Logistic regression with sparse common and distinctive covariates

Having large sets of predictor variables from multiple sources concerning the same individuals is becoming increasingly common in behavioral research. On top of the variable selection problem, predicting a categorical outcome using such data gives rise to an additional challenge of identifying the processes at play underneath the predictors. These processes are of particular interest in the setting of multi-source data because they can either be associated individually with a single data source or jointly with multiple sources. Although many methods have addressed the classification problem in high dimensionality, the additional challenge of distinguishing such underlying predictor processes from multi-source data has not received sufficient attention. To this end, we propose the method of Sparse Common and Distinctive Covariates Logistic Regression (SCD-Cov-logR). The method is a multi-source extension of principal covariates regression that combines with generalized linear modeling framework to allow classification of a categorical outcome. In a simulation study, SCD-Cov-logR resulted in outperformance compared to related methods commonly used in behavioral sciences. We also demonstrate the practical usage of the method under an empirical dataset

Quantum fluctuations of D5dD_{5d} polarons on C60C_{60} molecules

The dynamic Jahn-Teller splitting of the six equivalent $D_{5d}$ polarons due to quantum fluctuations is studied in the framework of the Bogoliubov-de Gennes formalism. The tunneling induced level splittings are determined to be $^2 T_{1u} \bigoplus ^2 T_{2u}$ and $^1 A_g \bigoplus ^1 H_g$ for $C_{60}^{1-}$ and $C_{60}^{2-}$, respectively, which should give rise to observable effects in experiments.Comment: REVTEX 3.0, 13 pages, to be published in Phys. Rev.

EVAPORATION: a new vapour pressure estimation methodfor organic molecules including non-additivity and intramolecular interactions

We present EVAPORATION (Estimation of VApour Pressure of ORganics, Accounting for Temperature, Intramolecular, and Non-additivity effects), a method to predict (subcooled) liquid pure compound vapour pressure <i>p</i><sup>0</sup> of organic molecules that requires only molecular structure as input. The method is applicable to zero-, mono- and polyfunctional molecules. A simple formula to describe log<sub>10</sub><i>p</i><sup>0</sup>(<i>T</i>) is employed, that takes into account both a wide temperature dependence and the non-additivity of functional groups. In order to match the recent data on functionalised diacids an empirical modification to the method was introduced. Contributions due to carbon skeleton, functional groups, and intramolecular interaction between groups are included. Molecules typically originating from oxidation of biogenic molecules are within the scope of this method: aldehydes, ketones, alcohols, ethers, esters, nitrates, acids, peroxides, hydroperoxides, peroxy acyl nitrates and peracids. Therefore the method is especially suited to describe compounds forming secondary organic aerosol (SOA)

Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups

Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement invariance). Otherwise, one would be comparing apples and oranges. Nowadays, measurement invariance is often tested across a large number of groups by means of multigroup factor analysis. When the assumption is untenable, one may compare group-specific measurement models to pinpoint sources of noninvariance, but the number of pairwise comparisons exponentially increases with the number of groups. This makes it hard to unravel invariances from noninvariances and for which groups they apply, and it elevates the chances of falsely detecting noninvariance. An intuitive solution is clustering the groups into a few clusters based on the measurement model parameters. Therefore, we present mixture multigroup factor analysis (MMG-FA) which clusters the groups according to a specific level of measurement invariance. Specifically, in this article, clusters of groups with metric invariance (i.e., equal factor loadings) are obtained by making the loadings cluster-specific, whereas other parameters (i.e., intercepts, factor (co)variances, residual variances) are still allowed to differ between groups within a cluster. MMG-FA was found to perform well in an extensive simulation study, but a larger sample size within groups is required for recovering more subtle loading differences. Its empirical value is illustrated for data on the social value of emotions and data on emotional acculturation

Een wereld van verschil: zien baby’s aantallen?

A

Parenting Strategies Used by Parents of Children with ASD: Differential Links with Child Problem Behaviour

Here, we explored the structure of the ‘Parenting Strategies Questionnaire’, a new scale designed to measure parenting strategies for problem behaviour in ASD. We then examined links between child behaviour and parenting in a sample of 222 predominantly-UK parents of ASD children exhibiting behaviour found difficult or challenging. Analysis revealed three parenting subscales: Accommodation, Reinforcement Approaches and Reducing Uncertainty. Both Accommodation and Reducing Uncertainty were linked to child problem behaviour. Child factors explained up to 29% of the variance in Accommodation, with Socially Inflexible Non-compliance the strongest predictor, and up to 24% of the variance in Reducing Uncertainty, with Intolerance of Uncertainty the strongest predictor. Child factors were not related to Reinforcement Approaches. Longitudinal studies investigating these relationships are needed. Autism Spectrum Disorders (ASD) are neurodevelopmental impairments characterised by difficulties with communication, socialisation, and rigid and repetitive behaviours (Americal Psychiatric Association 2013). Problem behaviour (also referred to as ‘behaviour that challenges’ or, in the past, ‘challenging behaviour’) often occurs in children with ASD, and is more severe in ASD than in other clinical populations (e.g., Brereton et al. 2006; Estes et al. 2009). Forms of problem behaviour include oppositionality, failures to comply, destructiveness and explosiveness (e.g., Gadow et al. 2004). These behaviours are thought to reflect a dysregulated emotional state, resulting in outbursts and prolonged emotional reactions (Mazefsky et al. 2018a, b). Problem behaviour may reflect attempts by the child to reduce anxiety or distress by escaping aversive activities, or reactivity reflecting frustration when things are not on their terms (Brewer et al. 2014; Larson 2006). Demands to comply have been identified as a key trigger of reactivity in ASD (Chowdhury et al. 2016). Some individuals appear more reactive to routine demands (e.g., to wash or get dressed), and others to demands in socially challenging or novel situations (e.g., when visiting friends) (Chowdhury et al. 2016). The former ‘demand-specific’ profile resembles accounts of extreme/‘pathological’ demand avoidance (‘PDA’), which describe avoidance of and reactivity to routine demands in children with ASD (Newson et al. 2003). Some accounts of PDA explicitly attribute these behaviours to elevated anxiety and distress in the context of demands (Newson et al. 2003). In contrast, the latter ‘socially inflexible’ profile, may particularly reflect intolerance of uncertainty: the tendency to “react negatively on an emotional, cognitive, and behavioural level to uncertain situations and events” (Buhr and Dugas 2009, p. 216), which characterizes some children with ASD (Boulter et al. 2014; Larson 2006)