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KAMP bivariate variance

kamp_variance_biv.Rd

Computes the KAMP (K-function Adjusted for Marked Permutations) variance for bivariate point patterns. This function calculates Ripley's K using both the traditional Ripley's K method and the KAMP-adjusted CSR baseline by using a matrix-based implementation.

The KAMP-adjusted CSR represents a more realistic baseline for K (compared to traditional CSR) that accounts for spatial clustering or inhomogeneity in a point pattern compared to the traditional CSR assumption, while avoiding the computational burden of permuting the point pattern.

Note: This function implements a slower, matrix-based implementation of the KAMP variance. It is a wrapper around the kamp_variance_biv_helper function that calculates the KAMP variance at one radius and maps it over a vector of radii.

Usage

kamp_variance_biv(
  ppp_obj,
  rvec = c(0, 0.05, 0.075, 0.1, 0.15, 0.2),
  correction = "trans",
  markvar1 = "immune1",
  markvar2 = "immune2",
  thin_pct = 0
)

Arguments

ppp_obj

A point pattern object from the spatstat.geom package.

rvec

A vector of radii at which to calculate the KAMP expectation. Defaults to c(0, 0.05, 0.075, 0.1, 0.15, 0.2).

correction

Type of edge correction. Defaults to translational.

markvar1

Variable used to mark the points in the point pattern object for the first type. Default is "immune1".

markvar2

Variable used to mark the points in the point pattern object for the second type. Default is "immune2".

thin_pct

Percentage that determines how much to thin the amount of points in the point pattern object. Default is 0.

Value

A dataframe with the following columns:

r

The radius at which K was calculated.

k

The observed K value

theo_csr

The theoretical K under CSR

kamp_csr

The adjusted CSR representing the KAMP permuted expectation.

var

Variance of K under the permutation null distribution

z

Z statistic, calculated by normalizing K using the formula: (K - KAMP)/sqrt(var)

pval

P-value, calculated using the formula: pnorm(-z)

Details

Computes KAMP Variance for Bivariate Point Patterns

Examples


if (requireNamespace("spatstat.geom", quietly = TRUE) &&
    requireNamespace("spatstat.random", quietly = TRUE)) {

  # Simulates a window
  win <- spatstat.geom::owin(c(0, 1), c(0, 1))

  # Generates 200 total points
  pp <- spatstat.random::rpoispp(lambda = 200, win = win)

  # Assigns three marks: immune1, immune2, and background
  marks <- sample(c("immune1", "immune2", "background"), pp$n, replace = TRUE, prob = c(0.3, 0.3, 0.4))

  # Creates marked point pattern
  marked_pp <- spatstat.geom::ppp(pp$x, pp$y, window = win, marks = factor(marks))

  # Computes KAMP variance
  result <- kamp_variance_biv(marked_pp, markvar1 = "immune1", markvar2 = "immune2")
  print(result)
}
#> Error in check_valid_inputs_biv(ppp_obj = ppp_obj, rvec = rvec, correction = correction,     markvar1 = markvar1, markvar2 = markvar2, thin_pct = thin_pct): unused arguments (markvar1 = markvar1, markvar2 = markvar2)