EvaluateLinearMTModel: Evaluate LinearMTL model.
In tohein/linearMTL: Linear Models for Multi-Task Learning

Compute training and test errors for the given indices as well as error changes for excluding features. If task.grouping is supplied, identify top regulators per group and plot clustering of the regression coefficients along with the most important features. Save results to directory out.dir.

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EvaluateLinearMTModel(X = NULL, task.specific.features = list(), Y,
  LMTL.model, train.idx, test.idx, task.grouping = NULL,
  feature.names = NULL, task.names = NULL, sd.threshold = 1.5, out.dir)

`X`	Column centered N by J input matrix of features common to all tasks.
`task.specific.features`	Named list of features which are specific to each task. Each entry contains an N by J2 column-centered matrix for one particular task (where columns are features). List has to be ordered according to the columns of Y.
`Y`	Column centered N by K output matrix for every task.
`LMTL.model`	Linear multi-task learning model (list containing B and intercept).
`train.idx`	Indices for the training set.
`test.idx`	Indices for the test set.
`task.grouping`	String vector of length K with group names for each task.
`feature.names`	Feature names.
`task.names`	Task names.
`sd.threshold`	All features with error changes above sd.threshold times the standard deviation will be taken as important.
`out.dir`	Output directory for results and plots.