Discrete Choice Models with Random Parameters in R: The Rchoice Package

Rchoice is a package in R for estimating models with individual heterogeneity for both cross-sectional and panel (longitudinal) data. In particular, the package allows binary, ordinal and count response, as well as continuous and discrete covariates. Individual heterogeneity is modeled by allowing the parameter associated with each observed variable (e.g., its coefficient) to vary randomly across individuals according to some pre-specified distribution. Simulated maximum likelihood method is implemented for the estimation of the moments of the distributions. In addition, functions for plotting the conditional individual-specific coefficients and their confidence interval are provided. This article is a general description of Rchoice and all functionalities are illustrated using real databases

Random Parameters and Spatial Heterogeneity using Rchoice package in R

This study focus on models with spatially varying coefficients using simulations.  As shown by Sarrias (2019), this modeling strategy is intended to complement the existing approaches by using variables at micro level and by adding flexibility and realism to the potential domain of the coefficient on the geographical space. Spatial heterogeneity is modelled by allowing the parameters associated with each observed variable to vary “randomly” across space according to some distribution. To show the main advantages of this modeling strategy, the Rchoice package in R is used

GMM Estimators for Binary Spatial Models in R

Despite the huge availability of software to estimate cross-sectional spatial models, there are only few functions to estimate models dealing with spatial limited dependent variable. This paper fills this gap introducing the new R package spldv. The package is based on generalized methods of moment (GMM) estimators and includes a series of one- and two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance-covariance matrix of the estimated coefficients. An important feature of spldv is that users can estimate the spatial Durbin model and compute the direct, indirect, and total effects in a friendly and flexible way

Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package

This paper introduces the package gmnl in R for estimation of multinomial logit models with unobserved heterogeneity across individuals for cross-sectional and panel (longitudinal) data. Unobserved heterogeneity is modeled by allowing the parameters to vary randomly over individuals according to a continuous, discrete, or discrete-continuous mixture distribution, which must be chosen a priori by the researcher. In particular, the models supported by gmnl are the multinomial or conditional logit, the mixed multinomial logit, the scale heterogeneity multinomial logit, the generalized multinomial logit, the latent class logit, and the mixed-mixed multinomial logit. These models are estimated using either the maximum likelihood estimator or the maximum simulated likelihood estimator. This article describes and illustrates with real databases all functionalities of gmnl, including the derivation of individual conditional estimates of both the random parameters and willingness-to-pay measures

Circulating soluble CD36 is similar in type 1 and type 2 diabetes mellitus versus non-diabetic subjects

The aim of this study was to determine whether plasma concentrations of sCD36 (soluble CD36) are associated with the presence of type 1 or type 2 diabetes. Plasma levels of sCD36 were analysed in 1023 subjects (225 type 1 diabetes (T1D) patients, 276 type 2 diabetes (T2D) patients, and 522 non-diabetic control subjects) using an enzyme-linked immunosorbent assay (ELISA). Multinomial and logistic regression models were performed to evaluate associations with sCD36 and its association with diabetes types. There were no significant differences in sCD36 (p = 0.144) among study groups, neither in head-to-head comparisons: non-diabetic versus T1D subjects (p = 0.180), non-diabetic versus T2D subjects (p = 0.583), and T1D versus T2D patients (p = 0.151). In the multinomial model, lower sCD36 concentrations were associated with older age (p < 0.001), tobacco exposure (p = 0.006), T2D (p = 0.020), and a higher-platelets count (p = 0.004). However, in logistic regression models of diabetes, sCD36 showed only a weak association with T2D. The current findings show a weak association of circulating sCD36 with type 2 diabetes and no association with T1D

Role of the Scavenger Receptor CD36 in Accelerated Diabetic Atherosclerosis

Diabetes mellitus entails increased atherosclerotic burden and medial arterial calcification, but the precise mechanisms are not fully elucidated. We aimed to investigate the implication of CD36 in inflammation and calcification processes orchestrated by vascular smooth muscle cells (VSMCs) under hyperglycemic and atherogenic conditions. We examined the expression of CD36, pro-inflammatory cytokines, endoplasmic reticulum (ER) stress markers, and mineralization-regulating enzymes by RT-PCR in human VSMCs, cultured in a medium containing normal (5 mM) or high glucose (22 mM) for 72 h with or without oxidized low-density lipoprotein (oxLDL) (24 h). The uptake of 1,1'-dioctadecyl-3,3,3',3-tetramethylindocarbocyanine perchlorate-fluorescently (DiI) labeled oxLDL was quantified by flow cytometry and fluorimetry and calcification assays were performed in VSMC cultured in osteogenic medium and stained by alizarin red. We observed induction in the expression of CD36, cytokines, calcification markers, and ER stress markers under high glucose that was exacerbated by oxLDL. These results were confirmed in carotid plaques from subjects with diabetes versus non-diabetic subjects. Accordingly, the uptake of DiI-labeled oxLDL was increased after exposure to high glucose. The silencing of CD36 reduced the induction of CD36 and the expression of calcification enzymes and mineralization of VSMC. Our results indicate that CD36 signaling is partially involved in hyperglycemia and oxLDL-induced vascular calcification in diabetes

Circulating CD5L is associated with cardiovascular events and all-cause mortality in individuals with chronic kidney disease

This study assessed the association of CD5L and soluble CD36 (sCD36) with the risk of a cardiovascular event (CVE), including CV death and all-cause mortality in CKD. We evaluated the association of CD5L and sCD36 with a predefined composite CV endpoint (unstable angina, myocardial infarction, transient ischemic attack, cerebrovascular accident, congestive heart failure, arrhythmia, peripheral arterial disease [PAD] or amputation by PAD, aortic aneurysm, or death from CV causes) and all-cause mortality using Cox proportional hazards regression, adjusted for CV risk factors. The analysis included 1,516 participants free from pre-existing CV disease followed up for 4 years. The median age was 62 years, 38.8% were female, and 26.8% had diabetes. There were 98 (6.5%) CVEs and 72 (4.8%) deaths, of which 26 (36.1%) were of CV origin. Higher baseline CD5L concentration was associated with increased risk of CVE (HR, 95% CI, 1.17, 1.0-1.36), and all-cause mortality (1.22, 1.01-1.48) after adjusting for age, sex, diabetes, systolic blood pressure, dyslipidemia, waist circumference, smoking, and CKD stage. sCD36 showed no association with adverse CV outcomes or mortality. Our study showed for the first time that higher concentrations of CD5L are associated with future CVE and all-cause mortality in individuals with CKD

Memorias de investigación: Feria de Semilleros y Jornadas de Investigación de uniminuto, Seccional Antioquia - Chocó.

Feria de Semilleros y Jornadas de Investigación de uniminuto, Seccional Antioquia - Chocó.Esta publicación busca divulgar investigaciones y producción académica en diferentes disciplinas, realizadas por estudiantes y docentes de UNIMINUTO Seccional Antioquia – Chocó, así como dar a conocer los semilleros de investigación que participaron en la V Feria de Semilleros, con el fin de visibilizar el trabajo que realiza el Centro de Investigación para el Desarrollo de UNIMINUTO Bello —CIDUB—, con respecto a debates académicos y espacios de interlocución. Igualmente, permite que la comunidad educativa conozca los temas de investigación y las discusiones que se están dando entre los semilleros y grupos de investigación, para así buscar puntos de encuentro y sinergias entre los investigadores. Adicionalmente, el texto se convierte en una invitación para que se vinculen otros investigadores, docentes, estudiantes e incluso otras instituciones a los procesos investigativos coordinados desde el CIDUB

Memorias de investigación: Feria de Semilleros y Jornadas de Investigación de uniminuto, Seccional Antioquia - Chocó.

Feria de Semilleros y Jornadas de Investigación de uniminuto, Seccional Antioquia - Chocó.Esta publicación busca divulgar investigaciones y producción académica en diferentes disciplinas, realizadas por estudiantes y docentes de UNIMINUTO Seccional Antioquia – Chocó, así como dar a conocer los semilleros de investigación que participaron en la V Feria de Semilleros, con el fin de visibilizar el trabajo que realiza el Centro de Investigación para el Desarrollo de UNIMINUTO Bello —CIDUB—, con respecto a debates académicos y espacios de interlocución. Igualmente, permite que la comunidad educativa conozca los temas de investigación y las discusiones que se están dando entre los semilleros y grupos de investigación, para así buscar puntos de encuentro y sinergias entre los investigadores. Adicionalmente, el texto se convierte en una invitación para que se vinculen otros investigadores, docentes, estudiantes e incluso otras instituciones a los procesos investigativos coordinados desde el CIDUB

Three Essays On Continuous And Discrete Spatial Heterogeneity

Continuous and discrete unobserved heterogeneity have been widely used in modeling discrete choice models. In this dissertation I investigate how these modeling strategies can be used to capture and model spatial heterogeneity or locally varying coefficients for different latent structures. In the first chapter, I outline the main advantages and disadvantages of both continuous and discrete spatial modeling strategies. Then I conduct a simulation experiment in order to understand the ability of both approaches to retrieve the true representation of the spatially varying process under small sample size situations. The results show that the data requirement to achieve lower bias in the continuous case is substantial compared with the discrete case. I have also found that, as the number of individuals per spatial unit increases, both models are able to identify the regional-specific estimates. However, the discrete case is able to retrieve the true spatial heterogeneity surface with lower bias and better coverage. In the second chapter, I show the Rchoice package for R that allows estimating models with individual heterogeneity for both cross-sectional and panel data. In particular, the package allows binary, ordinal and count response, as well as continuous and discrete covariates. This chapter is a general description of Rchoice and all functionalities are illustrated using real databases. The last chapter shows how continuous and discrete spatial heterogeneity models can be applied in order to analyze whether monetary subjective well-being eval- uations vary across space using a cross-sectional dataset from Chile. The results show that focusing just on the average estimates of compensating variations veils useful local variation. Moreover, the discrete approach shows some weak superiority over the continuous case in terms of model fit and interpretation