CRAN Package Check Results for Package momentuHMM

Last updated on 2018-06-20 01:50:22 CEST.

Check Details

Version: 1.4.1
Check: examples
Result: ERROR
    Running examples in ‘momentuHMM-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: simData
    > ### Title: Simulation tool
    > ### Aliases: simData
    >
    > ### ** Examples
    >
    > # 1. Pass a fitted model to simulate from
    > # (m is a momentuHMM 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)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 2. Pass the parameters of the model to simulate from
    > stepPar <- c(1,10,1,5,0.2,0.3) # mean_1, mean_2, sd_1, sd_2, zeromass_1, zeromass_2
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > omegaPar <- c(1,10,10,1) # shape1_1, shape1_2, shape2_1, shape2_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > omegaDist <- "beta"
    > data <- simData(nbAnimals=4,nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + zeroInflation=list(step=TRUE),
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1, zeromass=~1)
     angle ~ vm(mean=~1, concentration=~1)
     omega ~ beta(shape1=~1, shape2=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 3. Include covariates
    > # (note that it is useless to specify "nbCovs", which are overruled
    > # by the number of columns of "cov")
    > cov <- data.frame(temp=log(rnorm(500,20,5)))
    > stepPar <- c(log(10),0.1,log(100),-0.1,log(5),log(25)) # working scale parameters for step DM
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > data <- simData(nbAnimals=2,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=stepPar,angle=anglePar),
    + DM=list(step=list(mean=~temp,sd=~1)),
    + covs=cov,
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~temp, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 4. Include example 'forest' spatial covariate raster layer
    > # nbAnimals and obsPerAnimal kept small to reduce example run time
    > spatialCov<-list(forest=forest)
    > data <- simData(nbAnimals=1,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=c(100,1000,50,100),angle=c(0,0,0.1,5)),
    + beta=matrix(c(5,-10,-25,50),nrow=2,ncol=2,byrow=TRUE),
    + formula=~forest,spatialCovs=spatialCov,
    + obsPerAnimal=250,states=TRUE,
    + retrySims=100)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~forest
    
     Initial distribution formula: ~1
    =======================================================================
    Attempting to simulate tracks within spatial extent(s) of raster layers(s). Press 'esc' to force exit from 'simData'
    
     Attempt 1 of 100...DONE
    >
    > # 5. Specify design matrix for 'omega' data stream
    > # natural scale parameters for step and angle
    > stepPar <- c(1,10,1,5) # shape_1, shape_2, scale_1, scale_2
    > anglePar <- c(pi,0,0.5,0.7) # mean_1, mean_2, concentration_1, concentration_2
    >
    > # working scale parameters for omega DM
    > omegaPar <- c(log(1),0.1,log(10),-0.1,log(10),-0.1,log(1),0.1)
    >
    > stepDist <- "weibull"
    > angleDist <- "wrpcauchy"
    > omegaDist <- "beta"
    >
    > data <- simData(nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + DM=list(omega=list(shape1=~cov1,shape2=~cov2)),
    + obsPerAnimal=obsPerAnimal,states=TRUE)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ weibull(shape=~1, scale=~1)
     angle ~ wrpcauchy(mean=~1, concentration=~1)
     omega ~ beta(shape1=~cov1, shape2=~cov2)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 6. Include temporal irregularity and location measurement error
    > lambda <- 2 # expect 2 observations per time step
    > errorEllipse <- list(M=50,m=25,r=180)
    > obsData <- simData(model=m,obsPerAnimal=obsPerAnimal,
    + lambda=lambda, errorEllipse=errorEllipse)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 7. Cosinor and state-dependent formulas
    > nbStates<-2
    > dist<-list(step="gamma")
    > Par<-list(step=c(100,1000,50,100))
    >
    > # include 24-hour cycle on all transition probabilities
    > # include 12-hour cycle on transitions from state 2
    > formula=~cosinor(hour24,24)+state2(cosinor(hour12,12))
    >
    > # specify appropriate covariates
    > covs<-data.frame(hour24=0:23,hour12=0:11)
    >
    > beta<-matrix(c(-1.5,1,1,NA,NA,-1.5,-1,-1,1,1),5,2)
    > # row names for beta not required but can be helpful
    > rownames(beta)<-c("(Intercept)",
    + "cosinorCos(hour24, 24)",
    + "cosinorSin(hour24, 24)",
    + "cosinorCos(hour12, 12)",
    + "cosinorSin(hour12, 12)")
    > data.cos<-simData(nbStates=nbStates,dist=dist,Par=Par,
    + beta=beta,formula=formula,covs=covs)
    =======================================================================
    Simulating HMM with 2 states and 1 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
    
     Transition probability matrix formula: ~cosinor(hour24, 24) + state2(cosinor(hour12, 12))
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 8. Piecewise constant B-spline on step length mean and angle concentration
    > library(splines2)
    > nObs <- 1000 # length of simulated track
    > cov <- data.frame(time=1:nObs) # time covariate for splines
    > dist <- list(step="gamma",angle="vm")
    > stepDM <- list(mean=~bSpline(time,df=3,degree=0),sd=~1)
    > angleDM <- list(mean=~1,concentration=~bSpline(time,df=3,degree=0))
    > DM <- list(step=stepDM,angle=angleDM)
    > Par <- list(step=c(log(1000),1,-1,log(100)),angle=c(0,log(10),2,-5))
    >
    > data.spline<-simData(obsPerAnimal=nObs,nbStates=1,dist=dist,Par=Par,DM=DM,covs=cov)
    =======================================================================
    Simulating HMM with 1 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~bSpline(time, df = 3, degree = 0), sd=~1)
     angle ~ vm(mean=~1, concentration=~bSpline(time, df = 3, degree = 0))
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    Error in getDM(subCovs, inputs$DM, inputs$dist, nbStates, p$parNames, :
     Dimension mismatch between Par and DM for: step, angle
    Calls: simData -> getDM
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.4.1
Check: installed package size
Result: NOTE
     installed size is 7.5Mb
     sub-directories of 1Mb or more:
     data 2.1Mb
     doc 1.7Mb
     libs 2.8Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 1.4.1
Check: examples
Result: ERROR
    Running examples in ‘momentuHMM-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: simData
    > ### Title: Simulation tool
    > ### Aliases: simData
    >
    > ### ** Examples
    >
    > # 1. Pass a fitted model to simulate from
    > # (m is a momentuHMM 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)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 2. Pass the parameters of the model to simulate from
    > stepPar <- c(1,10,1,5,0.2,0.3) # mean_1, mean_2, sd_1, sd_2, zeromass_1, zeromass_2
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > omegaPar <- c(1,10,10,1) # shape1_1, shape1_2, shape2_1, shape2_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > omegaDist <- "beta"
    > data <- simData(nbAnimals=4,nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + zeroInflation=list(step=TRUE),
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1, zeromass=~1)
     angle ~ vm(mean=~1, concentration=~1)
     omega ~ beta(shape1=~1, shape2=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 3. Include covariates
    > # (note that it is useless to specify "nbCovs", which are overruled
    > # by the number of columns of "cov")
    > cov <- data.frame(temp=log(rnorm(500,20,5)))
    > stepPar <- c(log(10),0.1,log(100),-0.1,log(5),log(25)) # working scale parameters for step DM
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > data <- simData(nbAnimals=2,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=stepPar,angle=anglePar),
    + DM=list(step=list(mean=~temp,sd=~1)),
    + covs=cov,
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~temp, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 4. Include example 'forest' spatial covariate raster layer
    > # nbAnimals and obsPerAnimal kept small to reduce example run time
    > spatialCov<-list(forest=forest)
    > data <- simData(nbAnimals=1,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=c(100,1000,50,100),angle=c(0,0,0.1,5)),
    + beta=matrix(c(5,-10,-25,50),nrow=2,ncol=2,byrow=TRUE),
    + formula=~forest,spatialCovs=spatialCov,
    + obsPerAnimal=250,states=TRUE,
    + retrySims=100)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~forest
    
     Initial distribution formula: ~1
    =======================================================================
    Attempting to simulate tracks within spatial extent(s) of raster layers(s). Press 'esc' to force exit from 'simData'
    
     Attempt 1 of 100...DONE
    >
    > # 5. Specify design matrix for 'omega' data stream
    > # natural scale parameters for step and angle
    > stepPar <- c(1,10,1,5) # shape_1, shape_2, scale_1, scale_2
    > anglePar <- c(pi,0,0.5,0.7) # mean_1, mean_2, concentration_1, concentration_2
    >
    > # working scale parameters for omega DM
    > omegaPar <- c(log(1),0.1,log(10),-0.1,log(10),-0.1,log(1),0.1)
    >
    > stepDist <- "weibull"
    > angleDist <- "wrpcauchy"
    > omegaDist <- "beta"
    >
    > data <- simData(nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + DM=list(omega=list(shape1=~cov1,shape2=~cov2)),
    + obsPerAnimal=obsPerAnimal,states=TRUE)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ weibull(shape=~1, scale=~1)
     angle ~ wrpcauchy(mean=~1, concentration=~1)
     omega ~ beta(shape1=~cov1, shape2=~cov2)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 6. Include temporal irregularity and location measurement error
    > lambda <- 2 # expect 2 observations per time step
    > errorEllipse <- list(M=50,m=25,r=180)
    > obsData <- simData(model=m,obsPerAnimal=obsPerAnimal,
    + lambda=lambda, errorEllipse=errorEllipse)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 7. Cosinor and state-dependent formulas
    > nbStates<-2
    > dist<-list(step="gamma")
    > Par<-list(step=c(100,1000,50,100))
    >
    > # include 24-hour cycle on all transition probabilities
    > # include 12-hour cycle on transitions from state 2
    > formula=~cosinor(hour24,24)+state2(cosinor(hour12,12))
    >
    > # specify appropriate covariates
    > covs<-data.frame(hour24=0:23,hour12=0:11)
    >
    > beta<-matrix(c(-1.5,1,1,NA,NA,-1.5,-1,-1,1,1),5,2)
    > # row names for beta not required but can be helpful
    > rownames(beta)<-c("(Intercept)",
    + "cosinorCos(hour24, 24)",
    + "cosinorSin(hour24, 24)",
    + "cosinorCos(hour12, 12)",
    + "cosinorSin(hour12, 12)")
    > data.cos<-simData(nbStates=nbStates,dist=dist,Par=Par,
    + beta=beta,formula=formula,covs=covs)
    =======================================================================
    Simulating HMM with 2 states and 1 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
    
     Transition probability matrix formula: ~cosinor(hour24, 24) + state2(cosinor(hour12, 12))
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 8. Piecewise constant B-spline on step length mean and angle concentration
    > library(splines2)
    > nObs <- 1000 # length of simulated track
    > cov <- data.frame(time=1:nObs) # time covariate for splines
    > dist <- list(step="gamma",angle="vm")
    > stepDM <- list(mean=~bSpline(time,df=3,degree=0),sd=~1)
    > angleDM <- list(mean=~1,concentration=~bSpline(time,df=3,degree=0))
    > DM <- list(step=stepDM,angle=angleDM)
    > Par <- list(step=c(log(1000),1,-1,log(100)),angle=c(0,log(10),2,-5))
    >
    > data.spline<-simData(obsPerAnimal=nObs,nbStates=1,dist=dist,Par=Par,DM=DM,covs=cov)
    =======================================================================
    Simulating HMM with 1 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~bSpline(time, df = 3, degree = 0), sd=~1)
     angle ~ vm(mean=~1, concentration=~bSpline(time, df = 3, degree = 0))
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    Error in getDM(subCovs, inputs$DM, inputs$dist, nbStates, p$parNames, :
     Dimension mismatch between Par and DM for: step, angle
    Calls: simData -> getDM
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 1.4.1
Check: running examples for arch ‘i386’
Result: ERROR
    Running examples in 'momentuHMM-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: simData
    > ### Title: Simulation tool
    > ### Aliases: simData
    >
    > ### ** Examples
    >
    > # 1. Pass a fitted model to simulate from
    > # (m is a momentuHMM 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)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 2. Pass the parameters of the model to simulate from
    > stepPar <- c(1,10,1,5,0.2,0.3) # mean_1, mean_2, sd_1, sd_2, zeromass_1, zeromass_2
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > omegaPar <- c(1,10,10,1) # shape1_1, shape1_2, shape2_1, shape2_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > omegaDist <- "beta"
    > data <- simData(nbAnimals=4,nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + zeroInflation=list(step=TRUE),
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1, zeromass=~1)
     angle ~ vm(mean=~1, concentration=~1)
     omega ~ beta(shape1=~1, shape2=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 3. Include covariates
    > # (note that it is useless to specify "nbCovs", which are overruled
    > # by the number of columns of "cov")
    > cov <- data.frame(temp=log(rnorm(500,20,5)))
    > stepPar <- c(log(10),0.1,log(100),-0.1,log(5),log(25)) # working scale parameters for step DM
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > data <- simData(nbAnimals=2,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=stepPar,angle=anglePar),
    + DM=list(step=list(mean=~temp,sd=~1)),
    + covs=cov,
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~temp, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 4. Include example 'forest' spatial covariate raster layer
    > # nbAnimals and obsPerAnimal kept small to reduce example run time
    > spatialCov<-list(forest=forest)
    > data <- simData(nbAnimals=1,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=c(100,1000,50,100),angle=c(0,0,0.1,5)),
    + beta=matrix(c(5,-10,-25,50),nrow=2,ncol=2,byrow=TRUE),
    + formula=~forest,spatialCovs=spatialCov,
    + obsPerAnimal=250,states=TRUE,
    + retrySims=100)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~forest
    
     Initial distribution formula: ~1
    =======================================================================
    Attempting to simulate tracks within spatial extent(s) of raster layers(s). Press 'esc' to force exit from 'simData'
    
     Attempt 1 of 100...DONE
    >
    > # 5. Specify design matrix for 'omega' data stream
    > # natural scale parameters for step and angle
    > stepPar <- c(1,10,1,5) # shape_1, shape_2, scale_1, scale_2
    > anglePar <- c(pi,0,0.5,0.7) # mean_1, mean_2, concentration_1, concentration_2
    >
    > # working scale parameters for omega DM
    > omegaPar <- c(log(1),0.1,log(10),-0.1,log(10),-0.1,log(1),0.1)
    >
    > stepDist <- "weibull"
    > angleDist <- "wrpcauchy"
    > omegaDist <- "beta"
    >
    > data <- simData(nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + DM=list(omega=list(shape1=~cov1,shape2=~cov2)),
    + obsPerAnimal=obsPerAnimal,states=TRUE)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ weibull(shape=~1, scale=~1)
     angle ~ wrpcauchy(mean=~1, concentration=~1)
     omega ~ beta(shape1=~cov1, shape2=~cov2)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 6. Include temporal irregularity and location measurement error
    > lambda <- 2 # expect 2 observations per time step
    > errorEllipse <- list(M=50,m=25,r=180)
    > obsData <- simData(model=m,obsPerAnimal=obsPerAnimal,
    + lambda=lambda, errorEllipse=errorEllipse)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 7. Cosinor and state-dependent formulas
    > nbStates<-2
    > dist<-list(step="gamma")
    > Par<-list(step=c(100,1000,50,100))
    >
    > # include 24-hour cycle on all transition probabilities
    > # include 12-hour cycle on transitions from state 2
    > formula=~cosinor(hour24,24)+state2(cosinor(hour12,12))
    >
    > # specify appropriate covariates
    > covs<-data.frame(hour24=0:23,hour12=0:11)
    >
    > beta<-matrix(c(-1.5,1,1,NA,NA,-1.5,-1,-1,1,1),5,2)
    > # row names for beta not required but can be helpful
    > rownames(beta)<-c("(Intercept)",
    + "cosinorCos(hour24, 24)",
    + "cosinorSin(hour24, 24)",
    + "cosinorCos(hour12, 12)",
    + "cosinorSin(hour12, 12)")
    > data.cos<-simData(nbStates=nbStates,dist=dist,Par=Par,
    + beta=beta,formula=formula,covs=covs)
    =======================================================================
    Simulating HMM with 2 states and 1 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
    
     Transition probability matrix formula: ~cosinor(hour24, 24) + state2(cosinor(hour12, 12))
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 8. Piecewise constant B-spline on step length mean and angle concentration
    > library(splines2)
    > nObs <- 1000 # length of simulated track
    > cov <- data.frame(time=1:nObs) # time covariate for splines
    > dist <- list(step="gamma",angle="vm")
    > stepDM <- list(mean=~bSpline(time,df=3,degree=0),sd=~1)
    > angleDM <- list(mean=~1,concentration=~bSpline(time,df=3,degree=0))
    > DM <- list(step=stepDM,angle=angleDM)
    > Par <- list(step=c(log(1000),1,-1,log(100)),angle=c(0,log(10),2,-5))
    >
    > data.spline<-simData(obsPerAnimal=nObs,nbStates=1,dist=dist,Par=Par,DM=DM,covs=cov)
    =======================================================================
    Simulating HMM with 1 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~bSpline(time, df = 3, degree = 0), sd=~1)
     angle ~ vm(mean=~1, concentration=~bSpline(time, df = 3, degree = 0))
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    Error in getDM(subCovs, inputs$DM, inputs$dist, nbStates, p$parNames, :
     Dimension mismatch between Par and DM for: step, angle
    Calls: simData -> getDM
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.4.1
Check: running examples for arch ‘x64’
Result: ERROR
    Running examples in 'momentuHMM-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: simData
    > ### Title: Simulation tool
    > ### Aliases: simData
    >
    > ### ** Examples
    >
    > # 1. Pass a fitted model to simulate from
    > # (m is a momentuHMM 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)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 2. Pass the parameters of the model to simulate from
    > stepPar <- c(1,10,1,5,0.2,0.3) # mean_1, mean_2, sd_1, sd_2, zeromass_1, zeromass_2
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > omegaPar <- c(1,10,10,1) # shape1_1, shape1_2, shape2_1, shape2_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > omegaDist <- "beta"
    > data <- simData(nbAnimals=4,nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + zeroInflation=list(step=TRUE),
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1, zeromass=~1)
     angle ~ vm(mean=~1, concentration=~1)
     omega ~ beta(shape1=~1, shape2=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 3. Include covariates
    > # (note that it is useless to specify "nbCovs", which are overruled
    > # by the number of columns of "cov")
    > cov <- data.frame(temp=log(rnorm(500,20,5)))
    > stepPar <- c(log(10),0.1,log(100),-0.1,log(5),log(25)) # working scale parameters for step DM
    > anglePar <- c(pi,0,0.5,2) # mean_1, mean_2, concentration_1, concentration_2
    > stepDist <- "gamma"
    > angleDist <- "vm"
    > data <- simData(nbAnimals=2,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=stepPar,angle=anglePar),
    + DM=list(step=list(mean=~temp,sd=~1)),
    + covs=cov,
    + obsPerAnimal=obsPerAnimal)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~temp, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 4. Include example 'forest' spatial covariate raster layer
    > # nbAnimals and obsPerAnimal kept small to reduce example run time
    > spatialCov<-list(forest=forest)
    > data <- simData(nbAnimals=1,nbStates=2,dist=list(step=stepDist,angle=angleDist),
    + Par=list(step=c(100,1000,50,100),angle=c(0,0,0.1,5)),
    + beta=matrix(c(5,-10,-25,50),nrow=2,ncol=2,byrow=TRUE),
    + formula=~forest,spatialCovs=spatialCov,
    + obsPerAnimal=250,states=TRUE,
    + retrySims=100)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~forest
    
     Initial distribution formula: ~1
    =======================================================================
    Attempting to simulate tracks within spatial extent(s) of raster layers(s). Press 'esc' to force exit from 'simData'
    
     Attempt 1 of 100...DONE
    >
    > # 5. Specify design matrix for 'omega' data stream
    > # natural scale parameters for step and angle
    > stepPar <- c(1,10,1,5) # shape_1, shape_2, scale_1, scale_2
    > anglePar <- c(pi,0,0.5,0.7) # mean_1, mean_2, concentration_1, concentration_2
    >
    > # working scale parameters for omega DM
    > omegaPar <- c(log(1),0.1,log(10),-0.1,log(10),-0.1,log(1),0.1)
    >
    > stepDist <- "weibull"
    > angleDist <- "wrpcauchy"
    > omegaDist <- "beta"
    >
    > data <- simData(nbStates=2,dist=list(step=stepDist,angle=angleDist,omega=omegaDist),
    + Par=list(step=stepPar,angle=anglePar,omega=omegaPar),nbCovs=2,
    + DM=list(omega=list(shape1=~cov1,shape2=~cov2)),
    + obsPerAnimal=obsPerAnimal,states=TRUE)
    =======================================================================
    Simulating HMM with 2 states and 3 data streams
    -----------------------------------------------------------------------
    
     step ~ weibull(shape=~1, scale=~1)
     angle ~ wrpcauchy(mean=~1, concentration=~1)
     omega ~ beta(shape1=~cov1, shape2=~cov2)
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 6. Include temporal irregularity and location measurement error
    > lambda <- 2 # expect 2 observations per time step
    > errorEllipse <- list(M=50,m=25,r=180)
    > obsData <- simData(model=m,obsPerAnimal=obsPerAnimal,
    + lambda=lambda, errorEllipse=errorEllipse)
    =======================================================================
    Simulating HMM with 2 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
     angle ~ vm(mean=~1, concentration=~1)
    
     Transition probability matrix formula: ~cov1 + cos(cov2)
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 7. Cosinor and state-dependent formulas
    > nbStates<-2
    > dist<-list(step="gamma")
    > Par<-list(step=c(100,1000,50,100))
    >
    > # include 24-hour cycle on all transition probabilities
    > # include 12-hour cycle on transitions from state 2
    > formula=~cosinor(hour24,24)+state2(cosinor(hour12,12))
    >
    > # specify appropriate covariates
    > covs<-data.frame(hour24=0:23,hour12=0:11)
    >
    > beta<-matrix(c(-1.5,1,1,NA,NA,-1.5,-1,-1,1,1),5,2)
    > # row names for beta not required but can be helpful
    > rownames(beta)<-c("(Intercept)",
    + "cosinorCos(hour24, 24)",
    + "cosinorSin(hour24, 24)",
    + "cosinorCos(hour12, 12)",
    + "cosinorSin(hour12, 12)")
    > data.cos<-simData(nbStates=nbStates,dist=dist,Par=Par,
    + beta=beta,formula=formula,covs=covs)
    =======================================================================
    Simulating HMM with 2 states and 1 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~1, sd=~1)
    
     Transition probability matrix formula: ~cosinor(hour24, 24) + state2(cosinor(hour12, 12))
    
     Initial distribution formula: ~1
    =======================================================================
    DONE
    >
    > # 8. Piecewise constant B-spline on step length mean and angle concentration
    > library(splines2)
    > nObs <- 1000 # length of simulated track
    > cov <- data.frame(time=1:nObs) # time covariate for splines
    > dist <- list(step="gamma",angle="vm")
    > stepDM <- list(mean=~bSpline(time,df=3,degree=0),sd=~1)
    > angleDM <- list(mean=~1,concentration=~bSpline(time,df=3,degree=0))
    > DM <- list(step=stepDM,angle=angleDM)
    > Par <- list(step=c(log(1000),1,-1,log(100)),angle=c(0,log(10),2,-5))
    >
    > data.spline<-simData(obsPerAnimal=nObs,nbStates=1,dist=dist,Par=Par,DM=DM,covs=cov)
    =======================================================================
    Simulating HMM with 1 states and 2 data streams
    -----------------------------------------------------------------------
    
     step ~ gamma(mean=~bSpline(time, df = 3, degree = 0), sd=~1)
     angle ~ vm(mean=~1, concentration=~bSpline(time, df = 3, degree = 0))
    
     Transition probability matrix formula: ~1
    
     Initial distribution formula: ~1
    =======================================================================
    Error in getDM(subCovs, inputs$DM, inputs$dist, nbStates, p$parNames, :
     Dimension mismatch between Par and DM for: step, angle
    Calls: simData -> getDM
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64