Description Usage Arguments Details Value Warning Author(s) References See Also Examples
Tests the goodnessoffit of a Poisson point process model on a linear network, using the approach of Berman (1986).
1 2 3 4 5 6 7 8 9 
X 
A point pattern (object of class 
model 
A fitted point process model (object of class 
covariate 
The spatial covariate on which the test will be based.
An image (object of class 
which 
Character string specifying the choice of test. 
alternative 
Character string specifying the alternative hypothesis. 
... 
Additional arguments controlling the pixel resolution
(arguments 
These functions perform a goodnessoffit test of a Poisson point process model fitted to point pattern data. The observed distribution of the values of a spatial covariate at the data points, and the predicted distribution of the same values under the model, are compared using either of two test statistics Z[1] and Z[2] proposed by Berman (1986). The Z[1] test is also known as the LawsonWaller test.
The function berman.test
is generic, with methods for
point patterns ("ppp"
or "lpp"
)
and point process models ("ppm"
or "lppm"
).
See the help file for berman.test
for information on the generic function and the methods for data in
twodimensional space, classes "ppp"
and "ppm"
.
This help file describes the methods for data on a linear network,
classes "lpp"
and "lppm"
.
If X
is a point pattern dataset (object of class
"ppp"
or "lpp"
), then
berman.test(X, ...)
performs a goodnessoffit test of the
uniform Poisson point process (Complete Spatial Randomness, CSR)
for this dataset.
If model
is a fitted point process model
(object of class "ppm"
or "lppm"
)
then berman.test(model, ...)
performs
a test of goodnessoffit for this fitted model. In this case,
model
should be a Poisson point process.
The test is performed by comparing the observed distribution of the values of a spatial covariate at the data points, and the predicted distribution of the same covariate under the model. Thus, you must nominate a spatial covariate for this test.
The argument covariate
should be either a function(x,y)
or a pixel image (object of class "im"
containing the values
of a spatial function.
If covariate
is an image, it should have numeric values,
and its domain should cover the observation window of the
model
. If covariate
is a function, it should expect
two arguments x
and y
which are vectors of coordinates,
and it should return a numeric vector of the same length
as x
and y
.
First the original data point pattern is extracted from model
.
The values of the covariate
at these data points are
collected.
Next the values of the covariate
at all locations in the
observation window are evaluated. The point process intensity
of the fitted model is also evaluated at all locations in the window.
If which="Z1"
,
the test statistic Z[1] is computed as follows.
The sum S of the covariate values at all data
points is evaluated. The predicted mean μ and variance
σ^2 of S are computed
from the values of the covariate at all locations in the window.
Then we compute Z[1]=(Sμ)/σ.
Closelyrelated tests were proposed independently
by Waller et al (1993) and Lawson (1993)
so this test is often termed the
LawsonWaller test in epidemiological literature.
If which="Z2"
,
the test statistic Z[2] is computed as follows.
The values of the covariate
at all locations in the
observation window, weighted by the point process intensity,
are compiled into a cumulative distribution function F.
The probability integral transformation is then applied:
the values of the covariate
at the original data points
are transformed by the predicted cumulative distribution function
F into numbers between 0 and 1. If the model is correct,
these numbers are i.i.d. uniform random numbers.
The standardised sample mean of these numbers is the
statistic Z[2].
In both cases the null distribution of the test statistic is the standard normal distribution, approximately.
The return value is an object of class "htest"
containing the
results of the hypothesis test. The print method for this class
gives an informative summary of the test outcome.
An object of class "htest"
(hypothesis test)
and also of class "bermantest"
,
containing the results of the test. The return value can be
plotted (by plot.bermantest
) or printed
to give an informative summary of the test.
The meaning of a onesided test must be carefully scrutinised: see the printed output.
.
Berman, M. (1986) Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54–62.
Lawson, A.B. (1993) On the analysis of mortality events around a prespecified fixed point. Journal of the Royal Statistical Society, Series A 156 (3) 363–377.
Waller, L., Turnbull, B., Clark, L.C. and Nasca, P. (1992) Chronic Disease Surveillance and testing of clustering of disease and exposure: Application to leukaemia incidence and TCEcontaminated dumpsites in upstate New York. Environmetrics 3, 281–300.
cdf.test
,
quadrat.test
,
ppm
lppm
1 2 3 4 5  #' test of complete randomness
berman.test(spiders, "x")
#' test of fitted model
fit < lppm(spiders ~ x)
berman.test(fit, "y", "Z2")

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