problem with enveloped test in spatstat

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problem with enveloped test in spatstat

David Unwin
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

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Re: problem with enveloped test in spatstat

Marcelino de la Cruz Rot
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

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Re: problem with enveloped test in spatstat

David Unwin
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

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Re: problem with enveloped test in spatstat

Nicholas Bradley
Good morning

I hope you are well

I have a question regarding the persp and perspPoints functions

I have the x and y spatial coordinates and also the altitude of all these
coordinates. However, despite consulting several manuals, I have as yet
been unable to create a terrain elevation plot and also to plot the points.
I have only managed to create a persp image showing density

I would therefore appreciate very much if anyone could be so kind as to help

Thank you very much

Kind regards

Nicholas

On 22 April 2018 at 08:46, David Unwin <[hidden email]> wrote:

> 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
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>



--
Kind regards

Nicholas Bradley

PhD Candidate
School of International Relations
University of St Andrews

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Re: problem with enveloped test in spatstat

Rolf Turner
On 25/04/18 19:45, Nicholas Bradley wrote:

> Good morning
>
> I hope you are well
>
> I have a question regarding the persp and perspPoints functions
>
> I have the x and y spatial coordinates and also the altitude of all these
> coordinates. However, despite consulting several manuals, I have as yet
> been unable to create a terrain elevation plot and also to plot the points.
> I have only managed to create a persp image showing density
>
> I would therefore appreciate very much if anyone could be so kind as to help.

Please don't hijack threads.  Your question has nothing to do with
envelope tests.  You have a new question, so start a new thread with an
appropriate subject line.

I shall shortly attempt to answer your question in such a new thread.

cheers,

Rolf Turner


--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

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