scholarly journals Hierarchical generalized additive models in ecology: an introduction with mgcv

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6876 ◽  
Author(s):  
Eric J. Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Complex Structures ◽  
Ecological Data ◽  
Mixed Effect ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Statistical Issues

In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling 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 HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, themgcvpackage in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at:github.com/eric-pedersen/mixed-effect-gams.

scholarly journals Hierarchical generalized additive models: an introduction with mgcv

2018 ◽  
Author(s):  
Eric J Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Hierarchical Model ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
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.

scholarly journals Hierarchical generalized additive models: an introduction with mgcv

2018 ◽  
Author(s):  
Eric J Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Hierarchical Model ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
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.

scholarly journals Implementation of a Generalized Additive Model (GAM) for Soybean Maturity Prediction in African Environments

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1043
Author(s):  
Guillermo S. Marcillo ◽  
Nicolas F. Martin ◽  
Brian W. Diers ◽  
Michelle Da Fonseca Santos ◽  
Erica Pontes Leles ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Unbalanced Data ◽  
Genotypic Differences ◽  
Breeding Programs ◽  
Important Trait ◽  
New Crop

Time to maturity (TTM) is an important trait in soybean breeding programs. However, soybeans are a relatively new crop in Africa. As such, TTM information for soybeans is not yet as well defined as in other major producing areas. Multi-environment trials (METs) allow breeders to analyze crop performance across diverse conditions, but also pose statistical challenges (e.g., unbalanced data). Modern statistical methods, e.g., generalized additive models (GAMs), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from an MET breeding program in Africa to identify the best geographical and seasonal variables to explain site and genotypic differences in soybean TTM. Using soybean cycle features (e.g., minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM predicted soybean TTM within 10 days of the average observed TTM (RMSE = 10.3; x = 109 days post-planting). Furthermore, we found significant differences between cultivars (p < 0.05) in TTM sensitivity to minimum temperature and daylength. Our results show potential to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.

Generalized Additive Model of Air Pollution to Daily Mortality

2005 ◽  
Vol 277-279 ◽  
pp. 487-491
Author(s):  
Jae Hee Kim ◽  
Hee Eun Yang
Keyword(s):  
Cardiovascular Disease ◽  
Air Pollution ◽  
Wind Speed ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Daily Mortality ◽  
Pollution Levels ◽  
Poisson Models

The association of air pollution with daily mortality due to cardiovascular disease, respiratory disease, and old age (65 or older) in Seoul, Korea was investigated in 1999 using daily values of TSP, PM10, O3, SO2, NO2, and CO. Generalized additive Poisson models were applied to allow for the highly flexible fitting of daily trends in air pollution as well as nonlinear association with meteorological variables such as temperature, humidity, and wind speed. To estimate the effect of air pollution and weather on mortality, LOESS smoothing was used in generalized additive models. The findings suggest that air pollution levels affect significantly the daily mortality.

Crash Frequency Analysis with Generalized Additive Models

2008 ◽  
Vol 2061 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Yuanchang Xie ◽  
Yunlong Zhang
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Model ◽  
Negative Binomial ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Crash Frequency ◽  
Data Set ◽  
Explanatory Variables

Recent crash frequency studies have been based primarily on generalized linear models, in which a linear relationship is usually assumed between the logarithm of expected crash frequency and other explanatory variables. For some explanatory variables, such a linear assumption may be invalid. It is therefore worthwhile to investigate other forms of relationships. This paper introduces generalized additive models to model crash frequency. Generalized additive models use smooth functions of each explanatory variable and are very flexible in modeling nonlinear relationships. On the basis of an intersection crash frequency data set collected in Toronto, Canada, a negative binomial generalized additive model is compared with two negative binomial generalized linear models. The comparison results show that the negative binomial generalized additive model performs best for both the Akaike information criterion and the fitting and predicting performance.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

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

2007 ◽  
Vol 136 (3) ◽  
pp. 341-351 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
Z. SHKEDY ◽  
P. KUNG'U KIMANI ◽  
M. KOJOUHOROVA ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Penalized Regression ◽  
Incidence Rates ◽  
Additive Models ◽  
Time Dependent ◽  
Model Framework ◽  
Penalized Regression Splines ◽  
The Impact

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.

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

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Zouhour Hammouda ◽  
Leila Hedhili Zaier ◽  
Nadege Blond
Keyword(s):  
Particulate Matter ◽  
Tropospheric Ozone ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Meteorological Condition ◽  
Additive Models ◽  
Linear Functions ◽  
Meteorological Variables ◽  
Time Variables ◽  
The Impact

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.

scholarly journals GenoGAM: Genome-wide generalized additive models for ChIP-seq analysis

2016 ◽  
Author(s):  
Georg Stricker ◽  
Alexander Engelhardt ◽  
Daniel Schulz ◽  
Matthias Schmid ◽  
Achim Tresch ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Type I Error ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Supplementary Information ◽  
Type I ◽  
Smooth Functions ◽  
Base Level ◽  
Genome Wide

AbstractMotivationChromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective.ResultsHere, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad-hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays.AvailabilitySoftware is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/[email protected] informationSupplementary information is available at Bioinformatics online.

scholarly journals Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

2014 ◽  
Vol 48 (3) ◽  
pp. 451-458 ◽  
Author(s):  
Juliana Bottoni de Souza ◽  
Valdério Anselmo Reisen ◽  
Jane Méri Santos ◽  
Glaura Conceição Franco
Keyword(s):  
Relative Risk ◽  
Air Pollutants ◽  
Generalized Additive Model ◽  
Hospital Admissions ◽  
Additive Model ◽  
Principal Component ◽  
Component Analysis ◽  
Additive Models ◽  
Southeastern Brazil ◽  
Linear Modeling

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

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