Predict.bagging: Make predictions for new data from a 'bagging' object.
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/Predict.bagging.R
Description
This function makes predictions for new data from a bagging LASSO linear or logistic regression model, using the
stored 'bagging' object, with or without the use of trimmed bagging strategy.
Usage
1
Predict.bagging(object, newx, y = NULL, trimmed = FALSE, scale.trimmed = 0.75)
Arguments
object
a fitted 'bagging' object.
newx
matrix of new values for x at which predictions are to be made. Must be a
matrix. See documentation for Bagging.lasso.
y
response variable. Defaults to NULL. If the response variable for the newx
matrix is known and input, the corresponding validation measures can be calculated
for evaluating prediction performance.
trimmed
logical. Should a trimmed bagging strategy be performed? Defaults to
FALSE. This argument should correspond to the same setting in the Bagging.lasso function.
See documentation for Bagging.lasso.
scale.trimmed
the portion to trim of the "worst" based-level models, in the sense
of having the largest error rates, and to average only over the most accurate base-level
models. Defaults to 0.75.
Details
This function makes a prediction based on the object fitted by the Bagging.lasso model.
Value
y.new
the predicted values of response vector y.
probabilities
the predicted probabilities of response vector y.
predicted.matrix
the matrix of predicted values of response vector y based on the
base-level LASSO regression models.
bagging.prediction
the performance of bagging prediction accordig to the model
validation measures defined.
References
[1] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
[2] Croux, C., Joossens, K., & Lemmens, A. (2007). Trimmed bagging. Computational Statistics &
Data Analysis, 52(1), 362-368.
Examples
1
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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)
Bagging.fit
# Make predictions from a bagging LASSO linear regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -10], y=NULL)
pred
Example output
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
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 0.210387849
[7] 0.191697572 0.174667688 0.159150692 0.145012183 0.132129701 0.120391662
[13] 0.109696399 0.099951273 0.091071877 0.082981303 0.075609473 0.068892535
[19] 0.062772312 0.057195793 0.052114677 0.047484952 0.043266520 0.039422842
[25] 0.035920625 0.032729536 0.029821934 0.027172636 0.024758694 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 0.1474237588 0.1343270384 0.1223937945 0.1115206672
[11] 0.1016134785 0.0925864171 0.0843612950 0.0768668701 0.0700382292
[16] 0.0638162259 0.0581469683 0.0529813519 0.0482746346 0.0439860492
[21] 0.0400784498 0.0365179908 0.0332738331 0.0303178775 0.0276245208
[26] 0.0251704345 0.0229343625 0.0208969369 0.0190405107 0.0173490042
[31] 0.0158077665 0.0144034481 0.0131238854 0.0119579956 0.0108956802
[36] 0.0099277380 0.0090457852 0.0082421825 0.0075099698 0.0068428047
[41] 0.0062349088 0.0056810167 0.0051763308 0.0047164799 0.0042974808
[46] 0.0039157045 0.0035678441 0.0032508866 0.0029620868 0.0026989432
[51] 0.0024591765 0.0022407101 0.0020416516 0.0018602769 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,] 0.311002159 -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 -0.589501078
[9,] -0.002122263 0.071580627
[10,] -0.099000596 0.207375173
[11,] -0.284485102 0.154949136
[12,] -0.168291977 -0.177550882
[13,] -0.597520023 -0.629273337
[14,] -0.282717763 -0.251751645
[15,] -0.421894781 -0.144532890
[16,] -0.592388826 -0.751145286
[17,] -0.575571974 -0.449788213
[18,] -0.667244329 -0.563361743
[19,] -0.842102349 -0.192076351
[20,] -1.110285808 -0.523749436
attr(,"class")
[1] "BaggingPrediction"
SparseLearner documentation built on May 29, 2017, 9:18 p.m.
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Description Usage Arguments Details Value References Examples
View source: R/Predict.bagging.R
Description
This function makes predictions for new data from a bagging LASSO linear or logistic regression model, using the stored 'bagging' object, with or without the use of trimmed bagging strategy.
Usage
1 | Predict.bagging(object, newx, y = NULL, trimmed = FALSE, scale.trimmed = 0.75)
|
Arguments
object |
a fitted 'bagging' object. |
newx |
matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for Bagging.lasso. |
y |
response variable. Defaults to NULL. If the response variable for the newx matrix is known and input, the corresponding validation measures can be calculated for evaluating prediction performance. |
trimmed |
logical. Should a trimmed bagging strategy be performed? Defaults to FALSE. This argument should correspond to the same setting in the Bagging.lasso function. See documentation for Bagging.lasso. |
scale.trimmed |
the portion to trim of the "worst" based-level models, in the sense of having the largest error rates, and to average only over the most accurate base-level models. Defaults to 0.75. |
Details
This function makes a prediction based on the object fitted by the Bagging.lasso model.
Value
y.new |
the predicted values of response vector y. |
probabilities |
the predicted probabilities of response vector y. |
predicted.matrix |
the matrix of predicted values of response vector y based on the base-level LASSO regression models. |
bagging.prediction |
the performance of bagging prediction accordig to the model validation measures defined. |
References
[1] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
[2] Croux, C., Joossens, K., & Lemmens, A. (2007). Trimmed bagging. Computational Statistics & Data Analysis, 52(1), 362-368.
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 | 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)
Bagging.fit
# Make predictions from a bagging LASSO linear regression model.
pred <- Predict.bagging(Bagging.fit, newx=TestXY[, -10], y=NULL)
pred
|
Example output
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
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 0.210387849
[7] 0.191697572 0.174667688 0.159150692 0.145012183 0.132129701 0.120391662
[13] 0.109696399 0.099951273 0.091071877 0.082981303 0.075609473 0.068892535
[19] 0.062772312 0.057195793 0.052114677 0.047484952 0.043266520 0.039422842
[25] 0.035920625 0.032729536 0.029821934 0.027172636 0.024758694 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 0.1474237588 0.1343270384 0.1223937945 0.1115206672
[11] 0.1016134785 0.0925864171 0.0843612950 0.0768668701 0.0700382292
[16] 0.0638162259 0.0581469683 0.0529813519 0.0482746346 0.0439860492
[21] 0.0400784498 0.0365179908 0.0332738331 0.0303178775 0.0276245208
[26] 0.0251704345 0.0229343625 0.0208969369 0.0190405107 0.0173490042
[31] 0.0158077665 0.0144034481 0.0131238854 0.0119579956 0.0108956802
[36] 0.0099277380 0.0090457852 0.0082421825 0.0075099698 0.0068428047
[41] 0.0062349088 0.0056810167 0.0051763308 0.0047164799 0.0042974808
[46] 0.0039157045 0.0035678441 0.0032508866 0.0029620868 0.0026989432
[51] 0.0024591765 0.0022407101 0.0020416516 0.0018602769 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,] 0.311002159 -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 -0.589501078
[9,] -0.002122263 0.071580627
[10,] -0.099000596 0.207375173
[11,] -0.284485102 0.154949136
[12,] -0.168291977 -0.177550882
[13,] -0.597520023 -0.629273337
[14,] -0.282717763 -0.251751645
[15,] -0.421894781 -0.144532890
[16,] -0.592388826 -0.751145286
[17,] -0.575571974 -0.449788213
[18,] -0.667244329 -0.563361743
[19,] -0.842102349 -0.192076351
[20,] -1.110285808 -0.523749436
attr(,"class")
[1] "BaggingPrediction"
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