# lohboot

0th

Percentile

##### Bootstrap Confidence Bands for Summary Function

Computes a bootstrap confidence band for a summary function of a point process.

Keywords
spatial, nonparametric
##### Usage
lohboot(X,
fun=c("pcf", "Kest", "Lest", "pcfinhom", "Kinhom", "Linhom"),
…,
block=FALSE, global=FALSE, basicboot=FALSE, Vcorrection=FALSE,
confidence=0.95, nx = 4, ny = nx, nsim=200, type=7)
##### Arguments
X

A point pattern (object of class "ppp").

fun

Name of the summary function for which confidence intervals are desired: one of the strings "pcf", "Kest", "Lest", "pcfinhom", "Kinhom" or "Linhom". Alternatively, the function itself; it must be one of the functions listed here.

Arguments passed to the corresponding local version of the summary function (see Details).

block

Logical value indicating whether to use Loh's block bootstrap as originally proposed. Default is FALSE for consistency with older code. See Details.

global

Logical. If FALSE (the default), pointwise confidence intervals are constructed. If TRUE, a global (simultaneous) confidence band is constructed.

basicboot

Logical value indicating whether to use the so-called basic bootstrap confidence interval. See Details.

Vcorrection

Logical value indicating whether to use a variance correction when fun="Kest" or fun="Kinhom". See Details.

confidence

Confidence level, as a fraction between 0 and 1.

nx,ny

Integers. If block=TRUE, divide the window into nx*ny rectangles.

nsim

Number of bootstrap simulations.

type

Integer. Type of quantiles. Argument passed to quantile.default controlling the way the quantiles are calculated.

##### Details

This algorithm computes confidence bands for the true value of the summary function fun using the bootstrap method of Loh (2008) and a modification described in Baddeley, Rubak, Turner (2015).

If fun="pcf", for example, the algorithm computes a pointwise (100 * confidence)% confidence interval for the true value of the pair correlation function for the point process, normally estimated by pcf. It starts by computing the array of local pair correlation functions, localpcf, of the data pattern X. This array consists of the contributions to the estimate of the pair correlation function from each data point.

If block=FALSE, these contributions are resampled nsim times with replacement as described in Baddeley, Rubak, Turner (2015); from each resampled dataset the total contribution is computed, yielding nsim random pair correlation functions.

If block=TRUE, the (bounding box of the) window is divided into $$nx * ny$$ rectangles (blocks). The average contribution of a block is obtained by averaging the contribution of each point included in the block. Then, the average contributions on each block are resampled nsim times with replacement as described in Loh (2008) and Loh (2010); from each resampled dataset the total contribution is computed, yielding nsim random pair correlation functions. Notice that for non-rectangular windows any blocks not fully contained in the window are discarded before doing the resampling, so the effective number of blocks may be substantially smaller than $$nx * ny$$ in this case.

The pointwise alpha/2 and 1 - alpha/2 quantiles of these functions are computed, where alpha = 1 - confidence. The average of the local functions is also computed as an estimate of the pair correlation function.

There are several ways to define a bootstrap confidence interval. If basicbootstrap=TRUE, the so-called basic confidence bootstrap interval is used as described in Loh (2008).

It has been noticed in Loh (2010) that when the intensity of the point process is unknown, the bootstrap error estimate is larger than it should be. When the $$K$$ function is used, an adjustment procedure has been proposed in Loh (2010) that is used if Vcorrection=TRUE. In this case, the basic confidence bootstrap interval is implicitly used.

To control the estimation algorithm, use the arguments …, which are passed to the local version of the summary function, as shown below:

 fun local version pcf localpcf Kest localK Lest localK pcfinhom localpcfinhom Kinhom localKinhom

For fun="Lest", the calculations are first performed as if fun="Kest", and then the square-root transformation is applied to obtain the $$L$$-function.

Note that the confidence bands computed by lohboot(fun="pcf") may not contain the estimate of the pair correlation function computed by pcf, because of differences between the algorithm parameters (such as the choice of edge correction) in localpcf and pcf. If you are using lohboot, the appropriate point estimate of the pair correlation itself is the pointwise mean of the local estimates, which is provided in the result of lohboot and is shown in the default plot.

If the confidence bands seem unbelievably narrow, this may occur because the point pattern has a hard core (the true pair correlation function is zero for certain values of distance) or because of an optical illusion when the function is steeply sloping (remember the width of the confidence bands should be measured vertically).

An alternative to lohboot is varblock.

##### Value

A function value table (object of class "fv") containing columns giving the estimate of the summary function, the upper and lower limits of the bootstrap confidence interval, and the theoretical value of the summary function for a Poisson process.

##### References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

Loh, J.M. (2008) A valid and fast spatial bootstrap for correlation functions. The Astrophysical Journal, 681, 726--734.

Loh, J.M. (2010) Bootstrapping an inhomogeneous point process. Journal of Statistical Planning and Inference, 140, 734--749.

Summary functions Kest, pcf, Kinhom, pcfinhom, localK, localpcf, localKinhom, localpcfinhom.

See varblock for an alternative bootstrap technique.

• lohboot
##### Examples
# NOT RUN {
p <- lohboot(simdat, stoyan=0.5)
plot(p)
# }

Documentation reproduced from package spatstat, version 1.56-1, License: GPL (>= 2)

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