scan.test(X, r, ...,
method = c("poisson", "binomial"),
nsim = 19,
baseline = NULL,
case = 2,
alternative = c("greater", "less", "two.sided"),
verbose = TRUE)"ppp").as.mask to determine the
spatial resolution of the computations."poisson" or "binomial"
specifying the type of likelihood.method="poisson".
A pixel image or a function.method="binomial".
Integer or character string."greater" if the alternative
postulates that the mean number of points inside the circle
will be greater than expected under the null."htest" (hypothesis test)
which also belongs to the class "scan.test".
Printing this object gives the result of the test.
Plotting this object displays the Likelihood Ratio Test Statistic
as a function of the location of the centre of the circle.X.In a nutshell,
method="poisson"then
a significant result would mean that there is a circle of radiusr, located somewhere in the spatial domain of the data,
which contains a significantly higher than
expected number of points ofX. That is, the
patternXexhibits spatial clustering.method="binomial"thenXmust be a bivariate (two-type)
point pattern. By default, the first type of point is interpreted as
a control (non-event) and the second type of point as a case (event).
A significant result would mean that there is a
circle of radiusrwhich contains a significantly higher than
expected number of cases. That is, the cases are clustered together,
conditional on the locations of all points.Following is a more detailed explanation.
method="poisson"then the scan test based on Poisson
likelihood is performed (Kulldorf, 1997).
The datasetXis treated as an unmarked point pattern.
By default (ifbaselineis not specified)
the null hypothesis is complete spatial randomness CSR
(i.e. a uniform Poisson process).
The alternative hypothesis is a Poisson process with
one intensity$\beta_1$inside some circle of radiusrand another intensity$\beta_0$outside the
circle.
Ifbaselineis given, then it should be a pixel image
or afunction(x,y). The null hypothesis is
an inhomogeneous Poisson process with intensity proportional
tobaseline. The alternative hypothesis is an inhomogeneous
Poisson process with intensitybeta1 * baselineinside some circle of radiusr,
andbeta0 * baselineoutside the circle.method="binomial"then the scan test based on
binomial likelihood is performed (Kulldorf, 1997).
The datasetXmust be a bivariate point pattern,
i.e. a multitype point pattern with two types.
The null hypothesis is that all permutations of the type labels are
equally likely.
The alternative hypothesis is that some circle of radiusrhas a higher proportion of points of the second type,
than expected under the null hypothesis. The result of scan.test is a hypothesis test
(object of class "htest") which can be plotted to
report the results. The component p.value contains the
$p$-value.
The result of scan.test can also be plotted (using the plot
method for the class "scan.test"). The plot is
a pixel image of the Likelihood Ratio Test Statistic
(2 times the log likelihood ratio) as a function
of the location of the centre of the circle.
This pixel image can be extracted from the object
using as.im.
relrisknsim <- if(interactive()) 19 else 2
data(redwood)
scan.test(redwood, 0.1, method="poisson", nsim=nsim)
data(chorley)
scan.test(chorley, 1, method="binomial", case="larynx", nsim=nsim)Run the code above in your browser using DataLab