Planarplot: Visualize Separability of different classes
In CMA: Synthesis of microarray-based classification

Description Usage Arguments Value Author(s) See Also Examples

Given two variables, the methods trains a classifier (argument classifier) based on these two variables and plots the resulting class regions, learning- and test observations in the plane.

Appropriate variables are usually found by GeneSelection.

For S4 method information, s. Planarplot-methods.

1	Planarplot(X, y, f, learnind, predind, classifier, gridsize = 100, ...)

`X`	Gene expression data. Can be one of the following: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`.
`y`	Class labels. Can be one of the following: A `numeric` vector. A `factor`. A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable. `missing`, if `X` is a `data.frame` and a proper formula `f` is provided.
`f`	A two-sided formula, if `X` is a `data.frame`. The left part correspond to class labels, the right to variables.
`learnind`	An index vector specifying the observations that belong to the learning set. May be `missing`; in that case, the learning set consists of all observations and predictions are made on the learning set.
`predind`	A vector containing exactly two indices that denote the two variables used for classification.
`classifier`	Name of function ending with `CMA` indicating the classifier to be used.
`gridsize`	The gridsize used for two-dimensional plotting. For both variables specified in `predind`, an equidistant grid of size `gridsize` is created. The resulting two grids are then combined to obtain `gridsize^2` points in the real plane which are used to draw the class regions. Defaults to 100 which is usually a reasonable choice, but takes some time.
`...`	Further argument passed to `classifier`.

No return.

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de. Idea is from the MLInterfaces package, contributed by Jess Mar, Robert Gentleman and Vince Carey.

GeneSelection, compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA

### simple linear discrimination for the golub data:
data(golub)
golubY <- golub[,1]
golubX <- as.matrix(golub[,-1])
golubn <- nrow(golubX)
set.seed(111)
learnind <- sample(golubn, size=floor(2/3*golubn))
Planarplot(X=golubX, y=golubY, learnind=learnind, predind=c(2,4),
           classifier=ldaCMA)