# harmonic

0th

Percentile

##### Basis for Harmonic Functions

Evaluates a basis for the harmonic polynomials in $$x$$ and $$y$$ of degree less than or equal to $$n$$.

Keywords
models, spatial
##### Usage
harmonic(x, y, n)
##### Arguments
x

Vector of $$x$$ coordinates

y

Vector of $$y$$ coordinates

n

Maximum degree of polynomial

##### Details

This function computes a basis for the harmonic polynomials in two variables $$x$$ and $$y$$ up to a given degree $$n$$ and evaluates them at given $$x,y$$ locations. It can be used in model formulas (for example in the model-fitting functions lm,glm,gam and ppm) to specify a linear predictor which is a harmonic function.

A function $$f(x,y)$$ is harmonic if $$\frac{\partial^2}{\partial x^2} f + \frac{\partial^2}{\partial y^2}f = 0.$$ The harmonic polynomials of degree less than or equal to $$n$$ have a basis consisting of $$2 n$$ functions.

This function was implemented on a suggestion of P. McCullagh for fitting nonstationary spatial trend to point process models.

##### Value

A data frame with 2 * n columns giving the values of the basis functions at the coordinates. Each column is labelled by an algebraic expression for the corresponding basis function.

ppm, polynom

• harmonic
##### Examples
# NOT RUN {
# inhomogeneous point pattern
X <- unmark(longleaf)

# }
# NOT RUN {
# fit Poisson point process with log-cubic intensity
fit.3 <- ppm(X ~ polynom(x,y,3), Poisson())

# fit Poisson process with log-cubic-harmonic intensity
fit.h <- ppm(X ~ harmonic(x,y,3), Poisson())

# Likelihood ratio test
lrts <- 2 * (logLik(fit.3) - logLik(fit.h))
df <- with(coords(X),
ncol(polynom(x,y,3)) - ncol(harmonic(x,y,3)))
pval <- 1 - pchisq(lrts, df=df)
# }

Documentation reproduced from package spatstat, version 1.64-1, License: GPL (>= 2)

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