Predict method for ML objects.
# S3 method for class 'ML'
predict(object, newdata, verbose = FALSE, ...)Arguments
- object
A model fitting object from
SEMml()function.- newdata
A matrix containing new data with rows corresponding to subjects, and columns to variables.
- verbose
Print predicted out-of-sample MSE values (default = FALSE).
- ...
Currently ignored.
Value
A list of 2 objects:
"PE", vector of the prediction error equal to the Mean Squared Error (MSE) for each out-of-bag prediction. The first value of PE is the AMSE, where we average over all (sink and mediators) graph nodes.
"Yhat", the matrix of continuous predicted values of graph nodes (excluding source nodes) based on out-of-bag samples.
Examples
# \donttest{
# Load Amyotrophic Lateral Sclerosis (ALS)
data<- alsData$exprs; dim(data)
#> [1] 160 318
data<- transformData(data)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.
ig<- alsData$graph; gplot(ig)
#...with train-test (0.5-0.5) samples
set.seed(123)
train<- sample(1:nrow(data), 0.5*nrow(data))
start<- Sys.time()
# ... rf
res1<- SEMml(ig, data, train, algo="rf", vimp=FALSE)
#> 1 : z10452
#> 2 : z1432
#> 3 : z1616
#> 4 : z4217
#> 5 : z4741
#> 6 : z4744
#> 7 : z4747
#> 8 : z54205
#> 9 : z5530
#> 10 : z5532
#> 11 : z5533
#> 12 : z5534
#> 13 : z5535
#> 14 : z5600
#> 15 : z5603
#> 16 : z5606
#> 17 : z5608
#> 18 : z596
#> 19 : z6300
#> 20 : z79139
#> 21 : z836
#> 22 : z84134
#> 23 : z842
#>
#> RF solver ended normally after 23 iterations
#>
#> logL: -33.22824 srmr: 0.0859545
#>
mse1<- predict(res1, data[-train, ], verbose=TRUE)
#> amse 10452 1432 1616 4217 4741 4744 4747
#> 1.1087827 1.5751174 0.8407252 1.2992863 1.4710206 0.9542770 0.9797583 0.7995575
#> 54205 5530 5532 5533 5534 5535 5600 5603
#> 1.2948457 1.3477223 1.3374130 1.7462924 1.3393998 0.9579940 0.5885530 1.1292464
#> 5606 5608 596 6300 79139 836 84134 842
#> 1.1417869 1.1304838 1.2727327 0.6232077 0.8256068 0.8613661 1.1949609 0.7906486
# ... xgb
res2<- SEMml(ig, data, train, algo="xgb", vimp=FALSE)
#> 1 : z10452
#> 2 : z1432
#> 3 : z1616
#> 4 : z4217
#> 5 : z4741
#> 6 : z4744
#> 7 : z4747
#> 8 : z54205
#> 9 : z5530
#> 10 : z5532
#> 11 : z5533
#> 12 : z5534
#> 13 : z5535
#> 14 : z5600
#> 15 : z5603
#> 16 : z5606
#> 17 : z5608
#> 18 : z596
#> 19 : z6300
#> 20 : z79139
#> 21 : z836
#> 22 : z84134
#> 23 : z842
#>
#> XGB solver ended normally after 23 iterations
#>
#> logL: 70.10035 srmr: 0.0014393
#>
mse2<- predict(res2, data[-train, ], verbose=TRUE)
#> amse 10452 1432 1616 4217 4741 4744 4747
#> 1.5485977 2.3224700 1.0438189 2.0584053 2.1367318 0.9796140 1.1430960 0.7703339
#> 54205 5530 5532 5533 5534 5535 5600 5603
#> 1.4605175 2.0228881 2.0250110 2.2691126 2.0941554 1.5672913 0.8313812 1.2909388
#> 5606 5608 596 6300 79139 836 84134 842
#> 1.6402649 1.5021883 2.0930200 0.9203514 1.2559263 1.2960971 1.9018098 0.9923247
# ... nn
res3<- SEMml(ig, data, train, algo="nn", vimp=FALSE)
#> 1 : z10452
#> 2 : z1432
#> 3 : z1616
#> 4 : z4217
#> 5 : z4741
#> 6 : z4744
#> 7 : z4747
#> 8 : z54205
#> 9 : z5530
#> 10 : z5532
#> 11 : z5533
#> 12 : z5534
#> 13 : z5535
#> 14 : z5600
#> 15 : z5603
#> 16 : z5606
#> 17 : z5608
#> 18 : z596
#> 19 : z6300
#> 20 : z79139
#> 21 : z836
#> 22 : z84134
#> 23 : z842
#>
#> NN solver ended normally after 23 iterations
#>
#> logL: -36.07395 srmr: 0.194907
#>
mse3<- predict(res3, data[-train, ], verbose=TRUE)
#> amse 10452 1432 1616 4217 4741 4744 4747
#> 1.6357984 1.5177940 1.7251374 2.0771794 1.2273523 2.7357480 4.8539956 2.7193534
#> 54205 5530 5532 5533 5534 5535 5600 5603
#> 2.4691147 0.9825952 1.2431796 1.6832744 1.3556345 0.8070092 1.7176961 2.1512864
#> 5606 5608 596 6300 79139 836 84134 842
#> 0.9625083 1.1228321 1.3024315 0.9162324 1.1671646 0.7527348 1.0170158 1.1160940
# ... gam
res4<- SEMml(ig, data, train, algo="gam", vimp=FALSE)
#> 1 : z10452
#> 2 : z1432
#> 3 : z1616
#> 4 : z4217
#> 5 : z4741
#> 6 : z4744
#> 7 : z4747
#> 8 : z54205
#> 9 : z5530
#> 10 : z5532
#> 11 : z5533
#> 12 : z5534
#> 13 : z5535
#> 14 : z5600
#> 15 : z5603
#> 16 : z5606
#> 17 : z5608
#> 18 : z596
#> 19 : z6300
#> 20 : z79139
#> 21 : z836
#> 22 : z84134
#> 23 : z842
#>
#> GAM solver ended normally after 23 iterations
#>
#> logL: -46.77283 srmr: 0.3819281
#>
mse4<- predict(res4, data[-train, ], verbose=TRUE)
#> amse 10452 1432 1616 4217 4741 4744 4747
#> 0.9121870 1.1160997 0.8111562 1.1825384 1.0817833 1.0583429 1.1415647 0.6957230
#> 54205 5530 5532 5533 5534 5535 5600 5603
#> 1.1719439 0.9052094 0.8304518 1.3167643 0.8457815 0.7351789 0.5526870 1.0343893
#> 5606 5608 596 6300 79139 836 84134 842
#> 0.8886350 1.0385301 1.0676148 0.5683706 0.6435413 0.6957059 0.8558520 0.7424376
# ... sem
res5<- SEMml(ig, data, train, algo="sem", vimp=FALSE)
#> 1 : z10452
#> 2 : z1432
#> 3 : z1616
#> 4 : z4217
#> 5 : z4741
#> 6 : z4744
#> 7 : z4747
#> 8 : z54205
#> 9 : z5530
#> 10 : z5532
#> 11 : z5533
#> 12 : z5534
#> 13 : z5535
#> 14 : z5600
#> 15 : z5603
#> 16 : z5606
#> 17 : z5608
#> 18 : z596
#> 19 : z6300
#> 20 : z79139
#> 21 : z836
#> 22 : z84134
#> 23 : z842
#>
#> SEM solver ended normally after 23 iterations
#>
#> logL: -47.90891 srmr: 0.3040025
#>
mse5<- predict(res5, data[-train, ], verbose=TRUE)
#> amse 10452 1432 1616 4217 4741 4744 4747
#> 0.8973153 1.1160997 0.8122749 1.1257768 1.0817833 1.0773819 1.0733166 0.7435081
#> 54205 5530 5532 5533 5534 5535 5600 5603
#> 1.1161717 0.9041601 0.8519291 1.2303948 0.8115211 0.6638488 0.5717884 1.0343893
#> 5606 5608 596 6300 79139 836 84134 842
#> 0.8886350 0.9082540 1.0675990 0.6118950 0.6766548 0.6957059 0.8591412 0.7160223
end<- Sys.time()
print(end-start)
#> Time difference of 10.00831 secs
# }