Re: [R-sig-eco] Mixed models and multivariate methods for temporal-spatial nested data

[Tania Bird]

Many thanks for your useful advice Bob!
Unfortunately I did try to use my University's statistical consulting
department, but they were not able to provide advice at this level for
either the multivariate or mixed effect models. :(
I would be happy to consult with someone else if anyone if offering
such a service?

Tania Bird


On 27 February 2017 at 18:14, Bob OHara  wrote:
> Hm, this is a big job. The optimal solution is to see if your university
> offers a statistical consulting service. I don't see any big conceptual
> problems, but getting a good analysis will take a bit of time and
> exploration. I think you can probably 'just' use a GLMM, but getting the
> right GLMM and deciding what a good model is will take time and some poking
> of the data.
>
> Anyway, some answers below, which may (or may not) help.
>
>
> On 02/27/2017 04:27 PM, Tania Bird wrote:
>>
>> Hi all,
>>
>> I am seeking advice on how to analyse my unbalanced, multi-nested
>> multivariate data set. I realise there are many questions in this
>> email and I would be willing to consult with someone privately on this
>> if it is an option.
>>
>> I am using abundance data for insect species (I have the same
>> experimental design for reptiles, and annual plants as well). I use
>> Simpson's diversity as a univariate response and species composition
>> as a multivariate response.
>>
>> Experimental Design:
>> Plots are divided into three habitat types A, B, C based on vegetation.
>> Each habitat has 3 or 4 replicate control plots that are repeat
>> sampled (one sample a year always in spring).
>> In addition B and C have 3 or 4 treatment (vegetation removal) plots.
>>   'S' plots are disturbed( trampling and off-road vehicles) but the
>> disturbance is unquantified and I don't know the pre-disturbance
>> habitat type.
>>
>> The total data set is across a 12 year period, but the sampling was
>> unbalanced for various reasons. I attach a png of the metadata of the
>> plots over time to show the unbalanced sampling.
>> https://www.dropbox.com/s/7vxvo3x9lnywdbm/insects_years.gif?dl=0
>>
>> Each year the sampling across plots was conducted at the same time,
>> and so plots are comparable within a year.
>> In general, As were sampled every year and are considered the 'target'
>> habitat. B's were sampled in the earlier years and C's later on, and
>> in the last couple of years all three types were sampled together.
>>
>> The treatments on B & C were conducted using different methods and in
>> different years, so in principle I should probably test each
>> separately just against their own control pairs. However the
>> hypothesis for both treatments is that treated plots will be more
>> similar in composition to A plots than the paired control plots (if
>> possible I want to check if they become more or less similar to A over
>> time).
>>
>> So in that regard I thought there might be a way to include all
>> habitat types in one analysis? Perhaps using time as "number of years
>> since treatment" rather than a date? (Although I have no environmental
>> data with which to standardise).  S dunes have no "pre-treatment"  but
>> the hypothesis is that S plots will be most similar to A compared to
>> all other (treated and control) plot types.  I am not sure how to
>> include these plots in a testable model.
>>
>> Questions regarding the design:
>>
 Can I use all the habitat types in one model (preferable!) or can I only
 test B treated against B control etc?
>
> Yes you can. you obviously need a Treatment effect, and you should expect to
> have a Treatment by Habitat interaction.
>
> There may also be some sort of interaction with time (either as Time, or
> Time Since Treatment)
>
 Must I remove data to create blocks of sampled or is 'all data useful'?
>>
>> e.g. A's were the only plots sampled in 2010- should I remove that
>> year completely?
>> e.g. C1 & C5 were sampled in 2005 while the rest were not until 2011,-
>> should I only include data from 2011 onwards for all C's?
>> e.g. Should I remove A4 completely since its only sampled in the last
>> few years or its still useable?
>
> No, you should be able to use all of the data, you just have to be a bit
> careful about how you model Time.

 Can I include S in the analyses in order to compare them with B and C
 treated plots in relation to A plots?
>
> Yes, in principal. It just doesn't have a Habitat:Treatment interaction.
>
>> I have already analysed my first research question
>> Q1) To understanding the differences in diversity and composition
>> across control habitats, irrelevant of time.
>>
>> The analysis approach I used for this is:
>> i) Mixed effect model:  GLMM PQL (Penalised Quasi-Likelihood) using
>> MASS R package.
>>  Diversity ~   fixed effect = habitat type + random effect = year ,
>> Family = poisson
>
> There are better tools than glmmPQL nowadays. Have a look at the lme4
> package, for example.
>>
>> ii) Pairwise permutational 

Re: [R-sig-eco] Mixed models and multivariate methods for temporal-spatial nested data

[Bob OHara]

Hm, this is a big job. The optimal solution is to see if your university 
offers a statistical consulting service. I don't see any big conceptual 
problems, but getting a good analysis will take a bit of time and 
exploration. I think you can probably 'just' use a GLMM, but getting the 
right GLMM and deciding what a good model is will take time and some 
poking of the data.


Anyway, some answers below, which may (or may not) help.

On 02/27/2017 04:27 PM, Tania Bird wrote:

Hi all,

I am seeking advice on how to analyse my unbalanced, multi-nested
multivariate data set. I realise there are many questions in this
email and I would be willing to consult with someone privately on this
if it is an option.

I am using abundance data for insect species (I have the same
experimental design for reptiles, and annual plants as well). I use
Simpson's diversity as a univariate response and species composition
as a multivariate response.

Experimental Design:
Plots are divided into three habitat types A, B, C based on vegetation.
Each habitat has 3 or 4 replicate control plots that are repeat
sampled (one sample a year always in spring).
In addition B and C have 3 or 4 treatment (vegetation removal) plots.
  'S' plots are disturbed( trampling and off-road vehicles) but the
disturbance is unquantified and I don't know the pre-disturbance
habitat type.

The total data set is across a 12 year period, but the sampling was
unbalanced for various reasons. I attach a png of the metadata of the
plots over time to show the unbalanced sampling.
https://www.dropbox.com/s/7vxvo3x9lnywdbm/insects_years.gif?dl=0

Each year the sampling across plots was conducted at the same time,
and so plots are comparable within a year.
In general, As were sampled every year and are considered the 'target'
habitat. B's were sampled in the earlier years and C's later on, and
in the last couple of years all three types were sampled together.

The treatments on B & C were conducted using different methods and in
different years, so in principle I should probably test each
separately just against their own control pairs. However the
hypothesis for both treatments is that treated plots will be more
similar in composition to A plots than the paired control plots (if
possible I want to check if they become more or less similar to A over
time).

So in that regard I thought there might be a way to include all
habitat types in one analysis? Perhaps using time as "number of years
since treatment" rather than a date? (Although I have no environmental
data with which to standardise).  S dunes have no "pre-treatment"  but
the hypothesis is that S plots will be most similar to A compared to
all other (treated and control) plot types.  I am not sure how to
include these plots in a testable model.

Questions regarding the design:


Can I use all the habitat types in one model (preferable!) or can I only test B 
treated against B control etc?
Yes you can. you obviously need a Treatment effect, and you should 
expect to have a Treatment by Habitat interaction.


There may also be some sort of interaction with time (either as Time, or 
Time Since Treatment)



Must I remove data to create blocks of sampled or is 'all data useful'?

e.g. A's were the only plots sampled in 2010- should I remove that
year completely?
e.g. C1 & C5 were sampled in 2005 while the rest were not until 2011,-
should I only include data from 2011 onwards for all C's?
e.g. Should I remove A4 completely since its only sampled in the last
few years or its still useable?
No, you should be able to use all of the data, you just have to be a bit 
careful about how you model Time.

Can I include S in the analyses in order to compare them with B and C treated 
plots in relation to A plots?

Yes, in principal. It just doesn't have a Habitat:Treatment interaction.


I have already analysed my first research question
Q1) To understanding the differences in diversity and composition
across control habitats, irrelevant of time.

The analysis approach I used for this is:
i) Mixed effect model:  GLMM PQL (Penalised Quasi-Likelihood) using
MASS R package.
 Diversity ~   fixed effect = habitat type + random effect = year ,
Family = poisson
There are better tools than glmmPQL nowadays. Have a look at the lme4 
package, for example.

ii) Pairwise permutational multivariate analysis of variance (MANOVA)
with R code based on the adonis2 function, to determine if the
composition among habitats (visualised in NMDS) were significantly
different from each other.

iii) RDA with habitat as explanatory and year as covariate to test
explained variance.

Now I am trying to expand this analysis to include a temporal element
to answer Q2 & Q3
Q2) to understand the trends in diversity and composition over time in
control habitats
Q3) to understand the impact of treatment on diversity and composition
(over time if possible?)

The addition of time into the analyses is a bit difficult for me to
work out, due to the 

[R-sig-eco] Mixed models and multivariate methods for temporal-spatial nested data

[Tania Bird]

Hi all,

I am seeking advice on how to analyse my unbalanced, multi-nested
multivariate data set. I realise there are many questions in this
email and I would be willing to consult with someone privately on this
if it is an option.

I am using abundance data for insect species (I have the same
experimental design for reptiles, and annual plants as well). I use
Simpson's diversity as a univariate response and species composition
as a multivariate response.

Experimental Design:
Plots are divided into three habitat types A, B, C based on vegetation.
Each habitat has 3 or 4 replicate control plots that are repeat
sampled (one sample a year always in spring).
In addition B and C have 3 or 4 treatment (vegetation removal) plots.
 'S' plots are disturbed( trampling and off-road vehicles) but the
disturbance is unquantified and I don't know the pre-disturbance
habitat type.

The total data set is across a 12 year period, but the sampling was
unbalanced for various reasons. I attach a png of the metadata of the
plots over time to show the unbalanced sampling.
https://www.dropbox.com/s/7vxvo3x9lnywdbm/insects_years.gif?dl=0

Each year the sampling across plots was conducted at the same time,
and so plots are comparable within a year.
In general, As were sampled every year and are considered the 'target'
habitat. B's were sampled in the earlier years and C's later on, and
in the last couple of years all three types were sampled together.

The treatments on B & C were conducted using different methods and in
different years, so in principle I should probably test each
separately just against their own control pairs. However the
hypothesis for both treatments is that treated plots will be more
similar in composition to A plots than the paired control plots (if
possible I want to check if they become more or less similar to A over
time).

So in that regard I thought there might be a way to include all
habitat types in one analysis? Perhaps using time as "number of years
since treatment" rather than a date? (Although I have no environmental
data with which to standardise).  S dunes have no "pre-treatment"  but
the hypothesis is that S plots will be most similar to A compared to
all other (treated and control) plot types.  I am not sure how to
include these plots in a testable model.

Questions regarding the design:

>> Can I use all the habitat types in one model (preferable!) or can I only 
>> test B treated against B control etc?

>>Must I remove data to create blocks of sampled or is 'all data useful'?
e.g. A's were the only plots sampled in 2010- should I remove that
year completely?
e.g. C1 & C5 were sampled in 2005 while the rest were not until 2011,-
should I only include data from 2011 onwards for all C's?
e.g. Should I remove A4 completely since its only sampled in the last
few years or its still useable?

>> Can I include S in the analyses in order to compare them with B and C 
>> treated plots in relation to A plots?

I have already analysed my first research question
Q1) To understanding the differences in diversity and composition
across control habitats, irrelevant of time.

The analysis approach I used for this is:
i) Mixed effect model:  GLMM PQL (Penalised Quasi-Likelihood) using
MASS R package.
Diversity ~   fixed effect = habitat type + random effect = year ,
Family = poisson

ii) Pairwise permutational multivariate analysis of variance (MANOVA)
with R code based on the adonis2 function, to determine if the
composition among habitats (visualised in NMDS) were significantly
different from each other.

iii) RDA with habitat as explanatory and year as covariate to test
explained variance.

Now I am trying to expand this analysis to include a temporal element
to answer Q2 & Q3
Q2) to understand the trends in diversity and composition over time in
control habitats
Q3) to understand the impact of treatment on diversity and composition
(over time if possible?)

The addition of time into the analyses is a bit difficult for me to
work out, due to the multi-nested and unbalanced design of the data; I
am not sure what methods to use to include time as a variable for
looking at a) diversity and b) composition

Questions regarding analyses:
>1> Is there an appropriate mixed effect model I can use to look at differences 
>in diversity on different control plots and include time as a factor (rather 
>than as a random effect)?

>2> How can I appropriately test if different habitats exhibit different trends 
>in composition over time (ie. a multivariate approach). For example, I might 
>expect that A's will remain relatively stable over time, while C's will 
>exhibit high turnover (fluctuation) across years, or that B's will slowly 
>shift composition to be more similar to C. How can I test these directional 
>hypotheses?

I thought to create a Principle Response Curve to see relative
differences over time, but as far as I understand, I cannot use a
permutation test here due to the unbalance design. I also 

Re: [R-sig-eco] sending a query with a graphic

[Sarah Goslee]

Hello all,

Several useful suggestions have already been provided, but just for
clarification:

This list, like all the R-sig lists, follows the R mailing list
guidelines available at:
https://www.r-project.org/mail.html

Most binary files are stripped, but small PNG files are allowed, as
are PS and PDF.

Please also post in plain text rather than HTML.

Sarah, list owner



On Mon, Feb 27, 2017 at 5:02 AM, Tania Bird  wrote:
> Hi all,
>
> I just tried to post my first posting requesting help with some
> multivariate statistics to this mailing list, but I got an email saying it
> was rejected because it has a graphic.. I think the graphic is important to
> explain my question-
> Are we allowed to post small gif or jpeg images to aid the questions? What
> format can I use to attach or embed some low resolution format?
>
> Many thanks for your advice
>
>
> Tania Bird MSc
> *PhD Student*
> *Geo-Ecology Lab*
> *Ben Gurion University*
>
> [[alternative HTML version deleted]]
>
> ___
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
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-- 
Sarah Goslee
http://www.functionaldiversity.org

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Re: [R-sig-eco] sending a query with a graphic

[Thomas Petzoldt]

Am 27.02.2017 um 12:52 schrieb Evan Palmer-Young:

Try posting a link to a file on cloud storage


... or post a small R snippet that creates the figure.

Thomas


On Feb 27, 2017 05:03, "Tania Bird"  wrote:

Hi all,

I just tried to post my first posting requesting help with some
multivariate statistics to this mailing list, but I got an email saying it
was rejected because it has a graphic.. I think the graphic is important to
explain my question-
Are we allowed to post small gif or jpeg images to aid the questions? What
format can I use to attach or embed some low resolution format?

Many thanks for your advice


Tania Bird MSc
*PhD Student*
*Geo-Ecology Lab*
*Ben Gurion University*

[[alternative HTML version deleted]]



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Re: [R-sig-eco] sending a query with a graphic

[Evan Palmer-Young]

Try posting a link to a file on cloud storage

On Feb 27, 2017 05:03, "Tania Bird"  wrote:

Hi all,

I just tried to post my first posting requesting help with some
multivariate statistics to this mailing list, but I got an email saying it
was rejected because it has a graphic.. I think the graphic is important to
explain my question-
Are we allowed to post small gif or jpeg images to aid the questions? What
format can I use to attach or embed some low resolution format?

Many thanks for your advice


Tania Bird MSc
*PhD Student*
*Geo-Ecology Lab*
*Ben Gurion University*

[[alternative HTML version deleted]]

___
R-sig-ecology mailing list
R-sig-ecology@r-project.org
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[[alternative HTML version deleted]]

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