# Get starting values for new model from existing `momentuHMM` or `momentuHierHMM` model fit For nested models, this function will extract starting parameter values (i.e., `Par0` in `fitHMM` or `MIfitHMM`) from an existing `momentuHMM` or `momentuHierHMM` model fit based on the provided arguments for the new model. Any parameters that are not in common between `model` and the new model (as specified by the arguments) are set to `0`. This function is intended to help users incrementally build and fit more complicated models from simpler nested models (and vice versa). ```r getPar0(model, ...) ## Default S3 method: getPar0( model, nbStates = length(model$stateNames), estAngleMean = model$conditions$estAngleMean, circularAngleMean = model$conditions$circularAngleMean, formula = model$conditions$formula, formulaDelta = model$conditions$formulaDelta, stationary = model$conditions$stationary, mixtures = model$conditions$mixtures, formulaPi = model$conditions$formulaPi, DM = model$conditions$DM, betaRef = model$conditions$betaRef, stateNames = model$stateNames, ... ) ## S3 method for class 'hierarchical' getPar0( model, hierStates = model$conditions$hierStates, estAngleMean = model$conditions$estAngleMean, circularAngleMean = model$conditions$circularAngleMean, hierFormula = model$conditions$hierFormula, hierFormulaDelta = model$conditions$hierFormulaDelta, mixtures = model$conditions$mixtures, formulaPi = model$conditions$formulaPi, DM = model$conditions$DM, ... ) ``` ## Arguments - `model`: A `momentuHMM`, `momentuHierHMM`, `miHMM`, or `miSum` object (as returned by `fitHMM`, `MIfitHMM`, or `MIpool`) - `...`: further arguments passed to or from other methods - `nbStates`: Number of states in the new model. Default: `nbStates=length(model$stateNames)` - `estAngleMean`: Named list indicating whether or not the angle mean for data streams with angular distributions ('vm' and 'wrpcauchy') are to be estimated in the new model. Default: `estAngleMean=model$conditions$estAngleMean` - `circularAngleMean`: Named list indicating whether circular-linear or circular-circular regression on the mean of circular distributions ('vm' and 'wrpcauchy') for turning angles are to be used in the new model. See `fitHMM`. Default: `circularAngleMean=model$conditions$circularAngleMean` - `formula`: Regression formula for the transition probability covariates of the new model (see `fitHMM`). Default: `formula=model$conditions$formula`. - `formulaDelta`: Regression formula for the initial distribution covariates of the new model (see `fitHMM`). Default: `formulaDelta=model$conditions$formulaDelta`. - `stationary`: `FALSE` if there are time-varying covariates in `formula` or any covariates in `formulaDelta`. If `TRUE`, the initial distribution is considered equal to the stationary distribution. Default: `FALSE`. - `mixtures`: Number of mixtures for the state transition probabilities (see `fitHMM`). Default: `formula=model$conditions$mixtures`. - `formulaPi`: Regression formula for the mixture distribution probabilities (see `fitHMM`). Default: `formula=model$conditions$formulaPi`. - `DM`: Named list indicating the design matrices to be used for the probability distribution parameters of each data stream in the new model (see `fitHMM`). Only parameters with design matrix column names that match those in model$conditions$fullDM are extracted, so care must be taken in naming columns if any elements of `DM` are specified as matrices instead of formulas. Default: `DM=model$conditions$DM`. - `betaRef`: Numeric vector of length `nbStates` indicating the reference elements for the t.p.m. multinomial logit link. Default: `formula=model$conditions$betaRef`. - `stateNames`: Character vector of length `nbStates` indicating the names and order of the states in the new model. Default: `stateNames=model$stateNames[1:nbStates]`. - `hierStates`: A hierarchical model structure `Node` for the states (see `fitHMM`). Default: `hierStates=model$conditions$hierStates`. - `hierFormula`: A hierarchical formula structure for the transition probability covariates for each level of the hierarchy (see `fitHMM`). Default: `hierFormula=model$conditions$hierFormula`. - `hierFormulaDelta`: A hierarchical formula structure for the initial distribution covariates for each level of the hierarchy ('formulaDelta'). Default: `NULL` (no covariate effects and `fixPar$delta` is specified on the working scale). ## Returns A named list containing starting values suitable for `Par0` and `beta0` arguments in `fitHMM` or `MIfitHMM`: - **Par**: A list of vectors of state-dependent probability distribution parameters for each data stream specified in `model$conditions$dist` - **beta**: Matrix of regression coefficients for the transition probabilities - **delta**: Initial distribution of the HMM. Only returned if `stateNames` has the same membership as the state names for `model` ## Details All other `fitHMM` (or `MIfitHMM`) model specifications (e.g., `dist`, `hierDist`, `userBounds`, `workBounds`, etc.) and `data` are assumed to be the same for `model` and the new model (as specified by `nbStates`, `hierStates`, `estAngleMean`, `circularAngleMean`, `formula`, `hierFormula`, `formulaDelta`, `hierFormulaDelta`, `DM`, etc.). Note that for hierarchical models, `getPar0` will return hierarchical data.tree objects (`hierBeta` and `hierDelta`) with the default values for `fixPar`, `betaCons`, and `deltaCons`; if hierarchical t.p.m. or initial distribution parameters are subject to constraints, then these must be set manually on the list object returned by `getPar0`. ## Examples ```r # model is a momentuHMM object, automatically loaded with the package model <- example$m data <- model$data dist <- model$conditions$dist nbStates <- length(model$stateNames) estAngleMean <- model$conditions$estAngleMean newformula <- ~cov1+cov2 Par0 <- getPar0(model,formula=newformula) ## Not run: newModel <- fitHMM(model$data,dist=dist,nbStates=nbStates, Par0=Par0$Par,beta0=Par0$beta, formula=newformula, estAngleMean=estAngleMean) ## End(Not run) newDM1 <- list(step=list(mean=~cov1,sd=~cov1)) Par0 <- getPar0(model,DM=newDM1) ## Not run: newModel1 <- fitHMM(model$data,dist=dist,nbStates=nbStates, Par0=Par0$Par,beta0=Par0$beta, formula=model$conditions$formula, estAngleMean=estAngleMean, DM=newDM1) ## End(Not run) # same model but specify DM for step using matrices newDM2 <- list(step=matrix(c(1,0,0,0, "cov1",0,0,0, 0,1,0,0, 0,"cov1",0,0, 0,0,1,0, 0,0,"cov1",0, 0,0,0,1, 0,0,0,"cov1"),nrow=nbStates*2)) # to be extracted, new design matrix column names must match # column names of model$conditions$fullDM colnames(newDM2$step)<-paste0(rep(c("mean_","sd_"),each=2*nbStates), rep(1:nbStates,each=2), rep(c(":(Intercept)",":cov1"),2*nbStates)) Par0 <- getPar0(model,DM=newDM2) ## Not run: newModel2 <- fitHMM(model$data,dist=dist,nbStates=nbStates, Par0=Par0$Par,beta0=Par0$beta, formula=model$conditions$formula, estAngleMean=estAngleMean, DM=newDM2) ## End(Not run) ``` ## See Also `getPar`, `getParDM`, `fitHMM`, `MIfitHMM`

getPar0 function