R语言Caret中train支持的method
可以使用以下代码查看
library(caret)
names(getModelInfo())
> names(getModelInfo())
[1] "ada" "AdaBag" "AdaBoost.M1"
[4] "adaboost" "amdai" "ANFIS"
[7] "avNNet" "awnb" "awtan"
[10] "bag" "bagEarth" "bagEarthGCV"
[13] "bagFDA" "bagFDAGCV" "bam"
[16] "bartMachine" "bayesglm" "binda"
[19] "blackboost" "blasso" "blassoAveraged"
[22] "bridge" "brnn" "BstLm"
[25] "bstSm" "bstTree" "C5.0"
[28] "C5.0Cost" "C5.0Rules" "C5.0Tree"
[31] "cforest" "chaid" "CSimca"
[34] "ctree" "ctree2" "cubist"
[37] "dda" "deepboost" "DENFIS"
[40] "dnn" "dwdLinear" "dwdPoly"
[43] "dwdRadial" "earth" "elm"
[46] "enet" "evtree" "extraTrees"
[49] "fda" "FH.GBML" "FIR.DM"
[52] "foba" "FRBCS.CHI" "FRBCS.W"
[55] "FS.HGD" "gam" "gamboost"
[58] "gamLoess" "gamSpline" "gaussprLinear"
[61] "gaussprPoly" "gaussprRadial" "gbm_h2o"
[64] "gbm" "gcvEarth" "GFS.FR.MOGUL"
[67] "GFS.LT.RS" "GFS.THRIFT" "glm.nb"
[70] "glm" "glmboost" "glmnet_h2o"
[73] "glmnet" "glmStepAIC" "gpls"
[76] "hda" "hdda" "hdrda"
[79] "HYFIS" "icr" "J48"
[82] "JRip" "kernelpls" "kknn"
[85] "knn" "krlsPoly" "krlsRadial"
[88] "lars" "lars2" "lasso"
[91] "lda" "lda2" "leapBackward"
[94] "leapForward" "leapSeq" "Linda"
[97] "lm" "lmStepAIC" "LMT"
[100] "loclda" "logicBag" "LogitBoost"
[103] "logreg" "lssvmLinear" "lssvmPoly"
[106] "lssvmRadial" "lvq" "M5"
[109] "M5Rules" "manb" "mda"
[112] "Mlda" "mlp" "mlpKerasDecay"
[115] "mlpKerasDecayCost" "mlpKerasDropout" "mlpKerasDropoutCost"
[118] "mlpML" "mlpSGD" "mlpWeightDecay"
[121] "mlpWeightDecayML" "monmlp" "msaenet"
[124] "multinom" "mxnet" "mxnetAdam"
[127] "naive_bayes" "nb" "nbDiscrete"
[130] "nbSearch" "neuralnet" "nnet"
[133] "nnls" "nodeHarvest" "null"
[136] "OneR" "ordinalNet" "ordinalRF"
[139] "ORFlog" "ORFpls" "ORFridge"
[142] "ORFsvm" "ownn" "pam"
[145] "parRF" "PART" "partDSA"
[148] "pcaNNet" "pcr" "pda"
[151] "pda2" "penalized" "PenalizedLDA"
[154] "plr" "pls" "plsRglm"
[157] "polr" "ppr" "PRIM"
[160] "protoclass" "qda" "QdaCov"
[163] "qrf" "qrnn" "randomGLM"
[166] "ranger" "rbf" "rbfDDA"
[169] "Rborist" "rda" "regLogistic"
[172] "relaxo" "rf" "rFerns"
[175] "RFlda" "rfRules" "ridge"
[178] "rlda" "rlm" "rmda"
[181] "rocc" "rotationForest" "rotationForestCp"
[184] "rpart" "rpart1SE" "rpart2"
[187] "rpartCost" "rpartScore" "rqlasso"
[190] "rqnc" "RRF" "RRFglobal"
[193] "rrlda" "RSimca" "rvmLinear"
[196] "rvmPoly" "rvmRadial" "SBC"
[199] "sda" "sdwd" "simpls"
[202] "SLAVE" "slda" "smda"
[205] "snn" "sparseLDA" "spikeslab"
[208] "spls" "stepLDA" "stepQDA"
[211] "superpc" "svmBoundrangeString" "svmExpoString"
[214] "svmLinear" "svmLinear2" "svmLinear3"
[217] "svmLinearWeights" "svmLinearWeights2" "svmPoly"
[220] "svmRadial" "svmRadialCost" "svmRadialSigma"
[223] "svmRadialWeights" "svmSpectrumString" "tan"
[226] "tanSearch" "treebag" "vbmpRadial"
[229] "vglmAdjCat" "vglmContRatio" "vglmCumulative"
[232] "widekernelpls" "WM" "wsrf"
[235] "xgbDART" "xgbLinear" "xgbTree"
[238] "xyf"
关于每个缩写具体对应哪个模型，也可以查看详细说明
使用lm的情况
a=train(cmedv~.,data=w,method="lm")
关于其他方面的细节可以查看详细的参数说明