# icr.formula

##### Independent Component Regression

Fit a linear regression model using independent components

- Keywords
- multivariate

##### Usage

```
# S3 method for formula
icr(formula, data, weights, ..., subset, na.action,
contrasts = NULL)
```# S3 method for default
icr(x, y, ...)

# S3 method for 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.

- …
arguments passed to

`fastICA`

- 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 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.

- 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

the results of
`lm`

after the ICA transformation

pre-processing information

number of ICA components

column names of the original data

##### See Also

##### Examples

```
# NOT RUN {
data(BloodBrain)
icrFit <- icr(bbbDescr, logBBB, n.comp = 5)
icrFit
predict(icrFit, bbbDescr[1:5,])
# }
```

*Documentation reproduced from package caret, version 6.0-80, License: GPL (>= 2)*