# n2w: Scaling function: natural to working parameters. In moveHMM: Animal Movement Modelling using Hidden Markov Models

## Description

Scales each parameter from its natural interval to the set of real numbers, to allow for unconstrained optimization. Used during the optimization of the log-likelihood.

## Usage

 `1` ```n2w(par, bounds, beta, delta = NULL, nbStates, estAngleMean) ```

## Arguments

 `par` Vector of state-dependent distributions parameters. `bounds` Matrix with 2 columns and as many rows as there are elements in `par`. Each row contains the lower and upper bound for the correponding parameter. `beta` Matrix of regression coefficients for the transition probabilities. `delta` Initial distribution. Default: `NULL` ; if the initial distribution is not estimated. `nbStates` The number of states of the HMM. `estAngleMean` `TRUE` if the angle mean is estimated, `FALSE` otherwise.

## Value

A vector of unconstrained parameters.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## Not run: nbStates <- 3 par <- c(0.001,0.999,0.5,0.001,1500.3,7.1) bounds <- matrix(c(0,1, # bounds for first parameter 0,1, # bounds for second parameter 0,1, # ... 0,Inf, 0,Inf, 0,Inf), byrow=TRUE,ncol=2) beta <- matrix(rnorm(18),ncol=6,nrow=3) delta <- c(0.6,0.3,0.1) # vector of working parameters wpar <- n2w(par=par,bounds=bounds,beta=beta,delta=delta,nbStates=nbStates, estAngleMean=FALSE) ## End(Not run) ```

moveHMM documentation built on June 7, 2018, 5:05 p.m.