- Hello all -- I'm in the midst of modeling the distribution of

ice-hauling seals in relation to covariates such as day of year (DOY)

and ice cover (ICE.COV). By strip-transect sampling on 20 separate

days, I have created a lattice of cells containing seal counts and the

corresponding covariate measures. To remove a significant north-south

trend in seal counts, I extracted the residuals from a GAM loess smooth

of seal counts on the lat/long variables. Modeling the residuals, I

arrived at the following S+ best fit (using AIC):

glmmPQL(sealsum.gamfit ~ ICE.COV * DOY * SHIPACT2, random = ~ 1| DOY,

family = poisson, data = yakgrid.fit, correlation = corGaus(form = ~

lat.yak + lon.yak | DOY), verbose = T))

I determined that a gaussian variogram best fit the spatial

autocorrelation by exploring the data in Surfer, VarioWin, and S+. But

I have encountered a significant degree of both geometric and zonal

anisotropy. I believe the anisotropy is real as there are several

reasonable hypothesis that explain its presence which have to do with

the seals concentrating in a stream of ice, i.e., creating

discontinuities in variance and correlation within the study area.

Though complicated, I have been able to model the autocorrelation (in

VarioWin) by nesting two spatial structures (both Gaussian)

corresponding to the directions of maximum and minimum continuity. The

problem is that I now need to transport this nested model into the S+

spatial GLMM framework. The avenues I have explored thus far are:

1) create a new corStruct class in S+ that corresponds to a

gaussian-gaussian nested model. Problem: I have been unable to find

any documentation on how to create a new class though the online help

indicates that it is possible "by specifying a constructor function and

methods for the functions corMatrix and coef".

2) transforming/weighting the coordinates prior to running the model.

Problem: though a geometric transform of the coordinates is

straightforward it is less obvious how to conduct such a transform using

a nested model.

It may be I'm forcing a square peg (nested model) in a round hole (S+

spatial GLMM), but I'm hoping someone out there has done this in S+

before and can pass along their experience. Otherwise, I'm soliciting

the wisdom of those who know where to find the square hole that matches

my square peg, i.e., other software or techniques that allow one to

account for spatial correlation with nested structures with the main

focus of modeling the relation of animals to their environment. Much

gratitude to any assistance. Thanks, John Jansen

--

John K. Jansen

Wildlife Biologist

National Marine Mammal Laboratory

NOAA Fisheries

7600 Sand Point Way N.E. Bldg 4

Seattle, WA 98115-6349

voice: 206.526.4027

fax: 206.526.6615

email: John.Jansen@...

--

* 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.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org - A simple suggestion is to see how much "worse" your model fit becomes

with a "standard" variogram model. Having used S+ for this kind of work

a while ago I am not sure how straightforward it will be to do a the

non-standard variogram.

It looks like this page may help:

http://www.cas.umt.edu/math/graham/math595/math595.html

google is your friend

Steve

John Jansen wrote:

>Hello all -- I'm in the midst of modeling the distribution of

--

>ice-hauling seals in relation to covariates such as day of year (DOY)

>and ice cover (ICE.COV). By strip-transect sampling on 20 separate

>days, I have created a lattice of cells containing seal counts and the

>corresponding covariate measures. To remove a significant north-south

>trend in seal counts, I extracted the residuals from a GAM loess smooth

>of seal counts on the lat/long variables. Modeling the residuals, I

>arrived at the following S+ best fit (using AIC):

>

>glmmPQL(sealsum.gamfit ~ ICE.COV * DOY * SHIPACT2, random = ~ 1| DOY,

>family = poisson, data = yakgrid.fit, correlation = corGaus(form = ~

>lat.yak + lon.yak | DOY), verbose = T))

>

>I determined that a gaussian variogram best fit the spatial

>autocorrelation by exploring the data in Surfer, VarioWin, and S+. But

>I have encountered a significant degree of both geometric and zonal

>anisotropy. I believe the anisotropy is real as there are several

>reasonable hypothesis that explain its presence which have to do with

>the seals concentrating in a stream of ice, i.e., creating

>discontinuities in variance and correlation within the study area.

>Though complicated, I have been able to model the autocorrelation (in

>VarioWin) by nesting two spatial structures (both Gaussian)

>corresponding to the directions of maximum and minimum continuity. The

>problem is that I now need to transport this nested model into the S+

>spatial GLMM framework. The avenues I have explored thus far are:

>

>1) create a new corStruct class in S+ that corresponds to a

>gaussian-gaussian nested model. Problem: I have been unable to find

>any documentation on how to create a new class though the online help

>indicates that it is possible "by specifying a constructor function and

>methods for the functions corMatrix and coef".

>

>2) transforming/weighting the coordinates prior to running the model.

>Problem: though a geometric transform of the coordinates is

>straightforward it is less obvious how to conduct such a transform using

>a nested model.

>

>It may be I'm forcing a square peg (nested model) in a round hole (S+

>spatial GLMM), but I'm hoping someone out there has done this in S+

>before and can pass along their experience. Otherwise, I'm soliciting

>the wisdom of those who know where to find the square hole that matches

>my square peg, i.e., other software or techniques that allow one to

>account for spatial correlation with nested structures with the main

>focus of modeling the relation of animals to their environment. Much

>gratitude to any assistance. Thanks, John Jansen

>

>

>--

>John K. Jansen

>Wildlife Biologist

>National Marine Mammal Laboratory

>NOAA Fisheries

>7600 Sand Point Way N.E. Bldg 4

>Seattle, WA 98115-6349

>voice: 206.526.4027

>fax: 206.526.6615

>email: John.Jansen@...

>

>

>

>--

>* 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.

>* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

>* Support to the list is provided at http://www.ai-geostats.org

>

>

* 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.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org - John, the gstat R package/S-Plus library, found at

http://www.gstat.org/s.html

does provide nested variograms, each having their

own anisotropy parameters. However, it does not

do a fully integrated variogram parameter estimation

in a Poisson framework; you'd have to work with

residuals. More specificly, it does not fit anisotropy

ratios nor directions; only sills and ranges.

--

Edzer

John Jansen wrote:

>Hello all -- I'm in the midst of modeling the distribution of

--

>ice-hauling seals in relation to covariates such as day of year (DOY)

>and ice cover (ICE.COV). By strip-transect sampling on 20 separate

>days, I have created a lattice of cells containing seal counts and the

>corresponding covariate measures. To remove a significant north-south

>trend in seal counts, I extracted the residuals from a GAM loess smooth

>of seal counts on the lat/long variables. Modeling the residuals, I

>arrived at the following S+ best fit (using AIC):

>

>glmmPQL(sealsum.gamfit ~ ICE.COV * DOY * SHIPACT2, random = ~ 1| DOY,

>family = poisson, data = yakgrid.fit, correlation = corGaus(form = ~

>lat.yak + lon.yak | DOY), verbose = T))

>

>I determined that a gaussian variogram best fit the spatial

>autocorrelation by exploring the data in Surfer, VarioWin, and S+. But

>I have encountered a significant degree of both geometric and zonal

>anisotropy. I believe the anisotropy is real as there are several

>reasonable hypothesis that explain its presence which have to do with

>the seals concentrating in a stream of ice, i.e., creating

>discontinuities in variance and correlation within the study area.

>Though complicated, I have been able to model the autocorrelation (in

>VarioWin) by nesting two spatial structures (both Gaussian)

>corresponding to the directions of maximum and minimum continuity. The

>problem is that I now need to transport this nested model into the S+

>spatial GLMM framework. The avenues I have explored thus far are:

>

>1) create a new corStruct class in S+ that corresponds to a

>gaussian-gaussian nested model. Problem: I have been unable to find

>any documentation on how to create a new class though the online help

>indicates that it is possible "by specifying a constructor function and

>methods for the functions corMatrix and coef".

>

>2) transforming/weighting the coordinates prior to running the model.

>Problem: though a geometric transform of the coordinates is

>straightforward it is less obvious how to conduct such a transform using

>a nested model.

>

>It may be I'm forcing a square peg (nested model) in a round hole (S+

>spatial GLMM), but I'm hoping someone out there has done this in S+

>before and can pass along their experience. Otherwise, I'm soliciting

>the wisdom of those who know where to find the square hole that matches

>my square peg, i.e., other software or techniques that allow one to

>account for spatial correlation with nested structures with the main

>focus of modeling the relation of animals to their environment. Much

>gratitude to any assistance. Thanks, John Jansen

>

>

>--

>John K. Jansen

>Wildlife Biologist

>National Marine Mammal Laboratory

>NOAA Fisheries

>7600 Sand Point Way N.E. Bldg 4

>Seattle, WA 98115-6349

>voice: 206.526.4027

>fax: 206.526.6615

>email: John.Jansen@...

>

>

>

>--

>* 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.

>* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

>* Support to the list is provided at http://www.ai-geostats.org

>

>

* 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.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org - John: if by "nested" you mean hierarchical and if what you are working

with (some function of what originally were counts) may ostensibly be

viewed as normal, then you should be able to do this in SAS' PROC MIXED.

if your data remain counts then you may be able to do the same in the SAS

macro glimmix--under an over(under?)-dispersed Poisson assumption. glimmix

relies on a pseudolikelihood assumption, and relies on MIXED to analyze

pseudodata. as always, fitting complex structures may require more data

(at possibly multiple scales) than ecologists may have. 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> |

| | Sent by: |

| | ai-geostats-list@|

| | unil.ch |

| | |

| | |

| | 02/05/2004 02:15 |

| | AM |

| | Please respond to|

| | "Edzer J. |

| | Pebesma" |

| | |

|---------+---------------------------->>--------------------------------------------------------------------------------------------------------------------------------------------------|

| |

| To: John Jansen <John.Jansen@...> |

| cc: ai-geostats@... |

| Subject: Re: AI-GEOSTATS: spatial GLMM with nested correlation structure |>--------------------------------------------------------------------------------------------------------------------------------------------------|

John, the gstat R package/S-Plus library, found at

http://www.gstat.org/s.html

does provide nested variograms, each having their

own anisotropy parameters. However, it does not

do a fully integrated variogram parameter estimation

in a Poisson framework; you'd have to work with

residuals. More specificly, it does not fit anisotropy

ratios nor directions; only sills and ranges.

--

Edzer

John Jansen wrote:

>Hello all -- I'm in the midst of modeling the distribution of

= ~

>ice-hauling seals in relation to covariates such as day of year (DOY)

>and ice cover (ICE.COV). By strip-transect sampling on 20 separate

>days, I have created a lattice of cells containing seal counts and the

>corresponding covariate measures. To remove a significant north-south

>trend in seal counts, I extracted the residuals from a GAM loess smooth

>of seal counts on the lat/long variables. Modeling the residuals, I

>arrived at the following S+ best fit (using AIC):

>

>glmmPQL(sealsum.gamfit ~ ICE.COV * DOY * SHIPACT2, random = ~ 1| DOY,

>family = poisson, data = yakgrid.fit, correlation = corGaus(form

>

any useful responses to your questions.

>I determined that a gaussian variogram best fit the spatial

>autocorrelation by exploring the data in Surfer, VarioWin, and S+. But

>I have encountered a significant degree of both geometric and zonal

>anisotropy. I believe the anisotropy is real as there are several

>reasonable hypothesis that explain its presence which have to do with

>the seals concentrating in a stream of ice, i.e., creating

>discontinuities in variance and correlation within the study area.

>Though complicated, I have been able to model the autocorrelation (in

>VarioWin) by nesting two spatial structures (both Gaussian)

>corresponding to the directions of maximum and minimum continuity. The

>problem is that I now need to transport this nested model into the S+

>spatial GLMM framework. The avenues I have explored thus far are:

>

>1) create a new corStruct class in S+ that corresponds to a

>gaussian-gaussian nested model. Problem: I have been unable to find

>any documentation on how to create a new class though the online help

>indicates that it is possible "by specifying a constructor function and

>methods for the functions corMatrix and coef".

>

>2) transforming/weighting the coordinates prior to running the model.

>Problem: though a geometric transform of the coordinates is

>straightforward it is less obvious how to conduct such a transform using

>a nested model.

>

>It may be I'm forcing a square peg (nested model) in a round hole (S+

>spatial GLMM), but I'm hoping someone out there has done this in S+

>before and can pass along their experience. Otherwise, I'm soliciting

>the wisdom of those who know where to find the square hole that matches

>my square peg, i.e., other software or techniques that allow one to

>account for spatial correlation with nested structures with the main

>focus of modeling the relation of animals to their environment. Much

>gratitude to any assistance. Thanks, John Jansen

>

>

>--

>John K. Jansen

>Wildlife Biologist

>National Marine Mammal Laboratory

>NOAA Fisheries

>7600 Sand Point Way N.E. Bldg 4

>Seattle, WA 98115-6349

>voice: 206.526.4027

>fax: 206.526.6615

>email: John.Jansen@...

>

>

>

>--

>* 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

>* To unsubscribe, send an email to majordomo@... with no subject and

"unsubscribe ai-geostats" followed by "end" on the next line in the message

body. DO NOT SEND Subscribe/Unsubscribe requests to the list>* Support to the list is provided at http://www.ai-geostats.org

--

>

>

* 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.

* To unsubscribe, send an email to majordomo@... with no subject and

"unsubscribe ai-geostats" followed by "end" on the next line in the message

body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org

--

* 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.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org