R: Local Estimation for Lambda in Mixtures of Regressions

lambda {mixtools} | R Documentation

## Local Estimation for Lambda in Mixtures of Regressions

### Description

Return local estimates of the mixing proportions from each component of a mixture of regressions model using output from an EM algorithm.

### Usage

    lambda(z, x, xi, h = NULL, kernel = c("Gaussian", "Beta",
           "Triangle", "Cosinus", "Optcosinus"), g = 0)

### Arguments

`z` | An nxk matrix of posterior probabilities obtained from the EM algorithm.
`x` | A vector of values for which the local estimation is calculated.
`xi` | An nx(p-1) matrix of the predictor values.
`h` | The bandwidth controlling the size of the window used for the local estimation.
`kernel` | The type of kernel to be used for the local estimation.
`g` | A shape parameter required for the symmetric beta kernel. The default is `g` = 0 which yields the uniform kernel. Some common values are `g` = 1 for the Epanechnikov kernel, `g` = 2 for the biweight kernel, and `g` = 3 for the triweight kernel.

### Value

`lambda` returns local estimates of the mixing proportions for the inputted `x` vector.

### Note

`lambda` is for use within `regmixEM.loc`.

### See Also

`regmixEM.loc`

[Package mixtools version 1.2.0 Index]