checkPar0 {momentuHMM}

Check parameter length and order for a fitHMM (or MIfitHMM) model

Description

Prints parameters with labels based on DM, formula, and/or formulaDelta. See fitHMM for further argument details.

Usage

checkPar0(data, ...)

## Default S3 method:
checkPar0(
  data,
  nbStates,
  dist,
  Par0 = NULL,
  beta0 = NULL,
  delta0 = NULL,
  estAngleMean = NULL,
  circularAngleMean = NULL,
  formula = ~1,
  formulaDelta = NULL,
  stationary = FALSE,
  mixtures = 1,
  formulaPi = NULL,
  DM = NULL,
  userBounds = NULL,
  workBounds = NULL,
  betaCons = NULL,
  betaRef = NULL,
  deltaCons = NULL,
  stateNames = NULL,
  fixPar = NULL,
  prior = NULL,
  ...
)

## S3 method for class 'hierarchical'
checkPar0(
  data,
  hierStates,
  hierDist,
  Par0 = NULL,
  hierBeta = NULL,
  hierDelta = NULL,
  estAngleMean = NULL,
  circularAngleMean = NULL,
  hierFormula = NULL,
  hierFormulaDelta = NULL,
  mixtures = 1,
  formulaPi = NULL,
  DM = NULL,
  userBounds = NULL,
  workBounds = NULL,
  betaCons = NULL,
  deltaCons = NULL,
  fixPar = NULL,
  prior = NULL,
  ...
)

Arguments

data

momentuHMMData object, momentuHierHMMData object, or a data frame containing the data stream and covariate values

...

further arguments passed to or from other methods

nbStates

Number of states of the HMM.

dist

A named list indicating the probability distributions of the data streams.

Par0

Optional named list containing vectors of state-dependent probability distribution parameters for each data stream specified in dist. If Par0 is not provided, then ordered parameter indices are returned.

beta0

Optional matrix of regression coefficients for the transition probabilities. If beta0 is not provided, then ordered parameter indices are returned.

delta0

Optional values or regression coefficients for the initial distribution of the HMM. If delta0 is not provided, then ordered parameter indices are returned.

estAngleMean

An optional named list indicating whether or not to estimate the angle mean for data streams with angular distributions ('vm' and 'wrpcauchy').

circularAngleMean

An optional named list indicating whether to use circular-linear or circular-circular regression on the mean of circular distributions ('vm' and 'wrpcauchy') for turning angles.

formula

Regression formula for the transition probability covariates.

formulaDelta

Regression formula for the initial distribution.

stationary

FALSE if there are time-varying covariates in formula or any covariates in formulaDelta. If TRUE, the initial distribution is considered equal to the stationary distribution. Default: FALSE.

mixtures

Number of mixtures for the state transition probabilities.

formulaPi

Regression formula for the mixture distribution probabilities. Note that only the covariate values from the first row for each individual ID in data are used (i.e. time-varying covariates cannot be used for the mixture probabilties).

DM

An optional named list indicating the design matrices to be used for the probability distribution parameters of each data stream.

userBounds

An optional named list of 2-column matrices specifying bounds on the natural (i.e, real) scale of the probability distribution parameters for each data stream.

workBounds

An optional named list of 2-column matrices specifying bounds on the working scale of the probability distribution, transition probability, and initial distribution parameters.

betaCons

Matrix of the same dimension as beta0 composed of integers identifying any equality constraints among the t.p.m. parameters.

betaRef

Numeric vector of length nbStates indicating the reference elements for the t.p.m. multinomial logit link.

deltaCons

Matrix of the same dimension as delta0 composed of integers identifying any equality constraints among the initial distribution working scale parameters. Ignored unless a formula is provided in formulaDelta.

stateNames

Optional character vector of length nbStates indicating state names.

fixPar

An optional list of vectors indicating parameters which are assumed known prior to fitting the model.

prior

A function that returns the log-density of the working scale parameter prior distribution(s).

hierStates

A hierarchical model structure Node for the states ('state'). See fitHMM.

hierDist

A hierarchical data structure Node for the data streams ('dist'). See fitHMM.

hierBeta

A hierarchical data structure Node for the initial matrix of regression coefficients for the transition probabilities at each level of the hierarchy ('beta'). See fitHMM.

hierDelta

A hierarchical data structure Node for the initial values for the initial distribution at each level of the hierarchy ('delta'). See fitHMM.

hierFormula

A hierarchical formula structure for the transition probability covariates for each level of the hierarchy ('formula'). See fitHMM.

hierFormulaDelta

A hierarchical formula structure for the initial distribution covariates for each level of the hierarchy ('formulaDelta'). See fitHMM. Default: NULL (no covariate effects and fixPar$delta is specified on the working scale).

See Also

fitHMM, MIfitHMM

Examples

m <- example$m
checkPar0(data=m$data, nbStates=2, dist=m$conditions$dist,
          estAngleMean = m$conditions$estAngleMean,
          formula = m$conditions$formula)

par <- getPar(m)
checkPar0(data=m$data, nbStates=2, dist=m$conditions$dist,
          estAngleMean = m$conditions$estAngleMean,
          formula = m$conditions$formula,
          Par0=par$Par, beta0=par$beta, delta0=par$delta)
          
dummyDat <- data.frame(step=0,angle=0,cov1=0,cov2=0)
checkPar0(data=dummyDat, nbStates=2, dist=m$conditions$dist,
          estAngleMean = m$conditions$estAngleMean,
          formula = m$conditions$formula)

## Not run: 
simDat <- simData(nbStates=2, dist=m$conditions$dist, Par = par$Par,
                  spatialCovs = list(forest=forest),
                  centers = matrix(0,1,2),
                  nbCovs = 2)
checkPar0(data = simDat, nbStates=2, dist=m$conditions$dist,
          formula = ~forest,
          DM = list(step=list(mean=~cov1, sd=~cov2),
                    angle=list(mean=~center1.angle,concentration=~1)),
          estAngleMean=list(angle=TRUE),
          circularAngleMean=list(angle=TRUE))
          
par <- list(step=rnorm(8),angle=rnorm(4))
beta0 <- matrix(rnorm(4),2,2)
delta0 <- c(0.5,0.5)
checkPar0(data = simDat, nbStates=2, dist=m$conditions$dist,
          Par0 = par, beta0 = beta0, delta0 = delta0,
          formula = ~forest,
          DM = list(step=list(mean=~cov1, sd=~cov2),
                    angle=list(mean=~center1.angle,concentration=~1)),
          estAngleMean=list(angle=TRUE),
          circularAngleMean=list(angle=TRUE))                

## End(Not run)