update doc for fixPar

R/fitCTHMM.R

@@ -118,6 +118,11 @@ fitCTHMM <- function(data, ...) {
 #' For transition probability parameters, the corresponding element of \code{fixPar} must be named ``beta'' and have the same dimensions as \code{beta0}. 
 #' For initial distribution parameters, the corresponding element of \code{fixPar} must be named ``delta'' and have the same dimensions as \code{delta0}. 
 #' Each parameter should either be numeric (the fixed value of the parameter) or NA if the parameter is to be estimated. Corresponding \code{fixPar} parameters must be on the same scale as \code{Par0} (e.g. if \code{DM} is specified for a given data stream, any fixed parameters for this data stream must be on the working scale), \code{beta0}, and \code{delta0}.
+#'
+#' If \code{optMethod='TMB'} then \code{fixPar} is specified differently. In this case, \code{NA} instead indicates parameters that are fixed (to the value specified by \code{Par0}, \code{beta0}, or \code{delta0}) and the parameters that are not fixed are sequentially indexed (similar to \code{betaCons}) by parameter number (such that parameters can be constrained to be equal). For example, \code{fixPar=list(angle=c(NA,NA,1,2))} indicates the first two parameters of the 'angle' distribution are fixed and the other two parameters are freely estimated. \code{fixPar=list(angle=c(1,NA,2,2,3))} indicates the first angle parameter is freely estimated, the second parameter is fixed, the third and fourth parameters are constrained to be equal, and the fifth parameter is freely estimated.
+#' @param retryFits Non-negative integer indicating the number of times to attempt to iteratively fit the model using random perturbations of the current parameter estimates as the 
+#' initial values for likelihood optimization. Normal(0,\code{retrySD}^2) perturbations are used on the working scale parameters. Default: 0.  When \code{retryFits>0}, the model with the largest log likelihood 
+#' value is returned. Ignored if \code{fit=FALSE}.
 #' @param retryFits Non-negative integer indicating the number of times to attempt to iteratively fit the model using random perturbations of the current parameter estimates as the 
 #' initial values for likelihood optimization. Normal(0,\code{retrySD}^2) perturbations are used on the working scale parameters. Default: 0.  When \code{retryFits>0}, the model with the largest log likelihood 
 #' value is returned. Ignored if \code{fit=FALSE}.

R/fitHMM.R

@@ -117,6 +117,8 @@ fitHMM <- function(data, ...) {
 #' For transition probability parameters, the corresponding element of \code{fixPar} must be named ``beta'' and have the same dimensions as \code{beta0}. 
 #' For initial distribution parameters, the corresponding element of \code{fixPar} must be named ``delta'' and have the same dimensions as \code{delta0}. 
 #' Each parameter should either be numeric (the fixed value of the parameter) or NA if the parameter is to be estimated. Corresponding \code{fixPar} parameters must be on the same scale as \code{Par0} (e.g. if \code{DM} is specified for a given data stream, any fixed parameters for this data stream must be on the working scale), \code{beta0}, and \code{delta0}.
+#'
+#' If \code{optMethod='TMB'} then \code{fixPar} is specified differently. In this case, \code{NA} instead indicates parameters that are fixed (to the value specified by \code{Par0}, \code{beta0}, or \code{delta0}) and the parameters that are not fixed are sequentially indexed (similar to \code{betaCons}) by parameter number (such that parameters can be constrained to be equal). For example, \code{fixPar=list(angle=c(NA,NA,1,2))} indicates the first two parameters of the 'angle' distribution are fixed and the other two parameters are freely estimated. \code{fixPar=list(angle=c(1,NA,2,2,3))} indicates the first angle parameter is freely estimated, the second parameter is fixed, the third and fourth parameters are constrained to be equal, and the fifth parameter is freely estimated.
 #' @param retryFits Non-negative integer indicating the number of times to attempt to iteratively fit the model using random perturbations of the current parameter estimates as the 
 #' initial values for likelihood optimization. Normal(0,\code{retrySD}^2) perturbations are used on the working scale parameters. Default: 0.  When \code{retryFits>0}, the model with the largest log likelihood 
 #' value is returned. Ignored if \code{fit=FALSE}.