# icr.formula

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##### Independent Component Regression

Fit a linear regression model using independent components

Keywords
multivariate
##### Usage
"icr"(formula, data, weights, ..., subset, na.action, contrasts = NULL)
"icr"(x, y, ...)
"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 argument must be named.)
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
model
the results of lm after the ICA transformation
ica
pre-processing information
n.comp
number of ICA components
names
column names of the original data

fastICA, preProcess, lm

• icr.formula
• icr.default
• icr
• predict.icr
##### Examples
data(BloodBrain)

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

icrFit

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


Documentation reproduced from package caret, version 6.0-70, License: GPL (>= 2)

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