# 1. One-dimensional sampling: transect with 100 equispaced points.
# Generate transect points
tr100 <- 1 : 100
# Creation of the dbMEM eigenfunctions with Moran's I corresponding
# to positive spatial correlation: argument
# MEM.autocor = "positive" which is the default
tr100.dbmem.tmp <- dbmem(tr100, silent = FALSE)
tr100.dbmem <- as.data.frame(tr100.dbmem.tmp)
# Display the eigenvalues
attributes(tr100.dbmem.tmp)$values
# Number of (positive) eigenvalues
length(attributes(tr100.dbmem.tmp)$values)
d = 5
MEM1
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM2
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM5
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM10
[-2;-1] ]-1;0] ]0;1] ]1;2]
d = 5
MEM20
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM40
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM80
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM120
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
d = 5
MEM189
[-3;-2] ]-2;-1] ]-1;0] ]0;1] ]1;2] ]2;3]
Fig. 7.4 Some of the 189 dbMEM variables with positive eigenvalues built from a grid comprising
20 by 20 equispaced points
318
7 Spatial Analysis of Ecological Data