## AI-GEOSTATS: About gstat and binomial negative family data

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• Dear members I m newer to geostatistics analysis. So, my work now is probe to my lab.chief that geostat. anal. is better than other analysis to create
Message 1 of 8 , Nov 27, 2003
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Dear members

I'm newer to geostatistics analysis. So, my work now
is probe to my lab.chief that geostat. anal. is better
than other analysis to create probability maps from
fishery acoustic data of surveys in Brazil. Then,
generate kriging maps of probability distribution of
Maurolicus stehmanni (fish sp).

1)First step: using GMT, convert long, lat to linear
projection
[marcelo@localhost trab_R]\$ mapproject file1.dat
-R-53/-38/-35/-23 -Fn -Jm0/0/1c > file2.dat
where R is region Fn to nautic miles JM is mercartor
proj.
2) using "R" gstats:
compute variogram..., and points show no spatial
dependence! Range is 0.5 nautical miles!
3) selects limited areas, repeat step 1 and 2 and
result is equal!
4) remove nule values, repeat steps 1 and 2, and
result is equal!
5) change scale of projection, "-Jm0/0/10c", repeat
steps 1 and 2, result is equal.
Whats wrong? Someone could help me?
The family of distribution of M. stehmanni is binomial
negative, is possible define these prior to variogram
and then result better variograms?
My apologies for newbie questions, i'm very gratefully
for this list!
Marcelo
Ps:someone research could contact me in PVT.

=====
## ~~~~~~~ Oceanólogo ~~~~~~~ ##
# Marcelo Alexandre Bruno
# Linux User: 124592
# FUNDACAO UNIV. FEDERAL do RIO GRANDE
# Departamento de Oceanografia
# Lab. de Tecnologia Pesqueira e Hidroacústica
# AV. ITÁLIA km 8 s/n - CARREIROS
# 96201-900 (0xx53) 2336528
# Rio Grande - RS - BRAZIL
## ---------------------------------------- ##

______________________________________________________________________

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• Dear list members, I would like to know if anyone has information or bibliography on backtransformation of the variogram or the variogram model. I have 2 ref.
Message 2 of 8 , Nov 28, 2003
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Dear list members,

I would like to know if anyone has information or bibliography on
backtransformation of the variogram or the variogram model.
I have 2 ref. only (Armstrong and Guiblin et al. 1995).
Is this supose to give similar results to the log-normal kriging?
Could anyone point me bibliography for this, please....

Thank you very much in advance,
any help would be appreciated,
Best wishes
Marta

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• ... I don t know if something is wrong. Maybe your data don t exhibit much spatial correlation, maybe they are so skew that without transformation you just
Message 3 of 8 , Nov 28, 2003
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Marcelo Alexandre Bruno wrote:

>Whats wrong? Someone could help me?
>
I don't know if something is wrong. Maybe your data don't exhibit
much spatial correlation, maybe they are so skew that without
transformation you just don't see any in sample variograms.

>The family of distribution of M. stehmanni is binomial
>negative, is possible define these prior to variogram
>and then result better variograms?
>
Not without modifying the source code. You could, as a first shot, try
to look at Pearson residual variograms assuming a Poisson distribution.
This can be done with gstat (be it a little forceful); look at the variance
argument to the gstat function, define beta and the covariates such that
the trend value is set for each observation. (If the trend is constant, this
whole action is useless, given where you are now).
--
Edzer

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• you could modify the suggested approach by using a generalization of the Poisson, the neg binomial assumption you mention. most stat software allows negative
Message 4 of 8 , Nov 28, 2003
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you could modify the suggested approach by using a generalization of the
Poisson, the neg binomial assumption you mention. most stat software
allows negative binomial regression. in this case, the variance component
of the Chi-squared resids may be better approximated (than under the
Poisson assumption). as an aside, you may have a zillion zeroes with your
fisheries data. such data may be handled moderately well by the neg bin
assumption you mention. however, they may better be handled under the
assumption that some portion of the zeroes are structural (ie *can't*
generate a positive count) rather than stochastic. I haven't seen spatial
corr assessed under these assumptions in the published lit. regardless,
such "zero inflated" models are often considerably more complicated and may

****************************************************************
Brian Gray
USGS Upper Midwest Environmental Sciences Center
2630 Fanta Reed Road, La Crosse, WI 54602
608-783-7550 ext 19 - Onalaska campus or
608-781-6234 - La Crosse campus
fax 608-783-8058
brgray@...
*****************************************************************

|---------+---------------------------->
| | "Edzer J. |
| | Pebesma" |
| | <e.pebesma@geog.u|
| | u.nl> |
| | Sent by: |
| | ai-geostats-list@|
| | unil.ch |
| | |
| | |
| | 11/28/2003 09:44 |
| | AM |
| | "Edzer J. |
| | Pebesma" |
| | |
|---------+---------------------------->
>--------------------------------------------------------------------------------------------------------------|
| |
| To: Marcelo Alexandre Bruno <marcelo2lei@...> |
| cc: ai-geostats@... |
| Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
>--------------------------------------------------------------------------------------------------------------|

Marcelo Alexandre Bruno wrote:

>Whats wrong? Someone could help me?
>
I don't know if something is wrong. Maybe your data don't exhibit
much spatial correlation, maybe they are so skew that without
transformation you just don't see any in sample variograms.

>The family of distribution of M. stehmanni is binomial
>negative, is possible define these prior to variogram
>and then result better variograms?
>
Not without modifying the source code. You could, as a first shot, try
to look at Pearson residual variograms assuming a Poisson distribution.
This can be done with gstat (be it a little forceful); look at the variance
argument to the gstat function, define beta and the covariates such that
the trend value is set for each observation. (If the trend is constant,
this
whole action is useless, given where you are now).
--
Edzer

--
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• I know of a paper where people split up the process in begin zero or positive (binomial), and the value of the process given that it is positive (Poisson). In
Message 5 of 8 , Nov 29, 2003
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I know of a paper where people split up the process in begin zero or
positive (binomial), and the value of the process given that it is
positive (Poisson). In fact you're working with a composite pdf, two spatial
processes that have to be merged later on. The idea is attractive,
but not very easy. If you want the title of the paper, email me.
--
Edzer

Brian R Gray wrote:

>you could modify the suggested approach by using a generalization of the
>Poisson, the neg binomial assumption you mention. most stat software
>allows negative binomial regression. in this case, the variance component
>of the Chi-squared resids may be better approximated (than under the
>Poisson assumption). as an aside, you may have a zillion zeroes with your
>fisheries data. such data may be handled moderately well by the neg bin
>assumption you mention. however, they may better be handled under the
>assumption that some portion of the zeroes are structural (ie *can't*
>generate a positive count) rather than stochastic. I haven't seen spatial
>corr assessed under these assumptions in the published lit. regardless,
>such "zero inflated" models are often considerably more complicated and may
>
>****************************************************************
>
>

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• sounds like you are describing a two-part or hurdle model. a possibly more attractive but complex approach (zero-inflated count distributions) postulates two
Message 6 of 8 , Dec 1, 2003
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sounds like you are describing a two-part or hurdle model. a possibly more
attractive but complex approach (zero-inflated count distributions)
postulates two sources of zeroes: structural and stochastic. this doesn't
require working with a zero-truncated count distribution. the downside is
that the process defining how zeroes are separated is latent. brian

****************************************************************
Brian Gray, Ph.D.
USGS Upper Midwest Environmental Sciences Center
2630 Fanta Reed Road, La Crosse, WI 54602
608-783-7550 ext 19 - Onalaska campus or
608-781-6234 - La Crosse campus
fax 608-783-8058
brgray@...
*****************************************************************

|---------+---------------------------->
| | "Edzer J. |
| | Pebesma" |
| | <e.pebesma@geog.u|
| | u.nl> |
| | |
| | 11/29/2003 06:35 |
| | AM |
| | |
|---------+---------------------------->
>--------------------------------------------------------------------------------------------------------------------------------------------------|
| |
| To: Brian R Gray <brgray@...> |
| cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
| Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
>--------------------------------------------------------------------------------------------------------------------------------------------------|

I know of a paper where people split up the process in begin zero or
positive (binomial), and the value of the process given that it is
positive (Poisson). In fact you're working with a composite pdf, two
spatial
processes that have to be merged later on. The idea is attractive,
but not very easy. If you want the title of the paper, email me.
--
Edzer

Brian R Gray wrote:

>you could modify the suggested approach by using a generalization of the
>Poisson, the neg binomial assumption you mention. most stat software
>allows negative binomial regression. in this case, the variance component
>of the Chi-squared resids may be better approximated (than under the
>Poisson assumption). as an aside, you may have a zillion zeroes with your
>fisheries data. such data may be handled moderately well by the neg bin
>assumption you mention. however, they may better be handled under the
>assumption that some portion of the zeroes are structural (ie *can't*
>generate a positive count) rather than stochastic. I haven't seen spatial
>corr assessed under these assumptions in the published lit. regardless,
>such "zero inflated" models are often considerably more complicated and
may
>
>****************************************************************
>
>

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• If you can handle writing (or get someone to write) MCMC code/bayesian work then take a look a this zero inflated poisson with spatial effects. Zero-Inflated
Message 7 of 8 , Dec 1, 2003
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If you can handle writing (or get someone to write) MCMC code/bayesian
work then take a look a this zero inflated poisson with spatial effects.
Zero-Inflated Models with Application to Spatial Count Data
D.K.Agarwal,A.E.Gelfand and S.Citron-Pousty, Environmental and
Ecological Statistics 2002, vol 9, pp 341-355
Zero inflated poisson comes awfully close to a negative binomial, and
makes more "biological" sense (i.e. there are a lot more places with
zero counts and there is a process to account for them).
Thanks,
Steve

Brian R Gray wrote:

>sounds like you are describing a two-part or hurdle model. a possibly more
>attractive but complex approach (zero-inflated count distributions)
>postulates two sources of zeroes: structural and stochastic. this doesn't
>require working with a zero-truncated count distribution. the downside is
>that the process defining how zeroes are separated is latent. brian
>
>****************************************************************
>Brian Gray, Ph.D.
>USGS Upper Midwest Environmental Sciences Center
>2630 Fanta Reed Road, La Crosse, WI 54602
>608-783-7550 ext 19 - Onalaska campus or
>608-781-6234 - La Crosse campus
>fax 608-783-8058
>brgray@...
>*****************************************************************
>
>
>|---------+---------------------------->
>| | "Edzer J. |
>| | Pebesma" |
>| | <e.pebesma@geog.u|
>| | u.nl> |
>| | |
>| | 11/29/2003 06:35 |
>| | AM |
>| | |
>|---------+---------------------------->
> >--------------------------------------------------------------------------------------------------------------------------------------------------|
> | |
> | To: Brian R Gray <brgray@...> |
> | cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
> | Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data |
> >--------------------------------------------------------------------------------------------------------------------------------------------------|
>
>
>
>
>I know of a paper where people split up the process in begin zero or
>positive (binomial), and the value of the process given that it is
>positive (Poisson). In fact you're working with a composite pdf, two
>spatial
>processes that have to be merged later on. The idea is attractive,
>but not very easy. If you want the title of the paper, email me.
>--
>Edzer
>
>Brian R Gray wrote:
>
>
>
>>you could modify the suggested approach by using a generalization of the
>>Poisson, the neg binomial assumption you mention. most stat software
>>allows negative binomial regression. in this case, the variance component
>>of the Chi-squared resids may be better approximated (than under the
>>Poisson assumption). as an aside, you may have a zillion zeroes with your
>>fisheries data. such data may be handled moderately well by the neg bin
>>assumption you mention. however, they may better be handled under the
>>assumption that some portion of the zeroes are structural (ie *can't*
>>generate a positive count) rather than stochastic. I haven't seen spatial
>>corr assessed under these assumptions in the published lit. regardless,
>>such "zero inflated" models are often considerably more complicated and
>>
>>
>may
>
>
>>
>>****************************************************************
>>
>>
>>
>>
>
>
>
>
>
>
>
>
>--
>* To post a message to the list, send it to ai-geostats@...
>* As a general service to the users, please remember to post a summary of any useful responses to your questions.
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>
>

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• Here are the answers I had for my question: Thank you very much. Marta ... -- * To post a message to the list, send it to ai-geostats@unil.ch * As a general
Message 8 of 8 , Dec 2, 2003
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Thank you very much.
Marta

>>Dear list members,
>>
>>
>>I would like to know if anyone has information or bibliography on
>>backtransformation of the variogram or the variogram model.
>>I have 2 ref. only (Armstrong and Guiblin et al. 1995).
>>Is this supose to give similar results to the log-normal kriging?
>>Could anyone point me bibliography for this, please....
>>
>>Thank you very much in advance,
>>any help would be appreciated,
>>Best wishes
>>Marta

>It depends on the transformation you do!
>See GSLIB- book (1992) for normal score transform
>see
>G. Mateu-Figueras et al: Normal in R+ vs lognormal in R., 2002 (iamg
>meeting, berlin september 2002)

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