Linear Mixed Model Analysis in RCBD ( ~Location, year, and genotype) - Part I (Linear model) in скачать в хорошем качестве

Linear Mixed Model Analysis in RCBD ( ~Location, year, and genotype) - Part I (Linear model) in

Ecological and biological data are often complex and messy. We can have different grouping factors like populations, species, sites where we collect the data, etc. Sample sizes might leave something to be desired too, especially if we are trying to fit complicated models with many parameters. On top of that, our data points might not be truly independent. For instance, we might be using quadrats within our sites to collect the data (and so there is structure to our data: quadrats are nested within the sites). This is why mixed models were developed, to deal with such messy data and to allow us to use all our data, even when we have low sample sizes, structured data, and many covariates to fit. Oh, and on top of all that, mixed models allow us to save degrees of freedom compared to running standard linear models! Sounds good, doesn’t it? We will cover only linear mixed models here, but if you are trying to “extend” your linear model, fear not: there are generalized linear mixed-effects models out there, too.