Perform the Diggle (1986) / Cressie (1991) / Loosmore and Ford (2006) test or the Maximum Absolute Deviation test for a spatial point pattern.
dclf.test(X, ..., alternative=c("two.sided", "less", "greater"),
rinterval = NULL, leaveout=1,
scale=NULL, clamp=FALSE, interpolate=FALSE)mad.test(X, ..., alternative=c("two.sided", "less", "greater"),
rinterval = NULL, leaveout=1,
scale=NULL, clamp=FALSE, interpolate=FALSE)
An object of class "htest".
Printing this object gives a report on the result of the test.
The \(p\)-value is contained in the component p.value.
Data for the test.
Either a point pattern (object of class "ppp", "lpp"
or other class), a fitted point process model (object of class "ppm",
"kppm" or other class), a simulation envelope (object of class
"envelope") or a previous result of dclf.test or
mad.test.
Arguments passed to envelope.
Useful arguments include fun to determine the summary
function, nsim to specify the number of Monte Carlo
simulations, verbose=FALSE to turn off the messages,
savefuns or savepatterns to save the simulation
results, and use.theory described under Details.
The alternative hypothesis. A character string. The default is a two-sided alternative. See Details.
Interval of values of the summary function argument r
over which the maximum absolute deviation, or the integral,
will be computed for the test. A numeric vector of length 2.
Optional integer 0, 1 or 2 indicating how to calculate the deviation between the observed summary function and the nominal reference value, when the reference value must be estimated by simulation. See Details.
Optional. A function in the R language which determines the
relative scale of deviations, as a function of
distance \(r\). Summary function values for distance r
will be divided by scale(r) before the
test statistic is computed.
Logical value indicating how to compute deviations
in a one-sided test. Deviations of the observed
summary function from the theoretical summary function are initially
evaluated as signed real numbers, with large positive values indicating
consistency with the alternative hypothesis.
If clamp=FALSE (the default), these values are not changed.
If clamp=TRUE, any negative values are replaced by zero.
Logical value specifying whether to calculate the \(p\)-value
by interpolation.
If interpolate=FALSE (the default), a standard Monte Carlo test
is performed, yielding a \(p\)-value of the form \((k+1)/(n+1)\)
where \(n\) is the number of simulations and \(k\) is the number
of simulated values which are more extreme than the observed value.
If interpolate=TRUE, the \(p\)-value is calculated by
applying kernel density estimation to the simulated values, and
computing the tail probability for this estimated distribution.
If the observed value of the test statistic is equal to one or more of the simulated values (called a tied value), then the tied values will be assigned a random ordering, and a message will be printed.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Andrew Hardegen and Suman Rakshit.
These functions perform hypothesis tests for goodness-of-fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model.
dclf.test performs the test advocated by Loosmore and Ford (2006)
which is also described in Diggle (1986), Cressie (1991, page 667, equation
(8.5.42)) and Diggle (2003, page 14). See Baddeley et al (2014) for
detailed discussion.
mad.test performs the ‘global’ or
‘Maximum Absolute Deviation’ test described by Ripley (1977, 1981).
See Baddeley et al (2014).
The type of test depends on the type of argument X.
If X is some kind of point pattern, then a test of Complete
Spatial Randomness (CSR) will be performed. That is,
the null hypothesis is that the point pattern is completely random.
If X is a fitted point process model, then a test of
goodness-of-fit for the fitted model will be performed. The model object
contains the data point pattern to which it was originally fitted.
The null hypothesis is that the data point pattern is a realisation
of the model.
If X is an envelope object generated by envelope,
then it should have been generated with savefuns=TRUE or
savepatterns=TRUE so that it contains simulation results.
These simulations will be treated as realisations from the null
hypothesis.
Alternatively X could be a previously-performed
test of the same kind (i.e. the result of calling
dclf.test or mad.test).
The simulations used to perform the original test
will be re-used to perform the new test (provided these simulations
were saved in the original test, by setting savefuns=TRUE or
savepatterns=TRUE).
The argument alternative specifies the alternative hypothesis,
that is, the direction of deviation that will be considered
statistically significant. If alternative="two.sided" (the
default), both positive and negative deviations (between
the observed summary function and the theoretical function)
are significant. If alternative="less", then only negative
deviations (where the observed summary function is lower than the
theoretical function) are considered. If alternative="greater",
then only positive deviations (where the observed summary function is
higher than the theoretical function) are considered.
In all cases, the algorithm will first call envelope to
generate or extract the simulated summary functions.
The number of simulations that will be generated or extracted,
is determined by the argument nsim, and defaults to 99.
The summary function that will be computed is determined by the
argument fun (or the first unnamed argument in the list
...) and defaults to Kest (except when
X is an envelope object generated with savefuns=TRUE,
when these functions will be taken).
The choice of summary function fun affects the power of the
test. It is normally recommended to apply a variance-stabilising
transformation (Ripley, 1981). If you are using the \(K\) function,
the normal practice is to replace this by the \(L\) function
(Besag, 1977) computed by Lest. If you are using
the \(F\) or \(G\) functions, the recommended practice is to apply
Fisher's variance-stabilising transformation
\(\sin^{-1}\sqrt x\) using the argument
transform. See the Examples.
The argument rinterval specifies the interval of
distance values \(r\) which will contribute to the
test statistic (either maximising over this range of values
for mad.test, or integrating over this range of values
for dclf.test). This affects the power of the test.
General advice and experiments in Baddeley et al (2014) suggest
that the maximum \(r\) value should be slightly larger than
the maximum possible range of interaction between points. The
dclf.test is quite sensitive to this choice, while the
mad.test is relatively insensitive.
It is also possible to specify a pointwise test (i.e. taking
a single, fixed value of distance \(r\)) by specifing
rinterval = c(r,r).
The argument use.theory passed to envelope
determines whether to compare the summary function for the data
to its theoretical value for CSR (use.theory=TRUE)
or to the sample mean of simulations from CSR
(use.theory=FALSE). The test statistic \(T\) is defined in
equations (10.21) and (10.22) respectively on page 394 of Baddeley,
Rubak and Turner (2015).
The argument leaveout specifies how to calculate the
discrepancy between the summary function for the data and the
nominal reference value, when the reference value must be estimated
by simulation. The values leaveout=0 and
leaveout=1 are both algebraically equivalent (Baddeley et al, 2014,
Appendix) to computing the difference observed - reference
where the reference is the mean of simulated values.
The value leaveout=2 gives the leave-two-out discrepancy
proposed by Dao and Genton (2014).
Baddeley, A., Diggle, P.J., Hardegen, A., Lawrence, T., Milne, R.K. and Nair, G. (2014) On tests of spatial pattern based on simulation envelopes. Ecological Monographs 84(3) 477--489.
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
Besag, J. (1977) Discussion of Dr Ripley's paper. Journal of the Royal Statistical Society, Series B, 39, 193--195.
Cressie, N.A.C. (1991) Statistics for spatial data. John Wiley and Sons, 1991.
Dao, N.A. and Genton, M. (2014) A Monte Carlo adjusted goodness-of-fit test for parametric models describing spatial point patterns. Journal of Graphical and Computational Statistics 23, 497--517.
Diggle, P. J. (1986). Displaced amacrine cells in the retina of a rabbit : analysis of a bivariate spatial point pattern. J. Neuroscience Methods 18, 115--125.
Diggle, P.J. (2003) Statistical analysis of spatial point patterns, Second edition. Arnold.
Loosmore, N.B. and Ford, E.D. (2006) Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925--1931.
Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B, 39, 172 -- 212.
Ripley, B.D. (1981) Spatial statistics. John Wiley and Sons.
envelope,
dclf.progress
dclf.test(cells, Lest, nsim=39)
m <- mad.test(cells, Lest, verbose=FALSE, rinterval=c(0, 0.1), nsim=19)
m
# extract the p-value
m$p.value
# variance stabilised G function
dclf.test(cells, Gest, transform=expression(asin(sqrt(.))),
verbose=FALSE, nsim=19)
## one-sided test
ml <- mad.test(cells, Lest, verbose=FALSE, nsim=19, alternative="less")
## scaled
mad.test(cells, Kest, verbose=FALSE, nsim=19,
rinterval=c(0.05, 0.2),
scale=function(r) { r })
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