# icr.formula

From caret v6.0-24
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
`NA`

s 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 model the results of `lm`

after the ICA transformationica pre-processing information n.comp number of ICA components names column names of the original data

##### See Also

##### Examples

```
data(BloodBrain)
icrFit <- icr(bbbDescr, logBBB, n.comp = 5)
icrFit
predict(icrFit, bbbDescr[1:5,])
```

*Documentation reproduced from package caret, version 6.0-24, License: GPL-2*

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