Independent Component Regression
Fit a linear regression model using independent components
## S3 method for class 'formula' icr(formula, data, weights, ..., subset, na.action, contrasts = NULL) ## Default S3 method: icr(x, y, ...) ## S3 method for class 'icr' predict(object, newdata, ...)
formula |
A formula of the form |
data |
Data frame from which variables specified in |
weights |
(case) weights for each example - if missing defaults to 1. |
... |
arguments passed to |
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 |
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 |
y |
matrix or data frame of target values for examples. |
object |
an object of class |
newdata |
matrix or data frame of test examples. |
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.
For icr, a list with elements
model |
the results of
|
ica
|
pre-processing information |
n.comp |
number of ICA components |
names |
column names of the original data |
Max Kuhn
data(BloodBrain) icrFit <- icr(bbbDescr, logBBB, n.comp = 5) icrFit predict(icrFit, bbbDescr[1:5,])
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