README.md

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

A Random Forest Classifier for Multi-type Functional (RFCMFND)!

Package: RFCMFND

Type: Package

Title: A Random Forest Classifier for Multi-type Functional Neuroimaging Data

Version: 0.1

Date: 2015-12-18

Author(s): Nima Salehi Sadghiani, Amirhossein Meisami, Jian Kang

Description: In this package, we propose a modified Random Forest (RF) classifier for multi-type functional neuroimaging data (foci) and a K-Centroids Cluster Analysis (KCCA) algorithm to pre-process the foci.

License: University of Michigan

LazyData: TRUE

RoxygenNote: 5.0.1

Help File

imagePred {RFCMFND}

• Description: Prediction of the new dataset using the trained object time.
• Usage: imagePred(train, data)

• Arguments:

  • train: An object of class imageTrain.
  • data: A n by 5 data.frame representing n observations in 5 dimensions.

• Value:

  • pred : The prediction array.

• Warning: The NewData data.frame should be processed with the exact same options as the training dataset.

• Examples:

  • imagePred(train@Model, NewData)
  • imagePred(train@Model, imagePreProc (data, clusters=5, freq=TRUE,distorg=TRUE, dist=TRUE, cov=TRUE))

imagePreProc {RFCMFND}

• 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)

imageTrain {RFCMFND}

• Description: A modified version of RF classifier.
• Usage: imageTrain(data, cparallel = FALSE, accuracy = FALSE)

• Arguments:

  • data: A data.frame of the pre-processed inputs.
  • cparallel: If cparallel=TRUE, the RCPP Random Forest Classifier runs with parallel cores.
  • accuracy: If accuracy=TRUE, the accuracy of the classifier is shown.

• Value: Model and acc, a list in which the first element is the model object (named “Model”) and the second element is the list of accuracies across the classes (named “acc”).

• See Also:

  • https://cran.r-project.org/web/packages/randomForest/index.html
  • https://cran.r-project.org/web/packages/caret/index.html
  • https://cran.r-project.org/web/packages/Rborist/index.html

• Examples:

  • imageTrain(data, cparallel=TRUE, accuracy=TRUE)

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