tdmRegress: Core regression function of TDMR.
In TDMR: Tuned Data Mining in R

Description Usage Arguments Value Author(s) See Also Examples

tdmRegress is called by tdmRegressLoop and returns an object of class tdmRegre.
It trains a model on training set d_train and evaluates it on test set d_test. If this function is used for tuning, the test set d_test plays the role of a validation set.

tdmRegress(
  d_train,
  d_test,
  d_preproc,
  response.variables,
  input.variables,
  opts,
  tsetStr = c("Validation", "validation", ".vali")
)

`d_train`	training set
`d_test`	test set, same columns as training set
`d_preproc`	data used for preprocessing. May be NULL, if no preprocessing is done (opts$PRE.SFA=="none" and opts$PRE.PCA=="none"). If preprocessing is done, then d_preproc is usually all non-validation data.
`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 evaluation)
`input.variables`	vector with names of input columns
`opts`	additional parameters [defaults in brackets] `SRF.` several parameters for sorted_rf_importance (see tdmModelingUtils.r) `RF.` several parameters for RF (Random Forest, defaults are set, if omitted) `SVM.*` several parameters for SVM (Support Vector Machines, defaults are set, if omitted) `filename` `data.title` `MOD.method` ["RF"] the main training method ["RF"\|"SVM"\|"LM"]: use [Random forest\| SVM\| linear model] for the main model `MOD.SEED` =NULL: set the RNG to system time as seed (different RF trainings) =any value: set the random number seed to this value (+i) to get reproducible random numbers. In this way, the model training part (RF, NNET, ...) gets always a fixed seed. (see also TST.SEED in tdmRegressLoop) `OUTTRAFO` [NULL] string, apply a transformation to the output variable `fct.postproc` [NULL] name of a user-def'd function for postprocessing of predicted output `gr.log` =FALSE (def): make scatter plot as-is, =TRUE: transform output x with log(x+1) (x should be nonnegative) `GD.DEVICE` if !="non", then make a pairs-plot of the 5 most important variables and make a true-false bar plot `VERBOSE` [2] =2: most printed output, =1: less, =0: no output
`tsetStr`	[c("Validation", "validation",".vali")]

res, an object of class tdmRegre, this is a list containing

`d_train`	training set + predicted class column(s)
`d_test`	test set + predicted target output
`allRMAE`	data frame with columns = (rmae.train, rmae.test, theil.train, theil.test, ...) and rows = response variables. Here Theil's U is based on RMAE (relative mean absolute errror).
`allRMSE`	data frame with columns = (rmse.train, rmse.test, theil.train, theil.test, ...) and rows = response variables. Here Theil's U is based on RMSE (root mean square error).
`lastModel`	the last model built (e.g. the last Random Forest in the case of MOD.method=="RF")
`opts`	parameter list from input, some default values might have been added

The item lastModel is specific for the *last* model (the one built for the last response variable in the last run and last fold)

Wolfgang Konen, FHK, Sep'2009 - Jun'2012

print.tdmRegre tdmRegressLoop tdmClassifyLoop

#*# This example shows a simple data mining process (phase 1 of TDMR) for regression on
#*# dataset iris.
#*# The data mining process in tdmRegress calls randomForest as the prediction model.
#*# It is called  for 2 response variables. Therefore, the data frames allRMAE and allRMSE 
#*# have 2 rows.
#*#
opts=tdmOptsDefaultsSet()                       # set all defaults for data mining process
gdObj <- tdmGraAndLogInitialize(opts);          # init graphics and log file

data(iris)
response.variables=c("Petal.Length","Petal.Width")                # names, not data (!)
input.variables=setdiff(names(iris),response.variables)
opts$rgain.type="rmae"
opts$NRUN=1

idx_train = sample(nrow(iris))[1:110]
d_train=iris[idx_train,]
d_vali=iris[-idx_train,]
res <- tdmRegress(d_train,d_vali,NULL,response.variables,input.variables,opts)

print(res$allRMAE)
print(res$allRMSE)