This is sort of homework so I'm not really looking for an answer, just pointers.
I have a dataset with 2,871 sample points from an aerial photograph in a GIS. Each random point was scored as being in forest (tree = 1) or not (tree = 0). Using a digital elevation model (DEM) that gave the height to the nearest meter (Elevation.m) one other variable was derived. The other variable was a factor variable describing if the sample point was more or less east facing (E.vs.W. = east) or west facing (E.vs.W. = west).
E.vs.W,Elevation.m,tree west,7.896944379,1 west,6.897992018,1 west,-7.314651138,1 west,-10.88583519,1 west,128.6587367,1 west,102.8423517,1 west,205.0537347,1 west,169.6836871,1 west,201.2179048,1 west,210.6947441,1 ...
I'm to "explore the variables and their interactions to predict the presence of a tree. Find the best model while showing how it compares to the other models using ΔAIC."
This is what I have:
dat <- read.csv("Data/treeData.csv") head(dat) str(dat) mod1 <- lm(Elevation.m~tree, data=dat) mod2 <- lm(Elevation.m~E.vs.W, data=dat) AIC(mod1,mod2) plot(Elevation.m~E.vs.W, data=dat)
I guess I'm not sure how to pick the "best model". Am I approaching this correctly?