An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies

A common strategy for the analysis of object-attribute associations is to derive a low- dimensional spatial representation of objects and attributes which involves a compensatory model (e.g., principal components analysis) to explain the strength of object-attribute associations. As an alternative, probabilistic latent feature models assume that objects and attributes can be represented as a set of binary latent features and that the strength of object-attribute associations can be explained as a non-compensatory (e.g., disjunctive or conjunctive) mapping of latent features. In this paper, we describe the R package plfm which comprises functions for conducting both classical and Bayesian probabilistic latent feature analysis with disjunctive or a conjunctive mapping rules. Print and summary functions are included to summarize results on parameter estimation, model selection and the goodness of fit of the models. As an example the functions of plfm are used to analyze product-attribute data on the perception of car models, and situation-behavior associations on the situational determinants of anger-related behavior

Attribute non-attendance in choosing the bike as a transport mode in Belgium

Cycling is an important pillar of the global endeavor to have a more sustainable transportation system. Many papers have studied how trip and person characteristics affect selecting the bike as a transport mode but unlike other researchers, we model the probability of cycling using a binary item response model where the choice is modelled as a trade-off between the individuals' tendency to cycle and the threshold related to each cycling situation. We distinguish between frequent and occasional cyclists. The results show that occasional cyclists are more affected by adverse weather situations, darkness, and uphill slopes. Contrary to the previous studies, a separate bike path turned out a stronger motivator for the group of frequent cyclists. The model fit can substantially be improved by accounting for attribute non-attendance. The results show that weather and wind speed have the highest probability to be taken into account, and the bike path had the lowest probability of being considered by the respondents. Employing the attribute non-attendance model made it possible to make accurate and trustworthy conclusions about the attributes by focusing on the people who take into account the attributes. More specifically, it was found that the presence of a separate bike path and a 100% asphalt route can increase the average probability of taking the bike by up to 55 and 40 percentage points, respectively

Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data

The analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment. Latent class probabilistic latent feature models (LCPLFMs) may be used to explain binary object-attribute associations on the basis of a small number of binary latent variables (called latent features). As LCPLFMs aim to model object-attribute associations using a small number of latent features they may be more suited to analyze data with many objects/attributes than standard multilevel latent class models which do not include such a dimension reduction. In this paper we describe new functions of the plfm package for analyzing binary three-way data with LCPLFMs. The new functions provide a flexible modeling approach as they allow to (1) specify different assumptions for modeling statistical dependencies between object-attribute pairs, (2) use different assumptions for modeling parameter heterogeneity across persons, (3) conduct a confirmatory analysis by constraining specific parameters to pre-specified values, (4) inspect results with print, summary and plot methods. As an illustration, the models are applied to analyze data on the perception of midsize cars, and to study the situational determinants of anger-related behavior

Goal conflict in chronic pain : day reconstruction method

Background When suffering from chronic pain, attempts to control or avoid pain often compete with other daily activities. Engaging in one activity excludes engaging in another, equally valued activity, which is referred to as “goal conflict.” As yet, the presence and effects of goal conflicts in patients with chronic pain remain poorly understood. Methods This study systematically mapped the presence and experience of goal conflicts in patients with fibromyalgia compared to healthy controls. A total of 40 patients and 37 controls completed a semi-structured interview in which they first reconstructed the previous day, identified conflicts experienced during that day, and classified each of the conflicting goals in one of nine goal categories. Additionally, they assessed how they experienced the previous day and the reported conflicts. Results Results showed that patients did not experience more goal conflicts than healthy controls, but that they did differ in the type of conflicts experienced. Compared to controls, patients reported more conflicts related to pain, and fewer conflicts involving work-related, social or pleasure-related goals. Moreover, patients experienced conflicts as more aversive and more difficult to resolve than control participants. Discussion This study provides more insight in the dynamics of goal conflict in daily life, and indicates that patients experience conflict as more aversive than controls, and that conflict between pain control (and avoidance) and other valued activities is part of the life of patients

Pain Catastrophizing and Fear of Pain predict the Experience of Pain in Body Parts not targeted by a Delayed-Onset Muscle Soreness procedure

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The present study examined whether pain catastrophizing and pain-related fear predict the experience of pain in body regions that are not targeted by an experimental muscle injury protocol. A delayed-onset muscle soreness (DOMS) protocol was used to induce pain unilaterally in the pectoralis, serratus, trapezius, latissimus dorsi, and deltoid muscles. The day after the DOMS protocol, participants were asked to rate their pain as they lifted weighted canisters with their targeted (ie, injured) arm and their nontargeted arm. The lifting task is a nonnoxious stimulus unless participants are already experiencing musculoskeletal pain. Therefore, reports of pain on the nontargeted arm were operationalized as pain in response to a nonnoxious stimulus. Eighty-two healthy university students (54 men, 28 women) completed questionnaires on pain catastrophizing and fear of pain and went through the DOMS protocol. The analyses revealed that catastrophizing and pain-related fear prospectively predicted pain experience in response to a nonnoxious stimulus. The possible mechanisms underlying this effect and clinical implications are discussed

An R

A common strategy for the analysis of object-attribute associations is to derive a low- dimensional spatial representation of objects and attributes which involves a compensatory model (e.g., principal components analysis) to explain the strength of object-attribute associations. As an alternative, probabilistic latent feature models assume that objects and attributes can be represented as a set of binary latent features and that the strength of object-attribute associations can be explained as a non-compensatory (e.g., disjunctive or conjunctive) mapping of latent features. In this paper, we describe the R package plfm which comprises functions for conducting both classical and Bayesian probabilistic latent feature analysis with disjunctive or a conjunctive mapping rules. Print and summary functions are included to summarize results on parameter estimation, model selection and the goodness of fit of the models. As an example the functions of plfm are used to analyze product-attribute data on the perception of car models, and situation-behavior associations on the situational determinants of anger-related behavior

Probabilistic feature analysis of product perception based on pick any/n data

nrpages: 2status: publishe

Etude comparative de la physiologie des voies sensorielles primaires et des voies associatives : contrôle d'origine centrale des messages afférents

Thèse d'agrégation de l'enseignement supérieur (Faculté de médecine) -- UCL, 196