Deprecated-functions: DensityModel constructor has been deprecated.

Description Usage Arguments Value See Also

Description

DensityModel constructor has been deprecated.

Instantiates an instance of 'DensityModel' (or 'DensityBatchModel') from a MarginalModel or BatchModel object. See the corresponding class for additional details and examples.

Create an object for running hierarchical MCMC simulations.

DensityModel constructor and methods are Deprecated

Usage

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## S4 method for signature 'MarginalModel'
tau2(object)

## S4 method for signature 'MarginalModel'
bic(object)

## S4 method for signature 'MarginalModel'
theta(object)

## S4 method for signature 'MarginalModel'
sigma2(object)

## S4 method for signature 'BatchModel'
sigma2(object)

## S4 method for signature 'BatchModel'
tau2(object)

## S4 method for signature 'BatchModel'
theta(object)

## S4 method for signature 'MarginalModel'
marginalLikelihood(model, params = mlParams())

## S4 method for signature 'BatchModel'
marginalLikelihood(model, params = mlParams())

## S4 method for signature 'BatchModel'
ggMultiBatch(model, bins)

DensityModel(object, merge = FALSE)

## S4 method for signature 'DensityModel'
batch(object)

## S4 method for signature 'DensityModel'
modes(object)

## S4 method for signature 'DensityModel'
k(object)

## S4 method for signature 'DensityModel'
y(object)

## S4 method for signature 'DensityModel,ANY'
plot(x, y, ...)

## S4 method for signature 'MarginalModel,ANY'
plot(x, y, ...)

## S4 method for signature 'BatchModel,ANY'
plot(x, y, show.batch = TRUE, ...)

## S4 method for signature 'DensityBatchModel,ANY'
plot(x, show.batch = TRUE, ...)

HyperparametersBatch(k = 3L, mu.0 = 0, tau2.0 = 100, eta.0 = 1800,
  m2.0 = 1/60, alpha, beta = 0.1, a = 1.8, b = 6)

HyperparametersMarginal(k = 0L, mu.0 = 0, tau2.0 = 100, eta.0 = 1,
  m2.0 = 0.1, alpha, beta = 0.1, a = 1.8, b = 6)

BatchModelList(data = numeric(), k = numeric(), batch,
  mcmc.params = McmcParams(), ...)

BatchModel(data = numeric(), k = 3, batch, hypp, mcmc.params)

## S4 method for signature 'MixtureModel,integer'
posteriorSimulation(object, k)

## S4 method for signature 'MixtureModel,numeric'
posteriorSimulation(object, k)

DensityModel(object, merge = FALSE)

## S4 method for signature 'DensityModel'
batch(object)

## S4 method for signature 'DensityModel'
modes(object)

## S4 method for signature 'DensityModel'
k(object)

## S4 method for signature 'DensityModel'
y(object)

## S4 method for signature 'DensityModel,ANY'
plot(x, y, ...)

## S4 method for signature 'MarginalModel,ANY'
plot(x, y, ...)

## S4 method for signature 'BatchModel,ANY'
plot(x, y, show.batch = TRUE, ...)

## S4 method for signature 'DensityBatchModel,ANY'
plot(x, show.batch = TRUE, ...)

## S4 method for signature 'MultiBatchModel'
ggMultiBatch(model, bins)

## S4 method for signature 'MultiBatchPooled'
ggMultiBatch(model, bins)

## S4 method for signature 'MarginalModel'
ggSingleBatch(model, bins)

## S4 method for signature 'SingleBatchModel'
ggSingleBatch(model, bins)

## S4 method for signature 'MultiBatchCopyNumber'
ggMultiBatch(model, bins)

## S4 method for signature 'SingleBatchCopyNumber'
ggSingleBatch(model, bins)

downsample(batch.file, plate, y, ntiles = 250, THR = 0.1)

downSampleEachBatch(y, nt, batch)

MultiBatchModel(data = numeric(), k = 3, batch, hypp, mcmc.params)

## S4 method for signature 'list,ANY'
posteriorSimulation(object)

plot(x, y, ...)

clusters(object)

labelSwitching(object, merge = TRUE)

## S4 method for signature 'MixtureModel'
labelSwitching(object, merge = TRUE)

## S4 method for signature 'BatchModel,ANY,ANY,ANY'
x[i, j, ..., drop = FALSE]

SingleBatchModel(data = numeric(), k = 3, hypp, mcmc.params)

multiBatchDensities(model)

Arguments

object

see showMethods(DensityModel)

model

MarginalModel

params

list of parameters for computing marginal likelihood

bins

length-one numeric vector specifying number of bins for plotting

merge

Logical. Whether to use kmeans clustering to cluster the component means using the estimated modes from the overall density as the centers for the kmeans function.

x

a DensityModel-derived object, or a MixtureModel-derived object. numeric vector of the one-dimensional summaries for a given copy number polymorphism. If x is a MixtureModel, y is ignored.

y

in memory data

...

additional arguments to HyperparametersBatch

show.batch

a logical. If true, batch specific densities will be plotted.

k

length-one integer vector specifying number of components (typically 1 <= k <= 4)

mu.0

length-one numeric vector of the mean for the normal prior of the component means

tau2.0

length-one numeric vector of the variance for the normal prior of the component means

eta.0

length-one numeric vector of the shape parameter for the Inverse Gamma prior of the component variances. The shape parameter is parameterized as 1/2 * eta.0.

m2.0

length-one numeric vector of the rate parameter for the Inverse Gamma prior of the component variances. The rate parameter is parameterized as 1/2 * eta.0 * m2.0.

alpha

length-k numeric vector of the shape parameters for the dirichlet prior on the mixture probabilities

beta

length-one numeric vector for the parameter of the geometric prior for nu.0 (nu.0 is the shape parameter of the Inverse Gamma sampling distribution for the component-specific variances). beta is a probability and must be in the interval [0,1].

a

length-one numeric vector of the shape parameter for the Gamma prior used for sigma2.0 (sigma2.0 is the shape parameter of the Inverse Gamma sampling distribution for the component-specific variances)

b

a length-one numeric vector of the rate parameter for the Gamma prior used for sigma2.0 (sigma2.0 is the rate parameter of the Inverse Gamma sampling distribution for the component-specific variances)

data

numeric vector of average log R ratios

batch

a vector of the different batch numbers (must be sorted)

mcmc.params

a McmcParams object

hypp

An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

batch.file

the name of a file contaning RDS data to be read in.

plate

a vector containing the labels from which batch each observation came from.

ntiles

number of tiles in a batch

THR

threshold above which to merge batches in Kolmogorov-Smirnov test.

nt

the number of observations per batch

i

integer

j

integer

drop

Not used.

Value

An object of class 'DensityModel'

An object of class HyperparametersBatch

An object of class HyperparametersMarginal

a list. Each element of the list is a BatchModel

An object of class 'BatchModel'

An object of class 'DensityModel'

Tile labels for each observation

Tile labels for each observation

An object of class 'MultiBatchModel'

A plot showing the density estimate

A single proportion for a MarginalModel or a vector of proportions, one for each batch for a BatchModel

An object of class 'BatchModel'

See Also

See ggSingleBatch and ggMultiBatch for visualization

BatchModel. For single-batch data, use

DensityModel-class kmeans

ntile


CNPBayes documentation built on May 2, 2018, 3:57 a.m.