Description Usage Arguments Details Value Examples
Simulates movement data from an HMM.
1 2 3 4 5 
nbAnimals 
Number of observed individuals to simulate. 
nbStates 
Number of behavioural states to simulate. 
stepDist 
Name of the distribution of the step lengths (as a character string). Supported distributions are: gamma, weibull, lnorm, exp. Default: gamma. 
angleDist 
Name of the distribution of the turning angles (as a character string).
Supported distributions are: vm, wrpcauchy. Set to 
stepPar 
Parameters of the step length distribution. 
anglePar 
Parameters of the turning angle distribution. 
beta 
Matrix of regression parameters for the transition probabilities (more information in "Details"). 
covs 
Covariate values to include in the model, as a dataframe. Default: 
nbCovs 
Number of covariates to simulate (0 by default). Does not need to be specified of

zeroInflation 

obsPerAnimal 
Either the number of the number of observations per animal (if single value),
or the bounds of the number of observations per animal (if vector of two values). In the latter case,
the numbers of obervations generated for each animal are uniformously picked from this interval.
Default: 
model 
A moveHMM object. This option can be used to simulate from a fitted model. Default: NULL. Note that, if this argument is specified, most other arguments will be ignored – except for nbAnimals, obsPerAnimal, covs (if covariate values different from those in the data should be specified), and states. 
states 

The matrix beta
of regression coefficients for the transition probabilities has
one row for the intercept, plus one row for each covariate, and one column for
each nondiagonal element of the transition probability matrix. For example, in a 3state
HMM with 2 covariates, the matrix beta
has three rows (intercept + two covariates)
and six columns (six nondiagonal elements in the 3x3 transition probability matrix  filled in
rowwise).
In a covariatefree model (default), beta
has one row, for the intercept.
If the length of covariate values passed (either through 'covs', or 'model') is not the same as the number of observations suggested by 'nbAnimals' and 'obsPerAnimal', then the series of covariates is either shortened (removing last values  if too long) or extended (starting over from the first values  if too short).
An object moveData, i.e. a dataframe of:
ID 
The ID(s) of the observed animal(s) 
step 
The step lengths 
angle 
The turning angles (if any) 
x 
Either easting or longitude 
y 
Either northing or latitude 
... 
Covariates (if any) 
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  # 1. Pass a fitted model to simulate from
# (m is a moveHMM object  as returned by fitHMM  automatically loaded with the package)
# We keep the default nbAnimals=1.
m < example$m
obsPerAnimal=c(50,100)
data < simData(model=m,obsPerAnimal=obsPerAnimal)
# 2. Pass the parameters of the model to simulate from
stepPar < c(1,10,1,5,0.2,0.3) # mean1, mean2, sd1, sd2, z1, z2
anglePar < c(pi,0,0.5,2) # mean1, mean2, k1, k2
stepDist < "gamma"
angleDist < "vm"
data < simData(nbAnimals=5,nbStates=2,stepDist=stepDist,angleDist=angleDist,stepPar=stepPar,
anglePar=anglePar,nbCovs=2,zeroInflation=TRUE,obsPerAnimal=obsPerAnimal)
stepPar < c(1,10,1,5) # mean1, mean2, sd1, sd2
anglePar < c(pi,0,0.5,0.7) # mean1, mean2, k1, k2
stepDist < "weibull"
angleDist < "wrpcauchy"
data < simData(nbAnimals=5,nbStates=2,stepDist=stepDist,angleDist=angleDist,stepPar=stepPar,
anglePar=anglePar,obsPerAnimal=obsPerAnimal)
# step length only and zeroinflation
stepPar < c(1,10,1,5,0.2,0.3) # mean1, mean2, sd1, sd2, z1, z2
stepDist < "gamma"
data < simData(nbAnimals=5,nbStates=2,stepDist=stepDist,angleDist="none",stepPar=stepPar,
nbCovs=2,zeroInflation=TRUE,obsPerAnimal=obsPerAnimal)
# include covariates
# (note that it is useless to specify "nbCovs", which is determined
# by the number of columns of "cov")
cov < data.frame(temp=rnorm(500,20,5))
stepPar < c(1,10,1,5) # mean1, mean2, sd1, sd2
anglePar < c(pi,0,0.5,2) # mean1, mean2, k1, k2
stepDist < "gamma"
angleDist < "vm"
data < simData(nbAnimals=5,nbStates=2,stepDist=stepDist,angleDist=angleDist,stepPar=stepPar,
anglePar=anglePar,covs=cov)

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