A Reanalysis of the Handedness-Mathematics Hypothesis Using Generalized Additive Models

Additive Models ◽

School Level ◽

Linear Modeling ◽

Total N ◽

Math Ability ◽

Mathematical Performance ◽

Statistical Issues ◽

Sala et al. (Front Psychol. 2017;8) investigated the impact of handedness on children’s and adolescents’ performance in mathematics in five independent studies (Total N = 2,314). We concluded that (a) handedness played a nonnegligible role in affecting math ability and (b) its effects were mediated by the difficulty of the mathematical task and the participant’s gender and school level (e.g., primary school vs. high school).However, two statistical issues may have biased these findings. First, polynomial regression is a suboptimal (if not incorrect) option for modeling nonlinear relations between variables. Second, simple linear modeling assumes that the response variable is normally distributed in order to produce accurate estimates. However, mathematical performance did not follow a Gaussian distribution in any of the five studies.The present reanalysis overcomes the above methodological limitations by employing Generalized Additive Models (GAMs). While the significant effect of handedness is often confirmed, the GAMs produce somewhat different results compared to the previous analysis. The implications are discussed.

Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽

10.3390/math9040299 ◽

2021 ◽

Vol 9 (4) ◽

pp. 299

Author(s):

Jaime Pinilla ◽

Miguel Negrín

Keyword(s):

Linear Regression ◽

Regression Models ◽

Linear Trend ◽

Additive Models ◽

Linear Regression Models ◽

Non Linear ◽

Nonlinear Trends ◽

The Impact ◽

Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models

Psychological Medicine ◽

10.1017/s0033291721001148 ◽

2021 ◽

pp. 1-10

Author(s):

Hanna M. van Loo ◽

Lian Beijers ◽

Martijn Wieling ◽

Trynke R. de Jong ◽

Robert A. Schoevers ◽

...

Keyword(s):

Internalizing Symptoms ◽

Nonlinear Models ◽

Population Sample ◽

Internalizing Disorders ◽

Relative Difference ◽

Additive Models ◽

Point Prevalence ◽

Cross Sectional ◽

Abstract Background Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. Methods We used cross-sectional data on 146 315 subjects, aged 18–80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders – major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder – were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. Results The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). Conclusions The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

The Evolution of Data Sharing Practices in the Psychological Literature

10.31234/osf.io/3xdja ◽

2021 ◽

Author(s):

Judith Neve ◽

Guillaume A Rousselet

Keyword(s):

Data Sharing ◽

Linear Models ◽

Additive Models ◽

Future Research ◽

Science Data ◽

Editorial Policies ◽

Psychological Science ◽

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

Hierarchical generalized additive models: an introduction with mgcv

10.7287/peerj.preprints.27320v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Eric J Pedersen ◽

David L. Miller ◽

Gavin L. Simpson ◽

Noam Ross

Keyword(s):

Hierarchical Model ◽

Generalized Additive Model ◽

Additive Model ◽

Additive Models ◽

Complex Structures ◽

Ecological Data ◽

Nonlinear Functional ◽

Functional Relationships ◽

Statistical Issues

In this paper, we discuss an extension to two popular approaches to modelling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modelling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between these models, HGLMs and GAMs, explain how to model different assumptions about the degree of inter-group variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data.

An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

ICES Journal of Marine Science ◽

10.1093/icesjms/fsr150 ◽

2011 ◽

Vol 68 (10) ◽

pp. 2252-2263 ◽

Cited By ~ 16

Author(s):

Stéphanie Mahévas ◽

Youen Vermard ◽

Trevor Hutton ◽

Ane Iriondo ◽

Angélique Jadaud ◽

...

Keyword(s):

Linear Models ◽

Technical Information ◽

Fishing Effort ◽

Additive Models ◽

Induced Variation ◽

Fishing Fleets ◽

The Impact ◽

Selection Of ◽

Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents

Risks ◽

10.3390/risks8030091 ◽

2020 ◽

Vol 8 (3) ◽

pp. 91

Author(s):

Jean-Philippe Boucher ◽

Roxane Turcotte

Keyword(s):

Poisson Distribution ◽

Fixed Effects ◽

Additive Models ◽

Panel Count Data ◽

Insurance Company ◽

Smooth Functions ◽

Claim Frequency ◽

The Impact ◽

The Relationship

Using telematics data, we study the relationship between claim frequency and distance driven through different models by observing smooth functions. We used Generalized Additive Models (GAM) for a Poisson distribution, and Generalized Additive Models for Location, Scale, and Shape (GAMLSS) that we generalize for panel count data. To correctly observe the relationship between distance driven and claim frequency, we show that a Poisson distribution with fixed effects should be used because it removes residual heterogeneity that was incorrectly captured by previous models based on GAM and GAMLSS theory. We show that an approximately linear relationship between distance driven and claim frequency can be derived. We argue that this approach can be used to compute the premium surcharge for additional kilometers the insured wants to drive, or as the basis to construct Pay-as-you-drive (PAYD) insurance for self-service vehicles. All models are illustrated using data from a major Canadian insurance company.

Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B

Epidemiology and Infection ◽

10.1017/s0950268807008692 ◽

2007 ◽

Vol 136 (3) ◽

pp. 341-351 ◽

Cited By ~ 9

Author(s):

N. HENS ◽

M. AERTS ◽

Z. SHKEDY ◽

P. KUNG'U KIMANI ◽

M. KOJOUHOROVA ◽

...

Keyword(s):

Hepatitis B ◽

Additive Model ◽

Penalized Regression ◽

Incidence Rates ◽

Additive Models ◽

Time Dependent ◽

Model Framework ◽

Penalized Regression Splines ◽

SUMMARYThe objective of this study was to model the age–time-dependent incidence of hepatitis B while estimating the impact of vaccination. While stochastic models/time-series have been used before to model hepatitis B cases in the absence of knowledge on the number of susceptibles, this paper proposed using a method that fits into the generalized additive model framework. Generalized additive models with penalized regression splines are used to exploit the underlying continuity of both age and time in a flexible non-parametric way. Based on a unique case notification dataset, we have shown that the implemented immunization programme in Bulgaria resulted in a significant decrease in incidence for infants in their first year of life with 82% (79–84%). Moreover, we have shown that conditional on an assumed baseline susceptibility percentage, a smooth force-of-infection profile can be obtained from which two local maxima were observed at ages 9 and 24 years.

Percolation models of pathogen spillover

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0331 ◽

2019 ◽

Vol 374 (1782) ◽

pp. 20180331 ◽

Cited By ~ 6

Author(s):

Alex D. Washburne ◽

Daniel E. Crowley ◽

Daniel J. Becker ◽

Kezia R. Manlove ◽

Marissa L. Childs ◽

...

Keyword(s):

Statistical Analysis ◽

Linear Models ◽

Mechanistic Model ◽

Piecewise Linear ◽

Additive Models ◽

Management Intervention ◽

Management Concepts ◽

The Impact ◽

Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

Clean Air Journal ◽

10.17159/caj/2021/31/2.8880 ◽

2021 ◽

Vol 31 (2) ◽

Author(s):

Zouhour Hammouda ◽

Leila Hedhili Zaier ◽

Nadege Blond

Keyword(s):

Particulate Matter ◽

Tropospheric Ozone ◽

Additive Model ◽

Meteorological Condition ◽

Additive Models ◽

Linear Functions ◽

Meteorological Variables ◽

Time Variables ◽

The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

Cod pots in a Baltic fishery: are they efficient and what affects their efficiency?

ICES Journal of Marine Science ◽

10.1093/icesjms/fsu230 ◽

2015 ◽

Vol 72 (5) ◽

pp. 1545-1554 ◽

Cited By ~ 15

Author(s):

Sara J. Königson ◽

Ronny E. Fredriksson ◽

Sven-Gunnar Lunneryd ◽

Patrick Strömberg ◽

Ulf M. Bergström

Keyword(s):

Water Depth ◽

Additive Models ◽

Water Current ◽

Catch Per Unit Effort ◽

Seal Population ◽

Time Of Year ◽

The Baltic ◽

Longline Fisheries ◽

Abstract With the growing grey seal population in the Baltic Sea, the inshore cod fishery has suffered dramatic increases in both catch losses and damage to fishing gear. To mitigate this situation, cod pots were evaluated as an alternative to traditional gillnets and longlines. During a 3-year study, cod pots were used by commercial fishers in two areas off the coast of Sweden. Using the data from this study, we evaluated catches from pots in relation to other gear types and investigated the effects of environmental and fisheries-related variables such as depth and soak time. The comparison of pots with other gear types showed that, during the first half of the year, the pot fishery generated lower daily catches than the gillnet and longline fisheries at comparable fishing efforts. During the second half of the year, catches in the pot fishery exceeded or were equal to those in the traditional fisheries. Using generalized additive models to evaluate the impact of environmental and fisheries-related variables on pot catches, we showed that, in both areas, the catch per unit effort (cpue) of legal-sized cod was affected by the water depth, the time of year (months), and the soak time. In one of the areas, cpue was also affected by the direction of the water current in relation to the orientation of the string of pots. The cpue of undersized cod was affected by topographic variables such as the slope and the complexity of the bottom, in addition to the water depth, month of the year, and soak time. The results from the study indicate that pots can be a useful alternative gear in the Baltic cod fishery, at least during part of the year. By using our information on how catches are affected by environmental and fisheries-related variables, the pot fishery may be further optimized to increase catches.