Rnorm.exp {groHMM} | R Documentation

## Rnorm.exp fits a normal+exponential distribution to a specified data vector using maximum likelihood.

### Description

Distrubtion function devined by: alpha*Normal(mean, varience)+(1-alpha) *Exponential(lambda).

### Usage

    Rnorm.exp(xi, wi = rep(1, NROW(xi)), guess = c(0.5, 0, 1, 1),
      tol = sqrt(.Machine$double.eps), maxit = 10000)

### Arguments

`xi` | A vector of observations, assumed to be real numbers in the inveraval (-Inf,+Inf).
`wi` | A vector of weights. Default: vector of repeating 1; indicating all observations are weighted equally. (Are these normalized internally?! Or do they have to be [0,1]?)
`guess` | Initial guess for paremeters. Default: c(0.5, 0, 1, 1).
`tol` | Convergence tolerance. Default: sqrt(.Machine$double.eps).
`maxit` | Maximum number of iterations. Default: 10,000.

### Details

Fits nicely with data types that look normal overall, but have a long tail starting for positive values.

### Value

Returns a list of parameters for the best-fit normal distribution (alpha, mean, varience, and lambda).

### Author(s)

Charles G. Danko

[Package groHMM version 1.21.0 Index]