proteinAllocation | R Documentation |
Internal sampling function, not for outside use documented for completness
proteinAllocation(loglikelihoods, currentweights, alloctemp, cond) outlierAllocationProbs( outlierlikelihood, loglikelihoods, epsilon, alloctemp, cond ) sampleOutlier(allocoutlierprob) covOrganelle(object, fcol = "markers") pg_prior(object_cond1, object_cond2, K, pgPrior = NULL, fcol = "markers") sample_weights_pg(nk_mat, pgPrior, w, K, tau = 0.2) sample_weights_dir(nk_mat, dirPrior)
loglikelihoods |
the log likelihoods |
currentweights |
the current allocations weights |
alloctemp |
the current protein allocations |
cond |
the control = 1, treatment = 2 |
outlierlikelihood |
the outlier log likelihoods |
epsilon |
the outlier component weight |
allocoutlierprob |
the outlier probabilities |
object |
An instance of class |
fcol |
The feature column containing the markers. |
object_cond1 |
A list of instance of class |
object_cond2 |
A list of instance of class |
K |
The number of organelle classes |
pgPrior |
The Polya-Gamma prior |
nk_mat |
The summary matrix of allocations |
w |
The Polya-Gamma auxiliary variable |
tau |
The empirical bayes parameter for the Polya-Gamma variable. Defaults to 0.2. |
dirPrior |
The Dirichlet prior |
returns samples for protein allocations, log likelihoods and probabilities
returns outlier probabilities
returns outlier allocations
returns covariance of organelles using marker proteins
returns the Polya-Gamma prior
returns A sample of the weights using Polya-Gamma priors.
returns A sample of the weights using Dirichlet prior.
library(pRolocdata) data("tan2009r1") covOrganelle(object = tan2009r1) library(pRolocdata) data("tan2009r1") set.seed(1) tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L) d1 <- tansim$lopitrep control1 <- d1[1:3] treatment1 <- d1[4:6] out <- pg_prior(object_cond1 = control1, object_cond2 = treatment1, K = 11)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.