ECAS2019

Lab 1: Introduction

If you have not already done so, you’ll need to start R and load the spatstat package by typing

library(spatstat)

Exercise 1

We will study a dataset that records the locations of Ponderosa Pine trees (Pinus ponderosa) in a study region in the Klamath National Forest in northern California. The data are included with spatstat as the dataset ponderosa.

  1. assign the data to a shorter name, like X or P;

  2. plot the data;

  3. find out how many trees are recorded;

  4. find the dimensions of the study region;

  5. obtain an estimate of the average intensity of trees (number of trees per unit area).

Exercise 2

The Ponderosa data, continued:

  1. When you type plot(ponderosa), the command that is actually executed is plot.ppp, the plot method for point patterns. Read the help file for the function plot.ppp, and find out which argument to the function can be used to control the main title for the plot;

  2. plot the Ponderosa data with the title Ponderosa Pine Trees above it;

  3. from your reading of the help file, predict what will happen if we type

    plot(ponderosa, chars="X", cols="green")

    then check that your guess was correct;

  4. try different values of the argument chars, for example, one of the integers 0 to 25, or a letter of the alphabet. (Note the difference between chars=3 and chars="+", and the difference between chars=4 and chars="X").

Exercise 3

The dataset hamster is a multitype pattern representing the locations of cells of two types, dividing and pyknotic.

  1. plot the pattern;

  2. plot the pattern again, changing the colours and symbols used to represent the two types of cells;

  3. plot the patterns of pyknotic and dividing cells separately using plot(split(hamster)).

Exercise 4

The following vectors record the locations of 10 scintillation events observed under a microscope. Coordinates are given in microns, and the study region was 30 \times 30 microns, with the origin at the bottom left corner.

x <- c(13, 15, 27, 17, 8, 8, 1, 14, 19, 23)
y <- c(3, 15, 7, 11, 10, 17, 29, 22, 19, 29)

Create a point pattern A from the data by typing

A <- ppp(x, y, c(0, 30), c(0,30))

and plot the point pattern.

Exercise 5

The file anthills.txt is available in the Data directory on GitHub and downloadable by this direct link (right click and save).

It records the locations of anthills recorded in a 1200x1500 metre study region in northern Australia. Coordinates are given in metres, along with a letter code recording the ecological ‘status’ of each anthill (in this exercise we will ignore this letter code).

  1. read the data into R as a data frame, using the R function read.table. (Since the input file has a header line, you will need to use the argument header=TRUE when you call read.table.)

  2. check the data for any peculiarities.

  3. create a point pattern hills containing these data. Ensure that the unit of length is given its correct name, using the argument unitname.

  4. plot the data.