Analysis

Non-negative matrix factorization, as implemented in the LEA package, is a case of unsupervised machine learning; LEA uses least-squares estimates of ancestry coefficients.

Here, I present the prequel to the NALgen method: using multivariate analyses to infer population structure in the simulated data as well as to find covariance between genetic and environmental data. In upcoming posts about modeling neutral and adaptive genetic variation across continuous landscapes, information about population structure will be used in creating the response variable, while transformed environmental data (based on genetic-environmental covariance) will be used as predictors.