scholarly journals An introductory guide to statistical analysis-generalized linear models for count data using R.

2010 ◽  
Vol 55 (4) ◽  
pp. 287-294 ◽  
Author(s):  
Yoshiko Shimono
Keyword(s):  
Statistical Analysis ◽  
Generalized Linear Models ◽  
Count Data ◽  
Linear Models

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.

Changes in the distribution of Poplar Box (Eucalyptus populnea) on major soil groups: An application of the log-linear model.

1981 ◽  
Vol 3 (1) ◽  
pp. 33 ◽  
Author(s):  
RB Cunningham ◽  
AA Webb ◽  
A Mortlock
Keyword(s):  
Statistical Analysis ◽  
Linear Model ◽  
Generalized Linear Models ◽  
Gradient Analysis ◽  
Linear Models ◽  
Geographic Location ◽  
Significance Tests ◽  
Log Linear ◽  
Eucalyptus Populnea ◽  
Soil Groups

The association of poplar box (Eucalyptus populnea) with five main soil groups is examined. A statistical analysis, using a log- linear model, indicated that the relative frequencies of poplar box sites occumng on major soil groups changed with geographic location. The change in distribution is shown to relate to climate, as indicated by summer and winter moisture indices and the diff- erence between them. This study illustrates the use of log-linear models in ecology; such models, and more generally, Generalized Linear Models, in providing significance tests, have advantages over the non-statistical methods of gradient analysis.

scholarly journals A Statistical Analysis of the Lake Levels at Lake Neusiedl

2016 ◽  
Vol 37 (2) ◽  
Author(s):  
Johannes Ledolter
Keyword(s):  
Statistical Analysis ◽  
Generalized Linear Models ◽  
Lake Level ◽  
Linear Models ◽  
Daily Precipitation ◽  
Lake Levels ◽  
Lake Level Changes ◽  
Daily Data ◽  
Eastern Border ◽  
Logistic Regressions

A long record of daily data is used to study the lake levels of Lake Neusiedl, a large steppe lake at the eastern border of Austria. Daily lake level changes are modeled as functions of precipitation, temperature, and wind conditions. The occurrence and the amount of daily precipitation are modeled with logistic regressions and generalized linear models.

Generalized Linear Models for Count Data

2015 ◽  
pp. 429-504
Author(s):  
Michael Friendly ◽  
David Meyer ◽  
Achim Zeileis
Keyword(s):  
Generalized Linear Models ◽  
Count Data ◽  
Linear Models

scholarly journals glmGamPoi: Fitting Gamma-Poisson Generalized Linear Models on Single Cell Count Data

2020 ◽  
Author(s):  
Constantin Ahlmann-Eltze ◽  
Wolfgang Huber
Keyword(s):  
Single Cell ◽  
Poisson Distribution ◽  
Generalized Linear Models ◽  
Count Data ◽  
Linear Models ◽  
Differential Expression Analysis ◽  
Source Code ◽  
Principal Component ◽  
R Package ◽  
Single Cell Rna Sequencing

Abstract Motivation The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts (Grün et al., 2014; Svensson, 2020; Silverman et al., 2018; Hafemeister and Satija, 2019) and an essential building block for analysis approaches including differential expression analysis (Robinson et al., 2010; McCarthy et al., 2012; Anders and Huber, 2010; Love et al., 2014), principal component analysis (Townes et al., 2019) and factor analysis (Risso et al., 2018). Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which can comprise millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation. Results We present a new R package for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously. Availability The package glmGamPoi is available from Bioconductor for Windows, macOS, and Linux, and source code is available on github.com/const-ae/glmGamPoi under a GPL-3 license.

Sign in / Sign up

Export Citation Format

Share Document