 1.083419 1.086438 1.090247 1.094154 1.098015
[33] 1.102337 1.107088 1.112051 1.116835 1.121787 1.126853 1.131967 1.137072
[41] 1.142120 1.147071 1.151894 1.156561 1.161054 1.165352 1.169441 1.173328
[49] 1.176984 1.180454 1.183721 1.186783 1.189647 1.192315 1.194777 1.197087
[57] 1.199213 1.201198 1.203039 1.204717 1.206281 1.207707 1.209035 1.210263
[65] 1.211390
$cvsd
[1] 0.01364905 0.01398154 0.01353940 0.01589334 0.01981455 0.02347824
[7] 0.02760413 0.03108016 0.03420529 0.03784397 0.04209536 0.04684658
[13] 0.05266356 0.05914513 0.06560504 0.07180801 0.07834203 0.08502745
[19] 0.09142801 0.09762097 0.10356497 0.10925687 0.11463930 0.11984261
[25] 0.12470843 0.12893245 0.13303690 0.13708268 0.14105121 0.14497771
[31] 0.14880315 0.15230877 0.15572848 0.15906025 0.16227773 0.16532385
[37] 0.16825828 0.17107872 0.17378063 0.17636080 0.17881741 0.18114930
[43] 0.18335678 0.18544105 0.18740365 0.18924620 0.19097283 0.19258829
[49] 0.19409078 0.19549721 0.19680761 0.19802528 0.19915447 0.20019774
[55] 0.20115984 0.20204958 0.20286787 0.20362746 0.20432956 0.20497083
[61] 0.20556062 0.20609455 0.20659363 0.20705534 0.20747840
$cvup
[1] 1.403822 1.381884 1.355731 1.336602 1.323812 1.310158 1.287649 1.258743
[9] 1.231523 1.211034 1.196592 1.187531 1.183972 1.181616 1.180017 1.178710
[17] 1.178416 1.178219 1.179983 1.182387 1.185824 1.190625 1.194818 1.199353
[25] 1.204521 1.209193 1.214329 1.220501 1.227489 1.235225 1.242957 1.250324
[33] 1.258066 1.266148 1.274328 1.282159 1.290046 1.297932 1.305748 1.313433
[41] 1.320937 1.328220 1.335251 1.342002 1.348458 1.354598 1.360414 1.365917
[49] 1.371075 1.375952 1.380529 1.384808 1.388801 1.392513 1.395937 1.399137
[57] 1.402081 1.404826 1.407369 1.409688 1.411842 1.413802 1.415629 1.417318
[65] 1.418868
$cvlo
[1] 1.3765243 1.3539213 1.3286522 1.3048155 1.2841830 1.2632013 1.2324406
[8] 1.1965829 1.1631129 1.1353460 1.1124013 1.0938379 1.0786444 1.0633256
[15] 1.0488068 1.0350944 1.0217322 1.0081639 0.9971273 0.9871455 0.9786940
[22] 0.9721111 0.9655396 0.9596674 0.9551040 0.9513277 0.9482551 0.9463361
[29] 0.9453867 0.9452695 0.9453508 0.9457060 0.9466089 0.9480277 0.9497729
[36] 0.9515116 0.9535290 0.9557743 0.9581863 0.9607113 0.9633024 0.9659219
[43] 0.9685371 0.9711203 0.9736508 0.9761054 0.9784679 0.9807400 0.9828936
[50] 0.9849572 0.9869136 0.9887573 0.9904921 0.9921174 0.9936171 0.9950374
[57] 0.9963455 0.9975708 0.9987099 0.9997466 1.0007208 1.0016128 1.0024416
[64] 1.0032073 1.0039113
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 1 1 1 2 2 3 3 3 3 3 4 5 5 5 6 6 7 8 8
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62 s63 s64
9 9 9 9 9
$name
deviance
"Binomial Deviance"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = as.factor(trainY), family = "binomial")
Df %Dev Lambda
[1,] 0 8.023e-16 0.2318000
[2,] 1 2.647e-02 0.2112000
[3,] 1 4.860e-02 0.1924000
[4,] 1 6.724e-02 0.1753000
[5,] 2 8.603e-02 0.1598000
[6,] 2 1.110e-01 0.1456000
[7,] 3 1.357e-01 0.1326000
[8,] 3 1.662e-01 0.1209000
[9,] 3 1.923e-01 0.1101000
[10,] 3 2.148e-01 0.1003000
[11,] 3 2.343e-01 0.0914200
[12,] 4 2.520e-01 0.0833000
[13,] 5 2.698e-01 0.0759000
[14,] 5 2.874e-01 0.0691600
[15,] 5 3.027e-01 0.0630100
[16,] 6 3.181e-01 0.0574200
[17,] 6 3.321e-01 0.0523200
[18,] 7 3.460e-01 0.0476700
[19,] 8 3.594e-01 0.0434300
[20,] 8 3.716e-01 0.0395800
[21,] 8 3.824e-01 0.0360600
[22,] 8 3.920e-01 0.0328600
[23,] 8 4.004e-01 0.0299400
[24,] 8 4.078e-01 0.0272800
[25,] 8 4.143e-01 0.0248500
[26,] 9 4.210e-01 0.0226500
[27,] 9 4.270e-01 0.0206300
[28,] 9 4.323e-01 0.0188000
[29,] 9 4.369e-01 0.0171300
[30,] 9 4.409e-01 0.0156100
[31,] 9 4.444e-01 0.0142200
[32,] 9 4.475e-01 0.0129600
[33,] 9 4.501e-01 0.0118100
[34,] 9 4.524e-01 0.0107600
[35,] 9 4.544e-01 0.0098030
[36,] 9 4.561e-01 0.0089320
[37,] 9 4.576e-01 0.0081390
[38,] 9 4.588e-01 0.0074160
[39,] 9 4.599e-01 0.0067570
[40,] 9 4.608e-01 0.0061570
[41,] 9 4.616e-01 0.0056100
[42,] 9 4.622e-01 0.0051110
[43,] 9 4.628e-01 0.0046570
[44,] 9 4.633e-01 0.0042440
[45,] 9 4.637e-01 0.0038670
[46,] 9 4.640e-01 0.0035230
[47,] 9 4.643e-01 0.0032100
[48,] 9 4.645e-01 0.0029250
[49,] 9 4.647e-01 0.0026650
[50,] 9 4.649e-01 0.0024280
[51,] 9 4.651e-01 0.0022130
[52,] 9 4.652e-01 0.0020160
[53,] 9 4.653e-01 0.0018370
[54,] 9 4.654e-01 0.0016740
[55,] 9 4.654e-01 0.0015250
[56,] 9 4.655e-01 0.0013900
[57,] 9 4.655e-01 0.0012660
[58,] 9 4.656e-01 0.0011540
[59,] 9 4.656e-01 0.0010510
[60,] 9 4.656e-01 0.0009578
[61,] 9 4.657e-01 0.0008727
[62,] 9 4.657e-01 0.0007952
[63,] 9 4.657e-01 0.0007245
[64,] 9 4.657e-01 0.0006602
[65,] 9 4.657e-01 0.0006015
[66,] 9 4.657e-01 0.0005481
$lambda.min
[1] 0.02727762
$lambda.1se
[1] 0.1101202
attr(,"class")
[1] "cv.glmnet"
$models.fitted[[2]]
$lambda
[1] 0.2245280966 0.2045816393 0.1864071703 0.1698472711 0.1547585076
[6] 0.1410101884 0.1284832319 0.1170691357 0.1066690363 0.0971928531
[11] 0.0885585079 0.0806912141 0.0735228291 0.0669912636 0.0610399444
[16] 0.0556173239 0.0506764342 0.0461744794 0.0420724659 0.0383348640
[21] 0.0349293004 0.0318262776 0.0289989188 0.0264227348 0.0240754118
[26] 0.0219366186 0.0199878299 0.0182121662 0.0165942475 0.0151200603
[31] 0.0137768358 0.0125529397 0.0114377711 0.0104216711 0.0094958385
[36] 0.0086522544 0.0078836120 0.0071832536 0.0065451132 0.0059636633
[41] 0.0054338679 0.0049511381 0.0045112926 0.0041105218 0.0037453544
[46] 0.0034126274 0.0031094590 0.0028332232 0.0025815275 0.0023521917
[51] 0.0021432295 0.0019528309 0.0017793468 0.0016212745 0.0014772449
[56] 0.0013460106 0.0012264347 0.0011174816 0.0010182076 0.0009277529
[61] 0.0008453339 0.0007702367 0.0007018110
$cvm
[1] 1.436664 1.423640 1.398594 1.377528 1.354445 1.328559 1.295393 1.261669
[9] 1.231858 1.206731 1.185966 1.167854 1.152180 1.137593 1.125069 1.112630
[17] 1.101179 1.088093 1.075992 1.066618 1.057952 1.051075 1.046205 1.043135
[25] 1.041544 1.040995 1.041384 1.042551 1.044292 1.046487 1.049111 1.052060
[33] 1.055096 1.058048 1.061124 1.064315 1.067570 1.070847 1.074098 1.077316
[41] 1.080466 1.083330 1.085997 1.088573 1.091049 1.093416 1.095671 1.097687
[49] 1.099398 1.101037 1.102592 1.104056 1.105434 1.106698 1.107886 1.109012
[57] 1.110061 1.111036 1.111940 1.112778 1.113512 1.114225 1.114870
$cvsd
[1] 0.02516253 0.02666794 0.02625097 0.03000431 0.03516634 0.04126805
[7] 0.04801390 0.05488220 0.06169901 0.06846521 0.07524027 0.08207557
[13] 0.08905053 0.09601892 0.10323588 0.11050499 0.11712924 0.12123882
[19] 0.12540364 0.12970617 0.13410626 0.13848489 0.14277816 0.14706867
[25] 0.15137164 0.15567526 0.15992841 0.16416856 0.16838316 0.17255072
[31] 0.17663619 0.18062496 0.18450766 0.18834344 0.19206159 0.19563984
[37] 0.19907234 0.20235475 0.20547509 0.20844955 0.21126974 0.21386270
[43] 0.21626923 0.21853578 0.22066576 0.22266305 0.22453187 0.22618606
[49] 0.22757133 0.22888084 0.23010467 0.23124629 0.23230883 0.23327641
[55] 0.23418586 0.23503541 0.23582198 0.23654890 0.23721987 0.23783853
[61] 0.23838269 0.23890589 0.23938272
$cvup
[1] 1.461826 1.450308 1.424845 1.407533 1.389612 1.369827 1.343407 1.316551
[9] 1.293557 1.275196 1.261207 1.249930 1.241230 1.233612 1.228305 1.223135
[17] 1.218309 1.209332 1.201396 1.196324 1.192058 1.189560 1.188983 1.190204
[25] 1.192916 1.196671 1.201313 1.206720 1.212675 1.219038 1.225747 1.232685
[33] 1.239604 1.246392 1.253186 1.259955 1.266642 1.273201 1.279573 1.285766
[41] 1.291735 1.297192 1.302266 1.307109 1.311714 1.316079 1.320203 1.323873
[49] 1.326969 1.329918 1.332697 1.335303 1.337743 1.339975 1.342072 1.344047
[57] 1.345883 1.347585 1.349160 1.350616 1.351895 1.353130 1.354252
$cvlo
[1] 1.4115010 1.3969717 1.3723426 1.3475241 1.3192791 1.2872912 1.2473788
[8] 1.2067869 1.1701591 1.1382657 1.1107262 1.0857786 1.0631293 1.0415745
[15] 1.0218331 1.0021252 0.9840502 0.9668547 0.9505887 0.9369114 0.9238453
[22] 0.9125904 0.9034264 0.8960662 0.8901723 0.8853201 0.8814561 0.8783828
[29] 0.8759088 0.8739362 0.8724747 0.8714346 0.8705888 0.8697047 0.8690629
[36] 0.8686748 0.8684972 0.8684918 0.8686232 0.8688664 0.8691960 0.8694670
[43] 0.8697277 0.8700376 0.8703830 0.8707525 0.8711389 0.8715005 0.8718262
[50] 0.8721565 0.8724873 0.8728102 0.8731254 0.8734217 0.8737002 0.8739763
[57] 0.8742389 0.8744870 0.8747204 0.8749393 0.8751295 0.8753186 0.8754868
$nzero
s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19
0 1 1 1 4 4 4 4 4 4 6 6 6 6 7 7 8 8 8 8
s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39
8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9
s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 s52 s53 s54 s55 s56 s57 s58 s59
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
s60 s61 s62
9 9 9
$name
deviance
"Binomial Deviance"
$glmnet.fit
Call: glmnet(x = as.matrix(training_1), y = as.factor(trainY), family = "binomial")
Df %Dev Lambda
[1,] 0 6.418e-16 0.2245000
[2,] 1 2.484e-02 0.2046000
[3,] 1 4.563e-02 0.1864000
[4,] 1 6.312e-02 0.1698000
[5,] 4 9.377e-02 0.1548000
[6,] 4 1.284e-01 0.1410000
[7,] 4 1.582e-01 0.1285000
[8,] 4 1.840e-01 0.1171000
[9,] 4 2.065e-01 0.1067000
[10,] 4 2.260e-01 0.0971900
[11,] 6 2.452e-01 0.0885600
[12,] 6 2.627e-01 0.0806900
[13,] 6 2.780e-01 0.0735200
[14,] 6 2.913e-01 0.0669900
[15,] 7 3.064e-01 0.0610400
[16,] 7 3.204e-01 0.0556200
[17,] 8 3.340e-01 0.0506800
[18,] 8 3.471e-01 0.0461700
[19,] 8 3.586e-01 0.0420700
[20,] 8 3.687e-01 0.0383300
[21,] 8 3.777e-01 0.0349300
[22,] 8 3.855e-01 0.0318300
[23,] 8 3.924e-01 0.0290000
[24,] 8 3.984e-01 0.0264200
[25,] 8 4.037e-01 0.0240800
[26,] 8 4.083e-01 0.0219400
[27,] 8 4.124e-01 0.0199900
[28,] 9 4.162e-01 0.0182100
[29,] 9 4.198e-01 0.0165900
[30,] 9 4.229e-01 0.0151200
[31,] 9 4.256e-01 0.0137800
[32,] 9 4.279e-01 0.0125500
[33,] 9 4.300e-01 0.0114400
[34,] 9 4.317e-01 0.0104200
[35,] 9 4.332e-01 0.0094960
[36,] 9 4.345e-01 0.0086520
[37,] 9 4.356e-01 0.0078840
[38,] 9 4.366e-01 0.0071830
[39,] 9 4.374e-01 0.0065450
[40,] 9 4.381e-01 0.0059640
[41,] 9 4.387e-01 0.0054340
[42,] 9 4.392e-01 0.0049510
[43,] 9 4.396e-01 0.0045110
[44,] 9 4.400e-01 0.0041110
[45,] 9 4.403e-01 0.0037450
[46,] 9 4.406e-01 0.0034130
[47,] 9 4.408e-01 0.0031090
[48,] 9 4.410e-01 0.0028330
[49,] 9 4.411e-01 0.0025820
[50,] 9 4.413e-01 0.0023520
[51,] 9 4.414e-01 0.0021430
[52,] 9 4.415e-01 0.0019530
[53,] 9 4.416e-01 0.0017790
[54,] 9 4.416e-01 0.0016210
[55,] 9 4.417e-01 0.0014770
[56,] 9 4.417e-01 0.0013460
[57,] 9 4.418e-01 0.0012260
[58,] 9 4.418e-01 0.0011170
[59,] 9 4.418e-01 0.0010180
[60,] 9 4.418e-01 0.0009278
[61,] 9 4.419e-01 0.0008453
[62,] 9 4.419e-01 0.0007702
[63,] 9 4.419e-01 0.0007018
[64,] 9 4.419e-01 0.0006395
[65,] 9 4.419e-01 0.0005827
$lambda.min
[1] 0.02193662
$lambda.1se
[1] 0.08855851
attr(,"class")
[1] "cv.glmnet"
$models.trimmed
list()
$y.true
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[39] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[77] 1 1 1 1
Levels: 0 1
$conv.scores
$conv.scores$ranks
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "3" "4" "5" "6" "7" "10" "1" "2" "9" "8"
[2,] "1" "2" "5" "6" "8" "3" "4" "7" "9" "10"
[3,] "3" "4" "7" "10" "5" "6" "9" "1" "2" "8"
[4,] "1" "2" "7" "3" "5" "6" "9" "4" "10" "8"
[5,] "5" "6" "3" "4" "7" "10" "1" "2" "9" "8"
$conv.scores$weights
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.7500000 0.7500000 0.7500000 0.7500000 0.7500000 0.7500000 0.7187500
[2,] 0.8666667 0.8666667 0.8666667 0.8666667 0.8666667 0.8000000 0.8000000
[3,] 0.7058824 0.7058824 0.7058824 0.7058824 0.6470588 0.6470588 0.6470588
[4,] 0.7882353 0.7705882 0.7686275 0.7588235 0.7588235 0.7588235 0.7549020
[5,] 0.5057915 0.5057915 0.5019455 0.5019455 0.5019455 0.5019455 0.4461538
[,8] [,9] [,10]
[1,] 0.7187500 0.7187500 0.6875000
[2,] 0.8000000 0.8000000 0.8000000
[3,] 0.5882353 0.5882353 0.5294118
[4,] 0.7470588 0.7372549 0.7156863
[5,] 0.4461538 0.4418605 0.3869732
$importance
[,1]
A1 0.2758553
A2 0.5464931
A3 0.8955025
A4 0.2191840
A5 0.2008697
A6 0.1540545
A7 0.5832975
A8 0.7176415
A9 0.1270706
A10 0.5371627
attr(,"class")
[1] "bagging"
$y.new
[1] 0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1
Levels: 0 1
$probabilities
[1] 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 0.5 0.0
[20] 1.0
$predicted.matrix
[,1] [,2]
[1,] 0 0
[2,] 0 0
[3,] 0 0
[4,] 1 1
[5,] 0 0
[6,] 0 0
[7,] 1 1
[8,] 0 0
[9,] 0 0
[10,] 0 0
[11,] 0 0
[12,] 1 1
[13,] 1 1
[14,] 1 1
[15,] 0 0
[16,] 1 1
[17,] 0 0
[18,] 0 1
[19,] 0 0
[20,] 1 1
attr(,"class")
[1] "BaggingPrediction"
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