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Adonis for significance of clusteredness from hclust (vegan package)

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Adonis for significance of clusteredness from hclust (vegan package)

Ansley Silva
Hello:

I have created a dendrograms using hierarchical cluster analysis with the
vegan package (function: hclust).

By visually observing the dendrogram, I have determined that there are 3
main clusters if I "cut" the tree at the height 0.25  (please see the
dendrogram from the code).
I then created a new dataset, which is essentially the same as the
original, but I have added the categorical variable Group to represent
these 3 main clusters.
ST0 is group a, AP0 and AP100 is group b, and AP200 AP300 ST100 ST200 ST
300 is group c.
I want to now if they are significantly different from each other.  I
understand, from the output pasted below, that I can accept that there is a
significant effect of Group.  Is this the only thing I can say from
Permanova?  What would be the code for a follow up test to look at
pair-wise significant differences?
Thanks very much.

Call:
adonis(formula = species ~ Group, data = environ, permutations = 999)

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

          Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
Group      2   0.40244 0.201219   4.969 0.66528  0.007 **
Residuals  5   0.20248 0.040495         0.33472
Total      7   0.60492                  1.00000
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


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Re: Adonis for significance of clusteredness from hclust (vegan package)

Zoltan Botta-Dukat-2
Dear Ansley,

I cannot answer your question, I hope someone else will answer. I'd
rather point out a problem in your approach. Statistical tests were
developed for testing difference between a priori groups, thus estimated
Type I error rate is valid only for this situation. When you calculates
Type I error rate for comparison of groups created by cluster analysis
of the SAME data, the calculated error rate will be lower than the valid
error rate. So you cannot use the term "significant" in this situation.

Sorry for making you sadden by this information.

Zoltan

2016.10.03. 21:52 keltez�ssel, Ansley Silva �rta:

> Hello:
>
> I have created a dendrograms using hierarchical cluster analysis with the
> vegan package (function: hclust).
>
> By visually observing the dendrogram, I have determined that there are 3
> main clusters if I "cut" the tree at the height 0.25  (please see the
> dendrogram from the code).
> I then created a new dataset, which is essentially the same as the
> original, but I have added the categorical variable Group to represent
> these 3 main clusters.
> ST0 is group a, AP0 and AP100 is group b, and AP200 AP300 ST100 ST200 ST
> 300 is group c.
> I want to now if they are significantly different from each other.  I
> understand, from the output pasted below, that I can accept that there is a
> significant effect of Group.  Is this the only thing I can say from
> Permanova?  What would be the code for a follow up test to look at
> pair-wise significant differences?
> Thanks very much.
>
> Call:
> adonis(formula = species ~ Group, data = environ, permutations = 999)
>
> Permutation: free
> Number of permutations: 999
>
> Terms added sequentially (first to last)
>
>            Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
> Group      2   0.40244 0.201219   4.969 0.66528  0.007 **
> Residuals  5   0.20248 0.040495         0.33472
> Total      7   0.60492                  1.00000
> ---
> Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
>
>
>
>
> _______________________________________________
> R-sig-ecology mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology

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Re: Adonis for significance of clusteredness from hclust (vegan package)

Pierre THIRIET
Dear Ansley,

I agree with Zoltan.

I suggest SIMPROF test in package "clustsig". A complimentary package
for use with hclust; simprof tests to see which (if any) clusters are
statistically different. The null hypothesis is that there is no a
priori group structure.
See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008. Testing of
null hypothesis in exploratory community analyses: similarity profiles
and biota-environment linkage. J. Exp. Mar. Biol. Ecol. 366, 56-69

HTH
Pierre

Le 04/10/2016 à 14:14, Zoltan Botta-Dukat a écrit :

> Dear Ansley,
>
> I cannot answer your question, I hope someone else will answer. I'd
> rather point out a problem in your approach. Statistical tests were
> developed for testing difference between a priori groups, thus estimated
> Type I error rate is valid only for this situation. When you calculates
> Type I error rate for comparison of groups created by cluster analysis
> of the SAME data, the calculated error rate will be lower than the valid
> error rate. So you cannot use the term "significant" in this situation.
>
> Sorry for making you sadden by this information.
>
> Zoltan
>
> 2016.10.03. 21:52 keltez�ssel, Ansley Silva �rta:
>> Hello:
>>
>> I have created a dendrograms using hierarchical cluster analysis with the
>> vegan package (function: hclust).
>>
>> By visually observing the dendrogram, I have determined that there are 3
>> main clusters if I "cut" the tree at the height 0.25  (please see the
>> dendrogram from the code).
>> I then created a new dataset, which is essentially the same as the
>> original, but I have added the categorical variable Group to represent
>> these 3 main clusters.
>> ST0 is group a, AP0 and AP100 is group b, and AP200 AP300 ST100 ST200 ST
>> 300 is group c.
>> I want to now if they are significantly different from each other.  I
>> understand, from the output pasted below, that I can accept that there is a
>> significant effect of Group.  Is this the only thing I can say from
>> Permanova?  What would be the code for a follow up test to look at
>> pair-wise significant differences?
>> Thanks very much.
>>
>> Call:
>> adonis(formula = species ~ Group, data = environ, permutations = 999)
>>
>> Permutation: free
>> Number of permutations: 999
>>
>> Terms added sequentially (first to last)
>>
>>             Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
>> Group      2   0.40244 0.201219   4.969 0.66528  0.007 **
>> Residuals  5   0.20248 0.040495         0.33472
>> Total      7   0.60492                  1.00000
>> ---
>> Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
>>
>>
>>
>>
>> _______________________________________________
>> R-sig-ecology mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
>
>
> _______________________________________________
> R-sig-ecology mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology


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Re: Adonis for significance of clusteredness from hclust (vegan package)

Graciliano Galdino
pvclust is a good package to create and test significance of cluster groups.

2016-10-05 11:31 GMT-03:00 Pierre THIRIET <[hidden email]>:

> Dear Ansley,
>
> I agree with Zoltan.
>
> I suggest SIMPROF test in package "clustsig". A complimentary package
> for use with hclust; simprof tests to see which (if any) clusters are
> statistically different. The null hypothesis is that there is no a
> priori group structure.
> See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008. Testing of
> null hypothesis in exploratory community analyses: similarity profiles
> and biota-environment linkage. J. Exp. Mar. Biol. Ecol. 366, 56-69
>
> HTH
> Pierre
>
> Le 04/10/2016 à 14:14, Zoltan Botta-Dukat a écrit :
> > Dear Ansley,
> >
> > I cannot answer your question, I hope someone else will answer. I'd
> > rather point out a problem in your approach. Statistical tests were
> > developed for testing difference between a priori groups, thus estimated
> > Type I error rate is valid only for this situation. When you calculates
> > Type I error rate for comparison of groups created by cluster analysis
> > of the SAME data, the calculated error rate will be lower than the valid
> > error rate. So you cannot use the term "significant" in this situation.
> >
> > Sorry for making you sadden by this information.
> >
> > Zoltan
> >
> > 2016.10.03. 21:52 keltez�ssel, Ansley Silva �rta:
> >> Hello:
> >>
> >> I have created a dendrograms using hierarchical cluster analysis with
> the
> >> vegan package (function: hclust).
> >>
> >> By visually observing the dendrogram, I have determined that there are 3
> >> main clusters if I "cut" the tree at the height 0.25  (please see the
> >> dendrogram from the code).
> >> I then created a new dataset, which is essentially the same as the
> >> original, but I have added the categorical variable Group to represent
> >> these 3 main clusters.
> >> ST0 is group a, AP0 and AP100 is group b, and AP200 AP300 ST100 ST200 ST
> >> 300 is group c.
> >> I want to now if they are significantly different from each other.  I
> >> understand, from the output pasted below, that I can accept that there
> is a
> >> significant effect of Group.  Is this the only thing I can say from
> >> Permanova?  What would be the code for a follow up test to look at
> >> pair-wise significant differences?
> >> Thanks very much.
> >>
> >> Call:
> >> adonis(formula = species ~ Group, data = environ, permutations = 999)
> >>
> >> Permutation: free
> >> Number of permutations: 999
> >>
> >> Terms added sequentially (first to last)
> >>
> >>             Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
> >> Group      2   0.40244 0.201219   4.969 0.66528  0.007 **
> >> Residuals  5   0.20248 0.040495         0.33472
> >> Total      7   0.60492                  1.00000
> >> ---
> >> Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
> >>
> >>
> >>
> >>
> >> _______________________________________________
> >> R-sig-ecology mailing list
> >> [hidden email]
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> >
> >
> > _______________________________________________
> > R-sig-ecology mailing list
> > [hidden email]
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
>
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>
> _______________________________________________
> R-sig-ecology mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>



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Universidade Federal Rural da Amazônia - UFRA

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