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#' Lotka-Volterra model with pairwise alphas and no covariate effects
#'
#' @param par 1d vector of initial parameters: 'lambda', 'alpha_intra' (optional), 'alpha_inter', and 'sigma'
#' @param fitness 1d vector of fitness observations, in log scale
#' @param neigh_intra_matrix optional matrix of one column, number of intraspecific neighbours for each observation
#' @param neigh_inter_matrix matrix of arbitrary columns, number of interspecific neighbours for each observation
#' @param covariates included for compatibility, not used in this model
#' @param fixed_parameters optional list specifying values of fixed parameters,
#' with components "lambda","alpha_intra","alpha_inter".
#'
#' @return log-likelihood value
#' @export
LV_pm_alpha_pairwise_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_intra,alpha_inter,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$lambda_cov)){
# lambda_cov <- par[pos:(pos+ncol(covariates)-1)]
# pos <- pos + ncol(covariates)
# }else{
# lambda_cov <- fixed_parameters[["lambda_cov"]]
# }
if(!is.null(neigh_intra_matrix)){
# intra
if(is.null(fixed_parameters[["alpha_intra"]])){
alpha_intra <- par[pos]
pos <- pos + 1
}else{
alpha_intra <- fixed_parameters[["alpha_intra"]]
}
}else{
alpha_intra <- NULL
}
# inter
if(is.null(fixed_parameters[["alpha_inter"]])){
alpha_inter <- par[pos:(pos+ncol(neigh_inter_matrix)-1)]
pos <- pos + ncol(neigh_inter_matrix) -1
}else{
alpha_inter <- fixed_parameters[["alpha_inter"]]
}
# if(is.null(fixed_parameters$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 ---------------------------------------------------------
# we do not differentiate alpha_intra from alpha_inter in this model
# so, put together alpha_intra and alpha_inter, and the observations
# with intraspecific ones at the beginning
if(!is.null(alpha_intra)){
alpha <- c(alpha_intra,alpha_inter)
all_neigh_matrix <- cbind(neigh_intra_matrix,neigh_inter_matrix)
}else{
alpha <- alpha_inter
all_neigh_matrix <- neigh_inter_matrix
}
term = 0
for(z in 1:ncol(all_neigh_matrix)){
term <- term - all_neigh_matrix[,z]*alpha[z]
}
pred <- lambda+term
# 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))
}
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