icr.formula

From caret v4.48 by Max Kuhn

Independent Component Regression

Fit a linear regression model using independent components

Keywords: multivariate

Usage

## S3 method for class 'formula':
icr(formula, data, weights, ..., subset, na.action, contrasts = NULL)
## S3 method for class 'default':
icr(x, y, ...)
## S3 method for class 'icr':
predict(object, newdata, ...)

Arguments

formula: A formula of the form class ~ x1 + x2 + ...
data: Data frame from which variables specified in formula are preferentially to be taken.
weights: (case) weights for each example -- if missing defaults to 1.
subset: An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
na.action: A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this
contrasts: a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
...: arguments passed to fastICA
x: matrix or data frame of x values for examples.
y: matrix or data frame of target values for examples.
object: an object of class icr as returned by icr.
newdata: matrix or data frame of test examples.

Details

This produces a model analogous to Principal Components Regression (PCR) but uses Independent Component Analysis (ICA) to produce the scores. The user must specify a value of n.comp to pass to fastICA.

The function preProcess to produce the ICA scores for the original data and for newdata.

Value

For icr, a list with elements
modelthe results of lm after the ICA transformation
icapre-processing information
n.compnumber of ICA components
namescolumn names of the original data

Examples

data(BloodBrain)

icrFit <- icr(bbbDescr, logBBB, n.comp = 5)

icrFit

predict(icrFit, bbbDescr[1:5,])

Documentation reproduced from package caret, version 4.48, License: GPL-2

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