Problem 1

rpoispp(500) |> plot()

MyDis <- rpoispp(500)
  plot(MyDis)

Problem 2

It adds the heat map and contour lines onto the existing plot map, ensuring that they are all visualized together.

Problem 3

Those numbers are the dimensions of the grid.

Problem 4

Number of Points Number of Cells P-Values P-Value Interpretation
Instance 1 500 400 0.5033 Likely CSR
Instance 2 300 225 0.7817 Likely CSR
Instance 3 100 100 0.2164 Likely CSR

Problem 5

Instance 1: P-Value of 0.001088, unlikely to be CSR; Instance 2: P-Value of 0.00005977, unlikely to be CSR; Instance 3: P-Value of 0.00004596, unlikely to be CSR

Problem 6

Yes, it did. With a 4x4 grid, the P-Value was 0.09262, meaning that it was likely CSR. And with a 16x16 grid, the P-Value was 0.6983, meaning it was also likely CSR.

Problem 7

The area is 853,776 sq. ft.

Problem 8

There are 2251 trees in the data set.

Problem 9

The most abundant tree is the hickory. The least abundant is the black oak.

Problem 10

Hickory: aggregated; Maple: aggregated; Red Oak: dispersed; Black Oak: aggregated

Problem 11

Hickory: unlikely to be CSR; Maple: unlikely to be CSR; Red Oak: unlikely to be CSR; Black Oak: unlikely to be CSR

Problem 12

Yes, as the P-Values are consistently less than 0.05.

Problem 13

apply(nndist(rpoispp(500), k=1:500), 2, FUN = mean) |> plot()

MyNN <- apply(nndist(rpoispp(500), k=1:500), 2, FUN = mean)
plot(MyNN)

Problem 14

The plot suggests that the maple trees are dispersed.

Problem 15

The plot suggests that the cells are dispersed.

Problem 16

The hickory data does wander beyond the bounds of the confidence band, specifically, above it. This suggests clustering.

Problem 17

The black oak data does wander beyond the bounds of the confidence band, specifically, above it. This suggests clustering.

Problem 18

The cells data does wander beyond the bounds of the confidence band, specifically, below it. This suggests dispersion

Problem 19

The longleaf data does wander beyond the bounds of the confidence band, specifically, above it. This suggests clustering.