sdpar

sdpar-methods

sdpar,ANY-method

sdpar,dtiData-method

object

Suggested value for slot <code>level</code>. As a default the value in <code>object@level</code> 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.

level

Method for estimating voxelwise standard deviations if replicates of zero weighted images are available, either <code>"sd"</code> or <code>"mad"</code>.

sdmethod

If <code>TRUE</code> 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 tw

interactive

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

methods

Diffusion Weighted Imaging is a Magnetic Resonance Imaging
        modality, that measures diffusion of water in tissues like the
        human brain. The package contains R-functions to process
        diffusion-weighted data in the context of the diffusion tensor
        model (DTI). This includes the calculation of anisotropy
        measures and, most important, the implementation of our
        structural adaptive smoothing algorithm as described in K.
        Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss, Diffusion
        Tensor Imaging: Structural Adaptive Smoothing, Neuroimage
        39(4), 1763-1773 (2008).

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

Description

Usage

Arguments

Value

See Also

Examples