Testing difference between diversity indices with vegan::oecosimu

@David,
to ask for statistical *and* biological significance (effect size) is
indeed essential and
this statement does apply in general - but it does not contribute to a
solution of my problem at all..

@Chris,
as I want to test total diversity of each community (diversity calculated
from samples collapsed over groups) I have only two values and thus
ANOVA-like tests are not an option.

Kind regards,
Kay

ps: Sorry Chris for double posting.

2012/4/26 Chris Howden <chris at trickysolutions.com.au>

Standard Hypothesis statistical testing often starts with the null
hypothesis that 2 things are identical, or that 2 population means are
identical. The p value is then used to reject this null and accept the
alternative, that they are indeed different.

Practically we're actually asking if we have enough information to indeed
say they are different.

I do agree though that stopping there is a bit silly. If there is a
statistical difference then we next need to look at the effect size or in
other words the magnitude of the difference and decide if this is
ecologically meaningful.


Chris Howden B.Sc. (Hons) GStat.
Founding Partner
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling and Training
(mobile) 0410 689 945
(fax) +612 4782 9023
chris at trickysolutions.com.au




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-----Original Message-----
From: r-sig-ecology-bounces at r-project.org
[mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of David Valentim
Dias
Sent: Thursday, 26 April 2012 2:36 PM
To: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Testing difference between diversity indices with
vegan::oecosimu

Hello Cichini,

I cannot help with your code but seems like you have a silly hypothesis.
Think about it: Probability of two communities to be identical?
You need to restate it in some more useful way. We already know most
things are different but with what magnitude? Which factors are causing
these changes? How these changes matter from the environment and us?

2012/4/25 Chris Howden <chris at trickysolutions.com.au>

Why not try some type of ANOVA style glm?

Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and
Innovation, Data Analysis, Modelling and Training

(mobile) 0410 689 945
(fax / office)
chris at trickysolutions.com.au

Disclaimer: The information in this email and any attachments to it
are confidential and may contain legally privileged information. If
you are not the named or intended recipient, please delete this
communication and contact us immediately. Please note you are not
authorised to copy, use or disclose this communication or any
attachments without our consent. Although this email has been checked
by anti-virus software, there is a risk that email messages may be
corrupted or infected by viruses or other interferences. No
responsibility is accepted for such interference. Unless expressly
stated, the views of the writer are not those of the company. Tricky
Solutions always does our best to provide accurate forecasts and
analyses based on the data supplied, however it is possible that some
important predictors were not included in the data sent to us.
Information provided by us should not be solely relied upon when
making decisions and clients should use their own judgement.

On 26/04/2012, at 7:19, Kay Cichini <kay.cichini at gmail.com> wrote:

Hello all,

I'd like to test if total diversity differs between two communities.
For each community several samples were taken and abundances
collapsed over groups to compute total diversity for each group. I
tried to use vegan::oecosimu to test non-randomness of my statisitc
(difference in Simpson-Diversity indices of collapsed abundances) -
however, I am not quite sure if I oversee posssible pitfalls:

library(vegan)
data(dune)

# a grouping variable:
gr <- gl(2, nrow(dune)/2)

divdiff <- function(x) abs(diversity(colSums(x[gr == "1", ]), "simp")

-

                         diversity(colSums(x[gr == "2", ]),
"simp")) # testing function:
divdiff(dune)

oecosimu(dune, divdiff, "r2dtable", nsimul = 1999) # oecosimu with
1999 simulations # simulation method r2dtable # alternative
hypothesis: true mean is not equal to the statistic
#           statistic        z     2.5%      50% 97.5% Pr(sim.)
# statistic   0.00275 -0.20996  0.00013  0.00280  0.01     0.98

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