qnn.test calculates statistics related to the q
nearest neighbors method of comparing case and control
point patterns under the random labeling hypothesis.
qnn.test(x, q = 5, case = 2, nsim = 499, longlat = FALSE)Returns a list with the following components:
A dataframe with the number of neighbors (q), test statistic (Tq), and p-value for each test.
A dataframe with the contrasts (contrast), test statistic (Tcon), and p-value (pvalue) for the test of contrasts.
A ppp object with marks for the case
and control groups.
A vector of positive integers indicating the
values of q for which to do the q nearest
neighbors test.
The name of the desired "case" group in
levels(x$marks). Alternatively, the position of
the name of the "case" group in levels(x$marks).
Since we don't know the group names, the default is 2,
the second position of levels(x$marks).
x$marks is assumed to be a factor. Automatic
conversion is attempted if it is not.
The number of simulations from which to compute p-value.
A logical value indicating whether
Euclidean distance (FALSE) or Great Circle
(WGS84 ellipsoid, FALSE) should be used. Default
is FALSE, i.e., Euclidean distance.
Joshua French
Waller, L.A., and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley.
Cuzick, J., and Edwards, R. (1990). Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society. Series B (Methodological), 73-104.
Alt, K.W., and Vach, W. (1991). The reconstruction of "genetic kinship" in prehistoric burial complexes-problems and statistics. Classification, Data Analysis, and Knowledge Organization, 299-310.
data(grave)
qnn.test(grave, case = "affected", q = c(3, 5, 7, 9, 11, 13, 15))
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