performICA {psichomics} | R Documentation |
Perform independent component analysis after processing missing values
performICA( data, n.comp = min(5, ncol(data)), center = TRUE, scale. = FALSE, missingValues = round(0.05 * nrow(data)), alg.typ = c("parallel", "defaltion"), fun = c("logcosh", "exp"), alpha = 1, ... )
data |
an optional data frame (or similar: see
|
n.comp |
number of components to be extracted |
center |
a logical value indicating whether the variables
should be shifted to be zero centered. Alternately, a vector of
length equal the number of columns of |
scale. |
a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is |
missingValues |
Integer: number of tolerated missing values per column to be replaced with the mean of the values of that same column |
alg.typ |
if |
fun |
the functional form of the G function used in the approximation to neg-entropy (see ‘details’). |
alpha |
constant in range [1, 2] used in approximation to
neg-entropy when |
... |
Arguments passed on to |
ICA result in a prcomp
object
Other functions to analyse independent components:
plotICA()
performICA(USArrests)