dapcGraphics: Graphics for Discriminant Analysis of Principal Components...
In adegenet: Exploratory Analysis of Genetic and Genomic Data

dapc graphics

R Documentation

Graphics for Discriminant Analysis of Principal Components (DAPC)

Description

These functions provide graphic outputs for Discriminant Analysis of Principal Components (DAPC, Jombart et al. 2010). See ?dapc for details about this method. DAPC graphics are detailed in the DAPC tutorial accessible using vignette("adegenet-dapc").

These functions all require an object of class dapc (the ".dapc" can be ommitted when calling the functions):
- scatter.dapc: produces scatterplots of principal components (or 'discriminant functions'), with a screeplot of eigenvalues as inset.
- assignplot: plot showing the probabilities of assignment of individuals to the different clusters.

Usage

## S3 method for class 'dapc'
scatter(x, xax=1, yax=2, grp=x$grp, col=seasun(length(levels(grp))),
      pch=20, bg="white", solid=.7, scree.da=TRUE,
      scree.pca=FALSE, posi.da="bottomright",
      posi.pca="bottomleft", bg.inset="white", ratio.da=.25,
      ratio.pca=.25, inset.da=0.02, inset.pca=0.02,
      inset.solid=.5, onedim.filled=TRUE, mstree=FALSE, lwd=1,
      lty=1, segcol="black", legend=FALSE, posi.leg="topright",
      cleg=1, txt.leg=levels(grp), cstar = 1, cellipse = 1.5,
      axesell = FALSE, label = levels(grp), clabel = 1, xlim =
      NULL, ylim = NULL, grid = FALSE, addaxes = TRUE, origin =
      c(0,0), include.origin = TRUE, sub = "", csub = 1, possub =
      "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area
      = NULL, label.inds = NULL, ...)

assignplot(x, only.grp=NULL, subset=NULL, new.pred=NULL, cex.lab=.75,pch=3)

Arguments

`x`	a `dapc` object.
`xax,yax`	`integers` specifying which principal components of DAPC should be shown in x and y axes.
`grp`	a factor defining group membership for the individuals. The scatterplot is optimal only for the default group, i.e. the one used in the DAPC analysis.
`col`	a suitable color to be used for groups. The specified vector should match the number of groups, not the number of individuals.
`pch`	a `numeric` indicating the type of point to be used to indicate the prior group of individuals (see `points` documentation for more details); one value is expected for each group; recycled if necessary.
`bg`	the color used for the background of the scatterplot.
`solid`	a value between 0 and 1 indicating the alpha level for the colors of the plot; 0=full transparency, 1=solid colours.
`scree.da`	a logical indicating whether a screeplot of Discriminant Analysis eigenvalues should be displayed in inset (TRUE) or not (FALSE).
`scree.pca`	a logical indicating whether a screeplot of Principal Component Analysis eigenvalues should be displayed in inset (TRUE) or not (FALSE); retained axes are displayed in black.
`posi.da`	the position of the inset of DA eigenvalues; can match any combination of "top/bottom" and "left/right".
`posi.pca`	the position of the inset of PCA eigenvalues; can match any combination of "top/bottom" and "left/right".
`bg.inset`	the color to be used as background for the inset plots.
`ratio.da`	the size of the inset of DA eigenvalues as a proportion of the current plotting region.
`ratio.pca`	the size of the inset of PCA eigenvalues as a proportion of the current plotting region.
`inset.da`	a vector with two numeric values (recycled if needed) indicating the inset to be used for the screeplot of DA eigenvalues as a proportion of the current plotting region; see `?add.scatter` for more details.
`inset.pca`	a vector with two numeric values (recycled if needed) indicating the inset to be used for the screeplot of PCA eigenvalues as a proportion of the current plotting region; see `?add.scatter` for more details.
`inset.solid`	a value between 0 and 1 indicating the alpha level for the colors of the inset plots; 0=full transparency, 1=solid colours.
`onedim.filled`	a logical indicating whether curves should be filled when plotting a single discriminant function (TRUE), or not (FALSE).
`mstree`	a logical indicating whether a minimum spanning tree linking the groups and based on the squared distances between the groups inside the entire space should added to the plot (TRUE), or not (FALSE).
`lwd,lty,segcol`	the line width, line type, and segment colour to be used for the minimum spanning tree.
`legend`	a logical indicating whether a legend for group colours should added to the plot (TRUE), or not (FALSE).
`posi.leg`	the position of the legend for group colours; can match any combination of "top/bottom" and "left/right", or a set of x/y coordinates stored as a list (`locator` can be used).
`cleg`	a size factor used for the legend.
`cstar,cellipse,axesell,label,clabel,xlim,ylim,grid,addaxes,origin,include.origin,sub,csub,possub,cgrid,pixmap,contour,area`	arguments passed to `s.class`; see `?s.class` for more informations
`only.grp`	a `character` vector indicating which groups should be displayed. Values should match values of `x$grp`. If `NULL`, all results are displayed
`subset`	`integer` or `logical` vector indicating which individuals should be displayed. If `NULL`, all results are displayed
`new.pred`	an optional list, as returned by the `predict` method for `dapc` objects; if provided, the individuals with unknown groups are added at the bottom of the plot. To visualize these individuals only, specify `only.grp="unknown"`.
`cex.lab`	a `numeric` indicating the size of labels.
`txt.leg`	a character vector indicating the text to be used in the legend; if not provided, group names stored in `x$grp` are used.
`label.inds`	Named list of arguments passed to the `orditorp` function. This will label individual points witout overlapping. Arguments `x` and `display` are hardcoded and should not be specified by user.
`...`	further arguments to be passed to other functions. For `scatter`, arguments passed to `points`; for `compoplot`, arguments passed to `barplot`.

Details

See the documentation of dapc for more information about the method.

Value

All functions return the matched call.

Author(s)

Thibaut Jombart t.jombart@imperial.ac.uk

References

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

Examples

## Not run: 
data(H3N2)
dapc1 <- dapc(H3N2, pop=H3N2$other$epid, n.pca=30,n.da=6)

## defautl plot ##
scatter(dapc1)

## label individuals at the periphery
# air = 2 is a measure of how much space each label needs
# pch = NA suppresses plotting of points
scatter(dapc1, label.inds = list(air = 2, pch = NA))

## showing different scatter options ##
## remove internal segments and ellipses, different pch, add MStree
scatter(dapc1, pch=18:23, cstar=0, mstree=TRUE, lwd=2, lty=2, posi.da="topleft")

## only ellipse, custom labels, use insets
scatter(dapc1, cell=2, pch="", cstar=0, posi.pca="topleft", posi.da="topleft", scree.pca=TRUE,
inset.pca=c(.01,.3), label=paste("year\n",2001:2006), axesel=FALSE, col=terrain.colors(10))

## without ellipses, use legend for groups
scatter(dapc1, cell=0, cstar=0, scree.da=FALSE, clab=0, cex=3,
solid=.4, bg="white", leg=TRUE, posi.leg="topleft")

## only one axis
scatter(dapc1,1,1,scree.da=FALSE, legend=TRUE, solid=.4,bg="white")



## example using genlight objects ##
## simulate data
x <- glSim(50,4e3-50, 50, ploidy=2)
x
plot(x)

## perform DAPC
dapc2 <- dapc(x, n.pca=10, n.da=1)
dapc2

## plot results
scatter(dapc2, scree.da=FALSE, leg=TRUE, txt.leg=paste("group",
c('A','B')), col=c("red","blue"))

## SNP contributions
loadingplot(dapc2$var.contr)
loadingplot(tail(dapc2$var.contr, 100), main="Loading plot - last 100 SNPs")



## assignplot / compoplot ##
assignplot(dapc1, only.grp=2006)

data(microbov)
dapc3 <- dapc(microbov, n.pca=20, n.da=15)
compoplot(dapc3, lab="")

## End(Not run)

adegenet documentation built on Feb. 16, 2023, 6 p.m.