Multiple and complex regression
Extensions of simple linear regression Multiple regression models: predictor variables are continuous Analysis of variance: predictor variables are categorical (grouping variables), But… general linear models can include both continuous and categorical predictors
Relative abundance of C 3 and C 4 plants Paruelo & Lauenroth (1996) Geographic distribution and the effects of climate variables on the relative abundance of a number of plant functional types (PFTs): shrubs, forbs, succulents, C 3 grasses and C 4 grasses.
data Relative abundance of PTFs (based on cover, biomass, and primary production) for each site Longitude Latitude Mean annual temperature Mean annual precipitation Winter (%) precipitation Summer (%) precipitation Biomes (grassland, shrubland) 73 sites across temperate central North America Response variablePredictor variables
Box 6.1 Relative abundance transformed ln(dat+1) because positively skewed
Comparing l 10 vs ln
Collinearity Causes computational problems because it makes the determinant of the matrix of X-variables close to zero and matrix inversion basically involves dividing by the determinant (very sensitive to small differences in the numbers) Standard errors of the estimated regression slopes are inflated
Detecting collinearlity Check tolerance values Plot the variables Examine a matrix of correlation coefficients between predictor variables
Dealing with collinearity Omit predictor variables if they are highly correlated with other predictor variables that remain in the model
(lnC 3 )= β o + β 1 (lat)+ β 2 (long)+ β 3 (latxlong) After centering both lat and long
R 2 =0.514
Analysis of variance Source of variation SSdfMS RegressionΣ(y hat -Y) 2 p p ResidualΣ(y obs -y hat ) 2 n-p-1Σ(y obs -y hat ) 2 n-p-1 TotalΣ(y obs -Y) 2 n-1
Matrix algebra approach to OLS estimation of multiple regression models Y=βX+ε XXb=XY b=(XX) -1 (XY)
The forward selection is
The backward selection is
Criteria for best fitting in multiple regression with p predictors. CriterionFormula r2r2 Adjusted r 2 Akaike Information Criteria AIC
Hierarchical partitioning and model selection No predModelr2r2 Adjr 2 AIC (R)AIC 1 Lon Lat Lon + Lat Long +Lat + Lon x Lat