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DMRnet (Delete or Merge Regressors) is a suit of algorithms for linear and logistic model selection with high-dimensional data (i.e. the number of regressors may exceed the number of observations). The predictors can be continuous or categorical. The selected model consists of a subset of numerical regressors and partitions of levels of factors.

For information on how to get started using DMRnet, see our getting started vignette.

Installing DMRnet package

To install the development package version please execute


Alternatively, to install the current stable CRAN version please execute


After that, you can load the installed package into memory with a call to library(DMRnet).


GLAMER added

GLAMER was added in 0.3.1 version of the package. GLAMER stands for Group LAsso MERger and it is a new (simplified in relation to DMRnet) algorithm for which we prove partition selection consistency. It is the first result of that kind for high dimensional scenario. The relevant paper with algorithm description is the following: Szymon Nowakowski, Piotr Pokarowski and Wojciech Rejchel, 2021. “Group Lasso Merger for Sparse Prediction with High-Dimensional Categorical Data.” arXiv:2112.11114

To use GLAMER pass algorithm="glamer" in a call to DMRnet() or cross validation routine cv.DMRnet(). GLAMER is not supported in DMR.

Two cross validation routines

A new cross validation routine was introduced to improve the computed model quality. It indexes models by GIC. The method was proposed and first implemented for gaussian family by Piotr Pokarowski. Since 0.3.1 version of the package it has been built into DMRnet for both gaussian and binomial families.

All in all, the cross validation features in the package are the following:

  1. Models can be indexed by GIC or by model dimension. The relevant setting is selected with, respectively, indexation.mode="GIC" or indexation.mode="dimension" parameter in a call to cv.DMRnet(). The setting that indexes models by GIC has been the default since 0.3.1 version of the package.

  2. The net of lambda values is first calculated from the full data set and then this net is used for particular cross validation folds. The motivation behind this change is to stabilize the results.

  3. Apart from df.min, which is the model with minimal cross-validated error, the routines now return df.1se which is the smallest model falling under the upper curve of a prediction error plus one standard deviation. It can be used in predict() for inference by passing md="df.1se" instead of the default md="df.min".

  4. Cross validation handles the mismatched factor levels in a way that minimizes incorrect behavior (see Section Handling of mismatched factor levels).

Handling of mismatched factor levels

The new treatment of factors in cross validation/predict and in DMRnet/predict pairs is based on the following analysis:

Let us assume that - Xtr is training data in cross validation or in a regular call via DMRnet->model - Xte is test data in cross validation or in a regular call via model->predict

Without loss of generality, let us consider Xtr and Xte to be one column only, with factors.

Let us also consider the following definitions: - A is a true set of all factor levels in Xtr - B is a true set of all factor levels in Xte - C=levels(Xtr) is a set of factor levels in original data that Xtr originates from, but it is still assigned to Xtr via the levels() function. As a rule, when taking subsets, R does not eliminate redundant factors, so let us note that C is a superset of A.

There are 4 classes of problems:

  1. C is a strict superset of A.

    Then, if treated naively, DMRnet(...) when constructing a model would throw an error, because we would end up with NaN values in a column dedicated to this superfluous factor level (to be exact, it would happen when columns get normalized).

    The solution to that is straightforward. Before the model gets constructed in DMRnet we recalculate the factor level set, C_new. Then C_new=A.


  2. B does not contain a level(s) present in A.

    (sample case: we did sample to Xtr the single Dutch national from the Insurance data set, and he is not present in Xte, because there is only one instance of Dutch national in the whole Insurance data set). As a result predict(...) would throw an error, because expanded model-matrix dimensions would be conflicting.

    The solution is simple here, too: in constructing a model make a note about the true A set (technically, it gets stored into levels.listed variable in a model) and then in predict(...) assign the levels of Xte to be equal to A. Only then create the model-matrix.


  3. B contains a factor level(s) not present in A, AND we are doing CV, so we have access to Xtr.

    The solution is to remove the rows with levels that are going to cause problems later in predict(...) from Xte before the prediction. The other solution would be to predict using unknown.factor.levels=“NA” flag and then eliminate the NAs from comparisons (this solution is NOT used at present)


  4. B contains a factor level(s) not present in A, AND we are NOT doing CV, so we have no access to Xtr.

    This case is problematic because this situation gets identified too late - we are already in predict(...). At this point, only the model created by DMRnet(...) function (which got passed into predict(...) function) is known. We cannot perform inference and we cannot perform any imputation for the problematic data point, either (we don’t know Xtr and have no access to it).

    All that remains is to throw an error (when unknown.factor.levels="error", the default) OR eliminate the problematic rows, predict, and then replenish the result with NAs in place of problematic values (when unknown.factor.levels="NA").

    None of this solutions is fully satisfactory, thus this case remains PROBLEMATIC.

Stability improvements

Generally speaking, matrix rank in real world scenarios is more a numerical concept than a mathematical concept and its value may differ depending on a threshold. Thus various kinds of problems result from data matrices close to singular. Since 0.3.1 version of the package, the work has been devoted to improve stability of computations with such ill-defined matrices. See for more information on detailed stability improvements.

Weight parameterization

This remains to be introduced to GLAMER and DMRnet algorithms in future versions.