Browsing by Subject "Data analysis"

This thesis analyses the colonization success of lowland herbs in open tundra using Bayesian inference methods. This was done with four different models that analyse the the effects of different treatments, grazing levels and environmental covariates on the probability of a seed growing into a seedling. The thesis starts traditionally with an introduction chapter. The second chapter goes through the data; where and how it was collected, different treatments used and other relevant information. The third chapter goes through all the methods that you need to know to understand the analysis of this thesis, which are the basics of Bayesian inference, generalized linear models, generalized linear mixed models, model comparison and model assessment. The actual analysis starts in the fourth chapter that introduces the four models used in this thesis. All of the models are binomial generalized linear mixed models that have different variables. The first model only has the different treatments and grazing levels as variables. The second model also includes interactions between these treatment and grazing variables. The third and fourth models are otherwise the same as the first and the second but they also have some environmental covariates as additional variables. Every model also has the block number, where the seeds were sown as a random effect. The fifth chapter goes through the results of the models. First it shows the comparison of the predictive accuracy of all models. Then the gotten fixed effects, random effects and draws from posterior predictive distribution are presented for each model separately. Then the thesis ends with the sixth conclusions chapter