statismoMembers: Implementation/Emulation of the statismo StatisticalModel...

In zarquon42b/RvtkStatismo: Integrating statismo and R using the vtkStandardMeshRepresenter

Description

Implementation/Emulation of the statismo StatisticalModel class.

Usage

DrawMean(model)

DrawMeanAtPoint(model, pt)

DrawSample(model, coefficients = NULL, addNoise = FALSE)

DrawSampleVector(model, coefficients, addNoise = FALSE)

DrawSampleAtPoint(model, coefficients, pt, addNoise = FALSE)

ComputeCoefficientsForDataset(model, dataset)

ComputeCoefficientsForPointValues(model, sample, pt, ptNoise = 0)

GetDomainPoints(model)

GetDomainSize(model)

EvaluateSampleAtPoint(model, sample, pt)

GetModelInfo(model)

GetPCScores(model, scaled = TRUE)

## S4 method for signature 'pPCA'
DrawMean(model)

## S4 method for signature 'pPCA,numeric'
DrawMeanAtPoint(model, pt)

## S4 method for signature 'pPCA'
DrawSample(model, coefficients = NULL, addNoise = FALSE)

## S4 method for signature 'pPCA'
DrawSampleVector(model, coefficients, addNoise = FALSE)

## S4 method for signature 'pPCA,numeric,numeric'
DrawSampleAtPoint(model, coefficients, pt,
  addNoise = FALSE)

## S4 method for signature 'pPCA'
ComputeCoefficientsForDataset(model, dataset)

## S4 method for signature 'pPCA'
GetDomainPoints(model)

## S4 method for signature 'pPCA'
GetDomainSize(model)


  ## S4 method for signature 'pPCA,matrix,numeric,numeric'
ComputeCoefficientsForPointValues(model,
  sample, pt, ptNoise = 0)

## S4 method for signature 'pPCA,matrix,matrix,numeric'
ComputeCoefficientsForPointValues(model,
  sample, pt, ptNoise = 0)


  ## S4 method for signature 'pPCA,numeric,numeric,numeric'
ComputeCoefficientsForPointValues(model,
  sample, pt, ptNoise = 0)

## S4 method for signature 'pPCA,matrix,numeric'
EvaluateSampleAtPoint(model, sample, pt)

## S4 method for signature 'pPCA,mesh3d,numeric'
EvaluateSampleAtPoint(model, sample, pt)

## S4 method for signature 'pPCA'
GetModelInfo(model)

## S4 method for signature 'pPCA'
GetPCScores(model, scaled = TRUE)

Arguments

model	object of class pPCA
pt	either an integer pointing to a coordinate or a 3D-vector containing the coordinates of the domain point of interest. For ComputeCoefficientsForPointValues, this can also specify a matrix of coordinates on the domain.
coefficients	specify coefficients in the latent space to draw a sample
addNoise	logical: if TRUE noise as specified in the model will be added to the returned sample
dataset	an (already aligned) mesh or k x 3 matrix containing the datasets coordinates.
sample	depending on the function a matrix, a numeric vector or a mesh3d (see methods below)
ptNoise	specify the noise estimated in the points.
scaled	logical: if TRUE, the scores are scaled by their standard deviation.

Details

see http://statismo.github.io/docs/api/v0.10/html/classstatismo_1_1StatisticalModel.html for details.

Value

DrawMean	Get the mean (either a matrix or a mesh3d)
GetMeanVector	Get the mean vector
DrawMeanAtPoint	Get a specific point of the mean (numeric vector)
DrawSample	Draw a sample from the model (either a matrix or a mesh3d)
DrawMeanAtPoint	Get a specific point of the mean (numeric vector)
DrawSampleAtPoint	Draw a sample of a specific point from the model (numeric vector)
ComputeCoefficientsForDataset	Computes the coefficients of the latent variables
ComputeCoefficientsForPointValues	Returns the coefficients of the latent variables for the given values provided in two k x 3 matrices or two vectors of length 3, or one matrix/vector and a vector containing the indices on the domain corresponding to these points
GetDomainPoints	a matrix containing the points of the model's domain
GetDomainSize	get the size of the model's domain
EvaluateSampleAtPoint	Returns the value of the given sample at the point specified (either as point on the domain or as an index)
GetPCScores	get model's PC-scores, scaled or unscaled to unit variance, depending on the choice of scaled

zarquon42b/RvtkStatismo documentation built on May 4, 2019, 9:09 p.m.