Perform Analysis of Molecular Variance (AMOVA) on genind or genclone objects.
Description
This function simplifies the process necessary for performing AMOVA in R. It
gives user the choice of utilizing either the ade4 or the pegas
implementation of AMOVA. See amova
(ade4) and
amova
(pegas) for details on the specific
implementation.
Usage
1 2 3 4 5 
Arguments
x 
a 
hier 
a hierarchical 
clonecorrect 

within 

dist 
an optional distance matrix calculated on your data. If this is
set to 
squared 
if a distance matrix is supplied, this indicates whether or not it represents squared distances. 
correction 
a 
sep 
Deprecated. As of poppr version 2, this argument serves no purpose. 
filter 

threshold 
a number indicating the minimum distance two MLGs must be separated by to be considered different. Defaults to 0, which will reflect the original (naive) MLG definition. 
algorithm 
determines the type of clustering to be done.

missing 
specify method of correcting for missing data utilizing
options given in the function 
cutoff 
specify the level at which missing data should be
removed/modified. See 
quiet 

method 
Which method for calculating AMOVA should be used? Choices refer to package implementations: "ade4" (default) or "pegas". See details for differences. 
nperm 
the number of permutations passed to the pegas implementation of amova. 
Details
The poppr implementation of AMOVA is a very detailed wrapper for the
ade4 implementation. The output is an amova
class list
that contains the results in the first four elements. The inputs are
contained in the last three elements. The inputs required for the ade4
implementation are:
a distance matrix on all unique genotypes (haplotypes)
a data frame defining the hierarchy of the distance matrix
a genotype (haplotype) frequency table.
All of this data can be constructed from a genind
object, but can be daunting for a novice R user. This function
automates the entire process. Since there are many variables regarding
genetic data, some points need to be highlighted:
On Hierarchies:
The hierarchy is defined by different
population strata that separate your data hierarchically. These strata are
defined in the strata slot of genind
and
genclone
objects. They are useful for defining the
population factor for your data. See the function strata
for
details on how to properly define these strata.
On Within Individual Variance:
Heterozygosities within
diploid genotypes are sources of variation from within individuals and can
be quantified in AMOVA. When within = TRUE
, poppr will split diploid
genotypes into haplotypes and use those to calculate withinindividual
variance. No estimation of phase is made. This acts much like the default
settings for AMOVA in the Arlequin software package. Within individual
variance will not be calculated for haploid individuals or dominant
markers.
On Euclidean Distances:
AMOVA, as defined by
Excoffier et al., utilizes an absolute genetic distance measured in the
number of differences between two samples across all loci. With the ade4
implementation of AMOVA (utilized by poppr), distances must be Euclidean
(due to the nature of the calculations). Unfortunately, many genetic
distance measures are not always euclidean and must be corrected for before
being analyzed. Poppr automates this with three methods implemented in
ade4, quasieuclid
, lingoes
, and
cailliez
. The correction of these distances should not
adversely affect the outcome of the analysis.
On Filtering:
Filtering multilocus genotypes is performed by
mlg.filter
. This can necessarily only be done AMOVA tests
that do not account for withinindividual variance. The distance matrix used
to calculate the amova is derived from using mlg.filter
with
the option stats = "distance"
, which reports the distance between
multilocus genotype clusters. One useful way to utilize this feature is to
correct for genotypes that have equivalent distance due to missing data.
(See example below.)
On Methods:
Both ade4 and pegas have
implementations of AMOVA, both of which are appropriately called "amova".
The ade4 version is faster, but there have been questions raised as to the
validity of the code utilized. The pegas version is slower, but careful
measures have been implemented as to the accuracy of the method. It must be
noted that there appears to be a bug regarding permuting analyses where
within individual variance is accounted for (within = TRUE
) in the
pegas implementation. If you want to perform permutation analyses on the
pegas implementation, you must set within = FALSE
. In addition,
while clone correction is implemented for both methods, filtering is only
implemented for the ade4 version.
Value
a list of class amova
from the ade4 package. See
amova
for details.
Note
The ade4 function randtest.amova
contains a slight
bug as of version 1.7.4 which causes the wrong alternative hypothesis to be
applied on every 4th heirarchical level. Luckily, there is a way to fix it
by reconverting the results with the function
as.krandtest
. See examples for details.
References
Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479491.
See Also
amova
(ade4) amova
(pegas)
clonecorrect
diss.dist
missingno
is.euclid
strata
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  data(Aeut)
strata(Aeut) < other(Aeut)$population_hierarchy[1]
agc < as.genclone(Aeut)
agc
amova.result < poppr.amova(agc, ~Pop/Subpop)
amova.result
amova.test < randtest(amova.result) # Test for significance
plot(amova.test)
amova.test
## Not run:
# You can get the same results with the pegas implementation
amova.pegas < poppr.amova(agc, ~Pop/Subpop, method = "pegas")
amova.pegas
amova.pegas$varcomp/sum(amova.pegas$varcomp)
# Clone correction is possible
amova.cc.result < poppr.amova(agc, ~Pop/Subpop, clonecorrect = TRUE)
amova.cc.result
amova.cc.test < randtest(amova.cc.result)
plot(amova.cc.test)
amova.cc.test
# Example with filtering
data(monpop)
splitStrata(monpop) < ~Tree/Year/Symptom
poppr.amova(monpop, ~Symptom/Year) # gets a warning of zero distances
poppr.amova(monpop, ~Symptom/Year, filter = TRUE, threshold = 0.1) # no warning
# Correcting incorrect alternate hypotheses with >2 heirarchical levels
#
mon.amova < poppr.amova(monpop, ~Symptom/Year/Tree)
mon.test < randtest(mon.amova)
mon.test # Note alter is less, greater, greater, less
alt < c("less", "greater", "greater", "greater") # extend this to the number of levels
with(mon.test, as.krandtest(sim, obs, alter = alt, call = call, names = names))
## End(Not run)
