loglik.2binormal: Compute log-likelihood of parameterized bivariate 2-component...

In idr: Irreproducible Discovery Rate

Compute log-likelihood of parameterized bivariate 2-component Gaussian mixture models

Description

Compute the log-likelihood for parameterized bivariate 2-component Gaussian mixture models with (1-p)N(0, 0, 1, 1, 0) + pN(mu, mu, sigma, sigma, rho).

Usage

loglik.2binormal(z.1, z.2, mu, sigma, rho, p)

Arguments

z.1	a numerical data vector on coordinate 1.
z.2	a numerical data vector on coordinate 1.
mu	mean for the reproducible component.
sigma	standard deviation of the reproducible component.
rho	correlation coefficient of the reproducible component.
p	mixing proportion of the reproducible component.

Value

Log-likelihood of the bivariate 2-component Gaussian mixture models (1-p)N(0, 0, 1, 1, 0) + N(mu, mu, sigma, sigma, rho)$.

Author(s)

Qunhua Li

References

Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.

See Also

m.step.2normal, e.step.normal, est.IDR

Examples

z.1 <- c(rnorm(500, 0, 1), rnorm(500, 3, 1))
rho <- 0.8

## The component with higher values is correlated with correlation coefficient=0.8 
z.2 <- c(rnorm(500, 0, 1), rnorm(500, 3 + 0.8*(z.1[501:1000]-3), (1-rho^2)))

## Starting values
mu <- 3
sigma <- 1
rho <- 0.85
p <- 0.55

## The function is currently defined as
loglik <- loglik.2binormal(z.1, z.2, mu, sigma, rho, p) 

loglik

idr documentation built on June 21, 2022, 9:05 a.m.