<b>mplot</b>: An <i>R</i> Package for Graphical Model Stability and Variable Selection Procedures

Tarr, Garth; Müller, Samuel; Welsh, A. H.

doi:10.18637/jss.v083.i09

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…To visualise the variable selection property of APES, we perform bootstrap sampling on the n observations 100 times. For each bootstrap sample, we apply APES using the three different baseline model estimates

\overset{true^}{bold-italicπ} false(α_{b} false)

and construct a variable inclusion plot as in Tarr, Müller & Welsh (). Figure shows the performance of each baseline model with the active variables shown with solid lines.…”

Section: Resultsmentioning

confidence: 99%

“…In practice, the selection of a single model might not be desirable as doing so does not account for model selection uncertainty (Burnham & Anderson , Chapter 4). Instead, we may wish to explore a collection of models or some variable selection path as in Tarr, Müller & Welsh (). This will be explored further in Section 3.…”

Section: Apes: a New Framework For Approximate And Exhaustive Variablmentioning

confidence: 99%

“…variable (ftv), as a count response variable. We consider a Poisson regression model and use the mplot package (Tarr, Müller & Welsh ) to compute a genuine exhaustive search and compare that to APES. The results are shown in Supplementary Figure 2.…”

Section: Applicationsmentioning

confidence: 99%

“…However, exhaustive search is often dismissed as computationally intractable since a large number of candidate models needs to be considered. The R packages leaps (Lumley & Miller ) and bestglm (McLeod & Xu ), for instance, take considerable computation time with more than 50 variables when exhaustively searching linear regression models; and can take too long with as few as 20 variables in the GLM exhaustive search, respectively (Tarr, Müller & Welsh ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast and approximate exhaustive variable selection for generalised linear models with APES

Wang

Tarr

Yang

et al. 2019

Aus NZ J of Statistics

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We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps-and-bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super-model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best-subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.

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\overset{true^}{bold-italicπ} false(α_{b} false)

and construct a variable inclusion plot as in Tarr, Müller & Welsh (). Figure shows the performance of each baseline model with the active variables shown with solid lines.…”

Section: Resultsmentioning

confidence: 99%

Section: Apes: a New Framework For Approximate And Exhaustive Variablmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast and approximate exhaustive variable selection for generalised linear models with APES

Wang

Tarr

Yang

et al. 2019

Aus NZ J of Statistics

Self Cite

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show abstract

“…We also define a new measure of instability in the bootstrap variable selection. Complementary visualisation methods for bootstrap based variables selection are implemented in the mplot package of the statistical software system R, Tarr et al (2018). Riani and Atkinson (2010) propose a robust variables selection method involving exploration and visualisation of various models, although their aim is not the exploration of the variability of variable selection.…”

Section: Introductionmentioning

confidence: 99%

Exploration of the variability of variable selection based on distances between bootstrap sample results

Hennig

Sauerbrei

2019

Adv Data Anal Classif

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It is well known that variable selection in multiple regression can be unstable and that the model uncertainty can be considerable. The model uncertainty can be quantified and explored by bootstrap resampling, see Sauerbrei et al. (Biom J 57:531-555, 2015). Here approaches are introduced that use the results of bootstrap replications of the variable selection process to obtain more detailed information about the data. Analyses will be based on dissimilarities between the results of the analyses of different bootstrap samples. Dissimilarities are computed between the vector of predictions, and between the sets of selected variables. The dissimilarities are used to map the models by multidimensional scaling, to cluster them, and to construct heatplots. Clusters can point to different interpretations of the data that could arise from different selections of variables supported by different bootstrap samples. A new measure of variable selection instability is also defined. The methodology can be applied to various regression models, estimators, and variable selection methods. It will be illustrated by three real data examples, using linear regression and a Cox proportional hazards model, and model selection by AIC and BIC.

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COMBSS: best subset selection via continuous optimization

Moka,

Liquet,

Zhu

et al. 2024

Stat Comput

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The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy. Ideally, new methods perform best subset selection faster than existing optimal methods but with comparable accuracy, or, being more accurate than methods of comparable computational speed. Here, we propose a novel continuous optimization method that identifies a subset solution path, a small set of models of varying size, that consists of candidates for the single best subset of features, that is optimal in a specific sense in linear regression. Our method turns out to be fast, making the best subset selection possible when the number of features is well in excess of thousands. Because of the outstanding overall performance, framing the best subset selection challenge as a continuous optimization problem opens new research directions for feature extraction for a large variety of regression models.

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mplot: An R Package for Graphical Model Stability and Variable Selection Procedures

Cited by 11 publications

References 27 publications

Fast and approximate exhaustive variable selection for generalised linear models with APES

Fast and approximate exhaustive variable selection for generalised linear models with APES

Exploration of the variability of variable selection based on distances between bootstrap sample results

COMBSS: best subset selection via continuous optimization

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