modelLookup: Tools for Models Available in 'train'
In caret: Classification and Regression Training
Description
Usage
Arguments
Details
Value
Note
Author(s)
See Also
Examples
Description
These function show information about models and packages that are accessible via train
Usage
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modelLookup(model = NULL)
getModelInfo(model = NULL, regex = TRUE, ...)
checkInstall(pkg)
Arguments
model
a character string associated with the method argument of train. If no value is
passed, all models are returned. For getModelInfo, regular expressions can be used.
regex
a logical: should a regular expressions be used? If FALSE, a simple match is conducted against the whole name of the model.
pkg
a character string of package names.
...
options to pass to grepl
Details
modelLookup is good for getting information related to the tuning parameters for a model. getModelInfo will return all the functions and metadata associated with a model. Both of these functions will only search within the models bundled in this package.
checkInstall will check to see if packages are installed. If they are not and the session is interactive, an option is given to install the packages using install.packages using that functions default arguments (the missing packages are listed if you would like to install them with other options). If the session is not interactive, an error is thrown.
Value
modelLookup produces a data frame with columns
model
a character string for the model code
parameter
the tuning parameter name
label
a tuning parameter label (used in plots)
forReg
a logical; can the model be used for regression?
forClass
a logical; can the model be used for classification?
probModel
a logical; does the model produce class probabilities?
getModelInfo returns a list containing one or more lists of the standard model information.
checkInstall returns not value.
Note
The column seq is no longer included in the output of modelLookup.
Author(s)
Max Kuhn
See Also
train, install.packages, grepl
Examples
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modelLookup()
modelLookup("gbm")
getModelInfo("pls")
getModelInfo("^pls")
getModelInfo("pls", regex = FALSE)
## Not run:
checkInstall(getModelInfo("pls")$library)
## End(Not run)
Example output
Loading required package: lattice
Loading required package: ggplot2
model parameter
12 ANFIS num.labels
13 ANFIS max.iter
4 AdaBag mfinal
5 AdaBag maxdepth
6 AdaBoost.M1 mfinal
7 AdaBoost.M1 maxdepth
8 AdaBoost.M1 coeflearn
42 BstLm mstop
43 BstLm nu
49 C5.0 trials
50 C5.0 model
51 C5.0 winnow
52 C5.0Cost trials
53 C5.0Cost model
54 C5.0Cost winnow
55 C5.0Cost cost
56 C5.0Rules parameter
57 C5.0Tree parameter
62 CSimca parameter
75 DENFIS Dthr
76 DENFIS max.iter
102 FH.GBML max.num.rule
103 FH.GBML popu.size
104 FH.GBML max.gen
105 FIR.DM num.labels
106 FIR.DM max.iter
109 FRBCS.CHI num.labels
110 FRBCS.CHI type.mf
111 FRBCS.W num.labels
112 FRBCS.W type.mf
113 FS.HGD num.labels
114 FS.HGD max.iter
136 GFS.FR.MOGUL max.gen
137 GFS.FR.MOGUL max.iter
138 GFS.FR.MOGUL max.tune
139 GFS.LT.RS popu.size
140 GFS.LT.RS num.labels
141 GFS.LT.RS max.gen
142 GFS.THRIFT popu.size
143 GFS.THRIFT num.labels
144 GFS.THRIFT max.gen
163 HYFIS num.labels
164 HYFIS max.iter
166 J48 C
167 J48 M
168 JRip NumOpt
169 JRip NumFolds
170 JRip MinWeights
191 LMT iter
188 Linda parameter
195 LogitBoost nIter
206 M5 pruned
207 M5 smoothed
208 M5 rules
209 M5Rules pruned
210 M5Rules smoothed
214 Mlda parameter
312 ORFlog mtry
313 ORFpls mtry
314 ORFridge mtry
315 ORFsvm mtry
305 OneR parameter
319 PART threshold
320 PART pruned
339 PRIM peel.alpha
340 PRIM paste.alpha
341 PRIM mass.min
330 PenalizedLDA lambda
331 PenalizedLDA K
345 QdaCov parameter
367 RFlda q
393 RRF mtry
394 RRF coefReg
395 RRF coefImp
396 RRFglobal mtry
397 RRFglobal coefReg
401 RSimca parameter
356 Rborist predFixed
357 Rborist minNode
406 SBC r.a
407 SBC eps.high
408 SBC eps.low
414 SLAVE num.labels
415 SLAVE max.iter
416 SLAVE max.gen
476 WM num.labels
477 WM type.mf
1 ada iter
2 ada maxdepth
3 ada nu
9 adaboost nIter
10 adaboost method
11 amdai model
14 avNNet size
15 avNNet decay
16 avNNet bag
17 awnb smooth
18 awtan score
19 awtan smooth
20 bag vars
21 bagEarth nprune
22 bagEarth degree
23 bagEarthGCV degree
24 bagFDA degree
25 bagFDA nprune
26 bagFDAGCV degree
27 bam select
28 bam method
29 bartMachine num_trees
30 bartMachine k
31 bartMachine alpha
32 bartMachine beta
33 bartMachine nu
34 bayesglm parameter
35 binda lambda.freqs
36 blackboost mstop
37 blackboost maxdepth
38 blasso sparsity
39 blassoAveraged parameter
40 bridge parameter
41 brnn neurons
44 bstSm mstop
45 bstSm nu
46 bstTree mstop
47 bstTree maxdepth
48 bstTree nu
58 cforest mtry
59 chaid alpha2
60 chaid alpha3
61 chaid alpha4
63 ctree mincriterion
64 ctree2 maxdepth
65 ctree2 mincriterion
66 cubist committees
67 cubist neighbors
68 dda model
69 dda shrinkage
70 deepboost num_iter
71 deepboost tree_depth
72 deepboost beta
73 deepboost lambda
74 deepboost loss_type
77 dnn layer1
78 dnn layer2
79 dnn layer3
80 dnn hidden_dropout
81 dnn visible_dropout
82 dwdLinear lambda
83 dwdLinear qval
84 dwdPoly lambda
85 dwdPoly qval
86 dwdPoly degree
87 dwdPoly scale
88 dwdRadial lambda
89 dwdRadial qval
90 dwdRadial sigma
91 earth nprune
92 earth degree
93 elm nhid
94 elm actfun
95 enet fraction
96 enet lambda
97 evtree alpha
98 extraTrees mtry
99 extraTrees numRandomCuts
100 fda degree
101 fda nprune
107 foba k
108 foba lambda
115 gam select
116 gam method
119 gamLoess span
120 gamLoess degree
121 gamSpline df
117 gamboost mstop
118 gamboost prune
122 gaussprLinear parameter
123 gaussprPoly degree
124 gaussprPoly scale
125 gaussprRadial sigma
131 gbm n.trees
132 gbm interaction.depth
133 gbm shrinkage
134 gbm n.minobsinnode
126 gbm_h2o ntrees
127 gbm_h2o max_depth
128 gbm_h2o min_rows
129 gbm_h2o learn_rate
130 gbm_h2o col_sample_rate
135 gcvEarth degree
146 glm parameter
145 glm.nb link
153 glmStepAIC parameter
147 glmboost mstop
148 glmboost prune
151 glmnet alpha
152 glmnet lambda
149 glmnet_h2o alpha
150 glmnet_h2o lambda
154 gpls K.prov
155 hda gamma
156 hda lambda
157 hda newdim
158 hdda threshold
159 hdda model
160 hdrda gamma
161 hdrda lambda
162 hdrda shrinkage_type
165 icr n.comp
171 kernelpls ncomp
172 kknn kmax
173 kknn distance
174 kknn kernel
175 knn k
176 krlsPoly lambda
177 krlsPoly degree
178 krlsRadial lambda
179 krlsRadial sigma
180 lars fraction
181 lars2 step
182 lasso fraction
183 lda parameter
184 lda2 dimen
185 leapBackward nvmax
186 leapForward nvmax
187 leapSeq nvmax
189 lm intercept
190 lmStepAIC parameter
192 loclda k
193 logicBag nleaves
194 logicBag ntrees
196 logreg treesize
197 logreg ntrees
198 lssvmLinear tau
199 lssvmPoly degree
200 lssvmPoly scale
201 lssvmPoly tau
202 lssvmRadial sigma
203 lssvmRadial tau
204 lvq size
205 lvq k
211 manb smooth
212 manb prior
213 mda subclasses
215 mlp size
216 mlpKerasDecay size
217 mlpKerasDecay lambda
218 mlpKerasDecay batch_size
219 mlpKerasDecay lr
220 mlpKerasDecay rho
221 mlpKerasDecay decay
222 mlpKerasDecay activation
223 mlpKerasDecayCost size
224 mlpKerasDecayCost lambda
225 mlpKerasDecayCost batch_size
226 mlpKerasDecayCost lr
227 mlpKerasDecayCost rho
228 mlpKerasDecayCost decay
229 mlpKerasDecayCost cost
230 mlpKerasDecayCost activation
231 mlpKerasDropout size
232 mlpKerasDropout dropout
233 mlpKerasDropout batch_size
234 mlpKerasDropout lr
235 mlpKerasDropout rho
236 mlpKerasDropout decay
237 mlpKerasDropout activation
238 mlpKerasDropoutCost size
239 mlpKerasDropoutCost dropout
240 mlpKerasDropoutCost batch_size
241 mlpKerasDropoutCost lr
242 mlpKerasDropoutCost rho
243 mlpKerasDropoutCost decay
244 mlpKerasDropoutCost cost
245 mlpKerasDropoutCost activation
246 mlpML layer1
247 mlpML layer2
248 mlpML layer3
249 mlpSGD size
250 mlpSGD l2reg
251 mlpSGD lambda
252 mlpSGD learn_rate
253 mlpSGD momentum
254 mlpSGD gamma
255 mlpSGD minibatchsz
256 mlpSGD repeats
257 mlpWeightDecay size
258 mlpWeightDecay decay
259 mlpWeightDecayML layer1
260 mlpWeightDecayML layer2
261 mlpWeightDecayML layer3
262 mlpWeightDecayML decay
263 monmlp hidden1
264 monmlp n.ensemble
265 msaenet alphas
266 msaenet nsteps
267 msaenet scale
268 multinom decay
269 mxnet layer1
270 mxnet layer2
271 mxnet layer3
272 mxnet learning.rate
273 mxnet momentum
274 mxnet dropout
275 mxnet activation
276 mxnetAdam layer1
277 mxnetAdam layer2
278 mxnetAdam layer3
279 mxnetAdam dropout
280 mxnetAdam beta1
281 mxnetAdam beta2
282 mxnetAdam learningrate
283 mxnetAdam activation
284 naive_bayes laplace
285 naive_bayes usekernel
286 naive_bayes adjust
287 nb fL
288 nb usekernel
289 nb adjust
290 nbDiscrete smooth
291 nbSearch k
292 nbSearch epsilon
293 nbSearch smooth
294 nbSearch final_smooth
295 nbSearch direction
296 neuralnet layer1
297 neuralnet layer2
298 neuralnet layer3
299 nnet size
300 nnet decay
301 nnls parameter
302 nodeHarvest maxinter
303 nodeHarvest mode
304 null parameter
306 ordinalNet alpha
307 ordinalNet criteria
308 ordinalNet link
309 ordinalRF nsets
310 ordinalRF ntreeperdiv
311 ordinalRF ntreefinal
316 ownn K
317 pam threshold
318 parRF mtry
321 partDSA cut.off.growth
322 partDSA MPD
323 pcaNNet size
324 pcaNNet decay
325 pcr ncomp
326 pda lambda
327 pda2 df
328 penalized lambda1
329 penalized lambda2
332 plr lambda
333 plr cp
334 pls ncomp
335 plsRglm nt
336 plsRglm alpha.pvals.expli
337 polr method
338 ppr nterms
342 protoclass eps
343 protoclass Minkowski
344 qda parameter
346 qrf mtry
347 qrnn n.hidden
348 qrnn penalty
349 qrnn bag
366 rFerns depth
350 randomGLM maxInteractionOrder
351 ranger mtry
352 ranger splitrule
353 ranger min.node.size
354 rbf size
355 rbfDDA negativeThreshold
358 rda gamma
359 rda lambda
360 regLogistic cost
361 regLogistic loss
362 regLogistic epsilon
363 relaxo lambda
364 relaxo phi
365 rf mtry
368 rfRules mtry
369 rfRules maxdepth
370 ridge lambda
371 rlda estimator
372 rlm intercept
373 rlm psi
374 rmda K
375 rmda model
376 rocc xgenes
377 rotationForest K
378 rotationForest L
379 rotationForestCp K
380 rotationForestCp L
381 rotationForestCp cp
382 rpart cp
383 rpart1SE parameter
384 rpart2 maxdepth
385 rpartCost cp
386 rpartCost Cost
387 rpartScore cp
388 rpartScore split
389 rpartScore prune
390 rqlasso lambda
391 rqnc lambda
392 rqnc penalty
398 rrlda lambda
399 rrlda hp
400 rrlda penalty
402 rvmLinear parameter
403 rvmPoly scale
404 rvmPoly degree
405 rvmRadial sigma
409 sda diagonal
410 sda lambda
411 sdwd lambda
412 sdwd lambda2
413 simpls ncomp
417 slda parameter
418 smda NumVars
419 smda lambda
420 smda R
421 snn lambda
422 sparseLDA NumVars
423 sparseLDA lambda
424 spikeslab vars
425 spls K
426 spls eta
427 spls kappa
428 stepLDA maxvar
429 stepLDA direction
430 stepQDA maxvar
431 stepQDA direction
432 superpc threshold
433 superpc n.components
434 svmBoundrangeString length
435 svmBoundrangeString C
436 svmExpoString lambda
437 svmExpoString C
438 svmLinear C
439 svmLinear2 cost
440 svmLinear3 cost
441 svmLinear3 Loss
442 svmLinearWeights cost
443 svmLinearWeights weight
444 svmLinearWeights2 cost
445 svmLinearWeights2 Loss
446 svmLinearWeights2 weight
447 svmPoly degree
448 svmPoly scale
449 svmPoly C
450 svmRadial sigma
451 svmRadial C
452 svmRadialCost C
453 svmRadialSigma sigma
454 svmRadialSigma C
455 svmRadialWeights sigma
456 svmRadialWeights C
457 svmRadialWeights Weight
458 svmSpectrumString length
459 svmSpectrumString C
460 tan score
461 tan smooth
462 tanSearch k
463 tanSearch epsilon
464 tanSearch smooth
465 tanSearch final_smooth
466 tanSearch sp
467 treebag parameter
468 vbmpRadial estimateTheta
469 vglmAdjCat parallel
470 vglmAdjCat link
471 vglmContRatio parallel
472 vglmContRatio link
473 vglmCumulative parallel
474 vglmCumulative link
475 widekernelpls ncomp
478 wsrf mtry
479 xgbDART nrounds
480 xgbDART max_depth
481 xgbDART eta
482 xgbDART gamma
483 xgbDART subsample
484 xgbDART colsample_bytree
485 xgbDART rate_drop
486 xgbDART skip_drop
487 xgbDART min_child_weight
488 xgbLinear nrounds
489 xgbLinear lambda
490 xgbLinear alpha
491 xgbLinear eta
492 xgbTree nrounds
493 xgbTree max_depth
494 xgbTree eta
495 xgbTree gamma
496 xgbTree colsample_bytree
497 xgbTree min_child_weight
498 xgbTree subsample
499 xyf xdim
500 xyf ydim
501 xyf user.weights
502 xyf topo
label
12 #Fuzzy Terms
13 Max. Iterations
4 #Trees
5 Max Tree Depth
6 #Trees
7 Max Tree Depth
8 Coefficient Type
42 # Boosting Iterations
43 Shrinkage
49 # Boosting Iterations
50 Model Type
51 Winnow
52 # Boosting Iterations
53 Model Type
54 Winnow
55 Cost
56 none
57 none
62 parameter
75 Threshold
76 Max. Iterations
102 Max. #Rules
103 Population Size
104 Max. Generations
105 #Fuzzy Terms
106 Max. Iterations
109 #Fuzzy Terms
110 Membership Function
111 #Fuzzy Terms
112 Membership Function
113 #Fuzzy Terms
114 Max. Iterations
136 Max. Generations
137 Max. Iterations
138 Max. Tuning Iterations
139 Population Size
140 # Fuzzy Labels
141 Max. Generations
142 Population Size
143 # Fuzzy Labels
144 Max. Generations
163 #Fuzzy Terms
164 Max. Iterations
166 Confidence Threshold
167 Minimum Instances Per Leaf
168 # Optimizations
169 # Folds
170 Min Weights
191 # Iteratons
188 none
195 # Boosting Iterations
206 Pruned
207 Smoothed
208 Rules
209 Pruned
210 Smoothed
214 parameter
312 #Randomly Selected Predictors
313 #Randomly Selected Predictors
314 #Randomly Selected Predictors
315 #Randomly Selected Predictors
305 none
319 Confidence Threshold
320 Pruning
339 peeling quantile
340 pasting quantile
341 minimum mass
330 L1 Penalty
331 #Discriminant Functions
345 parameter
367 # Factors
393 #Randomly Selected Predictors
394 Regularization Value
395 Importance Coefficient
396 #Randomly Selected Predictors
397 Regularization Value
401 parameter
356 #Randomly Selected Predictors
357 Minimal Node Size
406 Radius
407 Upper Threshold
408 Lower Threshold
414 #Fuzzy Terms
415 Max. Iterations
416 Max. Generations
476 #Fuzzy Terms
477 Membership Function
1 #Trees
2 Max Tree Depth
3 Learning Rate
9 #Trees
10 Method
11 Model Type
14 #Hidden Units
15 Weight Decay
16 Bagging
17 Smoothing Parameter
18 Score Function
19 Smoothing Parameter
20 #Randomly Selected Predictors
21 #Terms
22 Product Degree
23 Product Degree
24 Product Degree
25 #Terms
26 Product Degree
27 Feature Selection
28 Method
29 #Trees
30 Prior Boundary
31 Base Terminal Node Hyperparameter
32 Power Terminal Node Hyperparameter
33 Degrees of Freedom
34 parameter
35 Shrinkage Intensity
36 #Trees
37 Max Tree Depth
38 Sparsity Threshold
39 parameter
40 parameter
41 # Neurons
44 # Boosting Iterations
45 Shrinkage
46 # Boosting Iterations
47 Max Tree Depth
48 Shrinkage
58 #Randomly Selected Predictors
59 Merging Threshold
60 Splitting former Merged Threshold
61 \n Splitting former Merged Threshold
63 1 - P-Value Threshold
64 Max Tree Depth
65 1 - P-Value Threshold
66 #Committees
67 #Instances
68 Model
69 Shrinkage Type
70 # Boosting Iterations
71 Tree Depth
72 L1 Regularization
73 Tree Depth Regularization
74 Loss
77 Hidden Layer 1
78 Hidden Layer 2
79 Hidden Layer 3
80 Hidden Dropouts
81 Visible Dropout
82 Regularization Parameter
83 q
84 Regularization Parameter
85 q
86 Polynomial Degree
87 Scale
88 Regularization Parameter
89 q
90 Sigma
91 #Terms
92 Product Degree
93 #Hidden Units
94 Activation Function
95 Fraction of Full Solution
96 Weight Decay
97 Complexity Parameter
98 # Randomly Selected Predictors
99 # Random Cuts
100 Product Degree
101 #Terms
107 #Variables Retained
108 L2 Penalty
115 Feature Selection
116 Method
119 Span
120 Degree
121 Degrees of Freedom
117 # Boosting Iterations
118 AIC Prune?
122 Parameter
123 Polynomial Degree
124 Scale
125 Sigma
131 # Boosting Iterations
132 Max Tree Depth
133 Shrinkage
134 Min. Terminal Node Size
126 # Boosting Iterations
127 Max Tree Depth
128 Min. Terminal Node Size
129 Shrinkage
130 #Randomly Selected Predictors
135 Product Degree
146 parameter
145 Link Function
153 parameter
147 # Boosting Iterations
148 AIC Prune?
151 Mixing Percentage
152 Regularization Parameter
149 Mixing Percentage
150 Regularization Parameter
154 #Components
155 Gamma
156 Lambda
157 Dimension of the Discriminative Subspace
158 Threshold
159 Model Type
160 Gamma
161 Lambda
162 Shrinkage Type
165 #Components
171 #Components
172 Max. #Neighbors
173 Distance
174 Kernel
175 #Neighbors
176 Regularization Parameter
177 Polynomial Degree
178 Regularization Parameter
179 Sigma
180 Fraction
181 #Steps
182 Fraction of Full Solution
183 parameter
184 #Discriminant Functions
185 Maximum Number of Predictors
186 Maximum Number of Predictors
187 Maximum Number of Predictors
189 intercept
190 parameter
192 #Nearest Neighbors
193 Maximum Number of Leaves
194 Number of Trees
196 Maximum Number of Leaves
197 Number of Trees
198 Regularization Parameter
199 Polynomial Degree
200 Scale
201 Regularization Parameter
202 Sigma
203 Regularization Parameter
204 Codebook Size
205 #Prototypes
211 Smoothing Parameter
212 Prior Probability
213 #Subclasses Per Class
215 #Hidden Units
216 #Hidden Units
217 L2 Regularization
218 Batch Size
219 Learning Rate
220 Rho
221 Learning Rate Decay
222 Activation Function
223 #Hidden Units
224 L2 Regularization
225 Batch Size
226 Learning Rate
227 Rho
228 Learning Rate Decay
229 Cost
230 Activation Function
231 #Hidden Units
232 Dropout Rate
233 Batch Size
234 Learning Rate
235 Rho
236 Learning Rate Decay
237 Activation Function
238 #Hidden Units
239 Dropout Rate
240 Batch Size
241 Learning Rate
242 Rho
243 Learning Rate Decay
244 Cost
245 Activation Function
246 #Hidden Units layer1
247 #Hidden Units layer2
248 #Hidden Units layer3
249 #Hidden Units
250 L2 Regularization
251 RMSE Gradient Scaling
252 Learning Rate
253 Momentum
254 Learning Rate Decay
255 Batch Size
256 #Models
257 #Hidden Units
258 Weight Decay
259 #Hidden Units layer1
260 #Hidden Units layer2
261 #Hidden Units layer3
262 Weight Decay
263 #Hidden Units
264 #Models
265 Alpha
266 #Adaptive Estimation Steps
267 Adaptive Weight Scaling Factor
268 Weight Decay
269 #Hidden Units in Layer 1
270 #Hidden Units in Layer 2
271 #Hidden Units in Layer 3
272 Learning Rate
273 Momentum
274 Dropout Rate
275 Activation Function
276 #Hidden Units in Layer 1
277 #Hidden Units in Layer 2
278 #Hidden Units in Layer 3
279 Dropout Rate
280 beta1
281 beta2
282 Learning Rate
283 Activation Function
284 Laplace Correction
285 Distribution Type
286 Bandwidth Adjustment
287 Laplace Correction
288 Distribution Type
289 Bandwidth Adjustment
290 Smoothing Parameter
291 #Folds
292 Minimum Absolute Improvement
293 Smoothing Parameter
294 Final Smoothing Parameter
295 Search Direction
296 #Hidden Units in Layer 1
297 #Hidden Units in Layer 2
298 #Hidden Units in Layer 3
299 #Hidden Units
300 Weight Decay
301 parameter
302 Maximum Interaction Depth
303 Prediction Mode
304 parameter
306 Mixing Percentage
307 Selection Criterion
308 Link Function
309 # score sets tried prior to the approximation
310 # of trees (small RFs)
311 # of trees (final RF)
316 #Neighbors
317 Shrinkage Threshold
318 #Randomly Selected Predictors
321 Number of Terminal Partitions
322 Minimum Percent Difference
323 #Hidden Units
324 Weight Decay
325 #Components
326 Shrinkage Penalty Coefficient
327 Degrees of Freedom
328 L1 Penalty
329 L2 Penalty
332 L2 Penalty
333 Complexity Parameter
334 #Components
335 #PLS Components
336 p-Value threshold
337 parameter
338 # Terms
342 Ball Size
343 Distance Order
344 parameter
346 #Randomly Selected Predictors
347 #Hidden Units
348 Weight Decay
349 Bagged Models?
366 Fern Depth
350 Interaction Order
351 #Randomly Selected Predictors
352 Splitting Rule
353 Minimal Node Size
354 #Hidden Units
355 Activation Limit for Conflicting Classes
358 Gamma
359 Lambda
360 Cost
361 Loss Function
362 Tolerance
363 Penalty Parameter
364 Relaxation Parameter
365 #Randomly Selected Predictors
368 #Randomly Selected Predictors
369 Maximum Rule Depth
370 Weight Decay
371 Regularization Method
372 intercept
373 psi
374 #Subclasses Per Class
375 Model
376 #Variables Retained
377 #Variable Subsets
378 Ensemble Size
379 #Variable Subsets
380 Ensemble Size
381 Complexity Parameter
382 Complexity Parameter
383 parameter
384 Max Tree Depth
385 Complexity Parameter
386 Cost
387 Complexity Parameter
388 Split Function
389 Pruning Measure
390 L1 Penalty
391 L1 Penalty
392 Penalty Type
398 Penalty Parameter
399 Robustness Parameter
400 Penalty Type
402 parameter
403 Scale
404 Polynomial Degree
405 Sigma
409 Diagonalize
410 shrinkage
411 L1 Penalty
412 L2 Penalty
413 #Components
417 none
418 # Predictors
419 Lambda
420 # Subclasses
421 Stabilization Parameter
422 # Predictors
423 Lambda
424 Variables Retained
425 #Components
426 Threshold
427 Kappa
428 Maximum #Variables
429 Search Direction
430 Maximum #Variables
431 Search Direction
432 Threshold
433 #Components
434 length
435 Cost
436 lambda
437 Cost
438 Cost
439 Cost
440 Cost
441 Loss Function
442 Cost
443 Class Weight
444 Cost
445 Loss Function
446 Class Weight
447 Polynomial Degree
448 Scale
449 Cost
450 Sigma
451 Cost
452 Cost
453 Sigma
454 Cost
455 Sigma
456 Cost
457 Weight
458 length
459 Cost
460 Score Function
461 Smoothing Parameter
462 #Folds
463 Minimum Absolute Improvement
464 Smoothing Parameter
465 Final Smoothing Parameter
466 Super-Parent
467 parameter
468 Theta Estimated
469 Parallel Curves
470 Link Function
471 Parallel Curves
472 Link Function
473 Parallel Curves
474 Link Function
475 #Components
478 #Randomly Selected Predictors
479 # Boosting Iterations
480 Max Tree Depth
481 Shrinkage
482 Minimum Loss Reduction
483 Subsample Percentage
484 Subsample Ratio of Columns
485 Fraction of Trees Dropped
486 Prob. of Skipping Drop-out
487 Minimum Sum of Instance Weight
488 # Boosting Iterations
489 L2 Regularization
490 L1 Regularization
491 Learning Rate
492 # Boosting Iterations
493 Max Tree Depth
494 Shrinkage
495 Minimum Loss Reduction
496 Subsample Ratio of Columns
497 Minimum Sum of Instance Weight
498 Subsample Percentage
499 Rows
500 Columns
501 Layer Weight
502 Topology
forReg forClass probModel
12 TRUE FALSE FALSE
13 TRUE FALSE FALSE
4 FALSE TRUE TRUE
5 FALSE TRUE TRUE
6 FALSE TRUE TRUE
7 FALSE TRUE TRUE
8 FALSE TRUE TRUE
42 TRUE TRUE FALSE
43 TRUE TRUE FALSE
49 FALSE TRUE TRUE
50 FALSE TRUE TRUE
51 FALSE TRUE TRUE
52 FALSE TRUE FALSE
53 FALSE TRUE FALSE
54 FALSE TRUE FALSE
55 FALSE TRUE FALSE
56 FALSE TRUE TRUE
57 FALSE TRUE TRUE
62 FALSE TRUE FALSE
75 TRUE FALSE FALSE
76 TRUE FALSE FALSE
102 FALSE TRUE FALSE
103 FALSE TRUE FALSE
104 FALSE TRUE FALSE
105 TRUE FALSE FALSE
106 TRUE FALSE FALSE
109 FALSE TRUE FALSE
110 FALSE TRUE FALSE
111 FALSE TRUE FALSE
112 FALSE TRUE FALSE
113 TRUE FALSE FALSE
114 TRUE FALSE FALSE
136 TRUE FALSE FALSE
137 TRUE FALSE FALSE
138 TRUE FALSE FALSE
139 TRUE FALSE FALSE
140 TRUE FALSE FALSE
141 TRUE FALSE FALSE
142 TRUE FALSE FALSE
143 TRUE FALSE FALSE
144 TRUE FALSE FALSE
163 TRUE FALSE FALSE
164 TRUE FALSE FALSE
166 FALSE TRUE TRUE
167 FALSE TRUE TRUE
168 FALSE TRUE TRUE
169 FALSE TRUE TRUE
170 FALSE TRUE TRUE
191 FALSE TRUE TRUE
188 FALSE TRUE TRUE
195 FALSE TRUE TRUE
206 TRUE FALSE FALSE
207 TRUE FALSE FALSE
208 TRUE FALSE FALSE
209 TRUE FALSE FALSE
210 TRUE FALSE FALSE
214 FALSE TRUE FALSE
312 FALSE TRUE TRUE
313 FALSE TRUE TRUE
314 FALSE TRUE TRUE
315 FALSE TRUE TRUE
305 FALSE TRUE TRUE
319 FALSE TRUE TRUE
320 FALSE TRUE TRUE
339 FALSE TRUE TRUE
340 FALSE TRUE TRUE
341 FALSE TRUE TRUE
330 FALSE TRUE FALSE
331 FALSE TRUE FALSE
345 FALSE TRUE TRUE
367 FALSE TRUE FALSE
393 TRUE TRUE TRUE
394 TRUE TRUE TRUE
395 TRUE TRUE TRUE
396 TRUE TRUE TRUE
397 TRUE TRUE TRUE
401 FALSE TRUE FALSE
356 TRUE TRUE TRUE
357 TRUE TRUE TRUE
406 TRUE FALSE FALSE
407 TRUE FALSE FALSE
408 TRUE FALSE FALSE
414 FALSE TRUE FALSE
415 FALSE TRUE FALSE
416 FALSE TRUE FALSE
476 TRUE FALSE FALSE
477 TRUE FALSE FALSE
1 FALSE TRUE TRUE
2 FALSE TRUE TRUE
3 FALSE TRUE TRUE
9 FALSE TRUE TRUE
10 FALSE TRUE TRUE
11 FALSE TRUE TRUE
14 TRUE TRUE TRUE
15 TRUE TRUE TRUE
16 TRUE TRUE TRUE
17 FALSE TRUE TRUE
18 FALSE TRUE TRUE
19 FALSE TRUE TRUE
20 TRUE TRUE TRUE
21 TRUE TRUE TRUE
22 TRUE TRUE TRUE
23 TRUE TRUE TRUE
24 FALSE TRUE TRUE
25 FALSE TRUE TRUE
26 FALSE TRUE TRUE
27 TRUE TRUE TRUE
28 TRUE TRUE TRUE
29 TRUE TRUE TRUE
30 TRUE TRUE TRUE
31 TRUE TRUE TRUE
32 TRUE TRUE TRUE
33 TRUE TRUE TRUE
34 TRUE TRUE TRUE
35 FALSE TRUE TRUE
36 TRUE TRUE TRUE
37 TRUE TRUE TRUE
38 TRUE FALSE FALSE
39 TRUE FALSE FALSE
40 TRUE FALSE FALSE
41 TRUE FALSE FALSE
44 TRUE TRUE FALSE
45 TRUE TRUE FALSE
46 TRUE TRUE FALSE
47 TRUE TRUE FALSE
48 TRUE TRUE FALSE
58 TRUE TRUE TRUE
59 FALSE TRUE TRUE
60 FALSE TRUE TRUE
61 FALSE TRUE TRUE
63 TRUE TRUE TRUE
64 TRUE TRUE TRUE
65 TRUE TRUE TRUE
66 TRUE FALSE FALSE
67 TRUE FALSE FALSE
68 FALSE TRUE TRUE
69 FALSE TRUE TRUE
70 FALSE TRUE FALSE
71 FALSE TRUE FALSE
72 FALSE TRUE FALSE
73 FALSE TRUE FALSE
74 FALSE TRUE FALSE
77 TRUE TRUE TRUE
78 TRUE TRUE TRUE
79 TRUE TRUE TRUE
80 TRUE TRUE TRUE
81 TRUE TRUE TRUE
82 FALSE TRUE TRUE
83 FALSE TRUE TRUE
84 FALSE TRUE TRUE
85 FALSE TRUE TRUE
86 FALSE TRUE TRUE
87 FALSE TRUE TRUE
88 FALSE TRUE TRUE
89 FALSE TRUE TRUE
90 FALSE TRUE TRUE
91 TRUE TRUE TRUE
92 TRUE TRUE TRUE
93 TRUE TRUE FALSE
94 TRUE TRUE FALSE
95 TRUE FALSE FALSE
96 TRUE FALSE FALSE
97 TRUE TRUE TRUE
98 TRUE TRUE TRUE
99 TRUE TRUE TRUE
100 FALSE TRUE TRUE
101 FALSE TRUE TRUE
107 TRUE FALSE FALSE
108 TRUE FALSE FALSE
115 TRUE TRUE TRUE
116 TRUE TRUE TRUE
119 TRUE TRUE TRUE
120 TRUE TRUE TRUE
121 TRUE TRUE TRUE
117 TRUE TRUE TRUE
118 TRUE TRUE TRUE
122 TRUE TRUE TRUE
123 TRUE TRUE TRUE
124 TRUE TRUE TRUE
125 TRUE TRUE TRUE
131 TRUE TRUE TRUE
132 TRUE TRUE TRUE
133 TRUE TRUE TRUE
134 TRUE TRUE TRUE
126 TRUE TRUE TRUE
127 TRUE TRUE TRUE
128 TRUE TRUE TRUE
129 TRUE TRUE TRUE
130 TRUE TRUE TRUE
135 TRUE TRUE TRUE
146 TRUE TRUE TRUE
145 TRUE FALSE FALSE
153 TRUE TRUE TRUE
147 TRUE TRUE TRUE
148 TRUE TRUE TRUE
151 TRUE TRUE TRUE
152 TRUE TRUE TRUE
149 TRUE TRUE TRUE
150 TRUE TRUE TRUE
154 FALSE TRUE TRUE
155 FALSE TRUE TRUE
156 FALSE TRUE TRUE
157 FALSE TRUE TRUE
158 FALSE TRUE TRUE
159 FALSE TRUE TRUE
160 FALSE TRUE TRUE
161 FALSE TRUE TRUE
162 FALSE TRUE TRUE
165 TRUE FALSE FALSE
171 TRUE TRUE TRUE
172 TRUE TRUE TRUE
173 TRUE TRUE TRUE
174 TRUE TRUE TRUE
175 TRUE TRUE TRUE
176 TRUE FALSE FALSE
177 TRUE FALSE FALSE
178 TRUE FALSE FALSE
179 TRUE FALSE FALSE
180 TRUE FALSE FALSE
181 TRUE FALSE FALSE
182 TRUE FALSE FALSE
183 FALSE TRUE TRUE
184 FALSE TRUE TRUE
185 TRUE FALSE FALSE
186 TRUE FALSE FALSE
187 TRUE FALSE FALSE
189 TRUE FALSE FALSE
190 TRUE FALSE FALSE
192 FALSE TRUE TRUE
193 TRUE TRUE TRUE
194 TRUE TRUE TRUE
196 TRUE TRUE TRUE
197 TRUE TRUE TRUE
198 FALSE TRUE FALSE
199 FALSE TRUE FALSE
200 FALSE TRUE FALSE
201 FALSE TRUE FALSE
202 FALSE TRUE FALSE
203 FALSE TRUE FALSE
204 FALSE TRUE FALSE
205 FALSE TRUE FALSE
211 FALSE TRUE TRUE
212 FALSE TRUE TRUE
213 FALSE TRUE TRUE
215 TRUE TRUE TRUE
216 TRUE TRUE TRUE
217 TRUE TRUE TRUE
218 TRUE TRUE TRUE
219 TRUE TRUE TRUE
220 TRUE TRUE TRUE
221 TRUE TRUE TRUE
222 TRUE TRUE TRUE
223 FALSE TRUE TRUE
224 FALSE TRUE TRUE
225 FALSE TRUE TRUE
226 FALSE TRUE TRUE
227 FALSE TRUE TRUE
228 FALSE TRUE TRUE
229 FALSE TRUE TRUE
230 FALSE TRUE TRUE
231 TRUE TRUE TRUE
232 TRUE TRUE TRUE
233 TRUE TRUE TRUE
234 TRUE TRUE TRUE
235 TRUE TRUE TRUE
236 TRUE TRUE TRUE
237 TRUE TRUE TRUE
238 FALSE TRUE TRUE
239 FALSE TRUE TRUE
240 FALSE TRUE TRUE
241 FALSE TRUE TRUE
242 FALSE TRUE TRUE
243 FALSE TRUE TRUE
244 FALSE TRUE TRUE
245 FALSE TRUE TRUE
246 TRUE TRUE TRUE
247 TRUE TRUE TRUE
248 TRUE TRUE TRUE
249 TRUE TRUE TRUE
250 TRUE TRUE TRUE
251 TRUE TRUE TRUE
252 TRUE TRUE TRUE
253 TRUE TRUE TRUE
254 TRUE TRUE TRUE
255 TRUE TRUE TRUE
256 TRUE TRUE TRUE
257 TRUE TRUE TRUE
258 TRUE TRUE TRUE
259 TRUE TRUE TRUE
260 TRUE TRUE TRUE
261 TRUE TRUE TRUE
262 TRUE TRUE TRUE
263 TRUE TRUE TRUE
264 TRUE TRUE TRUE
265 TRUE TRUE TRUE
266 TRUE TRUE TRUE
267 TRUE TRUE TRUE
268 FALSE TRUE TRUE
269 TRUE TRUE TRUE
270 TRUE TRUE TRUE
271 TRUE TRUE TRUE
272 TRUE TRUE TRUE
273 TRUE TRUE TRUE
274 TRUE TRUE TRUE
275 TRUE TRUE TRUE
276 TRUE TRUE TRUE
277 TRUE TRUE TRUE
278 TRUE TRUE TRUE
279 TRUE TRUE TRUE
280 TRUE TRUE TRUE
281 TRUE TRUE TRUE
282 TRUE TRUE TRUE
283 TRUE TRUE TRUE
284 FALSE TRUE TRUE
285 FALSE TRUE TRUE
286 FALSE TRUE TRUE
287 FALSE TRUE TRUE
288 FALSE TRUE TRUE
289 FALSE TRUE TRUE
290 FALSE TRUE TRUE
291 FALSE TRUE TRUE
292 FALSE TRUE TRUE
293 FALSE TRUE TRUE
294 FALSE TRUE TRUE
295 FALSE TRUE TRUE
296 TRUE FALSE FALSE
297 TRUE FALSE FALSE
298 TRUE FALSE FALSE
299 TRUE TRUE TRUE
300 TRUE TRUE TRUE
301 TRUE FALSE FALSE
302 TRUE TRUE TRUE
303 TRUE TRUE TRUE
304 TRUE TRUE TRUE
306 FALSE TRUE TRUE
307 FALSE TRUE TRUE
308 FALSE TRUE TRUE
309 FALSE TRUE TRUE
310 FALSE TRUE TRUE
311 FALSE TRUE TRUE
316 FALSE TRUE FALSE
317 FALSE TRUE TRUE
318 TRUE TRUE TRUE
321 TRUE TRUE FALSE
322 TRUE TRUE FALSE
323 TRUE TRUE TRUE
324 TRUE TRUE TRUE
325 TRUE FALSE FALSE
326 FALSE TRUE TRUE
327 FALSE TRUE TRUE
328 TRUE FALSE FALSE
329 TRUE FALSE FALSE
332 FALSE TRUE TRUE
333 FALSE TRUE TRUE
334 TRUE TRUE TRUE
335 TRUE TRUE TRUE
336 TRUE TRUE TRUE
337 FALSE TRUE TRUE
338 TRUE FALSE FALSE
342 FALSE TRUE FALSE
343 FALSE TRUE FALSE
344 FALSE TRUE TRUE
346 TRUE FALSE FALSE
347 TRUE FALSE FALSE
348 TRUE FALSE FALSE
349 TRUE FALSE FALSE
366 FALSE TRUE FALSE
350 TRUE TRUE TRUE
351 TRUE TRUE TRUE
352 TRUE TRUE TRUE
353 TRUE TRUE TRUE
354 TRUE TRUE TRUE
355 TRUE TRUE TRUE
358 FALSE TRUE TRUE
359 FALSE TRUE TRUE
360 FALSE TRUE TRUE
361 FALSE TRUE TRUE
362 FALSE TRUE TRUE
363 TRUE FALSE FALSE
364 TRUE FALSE FALSE
365 TRUE TRUE TRUE
368 TRUE TRUE FALSE
369 TRUE TRUE FALSE
370 TRUE FALSE FALSE
371 FALSE TRUE TRUE
372 TRUE FALSE FALSE
373 TRUE FALSE FALSE
374 FALSE TRUE TRUE
375 FALSE TRUE TRUE
376 FALSE TRUE FALSE
377 FALSE TRUE TRUE
378 FALSE TRUE TRUE
379 FALSE TRUE TRUE
380 FALSE TRUE TRUE
381 FALSE TRUE TRUE
382 TRUE TRUE TRUE
383 TRUE TRUE TRUE
384 TRUE TRUE TRUE
385 FALSE TRUE FALSE
386 FALSE TRUE FALSE
387 FALSE TRUE FALSE
388 FALSE TRUE FALSE
389 FALSE TRUE FALSE
390 TRUE FALSE FALSE
391 TRUE FALSE FALSE
392 TRUE FALSE FALSE
398 FALSE TRUE TRUE
399 FALSE TRUE TRUE
400 FALSE TRUE TRUE
402 TRUE FALSE FALSE
403 TRUE FALSE FALSE
404 TRUE FALSE FALSE
405 TRUE FALSE FALSE
409 FALSE TRUE TRUE
410 FALSE TRUE TRUE
411 FALSE TRUE TRUE
412 FALSE TRUE TRUE
413 TRUE TRUE TRUE
417 FALSE TRUE TRUE
418 FALSE TRUE FALSE
419 FALSE TRUE FALSE
420 FALSE TRUE FALSE
421 FALSE TRUE FALSE
422 FALSE TRUE TRUE
423 FALSE TRUE TRUE
424 TRUE FALSE FALSE
425 TRUE TRUE TRUE
426 TRUE TRUE TRUE
427 TRUE TRUE TRUE
428 FALSE TRUE TRUE
429 FALSE TRUE TRUE
430 FALSE TRUE TRUE
431 FALSE TRUE TRUE
432 TRUE FALSE FALSE
433 TRUE FALSE FALSE
434 TRUE TRUE TRUE
435 TRUE TRUE TRUE
436 TRUE TRUE TRUE
437 TRUE TRUE TRUE
438 TRUE TRUE TRUE
439 TRUE TRUE TRUE
440 TRUE TRUE FALSE
441 TRUE TRUE FALSE
442 FALSE TRUE TRUE
443 FALSE TRUE TRUE
444 FALSE TRUE FALSE
445 FALSE TRUE FALSE
446 FALSE TRUE FALSE
447 TRUE TRUE TRUE
448 TRUE TRUE TRUE
449 TRUE TRUE TRUE
450 TRUE TRUE TRUE
451 TRUE TRUE TRUE
452 TRUE TRUE TRUE
453 TRUE TRUE TRUE
454 TRUE TRUE TRUE
455 FALSE TRUE TRUE
456 FALSE TRUE TRUE
457 FALSE TRUE TRUE
458 TRUE TRUE TRUE
459 TRUE TRUE TRUE
460 FALSE TRUE TRUE
461 FALSE TRUE TRUE
462 FALSE TRUE TRUE
463 FALSE TRUE TRUE
464 FALSE TRUE TRUE
465 FALSE TRUE TRUE
466 FALSE TRUE TRUE
467 TRUE TRUE TRUE
468 FALSE TRUE TRUE
469 FALSE TRUE TRUE
470 FALSE TRUE TRUE
471 FALSE TRUE TRUE
472 FALSE TRUE TRUE
473 FALSE TRUE TRUE
474 FALSE TRUE TRUE
475 TRUE TRUE TRUE
478 FALSE TRUE TRUE
479 TRUE TRUE TRUE
480 TRUE TRUE TRUE
481 TRUE TRUE TRUE
482 TRUE TRUE TRUE
483 TRUE TRUE TRUE
484 TRUE TRUE TRUE
485 TRUE TRUE TRUE
486 TRUE TRUE TRUE
487 TRUE TRUE TRUE
488 TRUE TRUE TRUE
489 TRUE TRUE TRUE
490 TRUE TRUE TRUE
491 TRUE TRUE TRUE
492 TRUE TRUE TRUE
493 TRUE TRUE TRUE
494 TRUE TRUE TRUE
495 TRUE TRUE TRUE
496 TRUE TRUE TRUE
497 TRUE TRUE TRUE
498 TRUE TRUE TRUE
499 TRUE TRUE TRUE
500 TRUE TRUE TRUE
501 TRUE TRUE TRUE
502 TRUE TRUE TRUE
model parameter label forReg forClass probModel
1 gbm n.trees # Boosting Iterations TRUE TRUE TRUE
2 gbm interaction.depth Max Tree Depth TRUE TRUE TRUE
3 gbm shrinkage Shrinkage TRUE TRUE TRUE
4 gbm n.minobsinnode Min. Terminal Node Size TRUE TRUE TRUE
$gpls
$gpls$label
[1] "Generalized Partial Least Squares"
$gpls$library
[1] "gpls"
$gpls$loop
NULL
$gpls$type
[1] "Classification"
$gpls$parameters
parameter class label
1 K.prov numeric #Components
$gpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(K.prov = seq(1, len))
}
else {
out <- data.frame(K.prov = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$gpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
gpls::gpls(x, y, K.prov = param$K.prov, ...)
$gpls$predict
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata)$class
$gpls$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata)$predicted
out <- cbind(out, 1 - out)
colnames(out) <- modelFit$obsLevels
out
}
$gpls$predictors
function (x, ...)
{
out <- if (hasTerms(x))
predictors(x$terms)
else colnames(x$data$x.order)
out[!(out %in% "Intercept")]
}
$gpls$tags
[1] "Logistic Regression" "Partial Least Squares" "Linear Classifier"
$gpls$sort
function (x)
x[order(x[, 1]), ]
$gpls$levels
function (x)
x$obsLevels
$kernelpls
$kernelpls$label
[1] "Partial Least Squares"
$kernelpls$library
[1] "pls"
$kernelpls$type
[1] "Regression" "Classification"
$kernelpls$parameters
parameter class label
1 ncomp numeric #Components
$kernelpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$kernelpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$kernelpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
caret::plsda(x, y, method = "kernelpls", ncomp = ncomp,
...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "kernelpls",
ncomp = ncomp, ...)
}
out
}
$kernelpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$kernelpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$kernelpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- MSEP(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$kernelpls$predictors
function (x, ...)
rownames(x$projection)
$kernelpls$levels
function (x)
x$obsLevels
$kernelpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Kernel Method"
[4] "Linear Classifier" "Linear Regression"
$kernelpls$sort
function (x)
x[order(x[, 1]), ]
$ORFpls
$ORFpls$label
[1] "Oblique Random Forest"
$ORFpls$library
[1] "obliqueRF"
$ORFpls$loop
NULL
$ORFpls$type
[1] "Classification"
$ORFpls$parameters
parameter class label
1 mtry numeric #Randomly Selected Predictors
$ORFpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(mtry = caret::var_seq(p = ncol(x),
classification = is.factor(y), len = len))
}
else {
out <- data.frame(mtry = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$ORFpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(obliqueRF)
obliqueRF::obliqueRF(as.matrix(x), y, training_method = "pls",
...)
}
$ORFpls$predict
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata)
$ORFpls$prob
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata, type = "prob")
$ORFpls$levels
function (x)
x$obsLevels
$ORFpls$notes
[1] "Unlike other packages used by `train`, the `obliqueRF` package is fully loaded when this model is used."
$ORFpls$tags
[1] "Random Forest" "Oblique Tree"
[3] "Partial Least Squares" "Implicit Feature Selection"
[5] "Ensemble Model" "Two Class Only"
$ORFpls$sort
function (x)
x[order(x[, 1]), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
$plsRglm
$plsRglm$label
[1] "Partial Least Squares Generalized Linear Models "
$plsRglm$library
[1] "plsRglm"
$plsRglm$loop
NULL
$plsRglm$type
[1] "Classification" "Regression"
$plsRglm$parameters
parameter class label
1 nt numeric #PLS Components
2 alpha.pvals.expli numeric p-Value threshold
$plsRglm$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(nt = 1:len, alpha.pvals.expli = 10^(c(-2:(len -
3), 0)))
}
else {
out <- data.frame(nt = sample(1:ncol(x), size = len,
replace = TRUE), alpha.pvals.expli = runif(len, min = 0,
0.2))
}
out
}
$plsRglm$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(plsRglm)
if (is.factor(y)) {
lv <- levels(y)
y <- as.numeric(y) - 1
dst <- "pls-glm-logistic"
}
else {
lv <- NULL
dst <- "pls-glm-gaussian"
}
theDots <- list(...)
if (any(names(theDots) == "modele")) {
mod <- plsrRglm::plsRglm(y, x, nt = param$nt, pvals.expli = param$alpha.pvals.expli <
1, sparse = param$alpha.pvals.expli < 1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
else {
mod <- plsRglm::plsRglm(y, x, nt = param$nt, modele = dst,
pvals.expli = param$alpha.pvals.expli < 1, sparse = param$alpha.pvals.expli <
1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
mod
}
$plsRglm$predict
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
if (modelFit$problemType == "Classification") {
out <- factor(ifelse(out >= 0.5, modelFit$obsLevels[2],
modelFit$obsLevels[1]))
}
out
}
$plsRglm$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
out <- cbind(1 - out, out)
dimnames(out)[[2]] <- rev(modelFit$obsLevels)
out
}
$plsRglm$varImp
NULL
$plsRglm$predictors
function (x, ...)
{
vars <- names(which(coef(x)[[2]][, 1] != 0))
vars[vars != "Intercept"]
}
$plsRglm$notes
[1] "Unlike other packages used by `train`, the `plsRglm` package is fully loaded when this model is used."
$plsRglm$tags
[1] "Generalized Linear Models" "Partial Least Squares"
[3] "Two Class Only"
$plsRglm$levels
function (x)
x$lev
$plsRglm$sort
function (x)
x[order(-x$alpha.pvals.expli, x$nt), ]
$simpls
$simpls$label
[1] "Partial Least Squares"
$simpls$library
[1] "pls"
$simpls$type
[1] "Regression" "Classification"
$simpls$parameters
parameter class label
1 ncomp numeric #Components
$simpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:(ncol(x) -
1), size = len, replace = TRUE)))
}
out
}
$simpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$simpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "simpls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "simpls",
ncomp = ncomp, ...)
}
out
}
$simpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$simpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$simpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$simpls$levels
function (x)
x$obsLevels
$simpls$predictors
function (x, ...)
rownames(x$projection)
$simpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$simpls$sort
function (x)
x[order(x[, 1]), ]
$spls
$spls$label
[1] "Sparse Partial Least Squares"
$spls$library
[1] "spls"
$spls$type
[1] "Regression" "Classification"
$spls$parameters
parameter class label
1 K numeric #Components
2 eta numeric Threshold
3 kappa numeric Kappa
$spls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(K = 1:min(nrow(x), ncol(x)), eta = seq(0.1,
0.9, length = len), kappa = 0.5)
}
else {
out <- data.frame(kappa = runif(len, min = 0, max = 0.5),
eta = runif(len, min = 0, max = 1), K = sample(1:min(nrow(x),
ncol(x)), size = len, replace = TRUE))
}
out
}
$spls$loop
NULL
$spls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
param$K <- min(param$K, length(y))
if (is.factor(y)) {
caret:::splsda(x, y, K = param$K, eta = param$eta, kappa = param$kappa,
...)
}
else {
spls::spls(x, y, K = param$K, eta = param$eta, kappa = param$kappa,
...)
}
}
$spls$predict
function (modelFit, newdata, submodels = NULL)
{
if (length(modelFit$obsLevels) < 2) {
spls::predict.spls(modelFit, newdata)
}
else {
as.character(caret:::predict.splsda(modelFit, newdata,
type = "class"))
}
}
$spls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
caret:::predict.splsda(modelFit, newdata, type = "prob")
}
$spls$predictors
function (x, ...)
colnames(x$x)[x$A]
$spls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression" "L1 Regularization"
$spls$levels
function (x)
x$obsLevels
$spls$sort
function (x)
x[order(-x$eta, x$K), ]
$widekernelpls
$widekernelpls$label
[1] "Partial Least Squares"
$widekernelpls$library
[1] "pls"
$widekernelpls$type
[1] "Regression" "Classification"
$widekernelpls$parameters
parameter class label
1 ncomp numeric #Components
$widekernelpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:(ncol(x) -
1), size = len, replace = TRUE)))
}
out
}
$widekernelpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$widekernelpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
caret::plsda(x, y, method = "widekernelpls", ncomp = ncomp,
...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "widekernelpls",
ncomp = ncomp, ...)
}
out
}
$widekernelpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$widekernelpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$widekernelpls$predictors
function (x, ...)
rownames(x$projection)
$widekernelpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$widekernelpls$levels
function (x)
x$obsLevels
$widekernelpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$widekernelpls$sort
function (x)
x[order(x[, 1]), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
$plsRglm
$plsRglm$label
[1] "Partial Least Squares Generalized Linear Models "
$plsRglm$library
[1] "plsRglm"
$plsRglm$loop
NULL
$plsRglm$type
[1] "Classification" "Regression"
$plsRglm$parameters
parameter class label
1 nt numeric #PLS Components
2 alpha.pvals.expli numeric p-Value threshold
$plsRglm$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(nt = 1:len, alpha.pvals.expli = 10^(c(-2:(len -
3), 0)))
}
else {
out <- data.frame(nt = sample(1:ncol(x), size = len,
replace = TRUE), alpha.pvals.expli = runif(len, min = 0,
0.2))
}
out
}
$plsRglm$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(plsRglm)
if (is.factor(y)) {
lv <- levels(y)
y <- as.numeric(y) - 1
dst <- "pls-glm-logistic"
}
else {
lv <- NULL
dst <- "pls-glm-gaussian"
}
theDots <- list(...)
if (any(names(theDots) == "modele")) {
mod <- plsrRglm::plsRglm(y, x, nt = param$nt, pvals.expli = param$alpha.pvals.expli <
1, sparse = param$alpha.pvals.expli < 1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
else {
mod <- plsRglm::plsRglm(y, x, nt = param$nt, modele = dst,
pvals.expli = param$alpha.pvals.expli < 1, sparse = param$alpha.pvals.expli <
1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
mod
}
$plsRglm$predict
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
if (modelFit$problemType == "Classification") {
out <- factor(ifelse(out >= 0.5, modelFit$obsLevels[2],
modelFit$obsLevels[1]))
}
out
}
$plsRglm$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
out <- cbind(1 - out, out)
dimnames(out)[[2]] <- rev(modelFit$obsLevels)
out
}
$plsRglm$varImp
NULL
$plsRglm$predictors
function (x, ...)
{
vars <- names(which(coef(x)[[2]][, 1] != 0))
vars[vars != "Intercept"]
}
$plsRglm$notes
[1] "Unlike other packages used by `train`, the `plsRglm` package is fully loaded when this model is used."
$plsRglm$tags
[1] "Generalized Linear Models" "Partial Least Squares"
[3] "Two Class Only"
$plsRglm$levels
function (x)
x$lev
$plsRglm$sort
function (x)
x[order(-x$alpha.pvals.expli, x$nt), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
caret documentation built on May 2, 2019, 5:47 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note Author(s) See Also Examples
Description
These function show information about models and packages that are accessible via train
Usage
1 2 3 4 5 | modelLookup(model = NULL)
getModelInfo(model = NULL, regex = TRUE, ...)
checkInstall(pkg)
|
Arguments
model |
a character string associated with the |
regex |
a logical: should a regular expressions be used? If |
pkg |
a character string of package names. |
... |
options to pass to |
Details
modelLookup is good for getting information related to the tuning parameters for a model. getModelInfo will return all the functions and metadata associated with a model. Both of these functions will only search within the models bundled in this package.
checkInstall will check to see if packages are installed. If they are not and the session is interactive, an option is given to install the packages using install.packages using that functions default arguments (the missing packages are listed if you would like to install them with other options). If the session is not interactive, an error is thrown.
Value
modelLookup produces a data frame with columns
model |
a character string for the model code |
parameter |
the tuning parameter name |
label |
a tuning parameter label (used in plots) |
forReg |
a logical; can the model be used for regression? |
forClass |
a logical; can the model be used for classification? |
probModel |
a logical; does the model produce class probabilities? |
getModelInfo returns a list containing one or more lists of the standard model information.
checkInstall returns not value.
Note
The column seq is no longer included in the output of modelLookup.
Author(s)
Max Kuhn
See Also
train, install.packages, grepl
Examples
1 2 3 4 5 6 7 8 9 10 11 | modelLookup()
modelLookup("gbm")
getModelInfo("pls")
getModelInfo("^pls")
getModelInfo("pls", regex = FALSE)
## Not run:
checkInstall(getModelInfo("pls")$library)
## End(Not run)
|
Example output
Loading required package: lattice
Loading required package: ggplot2
model parameter
12 ANFIS num.labels
13 ANFIS max.iter
4 AdaBag mfinal
5 AdaBag maxdepth
6 AdaBoost.M1 mfinal
7 AdaBoost.M1 maxdepth
8 AdaBoost.M1 coeflearn
42 BstLm mstop
43 BstLm nu
49 C5.0 trials
50 C5.0 model
51 C5.0 winnow
52 C5.0Cost trials
53 C5.0Cost model
54 C5.0Cost winnow
55 C5.0Cost cost
56 C5.0Rules parameter
57 C5.0Tree parameter
62 CSimca parameter
75 DENFIS Dthr
76 DENFIS max.iter
102 FH.GBML max.num.rule
103 FH.GBML popu.size
104 FH.GBML max.gen
105 FIR.DM num.labels
106 FIR.DM max.iter
109 FRBCS.CHI num.labels
110 FRBCS.CHI type.mf
111 FRBCS.W num.labels
112 FRBCS.W type.mf
113 FS.HGD num.labels
114 FS.HGD max.iter
136 GFS.FR.MOGUL max.gen
137 GFS.FR.MOGUL max.iter
138 GFS.FR.MOGUL max.tune
139 GFS.LT.RS popu.size
140 GFS.LT.RS num.labels
141 GFS.LT.RS max.gen
142 GFS.THRIFT popu.size
143 GFS.THRIFT num.labels
144 GFS.THRIFT max.gen
163 HYFIS num.labels
164 HYFIS max.iter
166 J48 C
167 J48 M
168 JRip NumOpt
169 JRip NumFolds
170 JRip MinWeights
191 LMT iter
188 Linda parameter
195 LogitBoost nIter
206 M5 pruned
207 M5 smoothed
208 M5 rules
209 M5Rules pruned
210 M5Rules smoothed
214 Mlda parameter
312 ORFlog mtry
313 ORFpls mtry
314 ORFridge mtry
315 ORFsvm mtry
305 OneR parameter
319 PART threshold
320 PART pruned
339 PRIM peel.alpha
340 PRIM paste.alpha
341 PRIM mass.min
330 PenalizedLDA lambda
331 PenalizedLDA K
345 QdaCov parameter
367 RFlda q
393 RRF mtry
394 RRF coefReg
395 RRF coefImp
396 RRFglobal mtry
397 RRFglobal coefReg
401 RSimca parameter
356 Rborist predFixed
357 Rborist minNode
406 SBC r.a
407 SBC eps.high
408 SBC eps.low
414 SLAVE num.labels
415 SLAVE max.iter
416 SLAVE max.gen
476 WM num.labels
477 WM type.mf
1 ada iter
2 ada maxdepth
3 ada nu
9 adaboost nIter
10 adaboost method
11 amdai model
14 avNNet size
15 avNNet decay
16 avNNet bag
17 awnb smooth
18 awtan score
19 awtan smooth
20 bag vars
21 bagEarth nprune
22 bagEarth degree
23 bagEarthGCV degree
24 bagFDA degree
25 bagFDA nprune
26 bagFDAGCV degree
27 bam select
28 bam method
29 bartMachine num_trees
30 bartMachine k
31 bartMachine alpha
32 bartMachine beta
33 bartMachine nu
34 bayesglm parameter
35 binda lambda.freqs
36 blackboost mstop
37 blackboost maxdepth
38 blasso sparsity
39 blassoAveraged parameter
40 bridge parameter
41 brnn neurons
44 bstSm mstop
45 bstSm nu
46 bstTree mstop
47 bstTree maxdepth
48 bstTree nu
58 cforest mtry
59 chaid alpha2
60 chaid alpha3
61 chaid alpha4
63 ctree mincriterion
64 ctree2 maxdepth
65 ctree2 mincriterion
66 cubist committees
67 cubist neighbors
68 dda model
69 dda shrinkage
70 deepboost num_iter
71 deepboost tree_depth
72 deepboost beta
73 deepboost lambda
74 deepboost loss_type
77 dnn layer1
78 dnn layer2
79 dnn layer3
80 dnn hidden_dropout
81 dnn visible_dropout
82 dwdLinear lambda
83 dwdLinear qval
84 dwdPoly lambda
85 dwdPoly qval
86 dwdPoly degree
87 dwdPoly scale
88 dwdRadial lambda
89 dwdRadial qval
90 dwdRadial sigma
91 earth nprune
92 earth degree
93 elm nhid
94 elm actfun
95 enet fraction
96 enet lambda
97 evtree alpha
98 extraTrees mtry
99 extraTrees numRandomCuts
100 fda degree
101 fda nprune
107 foba k
108 foba lambda
115 gam select
116 gam method
119 gamLoess span
120 gamLoess degree
121 gamSpline df
117 gamboost mstop
118 gamboost prune
122 gaussprLinear parameter
123 gaussprPoly degree
124 gaussprPoly scale
125 gaussprRadial sigma
131 gbm n.trees
132 gbm interaction.depth
133 gbm shrinkage
134 gbm n.minobsinnode
126 gbm_h2o ntrees
127 gbm_h2o max_depth
128 gbm_h2o min_rows
129 gbm_h2o learn_rate
130 gbm_h2o col_sample_rate
135 gcvEarth degree
146 glm parameter
145 glm.nb link
153 glmStepAIC parameter
147 glmboost mstop
148 glmboost prune
151 glmnet alpha
152 glmnet lambda
149 glmnet_h2o alpha
150 glmnet_h2o lambda
154 gpls K.prov
155 hda gamma
156 hda lambda
157 hda newdim
158 hdda threshold
159 hdda model
160 hdrda gamma
161 hdrda lambda
162 hdrda shrinkage_type
165 icr n.comp
171 kernelpls ncomp
172 kknn kmax
173 kknn distance
174 kknn kernel
175 knn k
176 krlsPoly lambda
177 krlsPoly degree
178 krlsRadial lambda
179 krlsRadial sigma
180 lars fraction
181 lars2 step
182 lasso fraction
183 lda parameter
184 lda2 dimen
185 leapBackward nvmax
186 leapForward nvmax
187 leapSeq nvmax
189 lm intercept
190 lmStepAIC parameter
192 loclda k
193 logicBag nleaves
194 logicBag ntrees
196 logreg treesize
197 logreg ntrees
198 lssvmLinear tau
199 lssvmPoly degree
200 lssvmPoly scale
201 lssvmPoly tau
202 lssvmRadial sigma
203 lssvmRadial tau
204 lvq size
205 lvq k
211 manb smooth
212 manb prior
213 mda subclasses
215 mlp size
216 mlpKerasDecay size
217 mlpKerasDecay lambda
218 mlpKerasDecay batch_size
219 mlpKerasDecay lr
220 mlpKerasDecay rho
221 mlpKerasDecay decay
222 mlpKerasDecay activation
223 mlpKerasDecayCost size
224 mlpKerasDecayCost lambda
225 mlpKerasDecayCost batch_size
226 mlpKerasDecayCost lr
227 mlpKerasDecayCost rho
228 mlpKerasDecayCost decay
229 mlpKerasDecayCost cost
230 mlpKerasDecayCost activation
231 mlpKerasDropout size
232 mlpKerasDropout dropout
233 mlpKerasDropout batch_size
234 mlpKerasDropout lr
235 mlpKerasDropout rho
236 mlpKerasDropout decay
237 mlpKerasDropout activation
238 mlpKerasDropoutCost size
239 mlpKerasDropoutCost dropout
240 mlpKerasDropoutCost batch_size
241 mlpKerasDropoutCost lr
242 mlpKerasDropoutCost rho
243 mlpKerasDropoutCost decay
244 mlpKerasDropoutCost cost
245 mlpKerasDropoutCost activation
246 mlpML layer1
247 mlpML layer2
248 mlpML layer3
249 mlpSGD size
250 mlpSGD l2reg
251 mlpSGD lambda
252 mlpSGD learn_rate
253 mlpSGD momentum
254 mlpSGD gamma
255 mlpSGD minibatchsz
256 mlpSGD repeats
257 mlpWeightDecay size
258 mlpWeightDecay decay
259 mlpWeightDecayML layer1
260 mlpWeightDecayML layer2
261 mlpWeightDecayML layer3
262 mlpWeightDecayML decay
263 monmlp hidden1
264 monmlp n.ensemble
265 msaenet alphas
266 msaenet nsteps
267 msaenet scale
268 multinom decay
269 mxnet layer1
270 mxnet layer2
271 mxnet layer3
272 mxnet learning.rate
273 mxnet momentum
274 mxnet dropout
275 mxnet activation
276 mxnetAdam layer1
277 mxnetAdam layer2
278 mxnetAdam layer3
279 mxnetAdam dropout
280 mxnetAdam beta1
281 mxnetAdam beta2
282 mxnetAdam learningrate
283 mxnetAdam activation
284 naive_bayes laplace
285 naive_bayes usekernel
286 naive_bayes adjust
287 nb fL
288 nb usekernel
289 nb adjust
290 nbDiscrete smooth
291 nbSearch k
292 nbSearch epsilon
293 nbSearch smooth
294 nbSearch final_smooth
295 nbSearch direction
296 neuralnet layer1
297 neuralnet layer2
298 neuralnet layer3
299 nnet size
300 nnet decay
301 nnls parameter
302 nodeHarvest maxinter
303 nodeHarvest mode
304 null parameter
306 ordinalNet alpha
307 ordinalNet criteria
308 ordinalNet link
309 ordinalRF nsets
310 ordinalRF ntreeperdiv
311 ordinalRF ntreefinal
316 ownn K
317 pam threshold
318 parRF mtry
321 partDSA cut.off.growth
322 partDSA MPD
323 pcaNNet size
324 pcaNNet decay
325 pcr ncomp
326 pda lambda
327 pda2 df
328 penalized lambda1
329 penalized lambda2
332 plr lambda
333 plr cp
334 pls ncomp
335 plsRglm nt
336 plsRglm alpha.pvals.expli
337 polr method
338 ppr nterms
342 protoclass eps
343 protoclass Minkowski
344 qda parameter
346 qrf mtry
347 qrnn n.hidden
348 qrnn penalty
349 qrnn bag
366 rFerns depth
350 randomGLM maxInteractionOrder
351 ranger mtry
352 ranger splitrule
353 ranger min.node.size
354 rbf size
355 rbfDDA negativeThreshold
358 rda gamma
359 rda lambda
360 regLogistic cost
361 regLogistic loss
362 regLogistic epsilon
363 relaxo lambda
364 relaxo phi
365 rf mtry
368 rfRules mtry
369 rfRules maxdepth
370 ridge lambda
371 rlda estimator
372 rlm intercept
373 rlm psi
374 rmda K
375 rmda model
376 rocc xgenes
377 rotationForest K
378 rotationForest L
379 rotationForestCp K
380 rotationForestCp L
381 rotationForestCp cp
382 rpart cp
383 rpart1SE parameter
384 rpart2 maxdepth
385 rpartCost cp
386 rpartCost Cost
387 rpartScore cp
388 rpartScore split
389 rpartScore prune
390 rqlasso lambda
391 rqnc lambda
392 rqnc penalty
398 rrlda lambda
399 rrlda hp
400 rrlda penalty
402 rvmLinear parameter
403 rvmPoly scale
404 rvmPoly degree
405 rvmRadial sigma
409 sda diagonal
410 sda lambda
411 sdwd lambda
412 sdwd lambda2
413 simpls ncomp
417 slda parameter
418 smda NumVars
419 smda lambda
420 smda R
421 snn lambda
422 sparseLDA NumVars
423 sparseLDA lambda
424 spikeslab vars
425 spls K
426 spls eta
427 spls kappa
428 stepLDA maxvar
429 stepLDA direction
430 stepQDA maxvar
431 stepQDA direction
432 superpc threshold
433 superpc n.components
434 svmBoundrangeString length
435 svmBoundrangeString C
436 svmExpoString lambda
437 svmExpoString C
438 svmLinear C
439 svmLinear2 cost
440 svmLinear3 cost
441 svmLinear3 Loss
442 svmLinearWeights cost
443 svmLinearWeights weight
444 svmLinearWeights2 cost
445 svmLinearWeights2 Loss
446 svmLinearWeights2 weight
447 svmPoly degree
448 svmPoly scale
449 svmPoly C
450 svmRadial sigma
451 svmRadial C
452 svmRadialCost C
453 svmRadialSigma sigma
454 svmRadialSigma C
455 svmRadialWeights sigma
456 svmRadialWeights C
457 svmRadialWeights Weight
458 svmSpectrumString length
459 svmSpectrumString C
460 tan score
461 tan smooth
462 tanSearch k
463 tanSearch epsilon
464 tanSearch smooth
465 tanSearch final_smooth
466 tanSearch sp
467 treebag parameter
468 vbmpRadial estimateTheta
469 vglmAdjCat parallel
470 vglmAdjCat link
471 vglmContRatio parallel
472 vglmContRatio link
473 vglmCumulative parallel
474 vglmCumulative link
475 widekernelpls ncomp
478 wsrf mtry
479 xgbDART nrounds
480 xgbDART max_depth
481 xgbDART eta
482 xgbDART gamma
483 xgbDART subsample
484 xgbDART colsample_bytree
485 xgbDART rate_drop
486 xgbDART skip_drop
487 xgbDART min_child_weight
488 xgbLinear nrounds
489 xgbLinear lambda
490 xgbLinear alpha
491 xgbLinear eta
492 xgbTree nrounds
493 xgbTree max_depth
494 xgbTree eta
495 xgbTree gamma
496 xgbTree colsample_bytree
497 xgbTree min_child_weight
498 xgbTree subsample
499 xyf xdim
500 xyf ydim
501 xyf user.weights
502 xyf topo
label
12 #Fuzzy Terms
13 Max. Iterations
4 #Trees
5 Max Tree Depth
6 #Trees
7 Max Tree Depth
8 Coefficient Type
42 # Boosting Iterations
43 Shrinkage
49 # Boosting Iterations
50 Model Type
51 Winnow
52 # Boosting Iterations
53 Model Type
54 Winnow
55 Cost
56 none
57 none
62 parameter
75 Threshold
76 Max. Iterations
102 Max. #Rules
103 Population Size
104 Max. Generations
105 #Fuzzy Terms
106 Max. Iterations
109 #Fuzzy Terms
110 Membership Function
111 #Fuzzy Terms
112 Membership Function
113 #Fuzzy Terms
114 Max. Iterations
136 Max. Generations
137 Max. Iterations
138 Max. Tuning Iterations
139 Population Size
140 # Fuzzy Labels
141 Max. Generations
142 Population Size
143 # Fuzzy Labels
144 Max. Generations
163 #Fuzzy Terms
164 Max. Iterations
166 Confidence Threshold
167 Minimum Instances Per Leaf
168 # Optimizations
169 # Folds
170 Min Weights
191 # Iteratons
188 none
195 # Boosting Iterations
206 Pruned
207 Smoothed
208 Rules
209 Pruned
210 Smoothed
214 parameter
312 #Randomly Selected Predictors
313 #Randomly Selected Predictors
314 #Randomly Selected Predictors
315 #Randomly Selected Predictors
305 none
319 Confidence Threshold
320 Pruning
339 peeling quantile
340 pasting quantile
341 minimum mass
330 L1 Penalty
331 #Discriminant Functions
345 parameter
367 # Factors
393 #Randomly Selected Predictors
394 Regularization Value
395 Importance Coefficient
396 #Randomly Selected Predictors
397 Regularization Value
401 parameter
356 #Randomly Selected Predictors
357 Minimal Node Size
406 Radius
407 Upper Threshold
408 Lower Threshold
414 #Fuzzy Terms
415 Max. Iterations
416 Max. Generations
476 #Fuzzy Terms
477 Membership Function
1 #Trees
2 Max Tree Depth
3 Learning Rate
9 #Trees
10 Method
11 Model Type
14 #Hidden Units
15 Weight Decay
16 Bagging
17 Smoothing Parameter
18 Score Function
19 Smoothing Parameter
20 #Randomly Selected Predictors
21 #Terms
22 Product Degree
23 Product Degree
24 Product Degree
25 #Terms
26 Product Degree
27 Feature Selection
28 Method
29 #Trees
30 Prior Boundary
31 Base Terminal Node Hyperparameter
32 Power Terminal Node Hyperparameter
33 Degrees of Freedom
34 parameter
35 Shrinkage Intensity
36 #Trees
37 Max Tree Depth
38 Sparsity Threshold
39 parameter
40 parameter
41 # Neurons
44 # Boosting Iterations
45 Shrinkage
46 # Boosting Iterations
47 Max Tree Depth
48 Shrinkage
58 #Randomly Selected Predictors
59 Merging Threshold
60 Splitting former Merged Threshold
61 \n Splitting former Merged Threshold
63 1 - P-Value Threshold
64 Max Tree Depth
65 1 - P-Value Threshold
66 #Committees
67 #Instances
68 Model
69 Shrinkage Type
70 # Boosting Iterations
71 Tree Depth
72 L1 Regularization
73 Tree Depth Regularization
74 Loss
77 Hidden Layer 1
78 Hidden Layer 2
79 Hidden Layer 3
80 Hidden Dropouts
81 Visible Dropout
82 Regularization Parameter
83 q
84 Regularization Parameter
85 q
86 Polynomial Degree
87 Scale
88 Regularization Parameter
89 q
90 Sigma
91 #Terms
92 Product Degree
93 #Hidden Units
94 Activation Function
95 Fraction of Full Solution
96 Weight Decay
97 Complexity Parameter
98 # Randomly Selected Predictors
99 # Random Cuts
100 Product Degree
101 #Terms
107 #Variables Retained
108 L2 Penalty
115 Feature Selection
116 Method
119 Span
120 Degree
121 Degrees of Freedom
117 # Boosting Iterations
118 AIC Prune?
122 Parameter
123 Polynomial Degree
124 Scale
125 Sigma
131 # Boosting Iterations
132 Max Tree Depth
133 Shrinkage
134 Min. Terminal Node Size
126 # Boosting Iterations
127 Max Tree Depth
128 Min. Terminal Node Size
129 Shrinkage
130 #Randomly Selected Predictors
135 Product Degree
146 parameter
145 Link Function
153 parameter
147 # Boosting Iterations
148 AIC Prune?
151 Mixing Percentage
152 Regularization Parameter
149 Mixing Percentage
150 Regularization Parameter
154 #Components
155 Gamma
156 Lambda
157 Dimension of the Discriminative Subspace
158 Threshold
159 Model Type
160 Gamma
161 Lambda
162 Shrinkage Type
165 #Components
171 #Components
172 Max. #Neighbors
173 Distance
174 Kernel
175 #Neighbors
176 Regularization Parameter
177 Polynomial Degree
178 Regularization Parameter
179 Sigma
180 Fraction
181 #Steps
182 Fraction of Full Solution
183 parameter
184 #Discriminant Functions
185 Maximum Number of Predictors
186 Maximum Number of Predictors
187 Maximum Number of Predictors
189 intercept
190 parameter
192 #Nearest Neighbors
193 Maximum Number of Leaves
194 Number of Trees
196 Maximum Number of Leaves
197 Number of Trees
198 Regularization Parameter
199 Polynomial Degree
200 Scale
201 Regularization Parameter
202 Sigma
203 Regularization Parameter
204 Codebook Size
205 #Prototypes
211 Smoothing Parameter
212 Prior Probability
213 #Subclasses Per Class
215 #Hidden Units
216 #Hidden Units
217 L2 Regularization
218 Batch Size
219 Learning Rate
220 Rho
221 Learning Rate Decay
222 Activation Function
223 #Hidden Units
224 L2 Regularization
225 Batch Size
226 Learning Rate
227 Rho
228 Learning Rate Decay
229 Cost
230 Activation Function
231 #Hidden Units
232 Dropout Rate
233 Batch Size
234 Learning Rate
235 Rho
236 Learning Rate Decay
237 Activation Function
238 #Hidden Units
239 Dropout Rate
240 Batch Size
241 Learning Rate
242 Rho
243 Learning Rate Decay
244 Cost
245 Activation Function
246 #Hidden Units layer1
247 #Hidden Units layer2
248 #Hidden Units layer3
249 #Hidden Units
250 L2 Regularization
251 RMSE Gradient Scaling
252 Learning Rate
253 Momentum
254 Learning Rate Decay
255 Batch Size
256 #Models
257 #Hidden Units
258 Weight Decay
259 #Hidden Units layer1
260 #Hidden Units layer2
261 #Hidden Units layer3
262 Weight Decay
263 #Hidden Units
264 #Models
265 Alpha
266 #Adaptive Estimation Steps
267 Adaptive Weight Scaling Factor
268 Weight Decay
269 #Hidden Units in Layer 1
270 #Hidden Units in Layer 2
271 #Hidden Units in Layer 3
272 Learning Rate
273 Momentum
274 Dropout Rate
275 Activation Function
276 #Hidden Units in Layer 1
277 #Hidden Units in Layer 2
278 #Hidden Units in Layer 3
279 Dropout Rate
280 beta1
281 beta2
282 Learning Rate
283 Activation Function
284 Laplace Correction
285 Distribution Type
286 Bandwidth Adjustment
287 Laplace Correction
288 Distribution Type
289 Bandwidth Adjustment
290 Smoothing Parameter
291 #Folds
292 Minimum Absolute Improvement
293 Smoothing Parameter
294 Final Smoothing Parameter
295 Search Direction
296 #Hidden Units in Layer 1
297 #Hidden Units in Layer 2
298 #Hidden Units in Layer 3
299 #Hidden Units
300 Weight Decay
301 parameter
302 Maximum Interaction Depth
303 Prediction Mode
304 parameter
306 Mixing Percentage
307 Selection Criterion
308 Link Function
309 # score sets tried prior to the approximation
310 # of trees (small RFs)
311 # of trees (final RF)
316 #Neighbors
317 Shrinkage Threshold
318 #Randomly Selected Predictors
321 Number of Terminal Partitions
322 Minimum Percent Difference
323 #Hidden Units
324 Weight Decay
325 #Components
326 Shrinkage Penalty Coefficient
327 Degrees of Freedom
328 L1 Penalty
329 L2 Penalty
332 L2 Penalty
333 Complexity Parameter
334 #Components
335 #PLS Components
336 p-Value threshold
337 parameter
338 # Terms
342 Ball Size
343 Distance Order
344 parameter
346 #Randomly Selected Predictors
347 #Hidden Units
348 Weight Decay
349 Bagged Models?
366 Fern Depth
350 Interaction Order
351 #Randomly Selected Predictors
352 Splitting Rule
353 Minimal Node Size
354 #Hidden Units
355 Activation Limit for Conflicting Classes
358 Gamma
359 Lambda
360 Cost
361 Loss Function
362 Tolerance
363 Penalty Parameter
364 Relaxation Parameter
365 #Randomly Selected Predictors
368 #Randomly Selected Predictors
369 Maximum Rule Depth
370 Weight Decay
371 Regularization Method
372 intercept
373 psi
374 #Subclasses Per Class
375 Model
376 #Variables Retained
377 #Variable Subsets
378 Ensemble Size
379 #Variable Subsets
380 Ensemble Size
381 Complexity Parameter
382 Complexity Parameter
383 parameter
384 Max Tree Depth
385 Complexity Parameter
386 Cost
387 Complexity Parameter
388 Split Function
389 Pruning Measure
390 L1 Penalty
391 L1 Penalty
392 Penalty Type
398 Penalty Parameter
399 Robustness Parameter
400 Penalty Type
402 parameter
403 Scale
404 Polynomial Degree
405 Sigma
409 Diagonalize
410 shrinkage
411 L1 Penalty
412 L2 Penalty
413 #Components
417 none
418 # Predictors
419 Lambda
420 # Subclasses
421 Stabilization Parameter
422 # Predictors
423 Lambda
424 Variables Retained
425 #Components
426 Threshold
427 Kappa
428 Maximum #Variables
429 Search Direction
430 Maximum #Variables
431 Search Direction
432 Threshold
433 #Components
434 length
435 Cost
436 lambda
437 Cost
438 Cost
439 Cost
440 Cost
441 Loss Function
442 Cost
443 Class Weight
444 Cost
445 Loss Function
446 Class Weight
447 Polynomial Degree
448 Scale
449 Cost
450 Sigma
451 Cost
452 Cost
453 Sigma
454 Cost
455 Sigma
456 Cost
457 Weight
458 length
459 Cost
460 Score Function
461 Smoothing Parameter
462 #Folds
463 Minimum Absolute Improvement
464 Smoothing Parameter
465 Final Smoothing Parameter
466 Super-Parent
467 parameter
468 Theta Estimated
469 Parallel Curves
470 Link Function
471 Parallel Curves
472 Link Function
473 Parallel Curves
474 Link Function
475 #Components
478 #Randomly Selected Predictors
479 # Boosting Iterations
480 Max Tree Depth
481 Shrinkage
482 Minimum Loss Reduction
483 Subsample Percentage
484 Subsample Ratio of Columns
485 Fraction of Trees Dropped
486 Prob. of Skipping Drop-out
487 Minimum Sum of Instance Weight
488 # Boosting Iterations
489 L2 Regularization
490 L1 Regularization
491 Learning Rate
492 # Boosting Iterations
493 Max Tree Depth
494 Shrinkage
495 Minimum Loss Reduction
496 Subsample Ratio of Columns
497 Minimum Sum of Instance Weight
498 Subsample Percentage
499 Rows
500 Columns
501 Layer Weight
502 Topology
forReg forClass probModel
12 TRUE FALSE FALSE
13 TRUE FALSE FALSE
4 FALSE TRUE TRUE
5 FALSE TRUE TRUE
6 FALSE TRUE TRUE
7 FALSE TRUE TRUE
8 FALSE TRUE TRUE
42 TRUE TRUE FALSE
43 TRUE TRUE FALSE
49 FALSE TRUE TRUE
50 FALSE TRUE TRUE
51 FALSE TRUE TRUE
52 FALSE TRUE FALSE
53 FALSE TRUE FALSE
54 FALSE TRUE FALSE
55 FALSE TRUE FALSE
56 FALSE TRUE TRUE
57 FALSE TRUE TRUE
62 FALSE TRUE FALSE
75 TRUE FALSE FALSE
76 TRUE FALSE FALSE
102 FALSE TRUE FALSE
103 FALSE TRUE FALSE
104 FALSE TRUE FALSE
105 TRUE FALSE FALSE
106 TRUE FALSE FALSE
109 FALSE TRUE FALSE
110 FALSE TRUE FALSE
111 FALSE TRUE FALSE
112 FALSE TRUE FALSE
113 TRUE FALSE FALSE
114 TRUE FALSE FALSE
136 TRUE FALSE FALSE
137 TRUE FALSE FALSE
138 TRUE FALSE FALSE
139 TRUE FALSE FALSE
140 TRUE FALSE FALSE
141 TRUE FALSE FALSE
142 TRUE FALSE FALSE
143 TRUE FALSE FALSE
144 TRUE FALSE FALSE
163 TRUE FALSE FALSE
164 TRUE FALSE FALSE
166 FALSE TRUE TRUE
167 FALSE TRUE TRUE
168 FALSE TRUE TRUE
169 FALSE TRUE TRUE
170 FALSE TRUE TRUE
191 FALSE TRUE TRUE
188 FALSE TRUE TRUE
195 FALSE TRUE TRUE
206 TRUE FALSE FALSE
207 TRUE FALSE FALSE
208 TRUE FALSE FALSE
209 TRUE FALSE FALSE
210 TRUE FALSE FALSE
214 FALSE TRUE FALSE
312 FALSE TRUE TRUE
313 FALSE TRUE TRUE
314 FALSE TRUE TRUE
315 FALSE TRUE TRUE
305 FALSE TRUE TRUE
319 FALSE TRUE TRUE
320 FALSE TRUE TRUE
339 FALSE TRUE TRUE
340 FALSE TRUE TRUE
341 FALSE TRUE TRUE
330 FALSE TRUE FALSE
331 FALSE TRUE FALSE
345 FALSE TRUE TRUE
367 FALSE TRUE FALSE
393 TRUE TRUE TRUE
394 TRUE TRUE TRUE
395 TRUE TRUE TRUE
396 TRUE TRUE TRUE
397 TRUE TRUE TRUE
401 FALSE TRUE FALSE
356 TRUE TRUE TRUE
357 TRUE TRUE TRUE
406 TRUE FALSE FALSE
407 TRUE FALSE FALSE
408 TRUE FALSE FALSE
414 FALSE TRUE FALSE
415 FALSE TRUE FALSE
416 FALSE TRUE FALSE
476 TRUE FALSE FALSE
477 TRUE FALSE FALSE
1 FALSE TRUE TRUE
2 FALSE TRUE TRUE
3 FALSE TRUE TRUE
9 FALSE TRUE TRUE
10 FALSE TRUE TRUE
11 FALSE TRUE TRUE
14 TRUE TRUE TRUE
15 TRUE TRUE TRUE
16 TRUE TRUE TRUE
17 FALSE TRUE TRUE
18 FALSE TRUE TRUE
19 FALSE TRUE TRUE
20 TRUE TRUE TRUE
21 TRUE TRUE TRUE
22 TRUE TRUE TRUE
23 TRUE TRUE TRUE
24 FALSE TRUE TRUE
25 FALSE TRUE TRUE
26 FALSE TRUE TRUE
27 TRUE TRUE TRUE
28 TRUE TRUE TRUE
29 TRUE TRUE TRUE
30 TRUE TRUE TRUE
31 TRUE TRUE TRUE
32 TRUE TRUE TRUE
33 TRUE TRUE TRUE
34 TRUE TRUE TRUE
35 FALSE TRUE TRUE
36 TRUE TRUE TRUE
37 TRUE TRUE TRUE
38 TRUE FALSE FALSE
39 TRUE FALSE FALSE
40 TRUE FALSE FALSE
41 TRUE FALSE FALSE
44 TRUE TRUE FALSE
45 TRUE TRUE FALSE
46 TRUE TRUE FALSE
47 TRUE TRUE FALSE
48 TRUE TRUE FALSE
58 TRUE TRUE TRUE
59 FALSE TRUE TRUE
60 FALSE TRUE TRUE
61 FALSE TRUE TRUE
63 TRUE TRUE TRUE
64 TRUE TRUE TRUE
65 TRUE TRUE TRUE
66 TRUE FALSE FALSE
67 TRUE FALSE FALSE
68 FALSE TRUE TRUE
69 FALSE TRUE TRUE
70 FALSE TRUE FALSE
71 FALSE TRUE FALSE
72 FALSE TRUE FALSE
73 FALSE TRUE FALSE
74 FALSE TRUE FALSE
77 TRUE TRUE TRUE
78 TRUE TRUE TRUE
79 TRUE TRUE TRUE
80 TRUE TRUE TRUE
81 TRUE TRUE TRUE
82 FALSE TRUE TRUE
83 FALSE TRUE TRUE
84 FALSE TRUE TRUE
85 FALSE TRUE TRUE
86 FALSE TRUE TRUE
87 FALSE TRUE TRUE
88 FALSE TRUE TRUE
89 FALSE TRUE TRUE
90 FALSE TRUE TRUE
91 TRUE TRUE TRUE
92 TRUE TRUE TRUE
93 TRUE TRUE FALSE
94 TRUE TRUE FALSE
95 TRUE FALSE FALSE
96 TRUE FALSE FALSE
97 TRUE TRUE TRUE
98 TRUE TRUE TRUE
99 TRUE TRUE TRUE
100 FALSE TRUE TRUE
101 FALSE TRUE TRUE
107 TRUE FALSE FALSE
108 TRUE FALSE FALSE
115 TRUE TRUE TRUE
116 TRUE TRUE TRUE
119 TRUE TRUE TRUE
120 TRUE TRUE TRUE
121 TRUE TRUE TRUE
117 TRUE TRUE TRUE
118 TRUE TRUE TRUE
122 TRUE TRUE TRUE
123 TRUE TRUE TRUE
124 TRUE TRUE TRUE
125 TRUE TRUE TRUE
131 TRUE TRUE TRUE
132 TRUE TRUE TRUE
133 TRUE TRUE TRUE
134 TRUE TRUE TRUE
126 TRUE TRUE TRUE
127 TRUE TRUE TRUE
128 TRUE TRUE TRUE
129 TRUE TRUE TRUE
130 TRUE TRUE TRUE
135 TRUE TRUE TRUE
146 TRUE TRUE TRUE
145 TRUE FALSE FALSE
153 TRUE TRUE TRUE
147 TRUE TRUE TRUE
148 TRUE TRUE TRUE
151 TRUE TRUE TRUE
152 TRUE TRUE TRUE
149 TRUE TRUE TRUE
150 TRUE TRUE TRUE
154 FALSE TRUE TRUE
155 FALSE TRUE TRUE
156 FALSE TRUE TRUE
157 FALSE TRUE TRUE
158 FALSE TRUE TRUE
159 FALSE TRUE TRUE
160 FALSE TRUE TRUE
161 FALSE TRUE TRUE
162 FALSE TRUE TRUE
165 TRUE FALSE FALSE
171 TRUE TRUE TRUE
172 TRUE TRUE TRUE
173 TRUE TRUE TRUE
174 TRUE TRUE TRUE
175 TRUE TRUE TRUE
176 TRUE FALSE FALSE
177 TRUE FALSE FALSE
178 TRUE FALSE FALSE
179 TRUE FALSE FALSE
180 TRUE FALSE FALSE
181 TRUE FALSE FALSE
182 TRUE FALSE FALSE
183 FALSE TRUE TRUE
184 FALSE TRUE TRUE
185 TRUE FALSE FALSE
186 TRUE FALSE FALSE
187 TRUE FALSE FALSE
189 TRUE FALSE FALSE
190 TRUE FALSE FALSE
192 FALSE TRUE TRUE
193 TRUE TRUE TRUE
194 TRUE TRUE TRUE
196 TRUE TRUE TRUE
197 TRUE TRUE TRUE
198 FALSE TRUE FALSE
199 FALSE TRUE FALSE
200 FALSE TRUE FALSE
201 FALSE TRUE FALSE
202 FALSE TRUE FALSE
203 FALSE TRUE FALSE
204 FALSE TRUE FALSE
205 FALSE TRUE FALSE
211 FALSE TRUE TRUE
212 FALSE TRUE TRUE
213 FALSE TRUE TRUE
215 TRUE TRUE TRUE
216 TRUE TRUE TRUE
217 TRUE TRUE TRUE
218 TRUE TRUE TRUE
219 TRUE TRUE TRUE
220 TRUE TRUE TRUE
221 TRUE TRUE TRUE
222 TRUE TRUE TRUE
223 FALSE TRUE TRUE
224 FALSE TRUE TRUE
225 FALSE TRUE TRUE
226 FALSE TRUE TRUE
227 FALSE TRUE TRUE
228 FALSE TRUE TRUE
229 FALSE TRUE TRUE
230 FALSE TRUE TRUE
231 TRUE TRUE TRUE
232 TRUE TRUE TRUE
233 TRUE TRUE TRUE
234 TRUE TRUE TRUE
235 TRUE TRUE TRUE
236 TRUE TRUE TRUE
237 TRUE TRUE TRUE
238 FALSE TRUE TRUE
239 FALSE TRUE TRUE
240 FALSE TRUE TRUE
241 FALSE TRUE TRUE
242 FALSE TRUE TRUE
243 FALSE TRUE TRUE
244 FALSE TRUE TRUE
245 FALSE TRUE TRUE
246 TRUE TRUE TRUE
247 TRUE TRUE TRUE
248 TRUE TRUE TRUE
249 TRUE TRUE TRUE
250 TRUE TRUE TRUE
251 TRUE TRUE TRUE
252 TRUE TRUE TRUE
253 TRUE TRUE TRUE
254 TRUE TRUE TRUE
255 TRUE TRUE TRUE
256 TRUE TRUE TRUE
257 TRUE TRUE TRUE
258 TRUE TRUE TRUE
259 TRUE TRUE TRUE
260 TRUE TRUE TRUE
261 TRUE TRUE TRUE
262 TRUE TRUE TRUE
263 TRUE TRUE TRUE
264 TRUE TRUE TRUE
265 TRUE TRUE TRUE
266 TRUE TRUE TRUE
267 TRUE TRUE TRUE
268 FALSE TRUE TRUE
269 TRUE TRUE TRUE
270 TRUE TRUE TRUE
271 TRUE TRUE TRUE
272 TRUE TRUE TRUE
273 TRUE TRUE TRUE
274 TRUE TRUE TRUE
275 TRUE TRUE TRUE
276 TRUE TRUE TRUE
277 TRUE TRUE TRUE
278 TRUE TRUE TRUE
279 TRUE TRUE TRUE
280 TRUE TRUE TRUE
281 TRUE TRUE TRUE
282 TRUE TRUE TRUE
283 TRUE TRUE TRUE
284 FALSE TRUE TRUE
285 FALSE TRUE TRUE
286 FALSE TRUE TRUE
287 FALSE TRUE TRUE
288 FALSE TRUE TRUE
289 FALSE TRUE TRUE
290 FALSE TRUE TRUE
291 FALSE TRUE TRUE
292 FALSE TRUE TRUE
293 FALSE TRUE TRUE
294 FALSE TRUE TRUE
295 FALSE TRUE TRUE
296 TRUE FALSE FALSE
297 TRUE FALSE FALSE
298 TRUE FALSE FALSE
299 TRUE TRUE TRUE
300 TRUE TRUE TRUE
301 TRUE FALSE FALSE
302 TRUE TRUE TRUE
303 TRUE TRUE TRUE
304 TRUE TRUE TRUE
306 FALSE TRUE TRUE
307 FALSE TRUE TRUE
308 FALSE TRUE TRUE
309 FALSE TRUE TRUE
310 FALSE TRUE TRUE
311 FALSE TRUE TRUE
316 FALSE TRUE FALSE
317 FALSE TRUE TRUE
318 TRUE TRUE TRUE
321 TRUE TRUE FALSE
322 TRUE TRUE FALSE
323 TRUE TRUE TRUE
324 TRUE TRUE TRUE
325 TRUE FALSE FALSE
326 FALSE TRUE TRUE
327 FALSE TRUE TRUE
328 TRUE FALSE FALSE
329 TRUE FALSE FALSE
332 FALSE TRUE TRUE
333 FALSE TRUE TRUE
334 TRUE TRUE TRUE
335 TRUE TRUE TRUE
336 TRUE TRUE TRUE
337 FALSE TRUE TRUE
338 TRUE FALSE FALSE
342 FALSE TRUE FALSE
343 FALSE TRUE FALSE
344 FALSE TRUE TRUE
346 TRUE FALSE FALSE
347 TRUE FALSE FALSE
348 TRUE FALSE FALSE
349 TRUE FALSE FALSE
366 FALSE TRUE FALSE
350 TRUE TRUE TRUE
351 TRUE TRUE TRUE
352 TRUE TRUE TRUE
353 TRUE TRUE TRUE
354 TRUE TRUE TRUE
355 TRUE TRUE TRUE
358 FALSE TRUE TRUE
359 FALSE TRUE TRUE
360 FALSE TRUE TRUE
361 FALSE TRUE TRUE
362 FALSE TRUE TRUE
363 TRUE FALSE FALSE
364 TRUE FALSE FALSE
365 TRUE TRUE TRUE
368 TRUE TRUE FALSE
369 TRUE TRUE FALSE
370 TRUE FALSE FALSE
371 FALSE TRUE TRUE
372 TRUE FALSE FALSE
373 TRUE FALSE FALSE
374 FALSE TRUE TRUE
375 FALSE TRUE TRUE
376 FALSE TRUE FALSE
377 FALSE TRUE TRUE
378 FALSE TRUE TRUE
379 FALSE TRUE TRUE
380 FALSE TRUE TRUE
381 FALSE TRUE TRUE
382 TRUE TRUE TRUE
383 TRUE TRUE TRUE
384 TRUE TRUE TRUE
385 FALSE TRUE FALSE
386 FALSE TRUE FALSE
387 FALSE TRUE FALSE
388 FALSE TRUE FALSE
389 FALSE TRUE FALSE
390 TRUE FALSE FALSE
391 TRUE FALSE FALSE
392 TRUE FALSE FALSE
398 FALSE TRUE TRUE
399 FALSE TRUE TRUE
400 FALSE TRUE TRUE
402 TRUE FALSE FALSE
403 TRUE FALSE FALSE
404 TRUE FALSE FALSE
405 TRUE FALSE FALSE
409 FALSE TRUE TRUE
410 FALSE TRUE TRUE
411 FALSE TRUE TRUE
412 FALSE TRUE TRUE
413 TRUE TRUE TRUE
417 FALSE TRUE TRUE
418 FALSE TRUE FALSE
419 FALSE TRUE FALSE
420 FALSE TRUE FALSE
421 FALSE TRUE FALSE
422 FALSE TRUE TRUE
423 FALSE TRUE TRUE
424 TRUE FALSE FALSE
425 TRUE TRUE TRUE
426 TRUE TRUE TRUE
427 TRUE TRUE TRUE
428 FALSE TRUE TRUE
429 FALSE TRUE TRUE
430 FALSE TRUE TRUE
431 FALSE TRUE TRUE
432 TRUE FALSE FALSE
433 TRUE FALSE FALSE
434 TRUE TRUE TRUE
435 TRUE TRUE TRUE
436 TRUE TRUE TRUE
437 TRUE TRUE TRUE
438 TRUE TRUE TRUE
439 TRUE TRUE TRUE
440 TRUE TRUE FALSE
441 TRUE TRUE FALSE
442 FALSE TRUE TRUE
443 FALSE TRUE TRUE
444 FALSE TRUE FALSE
445 FALSE TRUE FALSE
446 FALSE TRUE FALSE
447 TRUE TRUE TRUE
448 TRUE TRUE TRUE
449 TRUE TRUE TRUE
450 TRUE TRUE TRUE
451 TRUE TRUE TRUE
452 TRUE TRUE TRUE
453 TRUE TRUE TRUE
454 TRUE TRUE TRUE
455 FALSE TRUE TRUE
456 FALSE TRUE TRUE
457 FALSE TRUE TRUE
458 TRUE TRUE TRUE
459 TRUE TRUE TRUE
460 FALSE TRUE TRUE
461 FALSE TRUE TRUE
462 FALSE TRUE TRUE
463 FALSE TRUE TRUE
464 FALSE TRUE TRUE
465 FALSE TRUE TRUE
466 FALSE TRUE TRUE
467 TRUE TRUE TRUE
468 FALSE TRUE TRUE
469 FALSE TRUE TRUE
470 FALSE TRUE TRUE
471 FALSE TRUE TRUE
472 FALSE TRUE TRUE
473 FALSE TRUE TRUE
474 FALSE TRUE TRUE
475 TRUE TRUE TRUE
478 FALSE TRUE TRUE
479 TRUE TRUE TRUE
480 TRUE TRUE TRUE
481 TRUE TRUE TRUE
482 TRUE TRUE TRUE
483 TRUE TRUE TRUE
484 TRUE TRUE TRUE
485 TRUE TRUE TRUE
486 TRUE TRUE TRUE
487 TRUE TRUE TRUE
488 TRUE TRUE TRUE
489 TRUE TRUE TRUE
490 TRUE TRUE TRUE
491 TRUE TRUE TRUE
492 TRUE TRUE TRUE
493 TRUE TRUE TRUE
494 TRUE TRUE TRUE
495 TRUE TRUE TRUE
496 TRUE TRUE TRUE
497 TRUE TRUE TRUE
498 TRUE TRUE TRUE
499 TRUE TRUE TRUE
500 TRUE TRUE TRUE
501 TRUE TRUE TRUE
502 TRUE TRUE TRUE
model parameter label forReg forClass probModel
1 gbm n.trees # Boosting Iterations TRUE TRUE TRUE
2 gbm interaction.depth Max Tree Depth TRUE TRUE TRUE
3 gbm shrinkage Shrinkage TRUE TRUE TRUE
4 gbm n.minobsinnode Min. Terminal Node Size TRUE TRUE TRUE
$gpls
$gpls$label
[1] "Generalized Partial Least Squares"
$gpls$library
[1] "gpls"
$gpls$loop
NULL
$gpls$type
[1] "Classification"
$gpls$parameters
parameter class label
1 K.prov numeric #Components
$gpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(K.prov = seq(1, len))
}
else {
out <- data.frame(K.prov = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$gpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
gpls::gpls(x, y, K.prov = param$K.prov, ...)
$gpls$predict
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata)$class
$gpls$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata)$predicted
out <- cbind(out, 1 - out)
colnames(out) <- modelFit$obsLevels
out
}
$gpls$predictors
function (x, ...)
{
out <- if (hasTerms(x))
predictors(x$terms)
else colnames(x$data$x.order)
out[!(out %in% "Intercept")]
}
$gpls$tags
[1] "Logistic Regression" "Partial Least Squares" "Linear Classifier"
$gpls$sort
function (x)
x[order(x[, 1]), ]
$gpls$levels
function (x)
x$obsLevels
$kernelpls
$kernelpls$label
[1] "Partial Least Squares"
$kernelpls$library
[1] "pls"
$kernelpls$type
[1] "Regression" "Classification"
$kernelpls$parameters
parameter class label
1 ncomp numeric #Components
$kernelpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$kernelpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$kernelpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
caret::plsda(x, y, method = "kernelpls", ncomp = ncomp,
...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "kernelpls",
ncomp = ncomp, ...)
}
out
}
$kernelpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$kernelpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$kernelpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- MSEP(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$kernelpls$predictors
function (x, ...)
rownames(x$projection)
$kernelpls$levels
function (x)
x$obsLevels
$kernelpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Kernel Method"
[4] "Linear Classifier" "Linear Regression"
$kernelpls$sort
function (x)
x[order(x[, 1]), ]
$ORFpls
$ORFpls$label
[1] "Oblique Random Forest"
$ORFpls$library
[1] "obliqueRF"
$ORFpls$loop
NULL
$ORFpls$type
[1] "Classification"
$ORFpls$parameters
parameter class label
1 mtry numeric #Randomly Selected Predictors
$ORFpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(mtry = caret::var_seq(p = ncol(x),
classification = is.factor(y), len = len))
}
else {
out <- data.frame(mtry = unique(sample(1:ncol(x), size = len,
replace = TRUE)))
}
out
}
$ORFpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(obliqueRF)
obliqueRF::obliqueRF(as.matrix(x), y, training_method = "pls",
...)
}
$ORFpls$predict
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata)
$ORFpls$prob
function (modelFit, newdata, submodels = NULL)
predict(modelFit, newdata, type = "prob")
$ORFpls$levels
function (x)
x$obsLevels
$ORFpls$notes
[1] "Unlike other packages used by `train`, the `obliqueRF` package is fully loaded when this model is used."
$ORFpls$tags
[1] "Random Forest" "Oblique Tree"
[3] "Partial Least Squares" "Implicit Feature Selection"
[5] "Ensemble Model" "Two Class Only"
$ORFpls$sort
function (x)
x[order(x[, 1]), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
$plsRglm
$plsRglm$label
[1] "Partial Least Squares Generalized Linear Models "
$plsRglm$library
[1] "plsRglm"
$plsRglm$loop
NULL
$plsRglm$type
[1] "Classification" "Regression"
$plsRglm$parameters
parameter class label
1 nt numeric #PLS Components
2 alpha.pvals.expli numeric p-Value threshold
$plsRglm$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(nt = 1:len, alpha.pvals.expli = 10^(c(-2:(len -
3), 0)))
}
else {
out <- data.frame(nt = sample(1:ncol(x), size = len,
replace = TRUE), alpha.pvals.expli = runif(len, min = 0,
0.2))
}
out
}
$plsRglm$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(plsRglm)
if (is.factor(y)) {
lv <- levels(y)
y <- as.numeric(y) - 1
dst <- "pls-glm-logistic"
}
else {
lv <- NULL
dst <- "pls-glm-gaussian"
}
theDots <- list(...)
if (any(names(theDots) == "modele")) {
mod <- plsrRglm::plsRglm(y, x, nt = param$nt, pvals.expli = param$alpha.pvals.expli <
1, sparse = param$alpha.pvals.expli < 1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
else {
mod <- plsRglm::plsRglm(y, x, nt = param$nt, modele = dst,
pvals.expli = param$alpha.pvals.expli < 1, sparse = param$alpha.pvals.expli <
1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
mod
}
$plsRglm$predict
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
if (modelFit$problemType == "Classification") {
out <- factor(ifelse(out >= 0.5, modelFit$obsLevels[2],
modelFit$obsLevels[1]))
}
out
}
$plsRglm$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
out <- cbind(1 - out, out)
dimnames(out)[[2]] <- rev(modelFit$obsLevels)
out
}
$plsRglm$varImp
NULL
$plsRglm$predictors
function (x, ...)
{
vars <- names(which(coef(x)[[2]][, 1] != 0))
vars[vars != "Intercept"]
}
$plsRglm$notes
[1] "Unlike other packages used by `train`, the `plsRglm` package is fully loaded when this model is used."
$plsRglm$tags
[1] "Generalized Linear Models" "Partial Least Squares"
[3] "Two Class Only"
$plsRglm$levels
function (x)
x$lev
$plsRglm$sort
function (x)
x[order(-x$alpha.pvals.expli, x$nt), ]
$simpls
$simpls$label
[1] "Partial Least Squares"
$simpls$library
[1] "pls"
$simpls$type
[1] "Regression" "Classification"
$simpls$parameters
parameter class label
1 ncomp numeric #Components
$simpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:(ncol(x) -
1), size = len, replace = TRUE)))
}
out
}
$simpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$simpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "simpls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "simpls",
ncomp = ncomp, ...)
}
out
}
$simpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$simpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$simpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$simpls$levels
function (x)
x$obsLevels
$simpls$predictors
function (x, ...)
rownames(x$projection)
$simpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$simpls$sort
function (x)
x[order(x[, 1]), ]
$spls
$spls$label
[1] "Sparse Partial Least Squares"
$spls$library
[1] "spls"
$spls$type
[1] "Regression" "Classification"
$spls$parameters
parameter class label
1 K numeric #Components
2 eta numeric Threshold
3 kappa numeric Kappa
$spls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(K = 1:min(nrow(x), ncol(x)), eta = seq(0.1,
0.9, length = len), kappa = 0.5)
}
else {
out <- data.frame(kappa = runif(len, min = 0, max = 0.5),
eta = runif(len, min = 0, max = 1), K = sample(1:min(nrow(x),
ncol(x)), size = len, replace = TRUE))
}
out
}
$spls$loop
NULL
$spls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
param$K <- min(param$K, length(y))
if (is.factor(y)) {
caret:::splsda(x, y, K = param$K, eta = param$eta, kappa = param$kappa,
...)
}
else {
spls::spls(x, y, K = param$K, eta = param$eta, kappa = param$kappa,
...)
}
}
$spls$predict
function (modelFit, newdata, submodels = NULL)
{
if (length(modelFit$obsLevels) < 2) {
spls::predict.spls(modelFit, newdata)
}
else {
as.character(caret:::predict.splsda(modelFit, newdata,
type = "class"))
}
}
$spls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
caret:::predict.splsda(modelFit, newdata, type = "prob")
}
$spls$predictors
function (x, ...)
colnames(x$x)[x$A]
$spls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression" "L1 Regularization"
$spls$levels
function (x)
x$obsLevels
$spls$sort
function (x)
x[order(-x$eta, x$K), ]
$widekernelpls
$widekernelpls$label
[1] "Partial Least Squares"
$widekernelpls$library
[1] "pls"
$widekernelpls$type
[1] "Regression" "Classification"
$widekernelpls$parameters
parameter class label
1 ncomp numeric #Components
$widekernelpls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:(ncol(x) -
1), size = len, replace = TRUE)))
}
out
}
$widekernelpls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$widekernelpls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
caret::plsda(x, y, method = "widekernelpls", ncomp = ncomp,
...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "widekernelpls",
ncomp = ncomp, ...)
}
out
}
$widekernelpls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$widekernelpls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels,
drop = FALSE])
}
out <- tmp
}
out
}
$widekernelpls$predictors
function (x, ...)
rownames(x$projection)
$widekernelpls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$widekernelpls$levels
function (x)
x$obsLevels
$widekernelpls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$widekernelpls$sort
function (x)
x[order(x[, 1]), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
$plsRglm
$plsRglm$label
[1] "Partial Least Squares Generalized Linear Models "
$plsRglm$library
[1] "plsRglm"
$plsRglm$loop
NULL
$plsRglm$type
[1] "Classification" "Regression"
$plsRglm$parameters
parameter class label
1 nt numeric #PLS Components
2 alpha.pvals.expli numeric p-Value threshold
$plsRglm$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- expand.grid(nt = 1:len, alpha.pvals.expli = 10^(c(-2:(len -
3), 0)))
}
else {
out <- data.frame(nt = sample(1:ncol(x), size = len,
replace = TRUE), alpha.pvals.expli = runif(len, min = 0,
0.2))
}
out
}
$plsRglm$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
require(plsRglm)
if (is.factor(y)) {
lv <- levels(y)
y <- as.numeric(y) - 1
dst <- "pls-glm-logistic"
}
else {
lv <- NULL
dst <- "pls-glm-gaussian"
}
theDots <- list(...)
if (any(names(theDots) == "modele")) {
mod <- plsrRglm::plsRglm(y, x, nt = param$nt, pvals.expli = param$alpha.pvals.expli <
1, sparse = param$alpha.pvals.expli < 1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
else {
mod <- plsRglm::plsRglm(y, x, nt = param$nt, modele = dst,
pvals.expli = param$alpha.pvals.expli < 1, sparse = param$alpha.pvals.expli <
1, alpha.pvals.expli = param$alpha.pvals.expli,
...)
}
mod
}
$plsRglm$predict
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
if (modelFit$problemType == "Classification") {
out <- factor(ifelse(out >= 0.5, modelFit$obsLevels[2],
modelFit$obsLevels[1]))
}
out
}
$plsRglm$prob
function (modelFit, newdata, submodels = NULL)
{
out <- predict(modelFit, newdata, type = "response")
out <- cbind(1 - out, out)
dimnames(out)[[2]] <- rev(modelFit$obsLevels)
out
}
$plsRglm$varImp
NULL
$plsRglm$predictors
function (x, ...)
{
vars <- names(which(coef(x)[[2]][, 1] != 0))
vars[vars != "Intercept"]
}
$plsRglm$notes
[1] "Unlike other packages used by `train`, the `plsRglm` package is fully loaded when this model is used."
$plsRglm$tags
[1] "Generalized Linear Models" "Partial Least Squares"
[3] "Two Class Only"
$plsRglm$levels
function (x)
x$lev
$plsRglm$sort
function (x)
x[order(-x$alpha.pvals.expli, x$nt), ]
$pls
$pls$label
[1] "Partial Least Squares"
$pls$library
[1] "pls"
$pls$type
[1] "Regression" "Classification"
$pls$parameters
parameter class label
1 ncomp numeric #Components
$pls$grid
function (x, y, len = NULL, search = "grid")
{
if (search == "grid") {
out <- data.frame(ncomp = seq(1, min(ncol(x) - 1, len),
by = 1))
}
else {
out <- data.frame(ncomp = unique(sample(1:ncol(x), replace = TRUE)))
}
out
}
$pls$loop
function (grid)
{
grid <- grid[order(grid$ncomp, decreasing = TRUE), , drop = FALSE]
loop <- grid[1, , drop = FALSE]
submodels <- list(grid[-1, , drop = FALSE])
list(loop = loop, submodels = submodels)
}
$pls$fit
function (x, y, wts, param, lev, last, classProbs, ...)
{
ncomp <- min(ncol(x), param$ncomp)
out <- if (is.factor(y)) {
plsda(x, y, method = "oscorespls", ncomp = ncomp, ...)
}
else {
dat <- if (is.data.frame(x))
x
else as.data.frame(x)
dat$.outcome <- y
pls::plsr(.outcome ~ ., data = dat, method = "oscorespls",
ncomp = ncomp, ...)
}
out
}
$pls$predict
function (modelFit, newdata, submodels = NULL)
{
out <- if (modelFit$problemType == "Classification") {
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "class")
}
else as.vector(pls:::predict.mvr(modelFit, newdata, ncomp = max(modelFit$ncomp)))
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels))
if (modelFit$problemType == "Classification") {
if (length(submodels$ncomp) > 1) {
tmp <- as.list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else tmp <- list(predict(modelFit, newdata, ncomp = submodels$ncomp))
}
else {
tmp <- as.list(as.data.frame(apply(predict(modelFit,
newdata, ncomp = submodels$ncomp), 3, function(x) list(x))))
}
out <- c(list(out), tmp)
}
out
}
$pls$prob
function (modelFit, newdata, submodels = NULL)
{
if (!is.matrix(newdata))
newdata <- as.matrix(newdata)
out <- predict(modelFit, newdata, type = "prob", ncomp = modelFit$tuneValue$ncomp)
if (length(dim(out)) == 3) {
if (dim(out)[1] > 1) {
out <- out[, , 1]
}
else {
out <- as.data.frame(t(out[, , 1]))
}
}
if (!is.null(submodels)) {
tmp <- vector(mode = "list", length = nrow(submodels) +
1)
tmp[[1]] <- out
for (j in seq(along = submodels$ncomp)) {
tmpProb <- predict(modelFit, newdata, type = "prob",
ncomp = submodels$ncomp[j])
if (length(dim(tmpProb)) == 3) {
if (dim(tmpProb)[1] > 1) {
tmpProb <- tmpProb[, , 1]
}
else {
tmpProb <- as.data.frame(t(tmpProb[, , 1]))
}
}
tmp[[j + 1]] <- as.data.frame(tmpProb[, modelFit$obsLevels])
}
out <- tmp
}
out
}
$pls$varImp
function (object, estimate = NULL, ...)
{
library(pls)
modelCoef <- coef(object, intercept = FALSE, comps = 1:object$ncomp)
perf <- pls:::MSEP.mvr(object)$val
nms <- dimnames(perf)
if (length(nms$estimate) > 1) {
pIndex <- if (is.null(estimate))
1
else which(nms$estimate == estimate)
perf <- perf[pIndex, , , drop = FALSE]
}
numResp <- dim(modelCoef)[2]
if (numResp <= 2) {
modelCoef <- modelCoef[, 1, , drop = FALSE]
perf <- perf[, 1, ]
delta <- -diff(perf)
delta <- delta/sum(delta)
out <- data.frame(Overall = apply(abs(modelCoef), 1,
weighted.mean, w = delta))
}
else {
perf <- -t(apply(perf[1, , ], 1, diff))
perf <- t(apply(perf, 1, function(u) u/sum(u)))
out <- matrix(NA, ncol = numResp, nrow = dim(modelCoef)[1])
for (i in 1:numResp) {
tmp <- abs(modelCoef[, i, , drop = FALSE])
out[, i] <- apply(tmp, 1, weighted.mean, w = perf[i,
])
}
colnames(out) <- dimnames(modelCoef)[[2]]
rownames(out) <- dimnames(modelCoef)[[1]]
}
as.data.frame(out)
}
$pls$predictors
function (x, ...)
rownames(x$projection)
$pls$levels
function (x)
x$obsLevels
$pls$tags
[1] "Partial Least Squares" "Feature Extraction" "Linear Classifier"
[4] "Linear Regression"
$pls$sort
function (x)
x[order(x[, 1]), ]
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