Cross-validation for Discriminant Analysis of Principal Components (DAPC)
The function xvalDapc performs stratified cross-validation of DAPC
using varying numbers of PCs (and keeping the number of discriminant
functions fixed); xvalDapc is a generic with methods for
data.frame and matrix.
xvalDapc(x, ...)
## Default S3 method:
xvalDapc(x, grp, n.pca.max = 300, n.da = NULL,
training.set = 0.9, result = c("groupMean", "overall"),
center = TRUE, scale = FALSE,
n.pca=NULL, n.rep = 30, xval.plot = TRUE, ...)
## S3 method for class 'data.frame'
xvalDapc(x, grp, n.pca.max = 300, n.da = NULL,
training.set = 0.9, result = c("groupMean", "overall"),
center = TRUE, scale = FALSE,
n.pca=NULL, n.rep = 30, xval.plot = TRUE, ...)
## S3 method for class 'matrix'
xvalDapc(x, grp, n.pca.max = 300, n.da = NULL,
training.set = 0.9, result = c("groupMean", "overall"),
center = TRUE, scale = FALSE,
n.pca=NULL, n.rep = 30, xval.plot = TRUE, ...)
## S3 method for class 'genlight'
xvalDapc(x, ...)
## S3 method for class 'genind'
xvalDapc(x, ...)x |
|
grp |
a |
n.pca.max |
maximum number of PCA components to retain. |
n.da |
an |
training.set |
the proportion of data (individuals) to be used for the training set; defaults to 0.9 if all groups have >= 10 members; otherwise, training.set scales automatically to the largest proportion that still ensures all groups will be present in both training and validation sets. |
result |
a character string; "groupMean" for group-wise assignment sucess, or "overall" for an overall mean assignment success; see details. |
center |
a |
scale |
a |
n.pca |
an |
n.rep |
the number of replicates to be carried out at each level of PC retention; defaults to 30. |
xval.plot |
a logical indicating whether a plot of the cross-validation results should be generated. |
... |
further arguments to be passed to |
The Discriminant Analysis of Principal Components (DAPC) relies on dimension reduction of the data using PCA followed by a linear discriminant analysis. How many PCA axes to retain is often a non-trivial question. Cross validation provides an objective way to decide how many axes to retain: different numbers are tried and the quality of the corresponding DAPC is assessed by cross- validation: DAPC is performed on a training set, typically made of 90% of the observations (comprising 90% of the observations in each subpopulation) , and then used to predict the groups of the 10% of remaining observations. The current method uses the average prediction success per group (result="groupMean"), or the overall prediction success (result="overall"). The number of PCs associated with the lowest Mean Squared Error is then retained in the DAPC.
The permutation of the data for cross-validation is performed in part by the
functionboot. If you have a modern computer, it is
likely that you have multiple cores on your system. R by default utilizes
only one of these cores unless you tell it otherwise. For details, please
see the documentation of boot. Basically, if you want to
use multiple cores, you need two arguments:
parallel - what R parallel system to use (see below)
ncpus - number of cores you want to use
If you are on a unix system (Linux or OSX), you will want to specify
parallel = "multicore". If you are on Windows, you will want to
specify parallel = "snow".
A list containing seven items, and a plot of the results. The
first is a data.frame with two columns, the first giving the number of
PCs of PCA retained in the corresponding DAPC, and the second giving the
proportion of successful group assignment for each replicate. The second item
gives the mean and confidence interval for random chance. The third gives the
mean successful assignment at each level of PC retention. The fourth indicates
which number of PCs is associated with the highest mean success. The fifth
gives the Root Mean Squared Error at each level of PC retention. The sixth
indicates which number of PCs is associated with the lowest MSE. The seventh
item contains the DAPC carried out with the optimal number of PCs, determined
with reference to MSE.
If xval.plot=TRUE a scatterplot of the results of cross-validation
will be displayed.
Caitlin Collins caitlin.collins12@imperial.ac.uk, Thibaut Jombart t.jombart@imperial.ac.uk, Zhian N. Kamvar kamvarz@science.oregonstate.edu
Jombart T, Devillard S and Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics11:94. doi:10.1186/1471-2156-11-94
## Not run:
## CROSS-VALIDATION ##
data(sim2pop)
xval <- xvalDapc(sim2pop@tab, pop(sim2pop), n.pca.max=100, n.rep=3)
xval
## 100 replicates ##
# Serial version (SLOW!)
system.time(xval <- xvalDapc(sim2pop@tab, pop(sim2pop), n.pca.max=100, n.rep=100))
# Parallel version (faster!)
system.time(xval <- xvalDapc(sim2pop@tab, pop(sim2pop), n.pca.max=100, n.rep=100,
parallel = "multicore", ncpus = 2))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.