[adegenet-forum] problems with na.replace and values for n.pca & n.da

Mon Oct 12 12:21:22 CEST 2015

Hi there,

your post suggests you are using an outdated version of adegenet - na.replace and some  arguments you are using have been removed from the package since version 2.0.0. This means you have been consulting an outdated version of the tutorials.. where did you find it? If there is outdated doc around I need to get rid of it.

Please update to the devel version (2.0.1) from github:
https://github.com/thibautjombart/adegenet

Current tutorials should answer your first question. Missing data are detected automatically if the right format is used. Let's wait to make sure your data were imported fine for the others.

Cheers
Thibaut

==============================
Dr Thibaut Jombart
MRC Centre for Outbreak Analysis and Modelling
Department of Infectious Disease Epidemiology
Imperial College - School of Public Health
Norfolk Place, London W2 1PG, UK
Tel. : 0044 (0)20 7594 3658
http://sites.google.com/site/thibautjombart/
http://sites.google.com/site/therepiproject/
http://adegenet.r-forge.r-project.org/
Twitter: @thibautjombart

________________________________
From: adegenet-forum-bounces at lists.r-forge.r-project.org [adegenet-forum-bounces at lists.r-forge.r-project.org] on behalf of D. Magdalena Sorger [dm.sorger at gmail.com]
Sent: 09 October 2015 20:00
To: adegenet-forum at lists.r-forge.r-project.org
Subject: [adegenet-forum] problems with na.replace and values for n.pca & n.da

Dear all,

I am trying to constuct a DAPC scatterplot with adegenet and have three questions that after consulting online resources, tutorials, etc. still haven't been answered definitively.

My data set consists of (diploid) microsatellite data (8 markers) for 20 ant colonies of 9-28 workers each (mean=15), 301 workers total. I have missing data in 7 spots (i.e. individuals with missing data at one or more loci). My first question is about reading in the genepop file:

1) What is the proper command for reading in my file in regards to missing data?

I had replaced all missing data ("0000") with "NA" in the genepop file and used the below code assuming that it would recognize my missing data as NA (first line of code) and replace missing values with means (second line):

msts_m2<-read.genepop("BOR-Od_m2_301w.gen",missing="NA")
na.replace(msts_m2,"mean", quiet=FALSE)

However, when I run this code, it informs me that it replaced 119 missing values. This obviously seems too much as it should have only replaced 7. I'm not sure why this isn't working, see output below

OUTPUT:
> na.replace(msts_m2,"mean", quiet=FALSE)

 Replaced 119 missing values

   #####################
   ### Genind object ###
   #####################
- genotypes of individuals -

S4 class:  genind
@call: read.genepop(file = "BOR-Od_m2_301w.gen", missing = "NA")

@tab:  301 x 93 matrix of genotypes

@ind.names: vector of  301 individual names
@loc.names: vector of  8 locus names
@loc.nall: number of alleles per locus
@loc.fac: locus factor for the  93 columns of @tab
@all.names: list of  8 components yielding allele names for each locus
@ploidy:  2
@type:  codom

Optionnal contents:
@pop:  factor giving the population of each individual
@pop.names:  factor giving the population of each individual

@other: - empty -

My second and third questions relate to the selection of the # of PCs and the # of discriminant functions to retain. It seems that each time I make slight changes to these numbers, the output changes vastly and so I want to make sure I input the proper numbers.

First I use the find.clusters function, I designate 20 for n.pca here since I have 20 colonies:

clusters<-find.clusters(msts_m2,max.n.pca=20)

When asked for the number of PCs to retain I select 30 which is the level at which the points seem to level off. The BIC graph looks nothing like the graph in the vignette (it does not level off but below a certain level shows a downward zigzag pattern) so I choose 20 given that I have 20 colonies.

dapc_m2<-dapc(msts_m2,clusters$grp)
[Inline image 1][Inline image 2]
Next, when asked for PCs to retain I again choose the level at which the points start to level off (30) but when asked for discriminant functions to retain, I'm at a loss. I have about 18 relatively constantly decreasing bars. I usually choose the point between the first few ones and the rest where there is some kind of bigger (sometimes arbitrary) break and use that number (in this case: 4):

2) How to choose the appropriate number for PCs to retain and discr. functions to retain?

[Inline image 3][Inline image 4]

best.n.pca<-a.score(dapc_m2)
temp<-optim.a.score(dapc_m2)

After running the optim.a.score function I receive a graph that tells me at the top "optimal number of PCs". This is the number I put into my last line of code (below) for n.pca, and for n.da I choose the number I used when prompted earlier:

3) Are these the correct numbers to use for this last line of code?

dapc_m2<-dapc(msts_m2,n.pca=7,n.da=4)

--
Magdalena Sorger
____________
Department of Applied Ecology
North Carolina State University
127 David Clark Labs, Box 7617
100 Eugene Brooks Ave.
Raleigh, NC 27695, USA
# 919-513-7464<tel:919-513-7464>
dmsorger at ncsu.edu<mailto:dmsorger at ncsu.edu>
www.theantlife.com<http://www.theantlife.com>
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