momentuHMM (version 1.4.3)

checkPar0: 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, nbStates, dist, Par0 = NULL, beta0 = NULL, delta0 = NULL,
  estAngleMean = NULL, circularAngleMean = NULL, formula = ~1,
  formulaDelta = NULL, stationary = FALSE, DM = NULL, cons = NULL,
  userBounds = NULL, workBounds = NULL, workcons = NULL,
  betaCons = NULL, betaRef = NULL, stateNames = NULL, fixPar = NULL)

Arguments

data

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

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 (FALSE) or circular-circular (TRUE) 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 covariates in formula or formulaDelta. If TRUE, the initial distribution is considered equal to the stationary distribution. Default: FALSE.

DM

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

cons

Deprecated: please use workBounds instead. An optional named list of vectors specifying a power to raise parameters corresponding to each column of the design matrix for 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.

workcons

Deprecated: please use workBounds instead. An optional named list of vectors specifying constants to add to the regression coefficients on the working scale for each data stream.

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.

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.

See Also

fitHMM, MIfitHMM

Examples

# NOT RUN {
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))                
# }