Gaussian Processes for Poisson-noised Data

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Documentation for package ‘gppois’ version 0.2-1

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A B C D E F G H I K L M N P Q R S T U V W X

gppois-package gppois (package)

-- A --

AddCovariance Add a new Covariance to this Model
AddCovariance.Model Add a new Covariance to this Model
Anscombe Anscombe transform for Poisson-noised data
AnscombeInverse Anscombe transform for Poisson-noised data

-- B --

BubblingRandomMatrix Generate marginally normal smooth random timetraces

-- C --

Clamp Clamps 'x' to within 'bounds'
clone.Covariance Deep-clone a Dataset
clone.Model Deep-clone a Model
Covariance Covariance: superclass for covariance functions
Covariance$id The ID for this Covariance
Covariance$lower Lower bounds for parameters using full names
Covariance$paramNames ID-decorated names of Covariance parameters
Covariance$params Parameter values for this Covariance
Covariance$paramsPlain Set parameters using undecorated names
Covariance$upper Upper bounds for parameters using full names
CovarianceNoise CovarianceNoise: i.i.d. Gaussian noise
CovarianceNoise$logspaceNames Names of "scale"-type parameters
CovarianceNoise$lowerPlain Lower bounds for params, with plain names
CovarianceNoise$paramNamesPlain Basenames of parameters
CovarianceNoise$paramsPlain Parameter values with plain names
CovarianceNoise$upperPlain Upper bounds for params, with plain names
CovarianceSE CovarianceSE: (S)quared-(E)xponential covariance
CovarianceSE$logspaceNames Names of "scale"-type parameters
CovarianceSE$lowerPlain Lower bounds for params, with plain names
CovarianceSE$paramNamesPlain Basenames of parameters
CovarianceSE$paramsPlain Parameter values with plain names
CovarianceSE$upperPlain Upper bounds for params, with plain names
CovarianceSEAniso2D 2D anisotropic SE covariance
CovarianceSEAniso2D$logspaceNames Names of "scale"-type parameters
CovarianceSEAniso2D$lowerPlain Lower bounds for params, with plain names
CovarianceSEAniso2D$paramNamesPlain Basenames of parameters
CovarianceSEAniso2D$paramsPlain Parameter values with plain names
CovarianceSEAniso2D$upperPlain Upper bounds for params, with plain names
CovarianceSELocalized Localized Squared-exponential Covariance
CovarianceSELocalized$logspaceNames Names of "scale"-type parameters
CovarianceSELocalized$lowerPlain Lower bounds for params, with plain names
CovarianceSELocalized$paramNamesPlain Basenames of parameters
CovarianceSELocalized$paramsPlain Parameter values with plain names
CovarianceSELocalized$upperPlain Upper bounds for params, with plain names
CovarianceSEVaryingEll Nonstationary Squared-Exponential Covariance
CovarianceSEVaryingEll$logspaceNames Names of "scale"-type parameters
CovarianceSEVaryingEll$lowerPlain Lower bounds for params, with plain names
CovarianceSEVaryingEll$paramNamesPlain Basenames of parameters
CovarianceSEVaryingEll$paramsPlain Parameter values with plain names
CovarianceSEVaryingEll$upperPlain Upper bounds for params, with plain names

-- D --

d The dimensionality of the Dataset
dataOffset The offset for this dataset
Dataset A wrapper class for data being analyzed
Dataset$id The id string for this Dataset
Dataset$quantity Currently selected quantity (column)
DebugIfError Runs a function with just-in-time debugging
DeleteRows Delete datapoints
DeleteRows.Dataset Delete datapoints
DemoPause Pause during demos
DistanceMatrix Pairwise distances between points in X and X.out
dpts Datapoint values

-- E --

ell ell(X)
ell.CovarianceSEVaryingEll ell(X)
erf Error function

-- F --

FindGapPoints Flag points which are "in the gap"
FixConstParam Set a parameter to a constant value.
FixConstParam.Covariance Set a parameter to a constant value.
flameSpeed Flame speed vs. fuel concentration
Forget Clear precomputed matrices from memory
Forget.Model Clear precomputed matrices from memory
Freeze Make some parameters constant
Freeze.Model Make some parameters constant

-- G --

getContributionIds Id's of each contributing Covariance
getContributionIds.Model Id's of each contributing Covariance
getD The dimensionality of the Dataset
getD.Dataset The dimensionality of the Dataset
getDataOffset The offset for this dataset
getDataOffset.Dataset The offset for this dataset
getDpts Datapoint values
getDpts.Dataset Datapoint values
getId The id string for this Dataset
getId.Covariance The ID for this Covariance
getId.Dataset The id string for this Dataset
getId.Model ID string for this Model
getIsPoisson Is the current column Poisson?
getIsPoisson.Dataset Is the current column Poisson?
getLogspaceNames Names of "scale"-type parameters
getLogspaceNames.CovarianceNoise Names of "scale"-type parameters
getLogspaceNames.CovarianceSE Names of "scale"-type parameters
getLogspaceNames.CovarianceSEAniso2D Names of "scale"-type parameters
getLogspaceNames.CovarianceSELocalized Names of "scale"-type parameters
getLogspaceNames.CovarianceSEVaryingEll Names of "scale"-type parameters
getLower Lower bounds for parameters
getLower.Covariance Lower bounds for parameters using full names
getLower.Model Lower bounds for parameters
getLowerPlain Lower bounds for plain-named parameters
getLowerPlain.CovarianceNoise Lower bounds for params, with plain names
getLowerPlain.CovarianceSE Lower bounds for params, with plain names
getLowerPlain.CovarianceSEAniso2D Lower bounds for params, with plain names
getLowerPlain.CovarianceSELocalized Lower bounds for params, with plain names
getLowerPlain.CovarianceSEVaryingEll Lower bounds for params, with plain names
getM The previously-computed matrix
getM.LazyMatrix The previously-computed matrix
getN The number of datapoints
getN.Dataset The number of datapoints
getNoiseVar Noise variance for current column
getNoiseVar.Dataset Noise variance for current column
getParamNames ID-decorated names of Covariance parameters
getParamNames.Covariance ID-decorated names of Covariance parameters
getParamNamesPlain Undecorated parameter names
getParamNamesPlain.CovarianceNoise Basenames of parameters
getParamNamesPlain.CovarianceSE Basenames of parameters
getParamNamesPlain.CovarianceSEAniso2D Basenames of parameters
getParamNamesPlain.CovarianceSELocalized Basenames of parameters
getParamNamesPlain.CovarianceSEVaryingEll Basenames of parameters
getParams Parameter values
getParams.Covariance Parameter values for this Covariance
getParams.Model Parameters for the Model
getParamsPlain Set parameters using undecorated names
getParamsPlain.Covariance Set parameters using undecorated names
getParamsPlain.CovarianceNoise Parameter values with plain names
getParamsPlain.CovarianceSE Parameter values with plain names
getParamsPlain.CovarianceSEAniso2D Parameter values with plain names
getParamsPlain.CovarianceSELocalized Parameter values with plain names
getParamsPlain.CovarianceSEVaryingEll Parameter values with plain names
getQuantity Currently selected quantity (column)
getQuantity.Dataset Currently selected quantity (column)
getSignalIds ID's of non-noise Covariances
getSignalIds.Model ID's of non-noise Covariances
getUpper Upper bounds for parameters
getUpper.Covariance Upper bounds for parameters using full names
getUpper.Model Upper bounds for parameters
getUpperPlain Upper bounds for plain-named parameters
getUpperPlain.CovarianceNoise Upper bounds for params, with plain names
getUpperPlain.CovarianceSE Upper bounds for params, with plain names
getUpperPlain.CovarianceSEAniso2D Upper bounds for params, with plain names
getUpperPlain.CovarianceSELocalized Upper bounds for params, with plain names
getUpperPlain.CovarianceSEVaryingEll Upper bounds for params, with plain names
getVaryingParamNames Non-constant Model parameters
getVaryingParamNames.Model Non-constant Model parameters
getX X-values
getX.Dataset X-values
getXformedDpts Transformed datapoint values
getXformedDpts.Dataset Transformed datapoint values
gppois gppois (package)
GradLogML Gradient of log marginal likelihood
GriddedConvexHull Cover a convex region with a hexagonal grid

-- H --

HexagonalGrid Hexagonal grid of points

-- I --

id The id string for this Dataset
isPoisson Is the current column Poisson?

-- K --

K.Covariance Covariance matrix
K.specific Covariance matrix implementation
K.specific.CovarianceNoise Noise Covariance matrix
K.specific.CovarianceSE Squared-Exponential Covariance matrix
K.specific.CovarianceSEAniso2D Anisotropic 2D SE Covariance matrix
K.specific.CovarianceSELocalized Localized Squared-Exponential Covariance matrix
K.specific.CovarianceSEVaryingEll Nonstationary Squared-exponential Covariance matrix
KDerivImplementation Element-wise derivatives of Covariance matrix
KDerivImplementation.CovarianceNoise Element-wise derivatives of Covariance matrix
KDerivImplementation.CovarianceSE Element-wise derivatives of Covariance matrix
KDerivImplementation.CovarianceSEAniso2D Element-wise derivatives of Covariance matrix
KDerivImplementation.CovarianceSELocalized Element-wise derivatives of Covariance matrix
KDerivImplementation.CovarianceSEVaryingEll Element-wise derivatives of Covariance matrix
KInIn Covariance matrix
KInIn.Covariance Covariance matrix
KInInDeriv Element-wise derivatives of covariance matrix
KInInDeriv.Covariance Element-wise derivatives of covariance matrix
KInOut Covariance matrix
KInOut.Covariance Covariance matrix
KOutIn Covariance matrix
KOutIn.Covariance Covariance matrix
KOutOut Covariance matrix
KOutOut.Covariance Covariance matrix

-- L --

L Lower Cholesky root of covariance matrix
L.Model Lower Cholesky root of covariance matrix
LazyMatrix Wrapper to avoid recomputing matrices
LogML Log of the Marginal Likelihood
lower Lower bounds for parameters
lowerPlain Lower bounds for plain-named parameters

-- M --

M The previously-computed matrix
Model Model: a trainable collection of Covariances
Model$contributionIds Id's of each contributing Covariance
Model$id ID string for this Model
Model$lower Lower bounds for parameters
Model$params Parameters for the Model
Model$signalIds ID's of non-noise Covariances
Model$upper Upper bounds for parameters
Model$varyingParamNames Non-constant Model parameters
MSR Mean square residuals
MSR.Dataset Mean square residuals

-- N --

n The number of datapoints
NamedCovariance Retrieve one contributing Covariance
NamedCovariance.Model Retrieve one contributing Covariance
NeedToRecalculate Decides whether we need to recompute
NeedToRecalculate.LazyMatrix Decides whether we need to recompute
noiseVar Noise variance for current column

-- P --

paramNamesPlain Undecorated parameter names
params Parameter values
paramsPlain Set parameters using undecorated names
Plot2D Scatterplot for 2D dataset
Plot2D.Dataset Scatterplot for 2D dataset
PlotBubblingSurfaces2D Animated uncertainty in a surface
PlotBubblingSurfaces2D.Model Animated uncertainty in a surface
PlotMatrixQuickAndDirty Plots a numeric matrix
PlotSurface Plot a triangulated surface using rgl
PosteriorInterval Best estimate, including uncertainty
PosteriorInterval.Model Best estimate, including uncertainty
PosteriorMean Best estimate of the true function
PosteriorMean.Model Best estimate of the true function
PosteriorStandardDeviation Pointwise uncertainty
PosteriorStandardDeviation.Model Pointwise uncertainty
PredictionMatrix Matrix connecting noisy data to true function
PredictionMatrix.Model Matrix connecting noisy data to true function
print.Covariance Pretty-printing for Covariance objects
print.Dataset Pretty-printing for Dataset objects
print.Model Pretty-printing for Model objects

-- Q --

quantity Currently selected quantity (column)

-- R --

RemoveRange Remove datapoints within a given range
RemoveRange.Dataset Remove datapoints within a given range

-- S --

Same Check whether two Datasets are identical
Same.Dataset Check whether two Datasets are identical
ScaleY Set the aspect ratio for rgl
setId The id string for this Dataset
setId.Covariance The ID for this Covariance
setId.Dataset The id string for this Dataset
setId.Model ID string for this Model
setLower Lower bounds for parameters
setLower.Covariance Lower bounds for parameters using full names
setLower.Model Lower bounds for parameters
setLowerPlain Lower bounds for plain-named parameters
setLowerPlain.CovarianceNoise Lower bounds for params, with plain names
setLowerPlain.CovarianceSE Lower bounds for params, with plain names
setLowerPlain.CovarianceSEAniso2D Lower bounds for params, with plain names
setLowerPlain.CovarianceSELocalized Lower bounds for params, with plain names
setLowerPlain.CovarianceSEVaryingEll Lower bounds for params, with plain names
SetNoiseBounds Uncertainty about the noise level
SetNoiseBounds.Model Uncertainty about the noise level
setParams Parameter values
setParams.Covariance Parameter values for this Covariance
setParams.Model Parameters for the Model
setParamsPlain Set parameters using undecorated names
setParamsPlain.Covariance Set parameters using undecorated names
setQuantity Currently selected quantity (column)
setQuantity.Dataset Currently selected quantity (column)
setUpper Upper bounds for parameters
setUpper.Covariance Upper bounds for parameters using full names
setUpper.Model Upper bounds for parameters
setUpperPlain Upper bounds for plain-named parameters
setUpperPlain.CovarianceNoise Upper bounds for params, with plain names
setUpperPlain.CovarianceSE Upper bounds for params, with plain names
setUpperPlain.CovarianceSEAniso2D Upper bounds for params, with plain names
setUpperPlain.CovarianceSELocalized Upper bounds for params, with plain names
setUpperPlain.CovarianceSEVaryingEll Upper bounds for params, with plain names
sigma.f sigma.f(X)
sigma.f.CovarianceSEVaryingEll sigma.f(X)
simXrayAu2nm Simulated X-ray diffraction from 2nm Au NPs
SmartTrace Fast trace for matrix product
steelStrain Strain on a stretched steel plate
steelStrainGap Strain on a stretched steel plate
StoreMatrix Stores a calculated matrix
StoreMatrix.LazyMatrix Stores a calculated matrix

-- T --

Train Train a Model on a Dataset
Train.Model Train a Model on a Dataset

-- U --

Untransform Undo any transformations on the datapoints
Untransform.Dataset Undo any transformations on the datapoints
upper Upper bounds for parameters
upperPlain Upper bounds for plain-named parameters

-- V --

Variance SE variance at each point
Variance.CovarianceNoise Noise variance at each point
Variance.CovarianceSE SE variance at each point
Variance.CovarianceSEAniso2D Anisotropic 2D SE variance at each point
Variance.CovarianceSELocalized Localized SE variance at each point
Variance.CovarianceSEVaryingEll Nonstationary SE variance at each point

-- W --

Widths Computes the width of each point in X.

-- X --

x X-values
xformedDpts Transformed datapoint values
xrayTiO2np20nm Powder x-ray diffraction from 20nm TiO2 NPs