combdist.mle {distributionsrd} R Documentation

## Combined distributions MLE

### Description

Maximum Likelihood estimation for combined ( single, composite and finite mixture) truncated or complete distributions.

### Usage

```combdist.mle(
x,
dist,
start = NULL,
lower = NULL,
upper = NULL,
components = 1,
nested = FALSE,
steps = 1,
lowertrunc = 0,
uppertrunc = Inf,
...
)
```

### Arguments

 `x` data vector `dist` character vector denoting the distribution(s). `start` named numeric vector holding the starting values for the coefficients. `lower, upper` Lower and upper bounds to the estimated coefficients, defaults to -Inf and Inf respectively. `components` number of components for a mixture distribution. `nested` logical indicating whether results should be returned in a nested list or a flat list form, defaults to FALSE. `steps` number of steps taken in stepflexmix, defaults to 1. `lowertrunc, uppertrunc` lowertrunc- and uppertrunc truncation points, defaults to 0 and Inf respectively `...` Additional arguments.

### Value

Returns a named list containing a

dist

Character vector denoting the distributions, separated by an underscore

components

Nr. of combined distributions

prior

Weights assigned to the respective component distributions

coefficients

Named vector of coefficients

convergence

logical indicator of convergence

n

Length of the fitted data vector

np

Nr. of coefficients

### Examples

```

x <- rdoubleparetolognormal(1e3)
combdist.mle(x = x, dist = "doubleparetolognormal") # Double-Pareto Lognormal
combdist.mle(x = x, components = 2, dist = "lnorm", steps = 20) # FMM with 2 components
combdist.mle( x = x, dist = c("invpareto", "lnorm", "pareto"),
start = c(coeff1.k = 1, coeff2.meanlog = mean(log(x)), coeff2.sdlog = sd(log(x)), coeff3.k = 1),
lower = c(1e-10, -Inf, 1e-10, 1e-10), upper = c(Inf, Inf, Inf, Inf), nested = TRUE)
# composite distribution

```

[Package distributionsrd version 0.0.6 Index]