Nothing
#' Ricker model with no alphas and no covariate effects
#'
#' This model, in all families, is simply given by lambda.
#'
#' @param par 1d vector of initial parameters: lambda and sigma
#' @param fitness 1d vector of fitness observations, in log scale
#' @param neigh_intra_matrix included for compatibility, not used in this model.
#' @param neigh_inter_matrix included for compatibility, not used in this model.
#' @param covariates included for compatibility, not used in this model
#' @param fixed_parameters included for compatibility, not used in this model
#'
#' @return log-likelihood value
#' @export
RK_pm_alpha_none_lambdacov_none_alphacov_none <- function(par,
fitness,
neigh_intra_matrix = NULL,
neigh_inter_matrix,
covariates,
fixed_parameters){
# retrieve parameters -----------------------------------------------------
# parameters to fit are all in the "par" vector,
# so we need to retrieve them one by one
# order is {lambda,lambda_cov,alpha,alpha_cov,sigma}
# comment or uncomment sections for the different parameters
# depending on whether your model includes them
pos <- 1
# if a parameter is passed within the "par" vector,
# it should be NULL in the "fixed_parameters" list
if(is.null(fixed_parameters[["lambda"]])){
lambda <- par[pos]
pos <- pos + 1
}else{
lambda <- fixed_parameters[["lambda"]]
}
# if(is.null(fixed_parameters$lambda_cov)){
# lambda_cov <- par[pos:(pos+ncol(covariates)-1)]
# pos <- pos + ncol(covariates)
# }else{
# lambda_cov <- fixed_parameters[["lambda_cov"]]
# }
# if(is.null(fixed_parameters$alpha)){
# alpha <- par[pos:(pos+ncol(neigh_matrix)-1)]
# pos <- pos + ncol(neigh_matrix)
# }else{
# alpha <- fixed_parameters[["alpha"]]
# }
# if(is.null(fixed_parameters$alpha_cov)){
# alpha.cov <- par[pos:(pos+(ncol(covariates)*ncol(neigh_matrix))-1)]
# pos <- pos + (ncol(covariates)*ncol(neigh_matrix))
# }else{
# alpha.cov <- fixed_parameters[["alpha.cov"]]
# }
sigma <- par[length(par)]
# now, parameters have appropriate values (or NULL)
# next section is where your model is coded
# MODEL CODE HERE ---------------------------------------------------------
pred <- rep(lambda, times=length(fitness))
# MODEL CODE ENDS HERE ----------------------------------------------------
# the routine returns the sum of log-likelihoods of the data and model:
# DO NOT CHANGE THIS
llik <- dnorm(fitness, mean = (log(pred)), sd = (sigma), log=TRUE)
return(sum(-1*llik))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.