fit2clusters: Flexible two-cluster mixture fit of a numeric vector
In IdMappingAnalysis: ID Mapping Analysis

Description Usage Arguments Details Value

fit2clusters uses an ECM algorithm to fit a two-component mixture model. It is more flexible than mclust in some ways, but it only deals with one-dimensional data.

  fit2clusters(Y, Ylabel = "correlation", Ysigsq,
    piStart = c(0.5, 0.5), VStart = c(0.1, 0.1),
    psiStart = c(0, 0.1), NinnerLoop = 1, nReps = 500,
    psi0Constraint, V0Constraint, sameV = FALSE,
    estimatesOnly = TRUE, plotMe = TRUE, testMe = FALSE,
    Ntest = 5000, simPsi = c(0, 0.4), simPi = c(2/3, 1/3),
    simV = c(0.05^2, 0.05^2), simAlpha = 5, simBeta = 400,
    seed, ...)

`Y`	The vector of numbers to fit.
`Ysigsq`	The vector of variance estimates for Y.
`Ylabel`	Label for the Y axis in a density fit figure.
`piStart`	Starting values for the component proportions.
`VStart`	Starting values for the component variances.
`psiStart`	Starting values for the component means
`NinnerLoop`	Number of iterations in the "C" loop of ECM.
`nReps`	Upper limit of number of EM steps.
`psi0Constraint`	If not missing, a fixed value for the first component mean.
`V0Constraint`	If not missing, a fixed value for the first component variance.
`sameV`	If TRUE, the components have the same variance.
`estimatesOnly`	If TRUE, return only the estimates. Otherwise, returns details per observations, and return the estimates as an attribute.
`plotMe`	If TRUE, plot the mixture density and kernel smooth estimates.
`testMe`	If TRUE, run a code test.
`Ntest`	For testing purposes, the number of replications of simulated data.
`simPsi`	For testing purposes, the true means.
`simPi`	For testing purposes, the true proportions
`simV`	For testing purposes, the true variances.
`simAlpha`	For testing purposes, alpha parameter in rgamma for measurement error variance.
`simBeta`	For testing purposes, beta parameter in rgamma for measurement error variance.
`seed`	For testing purposes, random seed.
`...`	Not used; testing roxygen2.

See the document "ECM_algorithm_for_two_clusters.pdf".

If estimatesOnly is TRUE, return only the estimates: Otherwise, return a dataframe of details per observations, and return the estimates as an attribute. The estimates details are:

`pi1`	The probability of the 2nd mixture component
`psi0`	The mean of the first component (psi0Constraint if provided)
`psi1`	The mean of the second component
`Var0`	The variance of the first component (V0Constraint if provided)
`Var1`	The variance of the second component

The observations details are:

`Y`	The original observations.
`Ysigsq`	The original measurement variances.
`posteriorOdds`	Posterior odds of being in component 2 of the mixture.
`postProbVar`	Estimated variance of the posterior probability, using the delta method.