lassopack: Model selection and prediction with regularized regression in Stata

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more information see https://statalasso.github.io

Changes in circle area after gravity compensation training in chronic stroke patients

After a stroke, many people experience difficulties to selectively activate muscles. As a result many patients move the affected arm in stereotypical patterns. Shoulder abduction is often accompanied by elbow flexion, reducing the ability to extend the elbow. This involuntary coupling reduces the patient's active range of motion. Gravity compensation reduces the activation level of shoulder abductors which limits the amount of coupled elbow flexion. As a result, stroke patients can instantaneously increase their active range of motion [1]. The objective of the present study is to examine whether training in a gravity compensated environment can also lead to an increased range of motion in an unsupported environment. Parts of this work have been presented at EMBC2009, Minneapolis, USA

Surface bubble nucleation phase space

Recent research has revealed several different techniques for nanoscopic gas nucleation on submerged surfaces, with findings seemingly in contradiction with each other. In response to this, we have systematically investigated the occurrence of surface nanobubbles on a hydrophobised silicon substrate for various different liquid temperatures and gas concentrations, which we controlled independently. We found that nanobubbles occupy a distinct region of this phase space, occurring for gas concentrations of approximately 100-110%. Below the nanobubble phase we did not detect any gaseous formations on the substrate, whereas micropancakes (micron wide, nanometer high gaseous domains) were found at higher temperatures and gas concentrations. We moreover find that supersaturation of dissolved gases is not a requirement for nucleation of bubbles.Comment: 4 pages, 4 figure

Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors

In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcome. Optimal Scaling (OS) transformations combined with GLMs can deal with this type of relations. Transformations of the predictors have been integrated in GLMs before, e.g. in Generalized Additive Models. However, the OS methodology has several benefits. For example, the levels of categorical predictors are quantified directly, such that they can be included in the model without defining dummy variables. This approach enhances the interpretation and visualization of the effect of different levels on the outcome. Furthermore, monotonicity restrictions can be applied to the OS transformations such that the original ordering of the category values is preserved. This improves the interpretation of the effect and may prevent overfitting. The scaling level can be chosen for each individual predictor such that models can include mixed scaling levels. In this way, a suitable transformation can be found for each predictor in the model. The implementation of OS in logistic regression is demonstrated using three datasets that contain a binary outcome variable and a set of categorical and/or continuous predictor variables.Comment: 35 pages, 4 figure