In the context of Redundancy Analysis (RDA), when examining the relationship between a single response variable, Y, such as Net Primary Productivity (NPP), and two sets of explanatory variables, X and Z, represented as matrices of climate data (e.g., Mean Annual Temperature, Mean Annual Precipitation, Seasonal Variation) and soil data (e.g., Soil Nitrogen, Soil Phosphorus, Soil Carbon), respectively:
Is RDA an appropriate method for analysis if Y is a single-column variable that does not pertain to community data or species abundance?
Alternatively, should multiple regression be considered?
Given the division of explanatory variables into two distinct matrices (climate data and soil data), how would one implement this analysis in R? Specifically, is the following R syntax correct for conducting an RDA and its subsequent VPA?
model_test <- rda(NPP ~ Climate dat + Soil dat, data = full_data)
anova(model_test)
vp <- varpart(NPP, Climate dat,Soil dat)
In the context of Redundancy Analysis (RDA), when examining the relationship between a single response variable, Y, such as Net Primary Productivity (NPP), and two sets of explanatory variables, X and Z, represented as matrices of climate data (e.g., Mean Annual Temperature, Mean Annual Precipitation, Seasonal Variation) and soil data (e.g., Soil Nitrogen, Soil Phosphorus, Soil Carbon), respectively:
Is RDA an appropriate method for analysis if Y is a single-column variable that does not pertain to community data or species abundance? Alternatively, should multiple regression be considered? Given the division of explanatory variables into two distinct matrices (climate data and soil data), how would one implement this analysis in R? Specifically, is the following R syntax correct for conducting an RDA and its subsequent VPA?
model_test <- rda(NPP ~ Climate dat + Soil dat, data = full_data) anova(model_test) vp <- varpart(NPP, Climate dat,Soil dat)