e.step.2normal {idr} | R Documentation |
E-step for parameterized bivariate 2-component Gaussian mixture models
Description
Expectation step in the EM algorithm for parameterized bivariate 2-component Gaussian mixture models with (1-p)N(0, 0, 1, 1, 0) + pN(mu, mu, sigma, sigma, rho).
Usage
e.step.2normal(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 2. |
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
e.z |
a numeric vector, where each entry represents the estimated expected conditional probability that an observation is in the reproducible component. |
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
, loglik.2binormal
, 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
mu0 <- 3
sigma0 <- 1
rho0 <- 0.85
p0 <- 0.55
e.z <- e.step.2normal(z.1, z.2, mu0, sigma0, rho0, p0)