mix.d.sum.of.mixtures.EXPLN: Density of the sum of mixtures of zero, one or more lognormal...

Description Usage Arguments Details Value Author(s) References

View source: R/mix.d.sum.of.mixtures.EXPLN.R

Description

Density of a sum of i.i.d. random variables, where each random variable is from the following mixture distribution: With probability p_i, it is of type i. For all but the largest i, it is lognormally distributed with log-mean mu_i and log-standard deviation sigma_i. Otherwise it is exponentially distributed with rate lambda. The density is somehow a "mixed" one, as for all values in n the density of the random variable is calculated and the weighted average is taken to be the density of this specific value.

Usage

1
mix.d.sum.of.mixtures.EXPLN(y, n.vector, p.vector, mu.vector, sigma.vector, lambda)

Arguments

y

the argument at which the density is evaluated

n.vector

the number of random variables entering each sum (in the considered application: number of cells per tissue sample). This can also be a vector stating how many cells are in each sample separatly

p.vector

vector (p1,p2,..,pT) containing the probabilities for each type of cell. Its elements have to sum up to one

mu.vector

vector (mu1,mu2,...,mu(T-1)) containing the log-means for each lognormal type (types 1 to T-1)

sigma.vector

vector (sigma1,...,sigma(T-1)) containing the log-standard deviations sigma for each lognormal type (types 1 to T-1)

lambda

the rate for the exponential type (type T)

Details

The lengths of mu.vector and sigma.vector have to be identical. p.vector has to have one component more. Its length automatically determines the number of different types. lambda has to be a scalar.

Value

'mix.d.sum.of.mixtures.EXPLN' gives the density of a random variable originating from one of the tissue samples in the mixed n-vector.

Author(s)

Lisa Amrhein, Christiane Fuchs

Maintainer: Lisa Amrhein <amrheinlisa@gmail.com>

References

"Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar*, Christiane Fuchs*, Andreas Roller, Fabian J Theis^ and Kevin A Janes^: PNAS 2014, 111(5), E626-635 (* joint first authors, ^ joint last authors) <doi:10.1073/pnas.1311647111>

"Pheno-seq - linking visual features and gene expression in 3D cell culture systems" by Stephan M. Tirier, Jeongbin Park, Friedrich Preusser, Lisa Amrhein, Zuguang Gu, Simon Steiger, Jan-Philipp Mallm, Teresa Krieger, Marcel Waschow, Bjoern Eismann, Marta Gut, Ivo G. Gut, Karsten Rippe, Matthias Schlesner, Fabian Theis, Christiane Fuchs, Claudia R. Ball, Hanno Glimm, Roland Eils & Christian Conrad: Sci Rep 9, 12367 (2019) <doi:10.1038/s41598-019-48771-4>


lisaamrhein/stochprofML documentation built on Dec. 25, 2021, 9:02 p.m.