normalise: Normalise

Description Usage Arguments Details Value Author(s) Examples

View source: R/rsurfer.R

Description

Performs ICV (intracranial volume) normalisation on a data frame of imported subjects in data.

Usage

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normalise(data, normalisationFunction, fieldType = "all", trainData = NULL,
  testData = NULL)

Arguments

data

The subject data to normalise

normalisationFunction

The normalisation function to use, see details for further information.

fieldType

The field set to normalise, see details for further information.

trainData

Data to train on, required for 'hconly' normalisation methods

testData

Unseen data, required for 'hconly' normalisation methods

Details

Performs ICV (intracranial volume) normalisation on a data frame of imported subjects in data. The normalisationFunction specifies which normalisation method to use:

normalisation.proportional = proportional ICV normalisation, the volumes of each subject are divided by their ICV

normalisation.residual = residual ICV normalisation, a linear regression model is built for each volume using the ICV as a predictor

normalisation.residualgender = residual ICV normalisation with a gender split, similar to residual ICV normalisation, except a separate linear regression model is built for Males and Females

normalisation.residualhconly = residual ICV normalisation creating a regression model based on healthy control patients only

The fieldType can be:

corticalvolumes = Normalise cortical volumes by ICV

subcortical = Normalise subcortical volumes by ICV

hippocampal = Normalise hippocampal volumes by ICV

corticalareas = Normalise cortical areas by ICV

corticalthicknesses = Normalise cortical thicknesses by ICV

corticalthicknessstds = Normalise cortical thicknesses standard deviations by ICV

corticalareastsa = Normalise cortical areas by total surface area

corticalthicknessesmct = Normalise cortical thicknesses by mean cortical thickness

Value

The normalised data

Author(s)

Alexander Luke Spedding, alexspedding271@gmail.com

Examples

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rsurfer documentation built on May 2, 2019, 4:20 a.m.