Perform Analysis of Molecular Variance (AMOVA) on genind or genclone objects.
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
poppr.amova(x, hier = NULL, clonecorrect = FALSE, within = TRUE, dist = NULL, squared = TRUE, correction = "quasieuclid", sep = "_", filter = FALSE, threshold = 0, algorithm = "farthest_neighbor", missing = "loci", cutoff = 0.05, quiet = FALSE, method = c("ade4", "pegas"), nperm = 0)
- a hierarchical
formulathat defines your population hierarchy. (e.g.: ~Population/Subpopulation). See Details below.
TRUE, the data set will be clone corrected with respect to the lowest level of the hierarchy. The default is set to
logical. When this is set to
TRUE(Default), variance within individuals are calculated as well. If this is set to
FALSE, The lowest level of the hierarchy will be the sample level. See Details below.
- an optional distance matrix calculated on your data. If this is
NULL(default), the raw pairwise distances will be calculated via
- if a distance matrix is supplied, this indicates whether or not it represents squared distances.
characterdefining the correction method for non-euclidean distances. Options are
cailliez. See Details below.
- Deprecated. As of poppr version 2, this argument serves no purpose.
logicalWhen set to
TRUE, mlg.filter will be run to determine genotypes from the distance matrix. It defaults to
FALSE. You can set the parameters with
thresholdarguments. Note that this will not be performed when
within = TRUE. Note that the threshold should be the number of allowable substitutions if you don't supply a distance matrix.
- 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.
- determines the type of clustering to be done.
- specify method of correcting for missing data utilizing
options given in the function
missingno. Default is
- specify the level at which missing data should be
FALSE(Default), messages regarding any corrections will be printed to the screen. If
TRUE, no messages will be printed.
- Which method for calculating AMOVA should be used? Choices refer to package implementations: "ade4" (default) or "pegas". See details for differences.
- the number of permutations passed to the pegas implementation of amova.
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
- a distance matrix on all unique genotypes (haplotypes)
- a data frame defining the hierarchy of the distance matrix
- a genotype (haplotype) frequency table.
genindobject, 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
gencloneobjects. They are useful for defining the population factor for your data. See the function
stratafor 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 within-individual
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
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
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 within-individual variance. The distance matrix used
to calculate the amova is derived from using
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.
a list of class
amovafrom the ade4 package. See
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 re-converting the results with the function
as.krandtest. See examples for details.
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, 479-491.
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)