R: Chi-Squared Test for Multiple Point Process Model Based on...

quadrat.test.mppm {spatstat.core} | R Documentation

## Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts

### Description

Performs a chi-squared goodness-of-fit test of a Poisson point process model fitted to multiple point patterns.

### Usage

      ## S3 method for class 'mppm'
    quadrat.test(X, ...)

### Arguments

`X` | An object of class `"mppm"` representing a point process model fitted to multiple point patterns. It should be a Poisson model.
`...` | Arguments passed to `quadrat.test.ppm` which determine the size of the quadrats.

### Details

This function performs a chi^2 test of goodness-of-fit for a Poisson point process model, based on quadrat counts. It can also be used to perform a test of Complete Spatial Randomness for a list of point patterns.

The function `quadrat.test` is generic, with methods for point patterns (class `"ppp"`), point process models (class `"ppm"`) and multiple point process models (class `"mppm"`).

For this function, the argument `X` should be a multiple point process model (object of class `"mppm"`) obtained by fitting a point process model to a list of point patterns using the function `mppm`.

To perform the test, the data point patterns are extracted from `X`. For each point pattern

- the window of observation is divided into rectangular tiles, and the number of data points in each tile is counted, as described in `quadratcount`.

- The expected number of points in each quadrat is calculated, as determined by the fitted model.

Then we perform a single chi^2 test of goodness-of-fit based on these observed and expected counts.

### Value

An object of class `"htest"`. Printing the object gives comprehensible output about the outcome of the test. The p-value of the test is stored in the component `p.value`.

The return value also belongs to the special class `"quadrat.test"`. Plotting the object will display, for each window, the position of the quadrats, annotated by their observed and expected counts and the Pearson residuals. See the examples.

The return value also has an attribute `"components"` which is a list containing the results of chi^2 tests of goodness-of-fit for each individual point pattern.

### Testing Complete Spatial Randomness

If the intention is to test Complete Spatial Randomness (CSR) there are two options:

- CSR with the same intensity of points in each point pattern;

- CSR with a different, unrelated intensity of points in each point pattern.

In the first case, suppose `P` is a list of point patterns we want to test. Then fit the multiple model `fit1 <- mppm(P, ~1)` which signifies a Poisson point process model with a constant intensity. Then apply `quadrat.test(fit1)`.

In the second case, fit the model codefit2 <- mppm(P, ~id) which signifies a Poisson point process with a different constant intensity for each point pattern. Then apply `quadrat.test(fit2)`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Ida-Maria Sintorn and Leanne Bischoff. Implemented by Adrian Baddeley Adrian.Baddeley@curtin.edu.au

Rolf Turner r.turner@auckland.ac.nz

and Ege Rubak rubak@math.aau.dk

### References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: Chapman and Hall/CRC Press.

### See Also

`mppm`, `quadrat.test`

### Examples

      H <- hyperframe(X=waterstriders)
      # Poisson with constant intensity for all patterns
      fit1 <- mppm(X~1, H)
      quadrat.test(fit1, nx=2)

      # uniform Poisson with different intensity for each pattern
      fit2 <- mppm(X ~ id, H)
      quadrat.test(fit2, nx=2)

[Package spatstat.core version 1.65-5 Index]