Obj: to isolate spatially explicit determinants affecting pathogen prevalence

Normally, LR (logistic regression) would be used in such cases to predict pathogen prevalence affected by covariates. Here they compare LR with classification trees (CT)

They used weather data for a few days prior to sample collection, using time windows of different sizes and using them for analysis like correlation, LR and CT.

Freeze-thaw cycles were calculated as described in Williams, C. et al, 2006. Days with min below 0 amd max above zero, or consecutuve days with such conditions were count as one cycle. Such cycles were also constructed for different periods, like from 1 day to a period of 5 days when the cycle could have occured.

PCA was done for weather variables. Since the units/measurements are different for the variables, data were standardised by subtracting the mean and dividing by the sd– is this the equivalent of performing the eigenanalysis of the correlation matrix? as they claim?