Melb2018

Lab 3: Intensity dependent on covariate

This session covers tools for investigating intensity depending on a covariate. The lecturer’s R script is available here (right click and save).

library(spatstat)

Exercise 1

The bei dataset gives the locations of trees in a survey area with additional covariate information in a list bei.extra.

  1. Assign the elevation covariate to a variable elev by typing

    elev <- bei.extra$elev
    

    OK, lets do that:

    elev <- bei.extra$elev
    
  2. Plot the trees on top of an image of the elevation covariate.

    plot(elev, main = "")
    plot(bei, add = TRUE, cex = 0.3, pch = 16, cols = "white")
    

  3. Cut the study region into 4 areas according to the value of the terrain elevation, and make a texture plot of the result.

    Z <- cut(elev, 4, labels=c("Low", "Med-Low", "Med-High", "High"))
    textureplot(Z, main = "")
    

  4. Convert the image from above to a tesselation, count the number of points in each region using quadratcount, and plot the quadrat counts.

    Y <- tess(image = Z)
    qc <- quadratcount(bei, tess = Y)
    
  5. Estimate the intensity in each of the four regions.

    intensity(qc)
    
    ## tile
    ##         Low     Med-Low    Med-High        High 
    ## 0.002259007 0.006372523 0.008562862 0.005843516
    

Exercise 2

Assume that the intensity of trees is a function (\lambda(u) = \rho(e(u))) where (e(u)) is the terrain elevation at location u.

  1. Compute a nonparametric estimate of the function (\rho) and plot it by

    rh <- rhohat(bei, elev)
    plot(rh)
    

    Repeating the R code:

    rh <- rhohat(bei, elev)
    plot(rh)
    

  2. Compute the predicted intensity based on this estimate of (\rho).

    prh <- predict(rh)
    plot(prh, main = "")
    plot(bei, add = TRUE, cols = "white", cex = .2, pch = 16)
    

  3. Compute a non-parametric estimate by kernel smoothing and compare with the predicted intensity above.

    The kernel density estimate of the points is computed and plotted with the following code:

    dbei <- density(bei, sigma = bw.scott)
    plot(dbei, main = "")
    plot(bei, add = TRUE, cols = "white", cex = .2, pch = 16)
    

    Which seems to be quite different form the predicted intentisty.

  4. Bonus info: To plot the two intensity estimates next to each other you collect the estimates as a spatial object list (solist) and plot the result (the estimates are called pred and ker below):

    l <- solist(pred, ker)
    plot(l, equal.ribbon = TRUE, main = "", 
         main.panel = c("rhohat prediction", "kernel smoothing"))
    
    l <- solist(prh, dbei)
    plot(l, equal.ribbon = TRUE, main = "",
         main.panel = c("rhohat prediction", "kernel smoothing"))
    

Exercise 3

  1. Continuing with the dataset bei conduct both Berman’s Z1 and Z2 tests for dependence on elev, and plot the results.

    The tests are done straightforwardly with berman.test:

    Z1 <- berman.test(bei, elev)
    print(Z1)
    
    ## 
    ##  Berman Z1 test of CSR in two dimensions
    ## 
    ## data:  covariate 'elev' evaluated at points of 'bei'
    ## Z1 = -0.72924, p-value = 0.4659
    ## alternative hypothesis: two-sided
    
    plot(Z1)
    

    Z2 <- berman.test(bei, elev, which = "Z2")
    print(Z2)
    
    ## 
    ##  Berman Z2 test of CSR in two dimensions
    ## 
    ## data:  covariate 'elev' evaluated at points of 'bei' 
    ##   and transformed to uniform distribution under CSR
    ## Z2 = 2.4514, p-value = 0.01423
    ## alternative hypothesis: two-sided
    
    plot(Z2)