imagePreProc: Defining new variables, running the KCCA

In nimasasa/RFCMFND: A Random Forest Classifier for Multi-type Functional Neuroimaging Data

Description

Defining new variables, running the KCCA

Usage

imagePreProc(data, clusters = 0, freq = TRUE, distorg = TRUE,
  dist = TRUE, cov = TRUE)

Arguments

data	A n by 5 data.frame representing n observations in 5 dimensions.
clusters	An Integer value for the number of clusters. The default value is 0.
freq	If freq=TRUE, the frequency column is added to the current input dataset.
distorg	If distorg=TRUE, the distance to origin column is added to the current input dataset.
dist	If dist=TRUE, the distance among points for each study is added to the current input dataset.
cov	If cov=TRUE, the covariances (XY, XZ, YZ) columns are added to the current input dataset.

Value

dfg A data.frame of the pre-processed inputs.

See Also

https://cran.r-project.org/web/packages/flexclust/index.html

Examples

imagePreProc (data, clusters=5, freq=TRUE,distorg=TRUE, dist=TRUE, cov=TRUE)

nimasasa/RFCMFND documentation built on May 23, 2019, 7:05 p.m.