sdpar-methods: Methods for Function 'sdpar' in Package 'dti'

In neuroconductor/dti: Analysis of Diffusion Weighted Imaging (DWI) Data

Description

This function estimates the parameters of a piecewise linear model for the dependence between error standard deviation and mean.

Usage

  ## S4 method for signature 'dtiData'
sdpar(object,level=NULL,sdmethod="none",interactive=TRUE,threshfactor=1)

Arguments

object	An object of class dtiData
level	Suggested value for slot level. As a default the value in object@level is used. The value determines the lower endpoint of the linear section in the model for error standard deviation as a function of the mean.
sdmethod	Method for estimating voxelwise standard deviations if replicates of zero weighted images are available, can be set to "sd" or "mad". "none" specifies that no variance model is fitted
interactive	If TRUE a density of values in zero weighted images is plotted together with the specification of the lower endpoint of the interval of linearity. A good choice of this point should correspond, if present, to the minimum between the first two modes of the density estimate. The value can be changed or accepted. If changed a new value for slot lambda is set.
threshfactor	Factor for threshold-value selected if function is run in interactive mode. May be used to correct results if automatic threshold selection fails.

Value

The function returns an object of class dtiData.

Methods

obj = "ANY"

Returns a warning

obj = "dtiData"

Estimate parameters of a model for the dependence between error standard deviation and mean.

Author(s)

Karsten Tabelow [e-mail]
J\"org Polzehl [e-mail]

See Also

dtiData, readDWIdata, dti.smooth, dtiTensor,

Examples

  ## Not run: demo(dti_art)

neuroconductor/dti documentation built on May 20, 2021, 4:28 p.m.