Fit a linear regression model using independent components
# 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, ...)
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. |
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
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
.
fastICA
, preProcess
,
lm
#> Error: package fastICA is requiredicrFit#> Error in eval(expr, envir, enclos): object 'icrFit' not found#> Error in predict(icrFit, bbbDescr[1:5, ]): object 'icrFit' not found