# plotDw: Plot of the estimated HSMM dwell-time distributions. In PHSMM: Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models

## Description

Plots the HSMM dwell-time distributions estimated using `pmleHSMM`.

## Usage

 `1` ```plotDw(mod, R_max, state='all', mfrow=NULL) ```

## Arguments

 `mod` model object as returned by `pmleHSMM`. `R_max` integer, maximum dwell time for which the dwell-time probabilities are plotted. `state` value determining the states for which the distributions are plotted. Either "all" (default) for plotting the dwell-time distributions of all states, or positive integer in 1,..,N. `mfrow` If `NULL` (default) and `state="all"`, the probability mass functions are plotted one below the other. Otherwise, a vector of length 2 which determines the number of rows (first element) and the number of columns (second argument) of the matrix of plots.

## Value

Plot of the estimated HSMM dwell-time distributions.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56``` ```# running this example might take a few minutes # # 1.) 2-state gamma-HSMM for hourly muskox step length # with an unstructured start of length of 10 # # initial values p_list0<-list() p_list0[]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2)) p_list0[]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2)) mu0<-c(5,150) sigma0<-c(3,180) # # fit 2-state gamma-HSMM with lambda=c(100,100) # and difference order 3 # estimation might take a few minutes PHSMM<-pmleHSMM(y=muskox\$step,N=2,p_list=p_list0,mu=mu0, sigma=sigma0,lambda=c(100,100),order_diff=3, y_dist='gamma') # # plot the estimated dwell-time distributions # for dwell-times up to 12 plotDw(mod=PHSMM,R_max=12) plotDw(mod=PHSMM,R_max=12,state=1) plotDw(mod=PHSMM,R_max=12,mfrow=c(1,2)) # running this example might take a few minutes # # 2.) 3-state gamma-HSMM for hourly muskox step length # with an unstructured start of length of 10 # # initial values p_list0<-list() p_list0[]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2)) p_list0[]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2)) p_list0[]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2)) omega0<-matrix(0.5,3,3) diag(omega0)<-0 mu0<-c(5,100,350) sigma0<-c(3,90,300) # # fit 3-state gamma-HSMM with lambda=c(1000,1000,1000) # and difference order 3 # estimation might take some minutes PHSMM<-pmleHSMM(y=muskox\$step,N=3,p_list=p_list0,mu=mu0, sigma=sigma0,omega=omega0, lambda=c(1000,1000,1000), order_diff=3,y_dist='gamma') # # plot the estimated dwell-time distributions # for dwell-times up to 15 plotDw(mod=PHSMM,R_max=15) plotDw(mod=PHSMM,R_max=15,state=1) plotDw(mod=PHSMM,R_max=15,mfrow=c(1,3)) ```

PHSMM documentation built on Feb. 9, 2021, 5:07 p.m.