tdmRegressLoop: Core regression double loop returning a 'TDMregressor'...

In TDMR: Tuned Data Mining in R

Description

tdmRegressLoop contains a double loop (opts$NRUN and CV-folds) and calls tdmRegress. It is called by all R-functions main_*.
It returns an object of class TDMregressor.

Usage

tdmRegressLoop(dset, response.variables, input.variables, opts, tset = NULL)

Arguments

dset	the data frame for which cvi is needed
response.variables	name of column which carries the target variable - or - vector of names specifying multiple target columns (these columns are not used during prediction, only for training and for evaluating the predicted result)
input.variables	vector with names of input columns
opts	a list from which we need here the following entries NRUN number of runs (outer loop) TST.SEED =NULL: leave the random number seed as it is. =any value: set the random number seed to this value to get reproducible random numbers and thus reproducible training-test-set-selection. (only relevant in case TST.kind=="cv" or "rand") (see also MOD.SEED in tdmClassify) TST.kind how to create cvi, handed over to tdmModCreateCVindex. If TST.kind="col", then cvi is taken from dset[,opts$TST.col]. GD.RESTART [TRUE] =TRUE/FALSE: do/don't restart graphic devices GRAPHDEV ["non"| other ]
tset	[NULL] If not NULL, this is the test data set. If NULL, we are in tuning and the validation data set is build from dset according to the procedure prescribed in opts$TST.*.

Value

result, an object of class TDMregressor, this is a list with results, containing

opts	the res$opts from tdmRegress
lastRes	last run, last fold: result from tdmRegress
R_train	RMAE / RMSE on training set (vector of length NRUN), depending on opts$rgain.type=="rmae" or "rmse"
S_train	RMSE on training set (vector of length NRUN)
T_train	Theil's U for RMAE on training set (vector of length NRUN)
*_test	— similar, with test set instead of training set —
Err	a data frame with as many rows as opts$NRUN and columns = (rmae.trn, rmse.trn made.trn, rmae.theil.trn, ntrn, rmae.tst, rmse.tst, made.tst, rmae.theil.tst, ntst)
predictions	last run: data frame with dimensions [nrow(dset),length(response.variable)]. In case of CV, all validation set predictions (for each record in dset), in other cases mixed validation / train set predictions.
predictTest	predictions on the test set tset (NULL if tset==NULL )

Author(s)

Wolfgang Konen ([e-mail]), THK

See Also

tdmRegress, tdmClassifyLoop, tdmClassify

Examples

#*# --------- demo/demo00-1regress.r ---------
#*# This demo shows a simple data mining process (phase 1 of TDMR) for regression on
#*# dataset iris.
#*# The data mining process in tdmRegressLoop calls randomForest as the prediction model.
#*# It is called opts$NRUN=2 times with different random train-validation set splits.
#*# Therefore data frame result$Err has 2 rows.
#*#
opts=tdmOptsDefaultsSet()                       # set all defaults for data mining process
gdObj <- tdmGraAndLogInitialize(opts);          # init graphics and log file

data(iris)
response.variables="Petal.Length"                # names, not data (!)
input.variables=setdiff(names(iris),"Petal.Length")
opts$rgain.type="rmae"

result = tdmRegressLoop(iris,response.variables,input.variables,opts)

print(result$Err)

TDMR documentation built on March 3, 2020, 1:06 a.m.