fit.mixture.model | R Documentation |
Fit a Gaussian mixture deconvolution model
fit.mixture.model( z, n = 2, ntry = 20, force.mu.zero = TRUE, diagnostics = FALSE )
z |
A vector of z-scores. |
n |
Number of mixture components. |
ntry |
Number of random initializations. |
force.mu.zero |
Should the means be forced to zero? |
diagnostics |
Logical indicator for showing diagnostic plots. |
This function assumes that z is distributed as N(γ, 1) and γ follows a Gaussian mixture model. It fits this deconvolution model by maximum likelihood and outputs the estimated mixture distribution.
A list of p
(mixture proportion), mu
(mean), sigma
(standard deviation).
z <- c(sqrt(2) * rnorm(900), sqrt(17) * rnorm(100)) ## So the correct sigma = (1, 4) and p = (0.9, 0.1) fit.mixture.model(z)
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