# [adegenet-forum] a.score versus cross validation and number of discriminant functions to retain

Ella Bowles ebowles at ucalgary.ca
Tue Oct 20 20:40:18 CEST 2015

```Hello,

I think I have worked my way through a DAPC analysis, and it's pretty neat.
I have five questions though. By way of background, I am using a SNP
dataset with 11 putative populations (clusters), containing 4099 SNPs. I've
converted a structure file to genInd, and am using that.

1) Am I correct in understanding that the number of clusters you find
should inform the number of colours that you list for your DAPC plot?

2) I'm not quite sure how to interpret the following. How do I know if the
fit is good?

[image: Inline image 1]

​3 and 4) Is there a function that I can use to correlate the colours with
my original populations. I do have this information in the datafile that I
fed in. And, does 300 sound reasonable for the number of discriminant
functions to retain?

> dapc1 <- dapc(data_full, NumClust\$grp)

Choose the number PCs to retain (>=1): 40

Choose the number discriminant functions to retain (>=1): 300

#making colours for 9 clusters, since optimal k was 9 with the data
containing zeros

myCol <- c("red", "orange", "yellow", "green", "blue", "purple", "violet",
"grey", "brown")

scatter(dapc1, scree.da=FALSE, bg="white", pch=20, cell=0, cstar=0,
col=myCol, solid=.4, cex=1, clab=0, leg=TRUE, txt.leg=paste("Cluster", 1:9))
[image: Inline image 2]​
​
5) I don't really understand the difference between the optim a score and
the cross validation analyses. Both seem to be determining what is the best
number of PCs to retain. However, they give very different results. Am I
misunderstanding what they are?

#for "data_full" dataset

dapc2 <- dapc(data_full, n.da=300, n.pca=50)

temp <- optim.a.score(dapc2)

#graph shows that highest alpha seems to be 8
​[image: Inline image 3]​
​#cross-validation for number of PCs to retain –can only do using data_full
(this is called “mat” here), couldn’t get it to work using data with zeros

mat <- scaleGen(data, NA.method="mean")

grp <- pop(data)

xval <- xvalDapc(mat, grp, n.pca.max = 100, training.set = 0.9, result =
"groupMean", center = TRUE, scale = FALSE, n.pca = NULL, n.rep = 30,
xval.plot = TRUE)

xval[2:6]

#results

Confidence Interval for Random Chance`

2.5%        50%      97.5%

0.05659207 0.09212947 0.14164194

\$`Mean Successful Assignment by Number of PCs of PCA`

10        20        30        40        50        60
70        80        90

0.8409091 0.8348485 0.8439394 0.8530303 0.8136364 0.8227273 0.8000000
0.8075758 0.8075758

\$`Number of PCs Achieving Highest Mean Success`

[1] "40"

\$`Root Mean Squared Error by Number of PCs of PCA`

10        20        30        40        50        60
70        80        90

0.1702777 0.1770200 0.1649359 0.1607061 0.2007218 0.1864929 0.2138458
0.2051338 0.2074707

\$`Number of PCs Achieving Lowest MSE`
[1] "40"
[image: Inline image 4]​

​Thank you very much for your time, and sincerely,
Ella Bowles​

--
Ella Bowles
PhD Candidate
Biological Sciences
University of Calgary

e-mail: ebowles at ucalgary.ca, bowlese at gmail.com
website: http://ellabowlesphd.wordpress.com/
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