Spatstat

analysing spatial point patterns

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RELEASE NOTES

spatstat 1.48-0

22 December 2016

We thank Kim Colyvas, Yongtao Guan, Gopalan Nair, Nader Najari, Suman Rakshit, Ian Renner and Hangsheng Wang for contributions.

OVERVIEW

• Sufficient Dimension Reduction for point processes.

• Alternating Gibbs Sampler for point process simulation.

• Intensity approximation for area-interaction and Geyer models.

• New class of spatially sampled functions.

• ROC and AUC extended to other types of point patterns and models.

• More support for linear networks.

• More support for infinite straight lines.

• Simulation of ‘rhohat’ objects.

• Kernel smoothing accelerated.

• Methods for ‘head’ and ‘tail’ for spatial patterns.

• More low-level functionality.

• Improvements and bug fixes.

• spatstat now has more than 1000 help files.

• Nickname: ‘Model Prisoner’

NEW CLASSES

• ssf
  Class of spatially sampled functions.

NEW FUNCTIONS

• sdr, dimhat
  Sufficient Dimension Reduction for point processes.
  Matlab code contributed by Yongtao Guan, translated by Suman Rakshit.

• rags, ragsAreaInter, ragsMultiHard
  Alternating Gibbs Sampler for point processes.

• psib
  Sibling probability (index of clustering strength in a cluster process).

• bugfixes
  List all bug fixes in recent versions of a package.

• roc.kppm, roc.lppm, roc.lpp
  Methods for ‘roc’ (receiver operating characteristic curve)
  for fitted models of class ‘kppm’ and ‘lppm’
  and point patterns of class ‘lpp’

• auc.kppm, auc.lppm, auc.lpp
  Methods for ‘auc’ (area under the ROC curve)
  for fitted models of class ‘kppm’ and ‘lppm’
  and point patterns of class ‘lpp’

• rlpp
  Random points on a linear network with a specified probability density.

• cut.lpp
  Method for ‘cut’ for point patterns on a linear network.

• crossing.linnet
  Find crossing points between a linear network and another set of lines.

• ssf
  Create a spatially sampled function

• print.ssf, plot.ssf, contour.ssf, image.ssf
  Display a spatially sampled function

• as.im.ssf, as.ppp.ssf, marks.ssf, marks<-.ssf, unmark.ssf, [.ssf, with.ssf
  Manipulate data in a spatially sampled function

• Smooth.ssf
  Smooth a spatially sampled function

• integral.ssf
  Approximate integral of spatially sampled function

• simulate.rhohat
  Generate a Poisson random point pattern with intensity that is
  a function of a covariate, given by a ‘rhohat’ object.

• head.ppp, head.ppx, head.psp, head.tess,
  tail.ppp, tail.ppx, tail.psp, tail.tess
  Methods for ‘head’ and ‘tail’ for spatial patterns.

• as.data.frame.tess
  Convert a tessellation to a data frame.

• timeTaken
  Extract the timing data from a ‘timed’ object or objects.

• rotate.infline, shift.infline, reflect.infline, flipxy.infline
  Geometrical transformations for infinite straight lines.

• whichhalfplane
  Determine which side of an infinite line a point lies on.

• points.lpp
  Method for ‘points’ for point patterns on a linear network.

• pairs.linim
  Pairs plot for images on a linear network.

• has.close
  Faster way to check whether a point has a close neighbour.

• closetriples
  Low-level function to find all close triples of points.

• matrixpower, matrixsqrt, matrixinvsqrt
  Raise a matrix to any power.

SIGNIFICANT USER-VISIBLE CHANGES

• intensity.ppm
  Intensity approximation is now available for the Geyer saturation process
  and the area-interaction process (results of research with Gopalan Nair).

• envelope.lpp, envelope.lppm
  New arguments ‘fix.n’ and ‘fix.marks’ allow envelopes to be computed
  using simulations conditional on the observed number of points.

• ”[.im”
  The subset index “i” can now be a linear network (object of class ‘linnet’).
  The result of “x[i, drop=FALSE]” is then a pixel image of class ‘linim’.

• cut.ppp
  Argument z can be “x” or “y” indicating one of the spatial coordinates.

• rThomas, rMatClust, rCauchy, rVarGamma, rPoissonCluster, rNeymanScott
  New argument ‘saveparents’.

• lintess
  Argument ‘df’ can be missing or NULL,
  resulting in a tesellation with only one tile.

• lpp
  X can be missing or NULL, resulting in an empty point pattern.

• plot.lintess
  Improved plot method, with more options.

• rpoisline
  Also returns information about the original infinite random lines.

• density.ppp, Smooth.ppp
  Accelerated.

• density.psp
  New argument ‘method’ controls the method of computation.
  New faster option ‘method=”FFT”’

• nndist.lpp
  Accelerated.

BUG FIXES

1. F3est
   Estimates of F(r) for the largest value of r were wildly incorrect.
   Fixed.

2. clip.infline
   Results were incorrect unless the midpoint of the window
   was the coordinate origin.
   Fixed.

3. integral.linim
   Results were inaccurate if many of the segment lengths were
   shorter than the width of a pixel.
   Fixed.

4. predict.lppm
   Bizarre error messages about ‘class too long’ or ‘names too long’
   occurred if the model was multitype.
   Fixed.

5. superimpose
   Point patterns containing 0 points were ignored
   when determining the list of possible marks.
   Fixed.

6. chop.tess
   Vertical lines were not handled correctly
   with pixellated tessellations.
   Fixed.

7. timed
   Argument ‘timetaken’ was ignored.
   Fixed.

8. ppm
   Crashed if method=”logi” and the ‘covariates’ were a data frame.
   [Spotted by Kim Colyvas and Ian Renner.]
   Fixed.

9. rpoislpp, runiflpp
   Crashed if nsim > 1.
   Fixed.

10. rpoisline
    Crashed if zero lines were generated.
    Fixed.

11. model.frame.ppm
    Crashed if the original model was fitted to a data frame of covariates
    and there were NA’s amongst the covariate values.
    [Spotted by Kim Colyvas.]
    Fixed.

12. any, all
    When applied to pixel images (objects of class ‘im’) the result
    was sometimes NA when a finite value should have been returned.
    Fixed.

13. predict.rhohat
    When the original data were on a linear network,
    the result of predict.rhohat did not belong to the correct class ‘linim’.
    Fixed.

Release notes are available in raw text format here.