coordinates. However, despite consulting several manuals, I have as yet

been unable to create a terrain elevation plot and also to plot the points.

> Many thanks. It is a superb book and amazing value for money. My only

> excuse is that I have not as yet got to page 392. Plotting the functions

> also makes it a lot clearer.

> Dave

>

> On 21 April 2018 at 22:50, Marcelino de la Cruz Rot <

>

[hidden email]> wrote:

>

> > Hi David,

> >

> > This is very clearly explained in page 393 of Baddeley et al. 2015.

> >

> > Basically, the MAD test is affected by transformations of the summary

> > function, with the L-function providing more powerful tests because of

> its

> > stabilization of the variance.

> >

> > If you are interested in point pattern analysis I would recommend you to

> > get a copy of this nice book.

> >

> >

> > Cheers,

> >

> > Marcelino

> >

> >

> > Adrian Baddeley, Ege Rubak, Rolf Turner (2015). Spatial Point Patterns:

> > Methodology and Applications with R. London: Chapman and

> > Hall/CRC Press.

> >

http://www.crcpress.com/Spatial-Point-Patterns-Methodology-> > and-Applications-with-R/Baddeley-Rubak-Turner/9781482210200/

> >

> >

> >

> >

> >

> >

> > El 21/04/2018 a las 18:05, David Unwin escribió:

> >

> >> Can any *spatstat* user explain to me why the two p-values obtained

> below

> >> for an envelope test against CSR are so different?

> >>

> >>

> >>

> >> data(swedishpines)

> >>> d<-swedishpines

> >>> plot(d)

> >>> mad.test(d,Kest,nsim=999,verbose=F)

> >>>

> >>

> >>

> >> Maximum absolute deviation test of CSR

> >>

> >> Monte Carlo test based on 999 simulations

> >>

> >> Summary function: K(r)

> >>

> >> Reference function: theoretical

> >>

> >> Alternative: two.sided

> >>

> >> Interval of distance values: [0, 24] units (one unit = 0.1

> >> metres)

> >>

> >> Test statistic: Maximum absolute deviation

> >>

> >> Deviation = observed minus theoretical

> >>

> >>

> >>

> >> data: d

> >>

> >> mad = 150.69, rank = 216, p-value = *0.216*

> >>

> >>

> >>

> >> mad.test(d,*Lest*,nsim=999,verbose=F)

> >>>

> >>

> >>

> >> Maximum absolute deviation test of CSR

> >>

> >> Monte Carlo test based on 999 simulations

> >>

> >> Summary function: L(r)

> >>

> >> Reference function: theoretical

> >>

> >> Alternative: two.sided

> >>

> >> Interval of distance values: [0, 24] units (one unit = 0.1

> >> metres)

> >>

> >> Test statistic: Maximum absolute deviation

> >>

> >> Deviation = observed minus theoretical

> >>

> >>

> >>

> >> data: d

> >>

> >> mad = 2.9921, rank = 9, p-value = *0.009*

> >>

> >>

> >>

> >> *!!!*

> >>

> >>

> >>

> >> These data are dispersed relative to CSR:

> >>

> >> kl<-envelope(d,Kest,nsim=999,correction="border")

> >>> plot(kl)

> >>>

> >>

> >> Dave Unwin

> >>

> >> [[alternative HTML version deleted]]

> >>

> >> _______________________________________________

> >> R-sig-Geo mailing list

> >>

[hidden email]
> >>

https://stat.ethz.ch/mailman/listinfo/r-sig-geo> >> .

> >>

> >>

> > --

> > Marcelino de la Cruz Rot

> > Depto. de Biología y Geología

> > Física y Química Inorgánica

> > Universidad Rey Juan Carlos

> > Móstoles España

> >

> >

>

>

> --

>

>

> David J. Unwin

> Professor Emeritus in Geography

> Birkbeck, University of London

> Phone +44(0)1604 686526 Mobile: +44(0)7840 297239 (text preferred)

> SKYPE: david.unwin99

>

> [[alternative HTML version deleted]]

>

> _______________________________________________

> R-sig-Geo mailing list

>

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>

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