Generalized linear modelling in periglacial studies: terrain parameters and patterned ground

2004 ◽  
Vol 15 (4) ◽  
pp. 327-338 ◽  
Author(s):  
Miska Luoto ◽  
Jan Hjort
Keyword(s):  
Patterned Ground ◽  
Generalized Linear Modelling ◽  
Linear Modelling ◽  
Terrain Parameters

Wellbeing and Running Events in Qatar: The Ooredoo Doha Marathon

2021 ◽  
Author(s):  
Kevin Filo ◽  
Millicent Kennelly ◽  
Richard J. Buning ◽  
Rana Sobh
Keyword(s):  
Goal Setting ◽  
Qualitative Data ◽  
Online Questionnaire ◽  
Sport Event ◽  
Sense Of Direction ◽  
Generalized Linear Modelling ◽  
Linear Modelling ◽  
Personal Connections ◽  
Males And Females

Research into participatory sport event experiences and wellbeing outcomes has increased, however, much of this work explores Western contexts. The purpose of the current research is to examine the wellbeing outcomes related to sport event participation in Qatar, as well as how these outcomes differ across nationalities and by gender. An online questionnaire was administered to participants of the 2017 Ooredoo Doha Marathon (ODM) (N = 239). Inductive coding of responses to the open-ended items uncovered activation of relationships, meaning and accomplishment. The qualitative data revealed that event participation allowed participants to strengthen personal connections with others, fostered a sense of direction and allowed for goal setting among participants, and enabled participants to experience achievement. Meanwhile, generalized linear modelling revealed Qatari nationals and expats as well as males and females differentially reported wellbeing based on PERMA domains.

More generalized linear modelling.

2021 ◽  
pp. 171-186
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Generalized Linear Models ◽  
Count Data ◽  
Binary Data ◽  
Linear Models ◽  
Explanatory Variable ◽  
Response Variable ◽  
Explanatory Variables ◽  
Continuous Response ◽  
Generalized Linear Modelling ◽  
Linear Modelling

Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.

Generalized linear modelling in geomorphology

1998 ◽  
Vol 23 (13) ◽  
pp. 1185-1195 ◽  
Author(s):  
Peter Atkinson ◽  
Hester Jiskoot ◽  
Remo Massari ◽  
Tavi Murray
Keyword(s):  
Generalized Linear Modelling ◽  
Linear Modelling

Modelling daily rainfall with climatological predictors: Poisson-gamma generalized linear modelling approach

2016 ◽  
Vol 37 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
Rossita M. Yunus ◽  
Masud M. Hasan ◽  
Nuradhiathy A. Razak ◽  
Yong Z. Zubairi ◽  
Peter K. Dunn
Keyword(s):  
Daily Rainfall ◽  
Generalized Linear Modelling ◽  
Linear Modelling ◽  
Modelling Approach

scholarly journals satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications

2021 ◽  
Author(s):  
Jeroen Gilis ◽  
Kristoffer Vitting-Seerup ◽  
Koen Van den Berge ◽  
Lieven Clement
Keyword(s):  
Rate Control ◽  
Single Gene ◽  
Experimental Designs ◽  
Rna Seq ◽  
Modelling Framework ◽  
Gene Dysregulation ◽  
False Discovery ◽  
Generalized Linear Modelling ◽  
Linear Modelling ◽  
Scalable Analysis

AbstractAlternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications.

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