R for Macroecology Spatial models. Next week Any topics that we haven’t talked about? Group...
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Transcript of R for Macroecology Spatial models. Next week Any topics that we haven’t talked about? Group...
R for Macroecology
Spatial models
Next week Any topics that we haven’t talked about?
Group projects
SAR Models Augment standard OLS with an additional
term to model the spatial autocorrelation We’ll focus on error SAR models, which
focuses on spatial pattern in the error part of the model
OLS Y = βX + εSARlag Y = ρWY + βX + εSARerror Y = βX + λWu+ ε
Defining the spatial weights matrix, W, is crucial
Neighborhoods in R spdep
dnearneigh() knearneigh()
dnearneigh(x, d1, d2, row.names = NULL, longlat = NULL)
Coordinates (matrix or SpatialPoints)
Minimum and maximum distances(in km if longlat = T)
Returns a list of vectors giving the neighbors for each point
Neighborhoods in R spdep
dnearneigh() knearneigh()> x = c(1,3,2,5)> y = c(3,2,4,4)> n = dnearneigh(cbind(x,y),d1 = 0,d2 = 3)> nNeighbour list object:Number of regions: 4 Number of nonzero links: 10 Percentage nonzero weights: 62.5 Average number of links: 2.5 > str(n)List of 4 $ : int [1:2] 2 3 $ : int [1:3] 1 3 4 $ : int [1:3] 1 2 4 $ : int [1:2] 2 3 - attr(*, "class")= chr "nb" - attr(*, "nbtype")= chr "distance”...
Converting a neighborhood to weights
nb2listw(neighbours, style="W", zero.policy=NULL)
neighbors list what to do with neighborless points
W = row standardized (rows sum to 1)B = binary (0/1)C = global standardized (all links sum to n)U = C/nS = variance stabilization (Tiefelsdorf et al. 1999)
Converting a neighborhood to weights> nb2listw(n,style = "W")$weights[[1]][1] 0.5 0.5
[[2]][1] 0.3333333 0.3333333 0.3333333
[[3]][1] 0.3333333 0.3333333 0.3333333
[[4]][1] 0.5 0.5> nb2listw(n,style = "B")$weights[[1]][1] 1 1
[[2]][1] 1 1 1
[[3]][1] 1 1 1
[[4]][1] 1 1
> nb2listw(n,style = "C")$weights[[1]][1] 0.4 0.4
[[2]][1] 0.4 0.4 0.4
[[3]][1] 0.4 0.4 0.4
[[4]][1] 0.4 0.4> > nb2listw(n,style = "S")$weights[[1]][1] 0.4494897 0.4494897
[[2]][1] 0.3670068 0.3670068 0.3670068
[[3]][1] 0.3670068 0.3670068 0.3670068
[[4]][1] 0.4494897 0.4494897
Converting a neighborhood to weights> nb2listw(n,style = "W")$weights[[1]][1] 0.5 0.5
[[2]][1] 0.3333333 0.3333333 0.3333333
[[3]][1] 0.3333333 0.3333333 0.3333333
[[4]][1] 0.5 0.5> nb2listw(n,style = "B")$weights[[1]][1] 1 1
[[2]][1] 1 1 1
[[3]][1] 1 1 1
[[4]][1] 1 1
> nb2listw(n,style = "C")$weights[[1]][1] 0.4 0.4
[[2]][1] 0.4 0.4 0.4
[[3]][1] 0.4 0.4 0.4
[[4]][1] 0.4 0.4 > nb2listw(n,style = "S")$weights[[1]][1] 0.4494897 0.4494897
[[2]][1] 0.3670068 0.3670068 0.3670068
[[3]][1] 0.3670068 0.3670068 0.3670068
[[4]][1] 0.4494897 0.4494897
Emphasizes weakly
connected points
Emphasizes strongly
connected points
Emphasizes strongly
connected points
Tries to balance
Lots of options – how to choose? Define the neighborhood Define the spatial weights matrix
Try things out! Look for stability in model estimates Look for residual autocorrelation
Defining the neighborhood - d#1. Small distancen = dnearneigh(cbind(x,y),d1 = 0, d2 = 0.1)w1 = nb2listw(n,zero.policy = T)
#2. Medium distancen = dnearneigh(cbind(x,y),d1 = 0, d2 = 0.3)w2 = nb2listw(n,zero.policy = T)
#2. Large distancen = dnearneigh(cbind(x,y),d1 = 0, d2 = 0.5)w3 = nb2listw(n,zero.policy = T)
par(mfrow = c(1,4))plot(x,y,axes = F,xlab = "",ylab = "")plot(w1,cbind(x,y))plot(w2,cbind(x,y))plot(w3,cbind(x,y))
Defining the neighborhood - K#4. 2 neighborsn = knn2nb(knearneigh(cbind(x,y),k=2,RANN = F))w4 = nb2listw(n,zero.policy = T)
#5. 4 neighborsn = knn2nb(knearneigh(cbind(x,y),k=4,RANN = F))w5 = nb2listw(n,zero.policy = T)
#6. 8 neighborsn = knn2nb(knearneigh(cbind(x,y),k=8,RANN = F))w6 = nb2listw(n,zero.policy = T)
par(mfrow = c(1,4))plot(x,y,axes = F,xlab = "",ylab = "")plot(w4,cbind(x,y))plot(w5,cbind(x,y))plot(w6,cbind(x,y))
Neighborhoods on gridsx = rep(1:20,20)y = rep(1:20,each = 20)plot(x,y)
n = dnearneigh(cbind(x,y),d1=0,d2 = 1)w = nb2listw(n)plot(w,cbind(x,y))
n = dnearneigh(cbind(x,y),d1=0,d2 = sqrt(2))w = nb2listw(n)plot(w,cbind(x,y))
Rook’s case Queen’s case
Data size SAR models can take a very long time to fit 2000 points is the maximum I have used sample() is useful again
Fitting the SAR model errorsarlm()
errorsarlm(formula, listw, zero.policy=NULL)
just like lm() what to do with neighborless points
The neighborhood weights
Try it out Build several SAR models with different W Which one works best?
Spatial eigenvector maps Generate new predictors that represent the
spatial structure of the data Three steps
Calculate a pairwise distance matrix Do a principal components analysis on this matrix Select some of these PCA axes to add to an OLS
model
Spatial eigenvector maps
Diniz-Filho and Bini 2005
Filter 1 Filter 2
Filter 3 Filter 4
Filter 10 Filter 20
Filter 30 Filter 40