Dijkstra, F.H. (2017) Generalized linear models for network analysis. Master's Thesis / Essay, Mathematics.
|
Text
Harmen_Dijkstra_2017_WM.pdf - Published Version Download (1MB) | Preview |
|
![[img]](https://fse.studenttheses.ub.rug.nl/style/images/fileicons/text.png) |
Text
Toestemming.pdf - Other Restricted to Backend only Download (77kB) |
Abstract
Network modelling appear in a variety of disciplines, we can find them among other disciplines in computer science, sociology, economics and biology. Exponential random graph models are widely used to model these networks. We will focus on generalized linear models as a new approach to analyse the data. The derived approach by generalized linear models is implemented in R and is used to analyse a gene regulatory network. The results with generalized linear models is compared with an already existing model: Network Enrichment Analysis Test. The data analysis with generalized linear models gives results that are partly in line with the results achieved by Network Enrichment Analysis Test.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Mathematics |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 08:27 |
| Last Modified: | 15 Feb 2018 08:27 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/15099 |
Actions (login required)
 |
View Item |