tau2,MarginalModel-method {CNPBayes} | R Documentation |
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
## 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)
object |
see |
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 |
x |
a |
y |
in memory data |
... |
additional arguments to |
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 |
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. |
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 ggSingleBatch
and ggMultiBatch
for visualization
BatchModel
. For single-batch data, use