Planarplot {CMA} | R Documentation |
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
.
Planarplot(X, y, f, learnind, predind, classifier, gridsize = 100, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
|
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix.
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)