diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..3274d2c --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,27 @@ +Package: anisoFun +Version: 1.0 +Date: 2022-08-3 +Title: Isotropic feasibility domains +Description: Tools to estimate extintion probabilities and anisotropic index. +Author: Alfonso Allen-Perkins +Maintainer: Alfonso Allen-Perkins +Depends: + foreach, + doParallel +Imports: + tibble, + dplyr, + magrittr, + stats, + mvtnorm, + boot, + pracma, + matlib, + zipfR +Suggests: + testthat, + knitr, + rmarkdown +VignetteBuilder: knitr +License: MIT + file LICENSE +RoxygenNote: 7.1.2 diff --git a/Data/caracoles_raw_data/01_05_lambda_homogeneous.csv b/Data/caracoles_raw_data/01_05_lambda_homogeneous.csv new file mode 100644 index 0000000..3fd05a3 --- /dev/null +++ b/Data/caracoles_raw_data/01_05_lambda_homogeneous.csv @@ -0,0 +1,856 @@ +"year";"plot";"sp";"lambda" +2015;1;"BEMA";141,0407839099 +2015;1;"CETE";632,432007235911 +2015;1;"CHFU";862,222023786659 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PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..35a1a88 --- /dev/null +++ b/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2022 +COPYRIGHT HOLDER: Alfonso Allen-Perkins \ No newline at end of file diff --git a/NAMESPACE b/NAMESPACE new file mode 100644 index 0000000..6bf2654 --- /dev/null +++ b/NAMESPACE @@ -0,0 +1,30 @@ +# Generated by roxygen2: do not edit by hand + +export(Omega_bootstrap) +export(anisotropy_index) +export(anisotropy_metrics_4_int_matrix) +export(boot_prob_excl_Omega_raw) +export(closest_to_exclusion) +export(cone_vertices_director_vertices) +export(furthest_from_exclusion) +export(incenter_inradius_isoprob_calculation) +export(max_min_intrinsic_growth_rates) +export(mean_pairwise_similarity) +export(prob_extinction_4_int_matrix) +export(small_omega_iso) +export(sp_distance_to_exclusion) +export(vertices_unit_ball) +import(doParallel) +import(foreach) +importFrom(boot,boot) +importFrom(dplyr,mutate) +importFrom(dplyr,pull) +importFrom(dplyr,select) +importFrom(magrittr,"%>%") +importFrom(matlib,inv) +importFrom(mvtnorm,pmvnorm) +importFrom(pracma,crossn) +importFrom(stats,quantile) +importFrom(tibble,as_tibble) +importFrom(tibble,tibble) +importFrom(zipfR,Rbeta) diff --git a/R/Omega_bootstrap.R b/R/Omega_bootstrap.R new file mode 100644 index 0000000..3fad3ed --- /dev/null +++ b/R/Omega_bootstrap.R @@ -0,0 +1,107 @@ + +#' Random estimations of capital Omega +#' +#' Given an interaction matrix, this function generates a vector with +#' estimations of capital Omega (i.e., the proportion of the feasible parameter +#' space inside the unit sphere). Those estimations are computed via a +#' quasi-Monte Carlo method for even relatively large communities. +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' @param replicates a number that specifies how many estimations of Omega +#' will be calculated. By default, this parameter is set to 1,000 replicates. +#' @param use_chol_decomp boolean that specifies if the QR decomposition should +#' be used to compute Omega. +#' +#' @import doParallel +#' @import foreach +#' @importFrom mvtnorm pmvnorm +#' @importFrom boot boot +#' +#' @return A vector whose length is equal to the parameter replicate, where each +#' element represents an estimation of capital Omega. +#' +#' @examples +#' numCores <- detectCores() +#' numCores +#' registerDoParallel(2) +#' A_int <- -1*diag(c(3,5,7)) +#' Omega_bootstrap(A_int, replicates = 1e3) +#' stopImplicitCluster() +#' +#' @references \url{https://doi.org/10.1016/j.jtbi.2018.04.030} +#' +#' @export + + +Omega_bootstrap <- function(A_int, replicates = 1e3, use_chol_decomp = F) { + + # check the input parameters + + is.wholenumber <- function(x){ + integer_part <- round(x,0) + decimal_part <- x - integer_part + return(decimal_part == 0) + } + + test_result_A_int <- check_A_int(A_int) + test_replicates <- is.wholenumber(replicates) + test_chol_decomp <- is.logical(use_chol_decomp) + + if((test_result_A_int[[1]]==T) & test_replicates & test_chol_decomp){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + if(use_chol_decomp == F){ + + Sigma <- solve(t(A_int) %*% A_int,tol = 1e-17) + + }else{ + + chol_A <- chol(t(A_int) %*% A_int) + Sigma <- chol2inv(chol_A) + + } + + S <- ncol(A_int) + + m <- matrix(0, S, 1) + a <- matrix(0, S, 1) + b <- matrix(Inf, S, 1) + + res_list <- foreach (i = 1:replicates, .combine=c) %dopar% { + + d <- try(mvtnorm::pmvnorm(lower = rep(0, S), + upper = rep(Inf, S), + mean = rep(0, S), sigma = Sigma),silent = TRUE) + out <- ifelse(class(d) == "try-error",0,d[1]) + + + } + + return(res_list) + + + }else{ + + if(test_result_A_int[[1]] != T){ + cat(test_result_A_int[[2]],"\n") + } + if(test_replicates != T){ + cat("replicates should be a whole number.","\n") + } + if(test_chol_decomp != T){ + cat("use_chol_decomp should be logical, that is, TRUE or FALSE.","\n") + } + + } + +} + + diff --git a/R/anisotropy_index.R b/R/anisotropy_index.R new file mode 100644 index 0000000..92706d4 --- /dev/null +++ b/R/anisotropy_index.R @@ -0,0 +1,44 @@ +#' Anisotropy index +#' +#' Given a vector whose elements are the average probabilities of extinction of +#' the species in a given community, this function calculates its anisotropy +#' index (i.e., the relative Shannon diversity index of such probabilities). +#' +#' @param probability_vector vector with length S whose elements are the +#' average probabilities of extinction of the species in a given community, +#' where S is the number of species. +#' +#' @return A non-negative number between 0 and 1 (isotropic community). +#' +#' @examples +#' probability_vector <- c(.25,.15,.60) +#' anisotropy_index(probability_vector) +#' +#' @export +anisotropy_index <- function(probability_vector){ + + # check the input parameter: probability_vector + + if(!all(class(probability_vector) %in% c("numeric"))){ + cat("Probabilities should be numbers.\n") + }else if(all(class(probability_vector) %in% c("matrix","array"))){ + cat("The input parameter should be vector.\n") + }else if(!all(probability_vector >=0)){ + cat("Probabilities should be non-negaive numbers.\n") + }else if(round(sum(probability_vector),2) != 1){ + cat("The sum of all probabilities should be equal to one.\n") + }else{ + + # If the vector with probabilities is OK, then the index is calculated. + + number_species <- length(probability_vector) + + probability_vector_positive <- probability_vector[probability_vector > 0] + + log_prob <- log(probability_vector_positive) + + return(-sum(probability_vector_positive*log_prob)/log(number_species)) + + } + +} diff --git a/R/anisotropy_metrics_4_int_matrix.R b/R/anisotropy_metrics_4_int_matrix.R new file mode 100644 index 0000000..877f286 --- /dev/null +++ b/R/anisotropy_metrics_4_int_matrix.R @@ -0,0 +1,145 @@ +#' Anisotropy metrics for a given interaction matrix +#' +#' Given an interaction matrix, this function uses the output of the function +#' boot_prob_excl_Omega_raw to estimate the mean values and 95% confidence +#' intervals of the following metrics: small omega (i.e., the S root of capital +#' Omega, that is, the proportion of the feasible parameter space inside the +#' unit sphere) and the anisotropy index (i.e., the relative Shannon diversity +#' index of the species' probabilities of exclusion). +#' +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' @param number_Omega_replicates a number that specifies how many estimations +#' of Omega will be calculated by the quasi-Monte Carlo method in function +#' boot_prob_excl_Omega_raw. By default, this parameter is set to 1,000 +#' replicates. +#' @param number_boot_replicates a number that specifies how many bootstrap +#' estimations of the probability of exclusion and Omega will be calculated +#' for each species in function boot_prob_excl_Omega_raw. By default, this +#' parameter is set to 1,000 replicates. +#' @param use_chol_decomp boolean that specifies if the QR decomposition should +#' be used to compute Omega. +#' +#' @import foreach +#' @import doParallel +#' @importFrom mvtnorm pmvnorm +#' @importFrom boot boot +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' @importFrom stats quantile +#' @importFrom tibble tibble +#' @importFrom dplyr pull +#' @importFrom dplyr select +#' @importFrom stats quantile +#' +#' @return A tibble with 1 row and 6 columns, that contain the following data: +#' \itemize{ +#' \item The mean value of small Omega. +#' \item The lower bound of the 95% confidence interval for small Omega. +#' \item The upper bound of the 95% confidence interval for small Omega. +#' \item The mean value of the anisotropy index. +#' \item The lower bound of the 95% confidence interval for the anisotropy +#' index. +#' \item The upper bound of the 95% confidence interval for the anisotropy +#' index. +#' } +#' +#' @examples +#' numCores <- detectCores() +#' numCores +#' registerDoParallel(2) +#' A_int <- -1*diag(c(3,5,7)) +#' anisotropy_metrics_4_int_matrix(A_int) +#' stopImplicitCluster() +#' +#' @references \url{https://doi.org/10.1016/j.jtbi.2018.04.030} +#' +#' @export +anisotropy_metrics_4_int_matrix <- function(A_int, + number_Omega_replicates = 1e3, + number_boot_replicates = 1e3, + use_chol_decomp = F){ + + # check the input parameters + + is.wholenumber <- function(x){ + integer_part <- round(x,0) + decimal_part <- x - integer_part + return(decimal_part == 0) + } + + test_result_A_int <- check_A_int(A_int) + test_omega_rep <- is.wholenumber(number_Omega_replicates) + test_boot_rep <- is.wholenumber(number_boot_replicates) + test_chol_decomp <- is.logical(use_chol_decomp) + + if((test_result_A_int[[1]]==T) & test_omega_rep & test_boot_rep & + test_chol_decomp){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + prob_excl_Omega_df <- boot_prob_excl_Omega_raw(A_int, number_Omega_replicates, + number_boot_replicates, + use_chol_decomp = F) + + anisotropy_agg_info <- tibble::as_tibble(data.frame(matrix(1))) + colnames(anisotropy_agg_info) <- "interaction_matrix" + + small_omega_mean <- NA + small_omega_lowerCI <- NA + small_omega_upperCI <- NA + anisotropy_index_mean <- NA + anisotropy_index_lowerCI <- NA + anisotropy_index_upperCI <- NA + + + Omega_rep <- prob_excl_Omega_df[1,(1+number_boot_replicates):(2*number_boot_replicates)] %>% + as.numeric() + small_omega_rep <- Omega_rep^(1/nrow(A_int)) + anisotropy_rep <- NULL + + for(i in 1:number_boot_replicates){ + + probability_vector <- prob_excl_Omega_df[,i+1] %>% dplyr::pull() + + anisotropy_rep <- c(anisotropy_rep, anisotropy_index(probability_vector)) + + } + + small_omega_rep_CI <- stats::quantile(small_omega_rep, prob=c(.025,.975)) %>% as.numeric() + anisotropy_rep_CI <- stats::quantile(anisotropy_rep, prob=c(.025,.975)) %>% as.numeric() + + anisotropy_agg_info$small_omega_mean <- mean(small_omega_rep, na.rm = T) + anisotropy_agg_info$small_omega_lowerCI <- small_omega_rep_CI[1] + anisotropy_agg_info$small_omega_upperCI <- small_omega_rep_CI[2] + anisotropy_agg_info$anisotropy_index_mean <- mean(anisotropy_rep, na.rm = T) + anisotropy_agg_info$anisotropy_index_lowerCI <- anisotropy_rep_CI[1] + anisotropy_agg_info$anisotropy_index_upperCI <- anisotropy_rep_CI[2] + + return(anisotropy_agg_info[,2:ncol(anisotropy_agg_info)]) + + }else{ + + if(test_result_A_int[[1]] != T){ + cat(test_result_A_int[[2]],"\n") + } + if(test_omega_rep != T){ + cat("number_Omega_replicates should be a whole number.","\n") + } + if(test_boot_rep != T){ + cat("number_boot_replicates should be a whole number.","\n") + } + if(test_chol_decomp != T){ + cat("use_chol_decomp should be logical, that is, TRUE or FALSE.","\n") + } + } + +} diff --git a/R/boot_prob_excl_Omega_raw.R b/R/boot_prob_excl_Omega_raw.R new file mode 100644 index 0000000..e60a58a --- /dev/null +++ b/R/boot_prob_excl_Omega_raw.R @@ -0,0 +1,184 @@ +#' Bootstrap estimation of the probabilities of exclusion and capital Omega +#' +#' Given an interaction matrix, this function generates a tibble with S rows, +#' where S is the number of species in the matrix. Each row contains the +#' following information for a given: replicates of its probability of exclusion +#' and the corresponding value of capital Omega for each replicate (i.e., the +#' proportion of the feasible parameter #' space inside the unit sphere). Those +#' estimations are computed via a quasi-Monte Carlo method and by +#' bootstrapping the quasi-Mote Carlo results. +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' @param number_Omega_replicates a number that specify how many estimations +#' of Omega will be calculated by the quasi-Monte Carlo method. By default, +#' this parameter is set to 1,000 replicates. +#' @param number_boot_replicates a number that specifies how many bootstrap +#' estimations of the probability of exclusion and Omega will be calculated +#' for each species. By default, this parameter is set to 1,000 replicates. +#' @param use_chol_decomp boolean that specifies if the QR decomposition should +#' be used to compute Omega. +#' +#' @import foreach +#' @import doParallel +#' @importFrom mvtnorm pmvnorm +#' @importFrom boot boot +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' @importFrom magrittr %>% +#' @importFrom tibble as_tibble +#' +#' @return A tibble with S rows and 2 x number_boot_replicates columns. Each row +#' contains the estimated probabilities of exclusion and values of Omega for +#' each species, when considering the intersection of the feasibility domain +#' and the unit ball. Specifically, each row contains: +#' \itemize{ +#' \item Resamples of the estimated probabilities of exclusion for species i +#' (prob_excl_rep_x), where x takes values from 1 to number_boot_replicates. +#' \item The corresponding value of capital Omega for species i and +#' a given prob_excl_rep_x (Omega_rep_x), where x takes values from 1 to +#' number_boot_replicates. +#' } +#' +#' @examples +#' numCores <- detectCores() +#' numCores +#' registerDoParallel(2) +#' A_int <- -1*diag(c(3,5,7)) +#' boot_prob_excl_Omega_raw(A_int) +#' stopImplicitCluster() +#' +#' @references \url{https://doi.org/10.1016/j.jtbi.2018.04.030} +#' +#' @export + + +boot_prob_excl_Omega_raw <- function(A_int, number_Omega_replicates = 1e3, + number_boot_replicates = 1e3, + use_chol_decomp = F){ + + + # check the input parameters + + is.wholenumber <- function(x){ + integer_part <- round(x,0) + decimal_part <- x - integer_part + return(decimal_part == 0) + } + + test_result_A_int <- check_A_int(A_int) + test_omega_rep <- is.wholenumber(number_Omega_replicates) + test_boot_rep <- is.wholenumber(number_boot_replicates) + test_chol_decomp <- is.logical(use_chol_decomp) + + if((test_result_A_int[[1]]==T) & test_omega_rep & test_boot_rep & + test_chol_decomp){ + + simple_mean <- function(x, indices){ + return(sum(x[indices])/length(indices)) + } + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + exclusion_probabilities_par_bootstrap <- function(A_int, I, replicates = 1e3, + use_chol_decomp = F){ + + dimensions <- ncol(A_int) + + all_vertices_data_aux <- vertices_unit_ball(A_int) + all_vertices_data <- all_vertices_data_aux[,c((dimensions+1):(2*dimensions))] + + feasibility_parts <- matrix(rep(0,nrow(A_int)*replicates), + nrow = nrow(A_int), ncol = replicates) + + names_sp <- colnames(A_int) + + for (i in 1:nrow(A_int)) { + + cat(names_sp[i],"\n") + + A_int_mod <- A_int + + A_int_mod[,i] <- -I + + feasibility_parts[i, ] <- Omega_bootstrap(A_int_mod, replicates, + use_chol_decomp = F) + + } + + return(feasibility_parts) + + } + + + dimensions <- ncol(A_int) + + incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) + + I <- incenter_inradius_isoprob[[1]] + + name_colums <- c("species", paste0("prob_excl_rep_",1:number_boot_replicates), + paste0("Omega_rep_",1:number_boot_replicates)) + + A_int_prob_exclusion_plants_mat <- matrix(rep(0, dimensions*(1 + 2*number_boot_replicates)), + nrow = dimensions, + ncol = (1 + 2*number_boot_replicates)) + + bootstrap_raw_data <- + exclusion_probabilities_par_bootstrap(A_int, I, + replicates = number_Omega_replicates, + use_chol_decomp = F) + + for (sp_i in 1:dimensions) { + + bootmean_area_excl_sp_i <- boot::boot(data = bootstrap_raw_data[sp_i,], + statistic = simple_mean, + R = number_boot_replicates) + + A_int_prob_exclusion_plants_mat[sp_i, 2:(1+number_boot_replicates)] <- + bootmean_area_excl_sp_i[["t"]] + + } + + Omega_values <- colSums(A_int_prob_exclusion_plants_mat[, c(2:(1+number_boot_replicates))]) + + A_int_prob_exclusion_plants_mat[, c((2+number_boot_replicates):(1 + 2*number_boot_replicates))] <- + matrix(rep(Omega_values,dimensions), nrow = number_boot_replicates) %>% t() + + for (sp_i in 1:nrow(A_int_prob_exclusion_plants_mat)) { + A_int_prob_exclusion_plants_mat[sp_i, c(2:(1+number_boot_replicates))] <- + A_int_prob_exclusion_plants_mat[sp_i, c(2:(1+number_boot_replicates))]/ + A_int_prob_exclusion_plants_mat[sp_i, c((2+number_boot_replicates):(1 + 2*number_boot_replicates))] + } + + A_int_prob_exclusion_plants_aux <- tibble::as_tibble(data.frame(A_int_prob_exclusion_plants_mat)) + colnames(A_int_prob_exclusion_plants_aux) <- name_colums + + A_int_prob_exclusion_plants_aux$species = colnames(A_int) + + return(A_int_prob_exclusion_plants_aux) + + }else{ + + if(test_result_A_int[[1]] != T){ + cat(test_result_A_int[[2]],"\n") + } + if(test_omega_rep != T){ + cat("number_Omega_replicates should be a whole number.","\n") + } + if(test_boot_rep != T){ + cat("number_boot_replicates should be a whole number.","\n") + } + if(test_chol_decomp != T){ + cat("use_chol_decomp should be logical, that is, TRUE or FALSE.","\n") + } + } + +} diff --git a/R/check_A_int.R b/R/check_A_int.R new file mode 100644 index 0000000..54e5253 --- /dev/null +++ b/R/check_A_int.R @@ -0,0 +1,36 @@ + +#' Internal, check the initial values of the interaction matrix +#' +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @return A list with 2 elements: +#' \itemize{ +#' \item The first element is a boolean that is equal to TRUE, if the +#' matrix passes all tests. +#' \item The second element is a string that contains an error or warning +#' message. +#' } +#' @noRd + +check_A_int <- function(A_int){ + + A_int_ok <- TRUE + A_int_message <- "" + + # A_int should be a square matrix + if(!all(class(A_int) %in% c("matrix","array"))){ + A_int_ok <- FALSE + A_int_message <- "The interaction matrix should be matrix." + }else if(!is.numeric(A_int)){ + A_int_ok <- FALSE + A_int_message <- "The interaction matrix should be numeric." + }else if(nrow(A_int)!=ncol(A_int)){ + A_int_ok <- FALSE + A_int_message <- "The interaction matrix should be square." + }else if(is.null(colnames(A_int)) & is.null(rownames(A_int))){ + A_int_message <- "Adding species labels to interaction matrix." + } + + return(list(A_int_ok,A_int_message)) + +} \ No newline at end of file diff --git a/R/check_center.R b/R/check_center.R new file mode 100644 index 0000000..0acb2a4 --- /dev/null +++ b/R/check_center.R @@ -0,0 +1,53 @@ +#' Internal, check the initial values of the interaction matrix +#' +#' @param center a vector with S elements, where S is the number of species. +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @return A list with 2 elements: +#' \itemize{ +#' \item The first element is a boolean that is equal to TRUE, if the +#' vector passes all tests. +#' \item The second element is a string that contains an error or warning +#' message. +#' } +#' @noRd + +check_center <- function(center, A_int){ + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + center_ok <- TRUE + center_message <- "" + + populations_center <- (-1)*matlib::inv(A_int) %*% center + module_center <- sqrt(sum(center*center)) + + if(!is.numeric(center)){ + center_ok <- FALSE + center_message <- "The vector that contains the initial growth rates should be numeric." + }else if(nrow(A_int)!=length(center)){ + center_ok <- FALSE + center_message <- "The vector that contains the initial growth rates and the interaction matrix should have the same number of species." + }else if(!all(populations_center > 0)){ + center_ok <- FALSE + center_message <- "The vector that contains the initial growth rates should be in the feasibility domain." + }else if(module_center != 1){ + center_message <- "Turning the initial growth rates into a unit vector."} + + return(list(center_ok,center_message)) + + }else{ + + center_ok <- FALSE + center_message <- "Invalid matrix does not allow to test the center" + + return(list(center_ok,center_message)) + + } + + + } + + diff --git a/R/closest_to_exclusion.R b/R/closest_to_exclusion.R new file mode 100644 index 0000000..554691f --- /dev/null +++ b/R/closest_to_exclusion.R @@ -0,0 +1,140 @@ +#' Parameters of the species closest to exclusion +#' +#' Given an interaction matrix and an initial vector of intrinsic growing rates, +#' denoted by center, this function computes the intrinsic growing +#' rates and abundances of the species that is/are closest to exclusion. +#' +#' @param center an S vector that contains the initial intrinsic growth +#' rates that are used to computed the minimal isotropic variation that +#' excludes one or more species from the community. +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @importFrom magrittr %>% +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' +#' @return A matrix with 2xS columns. The number of rows can take values +#' between 1 and S. Each row contains the information of one of species that +#' is closest to exclusion. Specifically, it shows: +#' \itemize{ +#' \item The intrinsic growth rate of the species when it is excluded +#' (r_T_i). +#' \item The abundance of species when it is excluded (N_T_i). +#' } +#' +#' @examples +#' A_int <- (-1) * diag(c(3,5,7)) +#' incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +#' I <- incenter_inradius_isoprob[[1]] +#' center <- I + runif(nrow(A_int)) +#' closest_to_exclusion(center, A_int) +#' +#' @export + +closest_to_exclusion <- function(center, A_int){ + + test_result_A_int <- check_A_int(A_int) + test_result_center <- check_center(center, A_int) + + if(test_result_A_int[[1]]==T & test_result_center[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + if(test_result_center[[2]] == "Turning the initial growth rates into a unit vector."){ + + center <- center/sqrt(sum(center*center)) + + cat(test_result_A_int[[2]],"\n") + + } + + dimensions <- ncol(A_int) + curves_data <- cone_vertices_director_vertices(A_int) + + col_number <- ncol(curves_data) + + dir_ver_mat <- as.matrix(curves_data[,(col_number-(dimensions-1)):col_number]) + + # we check the direction of the director vectors + correction_direction <- NULL + + for(i in 1: dimensions){ + + if(all((dir_ver_mat %*% as.numeric(curves_data[i,(1:dimensions)]) %>% round(12))>=0)){ + correction_direction <- c(correction_direction,1) + }else{ + correction_direction <- c(correction_direction,-1) + } + + } + + acr_distance_center_side <- NULL + + for(i in 1:nrow(curves_data)){ + + normal_vector_i <- correction_direction[i] * curves_data[i,c((col_number-(dimensions-1)):col_number)] + sin_theta <- sum(normal_vector_i*center) + acr_distance_center_side <- c(acr_distance_center_side,asin(sin_theta)) + + } + + min_arc_dist <- min(acr_distance_center_side) + vertex_min_arc_dist_index <- which(round(acr_distance_center_side,12) == round(min_arc_dist,12)) + + tangent_points_matrix <- matrix(rep(0,dimensions*length(vertex_min_arc_dist_index)), + nrow = length(vertex_min_arc_dist_index), + ncol = dimensions) + + colnames(tangent_points_matrix) <- paste0("r_T_",1:dimensions) + + for(i in 1:length(vertex_min_arc_dist_index)){ + + index <- vertex_min_arc_dist_index[i] + normal_vector_i <- correction_direction[index] * curves_data[index,c((col_number-(dimensions-1)):col_number)] + sin_theta <- sum(normal_vector_i*center) + tangent_aux <- center - sin_theta*normal_vector_i %>% as.numeric() + module_tangent_aux <- sqrt(sum(tangent_aux*tangent_aux)) + tangent_points_matrix[i,] <- tangent_aux / module_tangent_aux + + } + + N_tangent_points_matrix <- matrix(rep(0,dimensions*length(vertex_min_arc_dist_index)), + nrow = length(vertex_min_arc_dist_index), + ncol = dimensions) + + colnames(N_tangent_points_matrix) <- paste0("N_T_",1:dimensions) + + for(i in 1:nrow(N_tangent_points_matrix)){ + + r_T_i <- tangent_points_matrix[i,] %>% as.numeric() + r_T_i_column <- matrix(r_T_i, nrow = dimensions, ncol = 1) + N_T_i_colum <- (-1)*matlib::inv(A_int) %*% r_T_i_column %>% as.numeric() + N_tangent_points_matrix[i,] <- N_T_i_colum + + } + + return(cbind(tangent_points_matrix,N_tangent_points_matrix)) + + + }else{ + + if(test_result_A_int[[2]]!=""){ + error_messages <- paste0(test_result_A_int[[2]],"\n", + test_result_center[[2]],"\n") + }else{ + error_messages <- paste0(test_result_center[[2]],"\n") + } + + cat(error_messages) + + } + +} diff --git a/R/cone_vertices_director_vertices.R b/R/cone_vertices_director_vertices.R new file mode 100644 index 0000000..b9b6b82 --- /dev/null +++ b/R/cone_vertices_director_vertices.R @@ -0,0 +1,78 @@ +#' Vertices of the feasibility domain +#' +#' This function computes the vertices of the simplicial cone of the +#' feasibility domain, when it intersects the unit ball, as well as the normal +#' vector of the great circle that contains all the S-1 remaining vertices and +#' the origin of the unit ball (i.e., a point where all the intrinsic growth +#' rates are equal to zero). +#' +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @importFrom pracma crossn +#' @importFrom magrittr %>% +#' @importFrom dplyr mutate +#' +#' @return Dataframe with S rows and 2xS+2 columns. Each row contains the vertex +#' information for each species, when considering the intersection +#' of the feasibility domain and the unit ball. Specifically, each row +#' shows: +#' \itemize{ +#' \item The intrinsic growth rate of species i at the given vertex (r_i). +#' \item The vertex ID. +#' \item The vertex name. +#' \item the normal vector of the great circle that contains all the S-1 +#' remaining vertices and the origin of the unit ball. +#' } +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' cone_vertices_director_vertices(A_int) +#' +#' @export + +cone_vertices_director_vertices <- function(A_int){ + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + dimensions <- ncol(A_int) + all_vertices_data <- vertices_unit_ball(A_int) %>% dplyr::mutate(V_name=NA) + all_vertices_data$V_name <- 1:dimensions + all_vertices_r <- all_vertices_data[,(dimensions+1):ncol(all_vertices_data)] + + new_columns_names <- paste0("dir_vec_",1:dimensions) + all_vertices_r[,new_columns_names] <- NA + + for(i in 1:dimensions){ + + other_vertex_data <- all_vertices_r[-i,1:dimensions] + + director_vector_i_aux <- pracma::crossn(as.matrix(other_vertex_data)) + module_director_vector_i_aux <- sqrt(sum(director_vector_i_aux*director_vector_i_aux)) + director_vector_i <- director_vector_i_aux/module_director_vector_i_aux + all_vertices_r[i,new_columns_names] <- director_vector_i + + } + + return(all_vertices_r) + + }else{ + + cat(test_result_A_int[[2]],"\n") + + } + + + + +} diff --git a/R/exclusion_probabilities_par_bootstrap.R b/R/exclusion_probabilities_par_bootstrap.R new file mode 100644 index 0000000..692eb1b --- /dev/null +++ b/R/exclusion_probabilities_par_bootstrap.R @@ -0,0 +1,32 @@ + +exclusion_probabilities_par_bootstrap <- function(A_int, replicates = 1e3){ + + dimensions <- ncol(A_int) + + incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) + + I <- incenter_inradius_isoprob[[1]] + + all_vertices_data_aux <- vertices_unit_ball(A_int) + all_vertices_data <- all_vertices_data_aux[,c((dimensions+1):(2*dimensions))] + + feasibility_parts <- matrix(rep(0,nrow(A_int)*replicates), + nrow = nrow(A_int), ncol = replicates) + + names_sp <- colnames(A_int) + + for (i in 1:nrow(A_int)) { + + cat(names_sp[i],"\n") + + A_int_mod <- A_int + + A_int_mod[,i] <- -I + + feasibility_parts[i, ] <- Omega_bootstrap(A_int_mod, replicates) + + } + + return(feasibility_parts) + +} diff --git a/R/first_species_excluded_ODE.R b/R/first_species_excluded_ODE.R new file mode 100644 index 0000000..63b48ee --- /dev/null +++ b/R/first_species_excluded_ODE.R @@ -0,0 +1,34 @@ +# load necessary packages + +library(mvtnorm) +library(mgcv) +if(!require(ggtern)) {install.packages("ggtern"); library(ggtern)} +if(!require(deSolve)) {install.packages("deSolve"); library(deSolve)} +if(!require(matrixcalc)) {install.packages("matrixcalc"); library(matrixcalc)} + + + +first_species_excluded_ODE <- function(A_int, growth_rates_vector,initial_abundances, delta_t = 0.1, tmax = 1000) { + + number_species <- ncol(A_int) + species_names <- colnames(A_int) + closest_to_exclusion_estimation <- closest_to_exclusion(growth_rates_vector,A_int) + perturbed_growth_rates_vector <- closest_to_exclusion_estimation[1:number_species] %>% + as.numeric() + + alpha <- A_int + dimnames(alpha) <- NULL + + change_growth_vector <- perturbed_growth_rates_vector-growth_rates_vector + + time_step <- seq(0,tmax,by=delta_t) + r <- growth_rates_vector+1*change_growth_vector + N0 <- initial_abundances + parms <- list(r=r, alpha = alpha) #ODE + model <- function(t,N,parms){ dN <- N * (parms$r + parms$alpha %*% N); list(dN)} + sol <- ode(N0,time_step,model,parms, method = 'bdf', rtol = 1e-15, atol = 1e-15, maxsteps = 10000)# ode(N0,time_step,model,parms) + # plot(sol) + species_excluded <- which(min(sol[nrow(sol),2:(1+number_species)])==sol[nrow(sol),2:(1+number_species)]) %>% unname() + + return(species_names[species_excluded]) +} diff --git a/R/furthest_from_exclusion.R b/R/furthest_from_exclusion.R new file mode 100644 index 0000000..c0cfb38 --- /dev/null +++ b/R/furthest_from_exclusion.R @@ -0,0 +1,85 @@ +#' Parameters of the species furthest away from exclusion +#' +#' Given an interaction matrix and an initial vector of intrinsic growing rates, +#' denoted by center, this function returns the column number/s of the +#' interaction matrix with the species that is/are further away from exclusion. +#' +#' @param center a S vector that contains the initial intrinsic growth +#' rates that are used to computed the minimal isotropic variation that +#' excludes one or more species from the community. +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' +#' @return A vector whose length varies between 1 and S. It element contains the +#' column index of the species that is/are furthest away from exclusion. +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +#' I <- incenter_inradius_isoprob[[1]] +#' center <- I + runif(nrow(A_int)) +#' furthest_from_exclusion(center, A_int) +#' +#' @export + + +furthest_from_exclusion <- function(center, A_int){ + + + test_result_A_int <- check_A_int(A_int) + test_result_center <- check_center(center, A_int) + + if(test_result_A_int[[1]]==T & test_result_center[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + if(test_result_center[[2]] == "Turning the initial growth rates into a unit vector."){ + + center <- center/sqrt(sum(center*center)) + + cat(test_result_A_int[[2]],"\n") + + } + + + dimensions <- ncol(A_int) + curves_data <- cone_vertices_director_vertices(A_int) + acr_distance_center_vertex <- NULL + + for(i in 1:nrow(curves_data)){ + + vertex_i <- curves_data[i,c(1:dimensions)] + acr_distance_center_vertex <- c(acr_distance_center_vertex,acos(sum(vertex_i*center))) + + } + + max_arc_dist <- max(acr_distance_center_vertex) + vertex_max_arc_dist_index <- which(acr_distance_center_vertex == max_arc_dist) + + return(vertex_max_arc_dist_index) + + }else{ + + if(test_result_A_int[[2]]!=""){ + error_messages <- paste0(test_result_A_int[[2]],"\n", + test_result_center[[2]],"\n") + }else{ + error_messages <- paste0(test_result_center[[2]],"\n") + } + + cat(error_messages) + + } + + +} diff --git a/R/generate_random_feasible_growth_rates.R b/R/generate_random_feasible_growth_rates.R new file mode 100644 index 0000000..dc31fce --- /dev/null +++ b/R/generate_random_feasible_growth_rates.R @@ -0,0 +1,49 @@ +generate_random_feasible_growth_rates <- function(A_int, random_points=1000){ + + num_species <- ncol(A_int) + inv_A_int <- inv(A_int) + + incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) + incenter <- incenter_inradius_isoprob[[1]] + max_radius <- max_arc_distance_vertices_incenter(A_int, incenter) + + results <- data.frame(matrix(ncol = 2*ncol(A_int), nrow = random_points)) + r_names <- c(paste0("r_", 1:num_species),paste0("N_", 1:num_species)) + colnames(results) <- r_names + + number_feasible_points <- 0 + + while(number_feasible_points < random_points){ + + aux_growth_rate <- incenter+rnorm(num_species, mean = 0, sd = max_radius) + growth_rate <- aux_growth_rate/sqrt(sum(aux_growth_rate*aux_growth_rate)) + abundance <- -inv_A_int %*% growth_rate %>% as.numeric() + + if(all(abundance>0)){ + number_feasible_points <- 1+number_feasible_points + results[number_feasible_points,] <- c(growth_rate,abundance) + + } + + } + + return(results) +} + + +max_arc_distance_vertices_incenter <- function(A_int, incenter){ + + number_species <- ncol(A_int) + vertices <- vertices_unit_ball(A_int) + + vertices_growth_rates <- vertices[,(number_species+1):(2*number_species)] + arc_distances <- NULL + + for(j in 1:number_species){ + + arc_distances <- c(arc_distances,sum(as.numeric(vertices_growth_rates[j,])*incenter)) + + } + + return(max(abs(arc_distances))) +} diff --git a/R/incenter_inradius_isoprob_calculation.R b/R/incenter_inradius_isoprob_calculation.R new file mode 100644 index 0000000..f9f1d4c --- /dev/null +++ b/R/incenter_inradius_isoprob_calculation.R @@ -0,0 +1,93 @@ +#' Main parameters of the maximum isotropic cap +#' +#' Given an interaction matrix, this function computes the main parameters of +#' its maximum isotropic cap. +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' +#' @importFrom magrittr %>% +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' +#' @return A list with 3 elements: +#' \itemize{ +#' \item The first element is a vector that contains the intrinsic growth +#' rates of the feasibility domain's incenter, I. +#' \item The second element is a number that represents the great circle +#' distance from I to the border of the maximum isotropic cap, theta. +#' \item The third element is a number that shows area of the maximum +#' isotropic cap, Xi. +#' } +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' incenter_inradius_isoprob_calculation(A_int) +#' +#' @export + + +incenter_inradius_isoprob_calculation <- function(A_int){ + + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + dimensions <- ncol(A_int) + + curves_data <- cone_vertices_director_vertices(A_int) + + col_number <- ncol(curves_data) + + dir_ver_mat <- as.matrix(curves_data[,(col_number-(dimensions-1)):col_number]) + + + # we check the direction of the director vectors + correction_direction <- NULL + + for(i in 1: dimensions){ + + if(all((dir_ver_mat %*% as.numeric(curves_data[i,(1:dimensions)]) %>% round(12))>=0)){ + correction_direction <- c(correction_direction,1) + }else{ + correction_direction <- c(correction_direction,-1) + } + + } + + inv_dir_ver_mat <- matlib::inv(dir_ver_mat) + I_aux_mat <- inv_dir_ver_mat %*% matrix(correction_direction,nrow = dimensions, ncol = 1) + I_aux <- as.numeric(I_aux_mat) + sin_theta <- sqrt(1/sum(I_aux*I_aux)) # After imposing normalization + # cos_theta <- sqrt(1-sin_theta*sin_theta) + I_aux2 <- I_aux*sin_theta + I <- I_aux2/sqrt(sum(I_aux2*I_aux2)) + + dir_ver_mat_zero <- as.matrix(curves_data[,(dimensions+3):ncol(curves_data)]) + + dir_ver_mat_zero %*% I + + # probability of the isotropic area: Area of the spherical cap with angle theta/ Area of full circle + a_par <- 0.5*(dimensions-1) + b_par <- 0.5 + probability_sp_cap <- 0.5*zipfR::Rbeta(sin_theta*sin_theta,a_par,b_par) + + return(list(I,asin(sin_theta),probability_sp_cap)) + + }else{ + + cat(test_result_A_int[[2]],"\n") + + } + +} diff --git a/R/matrix_year_i.R b/R/matrix_year_i.R new file mode 100644 index 0000000..4f561ff --- /dev/null +++ b/R/matrix_year_i.R @@ -0,0 +1,53 @@ + +matrix_year_i <- function(matrix_entries_raw, year_i){ + + data_year_i <- matrix_entries_raw %>% filter(year == year_i, !is.na(magnitude)) + + + if(nrow(data_year_i)>0){ + + name_focals <- data_year_i$focal %>% unique() %>% sort() + name_neighbors <- data_year_i$neighbour %>% unique() %>% sort() + + # if(length(name_focals)% + filter(focal == name_plants[focal_i], + neighbour == name_plants[neigh_j]) %>% ungroup() %>% + select(magnitude) %>% as.numeric() + + } + + } + + matrix_year_i_out[is.na(matrix_year_i_out)] <- 0.0 + + return(matrix_year_i_out) + + }else{ + + matrix_year_i_out <- "Combination_absent" + + return(matrix_year_i_out) + } + + +} diff --git a/R/max_min_intrinsic_growth_rates.R b/R/max_min_intrinsic_growth_rates.R new file mode 100644 index 0000000..88e911b --- /dev/null +++ b/R/max_min_intrinsic_growth_rates.R @@ -0,0 +1,118 @@ +#' Maximum and minimum (feasible) intrinsic growth rates for the community's +#' species +#' +#' This function computes a rough estimation of the species range of feasible +#' intrinsic growth rates in a community with S species, under Lotka-Volterra +#' dynamics. To do so, for each species, we randomly select 1,000 intrinsic +#' growth rates vectors in the border where a given species goes extinct, and +#' then we extract the the minimum and maximum values for each component of the +#' 1,000xS selected vectors. +#' +#' @param A_int SxS interaction matrix, where S is the number of species. +#' @param number_points_per_species number of intrinsic growth rates vectors to +#' be randomly sampled in the border of the feasibility domain where a given +#' species goes excluded. +#' +#' @return Dataframe with S rows and 3 columns. Each row contains the +#' information for each species. Specifically, each row shows: +#' \itemize{ +#' \item The species name. +#' \item The minimum intrinsic growth rate estimated for such species. +#' \item The miaximum intrinsic growth rate estimated for such species. +#' } +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' max_min_intrinsic_growth_rates(A_int, number_points_per_species = 1000) +#' +#' @export + +max_min_intrinsic_growth_rates <- function(A_int, + number_points_per_species = 1000){ + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + limit_points <- NULL + number_species <- ncol(A_int) + + limits_feasibility_domain <- function(A_int, species_limiting, + number_points_per_species){ + + num_species <- 1:(ncol(A_int)) + + r_values <- data.frame(matrix(ncol = (2*ncol(A_int)), + nrow = number_points_per_species)) + r_names <- c(paste0("N_", num_species),paste0("r_", num_species)) + colnames(r_values) <- r_names + + for(j in 1:ncol(A_int)){ + + r_values[,j] <- stats::runif(number_points_per_species) # uniform on [-1, 1] + + } + + r_values[,species_limiting] <- 0 + + for(i in 1:nrow(r_values)){ + + N_column <- matrix(as.numeric(r_values[i,1:ncol(A_int)]), + ncol = 1, nrow = ncol(A_int) ) + + r_column <- (-1)*(A_int %*% as.matrix(N_column)) + + sq_sum <- sum(r_column*r_column) + + r_values[i,c((ncol(A_int)+1):(2*ncol(A_int)))] <- r_column/sqrt(sq_sum) + + } + + return(r_values) + + } + + + for(i in 1:nrow(A_int)){ + + limit_FD_i <- limits_feasibility_domain(A_int, species_limiting=i, + number_points_per_species=1000) + + limit_points <- dplyr::bind_rows(limit_points, limit_FD_i) + + } + + species_names <- colnames(A_int) + + if(is.null(species_names)){ + species_names <- paste0("sp_",1:number_species) + } + + min_intrinsic_growth_rates <- apply(limit_points,2,min) + max_intrinsic_growth_rates <- apply(limit_points,2,max) + + solution <- tibble( + species = species_names, + min_R = (min_intrinsic_growth_rates[(number_species+1):(2*number_species)] %>% as.numeric()), + max_R = (max_intrinsic_growth_rates[(number_species+1):(2*number_species)] %>% as.numeric()) + ) + + return(solution) + + }else{ + + cat(test_result_A_int[[2]],"\n") + + } + + +} diff --git a/R/mean_pairwise_similarity.R b/R/mean_pairwise_similarity.R new file mode 100644 index 0000000..37c91e8 --- /dev/null +++ b/R/mean_pairwise_similarity.R @@ -0,0 +1,54 @@ + +#' Mean pairwise similarity +#' +#' Given a vector whose elements are the average probabilities of extinction of +#' the species in a given community, this function calculates the mean pairwise +#' similarity of such probabilities. +#' +#' @param probability_vector a vector with length S whose elements are the +#' average probabilities of extinction of the species in a given community, +#' where S is the number of species. +#' +#' @return A non-negative number between 0 and 1 (isotropic community). +#' +#' @examples +#' probability_vector <- c(.25,.15,.60) +#' mean_pairwise_similarity(probability_vector) +#' +#' @export + +mean_pairwise_similarity <- function(probability_vector){ + + # check the input parameter: probability_vector + + if(!all(class(probability_vector) %in% c("numeric"))){ + cat("Probabilities should be numbers.\n") + }else if(all(class(probability_vector) %in% c("matrix","array"))){ + cat("The input parameter should be vector.\n") + }else if(!all(probability_vector >=0)){ + cat("Probabilities should be non-negaive numbers.\n") + }else if(round(sum(probability_vector),2) != 1){ + cat("The sum of all probabilities should be equal to one.\n") + }else{ + + # If the vector with probabilities is OK, then the index is calculated. + + number_species <- length(probability_vector) + + complement <- 0 + + for(i in 1:(number_species-1)){ + + for(j in (i+1):number_species){ + + complement <- complement + abs(probability_vector[i]-probability_vector[j]) + + } + + } + + return(1-complement/number_species) + + } + +} diff --git a/R/prob_extinction_4_int_matrix.R b/R/prob_extinction_4_int_matrix.R new file mode 100644 index 0000000..b92b2a6 --- /dev/null +++ b/R/prob_extinction_4_int_matrix.R @@ -0,0 +1,126 @@ + +#' Probabilities of exclusion +#' +#' Given an interaction matrix, this function uses the output of the function +#' boot_prob_excl_Omega_raw to estimate the mean values and 95% confidence +#' intervals of the species' probabilities of exclusion. +#' +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' @param number_Omega_replicates a number that specifies how many estimations +#' of Omega will be calculated by the quasi-Monte Carlo method in function +#' boot_prob_excl_Omega_raw. By default, this parameter is set to 1,000 +#' replicates. +#' @param number_boot_replicates a number that specifies how many bootstrap +#' estimations of the probability of exclusion and Omega will be calculated +#' for each species in function boot_prob_excl_Omega_raw. By default, this +#' parameter is set to 1,000 replicates. +#' @param use_chol_decomp boolean that specifies if the QR decomposition should +#' be used to compute Omega. +#' +#' @importFrom magrittr %>% +#' @importFrom tibble tibble +#' @importFrom stats quantile +#' @import foreach +#' @import doParallel +#' @importFrom mvtnorm pmvnorm +#' @importFrom boot boot +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' +#' @return A tibble with S rows and 4 columns, that contain the following data: +#' \itemize{ +#' \item The species ID. +#' \item The mean value of the species' probability of exclusion. +#' \item The lower bound of the 95% confidence interval for the species' +#' probability of exclusion. +#' \item The upper bound of the 95% confidence interval for the species' +#' probability of exclusion. +#' } +#' +#' @examples +#' numCores <- detectCores() +#' numCores +#' registerDoParallel(2) +#' A_int <- -1*diag(c(3,5,7)) +#' prob_extinction_4_int_matrix(A_int) +#' stopImplicitCluster() +#' +#' @references \url{https://doi.org/10.1016/j.jtbi.2018.04.030} +#' +#' @export + + +prob_extinction_4_int_matrix <- function(A_int, number_Omega_replicates = 1e3, + number_boot_replicates = 1e3, + use_chol_decomp = F){ + + # check the input parameters + + is.wholenumber <- function(x){ + integer_part <- round(x,0) + decimal_part <- x - integer_part + return(decimal_part == 0) + } + + test_result_A_int <- check_A_int(A_int) + test_omega_rep <- is.wholenumber(number_Omega_replicates) + test_boot_rep <- is.wholenumber(number_boot_replicates) + test_chol_decomp <- is.logical(use_chol_decomp) + + if((test_result_A_int[[1]]==T) & test_omega_rep & test_boot_rep & + test_chol_decomp){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + prob_excl_Omega_df <- boot_prob_excl_Omega_raw(A_int, number_Omega_replicates, + number_boot_replicates, + use_chol_decomp = F) + + prob_excl_agg_info <- tibble::tibble(species = colnames(A_int)) + prob_excl_agg_info$prob_excl_mean <- NA + prob_excl_agg_info$prob_excl_lowerCI <- NA + prob_excl_agg_info$prob_excl_upperCI <- NA + + prob_excl_df <- prob_excl_Omega_df[,2:(number_boot_replicates-1)] + + for(i in 1:nrow(prob_excl_df)){ + + probability_excl_Sp <- prob_excl_df[i, ] %>% as.numeric() + + probability_excl_CI <- stats::quantile(probability_excl_Sp, prob=c(.025,.975), na.rm = T) %>% as.numeric() + + prob_excl_agg_info$prob_excl_mean[i] <- mean(probability_excl_Sp, na.rm = T) + prob_excl_agg_info$prob_excl_lowerCI[i] <- probability_excl_CI[1] + prob_excl_agg_info$prob_excl_upperCI[i] <- probability_excl_CI[2] + + } + + return(prob_excl_agg_info) + + }else{ + + if(test_result_A_int[[1]] != T){ + cat(test_result_A_int[[2]],"\n") + } + if(test_omega_rep != T){ + cat("number_Omega_replicates should be a whole number.","\n") + } + if(test_boot_rep != T){ + cat("number_boot_replicates should be a whole number.","\n") + } + if(test_chol_decomp != T){ + cat("use_chol_decomp should be logical, that is, TRUE or FALSE.","\n") + } + + } + +} diff --git a/R/small_omega_iso.R b/R/small_omega_iso.R new file mode 100644 index 0000000..2d30496 --- /dev/null +++ b/R/small_omega_iso.R @@ -0,0 +1,60 @@ + +#' Small Omega for the maximum isotropic cap +#' +#' Given an SxS interaction matrix, this function computes the value of small +#' Omega for maximum isotropic cap (i.e., the S root of the maximum isotropic +#' cap, Xi). +#' +#' @param A_int a SxS interaction matrix, where S is the number of species. +#' +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom zipfR Rbeta +#' +#' @return A number that shows the value of value of small Omega, the S root of +#' the volume of the maximum isotropic cap, Xi. +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' small_omega_iso(A_int) +#' +#' @export + +small_omega_iso <- function(A_int){ + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + # coordinates of FD's Vertices + vertices_unit_ball(A_int) + + # Get main curves data: vertices and normal vectors for their hyperplanes + dimensions <- ncol(A_int) + + # Estimation of the incenter position and of the inradius for all the three + # vertices (of the simplicial cone) combinations + + incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) + + isoprobability <- incenter_inradius_isoprob[[3]] + + return(isoprobability^(1/nrow(A_int))) + + }else{ + + cat(test_result_A_int[[2]],"\n") + + } + +} + diff --git a/R/sp_distance_to_exclusion.R b/R/sp_distance_to_exclusion.R new file mode 100644 index 0000000..598ee06 --- /dev/null +++ b/R/sp_distance_to_exclusion.R @@ -0,0 +1,104 @@ +#' Parameters of the species closest to exclusion +#' +#' Given an interaction matrix and an initial vector of intrinsic growing rates, +#' denoted by center, this function computes the arc distance from each species +#' to the feasibility domain's border where only such species is excluded. +#' +#' @param center an S vector that contains the initial intrinsic growth +#' rates that are used to computed the minimal isotropic variation that +#' excludes one or more species from the community. +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @importFrom magrittr %>% +#' @importFrom pracma crossn +#' @importFrom matlib inv +#' @importFrom tibble tibble +#' +#' @return A tibble data frame with S rows and 2 columns. Each row contains the +#' species name and its great circle distance to the border where it goes +#' excluded (i.e, that distance is the absolute value of the angle between +#' the center and the tangent point in a given "edge" of the feasibility domain). +#' +#' @examples +#' A_int <- (-1) * diag(c(3,5,7)) +#' incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +#' I <- incenter_inradius_isoprob[[1]] +#' center <- I + runif(nrow(A_int)) +#' sp_distance_to_exclusion(center, A_int) +#' +#' @export + +sp_distance_to_exclusion <- function(center, A_int){ + + test_result_A_int <- check_A_int(A_int) + test_result_center <- check_center(center, A_int) + + if(test_result_A_int[[1]]==T & test_result_center[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + if(test_result_center[[2]] == "Turning the initial growth rates into a unit vector."){ + + center <- center/sqrt(sum(center*center)) + + cat(test_result_A_int[[2]],"\n") + + } + + dimensions <- ncol(A_int) + curves_data <- cone_vertices_director_vertices(A_int) + + col_number <- ncol(curves_data) + + dir_ver_mat <- as.matrix(curves_data[,(col_number-(dimensions-1)):col_number]) + + # we check the direction of the director vectors + correction_direction <- NULL + + for(i in 1: dimensions){ + + if(all((dir_ver_mat %*% as.numeric(curves_data[i,(1:dimensions)]) %>% round(12))>=0)){ + correction_direction <- c(correction_direction,1) + }else{ + correction_direction <- c(correction_direction,-1) + } + + } + + acr_distance <- NULL + + for(i in 1:nrow(curves_data)){ + + normal_vector_i <- correction_direction[i] * curves_data[i,c((col_number-(dimensions-1)):col_number)] + sin_theta <- sum(normal_vector_i*center) + acr_distance <- c(acr_distance,asin(sin_theta)) + + } + + result_rank <- tibble(species = colnames(A_int), + arc_distance = abs(acr_distance)) + + return(result_rank) + + + }else{ + + if(test_result_A_int[[2]]!=""){ + error_messages <- paste0(test_result_A_int[[2]],"\n", + test_result_center[[2]],"\n") + }else{ + error_messages <- paste0(test_result_center[[2]],"\n") + } + + cat(error_messages) + + } + +} diff --git a/R/vertices_unit_ball.R b/R/vertices_unit_ball.R new file mode 100644 index 0000000..20be786 --- /dev/null +++ b/R/vertices_unit_ball.R @@ -0,0 +1,82 @@ + +#' Vertices of the feasibility domain +#' +#' This function computes the vertices of the simplicial cone of the +#' feasibility domain when it intersects the unit ball. +#' +#' @param A_int SxS interaction matrix, where S is the number of species. +#' +#' @return Dataframe with S rows and 2xS+1 columns. Each row contains the vertex +#' information for each species, when considering the intersection +#' of the feasibility domain and the unit ball. Specifically, each row +#' shows: +#' \itemize{ +#' \item The abundance of species i at the given vertex (N_i). +#' \item The intrinsic growth rate of species i at the given vertex (r_i). +#' \item The vertex ID. +#' } +#' +#' @examples +#' A_int <- -1*diag(c(3,5,7)) +#' vertices_unit_ball(A_int) +#' +#' @export + +vertices_unit_ball <- function(A_int){ + + test_result_A_int <- check_A_int(A_int) + + if(test_result_A_int[[1]]==T){ + + if(test_result_A_int[[2]] == "Adding species labels to interaction matrix."){ + + colnames(A_int) <- paste0("sp_",1:nrow(A_int)) + rownames(A_int) <- paste0("sp_",1:nrow(A_int)) + + cat(test_result_A_int[[2]],"\n") + + } + + dimensions <- ncol(A_int) + + num_species <- 1:dimensions + + r_values <- data.frame(matrix(ncol = (2*dimensions), nrow = dimensions)) + r_names <- c(paste0("N_", num_species),paste0("r_", num_species)) + colnames(r_values) <- r_names + r_values$vertex_ID <- NA + + for(i in 1:dimensions){ + + sup_A_col_i <- A_int[,i] + + Nv_i_aux <- sqrt(1/sum( sup_A_col_i* sup_A_col_i)) + + # We add a zero in x_aux at position i + if(i==1){ + Nv_i <- c(Nv_i_aux,rep(0,dimensions-1)) + }else if(i==dimensions){ + Nv_i <- c(rep(0,dimensions-1),Nv_i_aux) + }else{ + Nv_i <- c(rep(0,i-1),Nv_i_aux,rep(0,dimensions-i)) + } + + Nv_i_mat <- matrix(Nv_i,nrow = dimensions, ncol = 1) + rv_i_mat <- (-1)*A_int %*% Nv_i_mat + rv_i <- as.numeric(rv_i_mat) + + r_values[i,1:dimensions] <- Nv_i + r_values[i,(dimensions+1):(2*dimensions)] <- rv_i + r_values$vertex_ID[i] <- paste0("vertex ",i) + + } + + return(r_values) + + }else{ + + cat(test_result_A_int[[2]],"\n") + + } + +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..cc4900b --- /dev/null +++ b/README.md @@ -0,0 +1,87 @@ +# anisoFun 1.0.0 + +anisoFun provides a complete toolbox for calculating i) the maximum isotropic cap, ii) average exclusion probabilities, iii) anisotropic index, and iv) values of (small and capital) Omega for a given multispecies community, whose population dynamics can be approximated by a Lotka-Volterra (LV) model. The functions report 95% confidence intervals for those features that are estimated via quasi-Monte Carlo methods. + +### Installation + +The development version can be installed via the `remotes` package: + +```R +install.packages("devtools") +require(devtools) +install_github("RadicalCommEcol/anisoFun") +library(anisoFun) +``` + +### Key features + +The package has several key functions: + +- `anisotropy_metrics_4_int_matrix()` +- `prob_extinction_4_int_matrix()` +- `incenter_inradius_isoprob_calculation()` +- `closest_to_exclusion()` + +Firstly, we create an interaction matrix for a 3 species community + +```R +A_int <- -1*diag(c(3,5,7)) +colnames(A_int) <- paste0("sp_",1:nrow(A_int)) +rownames(A_int) <- paste0("sp_",1:nrow(A_int)) +``` +We estimate the following parameters: +* `I`: the incenter of the feasibility domain. +* `theta`: the great circle distance between the incerter and the feasibility domain's border. +* `Xi`: the maximum isotropic cap that can be placed in the feasibility domain. + +```R +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +I <- incenter_inradius_isoprob[[1]] +theta <- incenter_inradius_isoprob[[2]] +Xi <- incenter_inradius_isoprob[[3]] +``` + +In case you want to calculate the value of small `omega_iso` for maximum isotropic cap (i.e., the S root of the maximum isotropic cap, Xi), just run: + +```R +omega_iso <- small_omega_iso(A_int) +``` + +For the above community, we can also estimate: +* The mean value of (small) `omega`, the `S` root of capital `Omega` (the proportion of the feasible parameter space inside the unit sphere), where `S` is the number of species in the community. We also provide the lower and upper bounds of the 95% confidence interval for small `omega`. +* The mean value of the anisotropy index `J`, along with the lower and upper bounds of the corresponding 95% confidence interval. + +To reduce calculation time, we rely on parallel computation. In next example, we show how to check the number of cores in our computer (in our case, `numCores` = 12 cores) and how to select -for instance- `numCores` minus 4 cores. +```R +# Select the number of cores to perform a parallel computation and reduce calculation time +numCores <- detectCores() +numCores_selected <- numCores-4 +registerDoParallel(numCores_selected) +``` +If you skip the number of cores selection, one core will be used by default. + +Once you selected the number of cores, to estimate (small) `omega` and the anisotropy index `J` and their confidence intervals, just run: + +```R +anisotropy_metrics_A_int <- anisotropy_metrics_4_int_matrix(A_int) +anisotropy_metrics_A_int +``` +To obtain the species' average probabilities of exclusion, execute the following command: + +```R +prob_extinction_sp <- prob_extinction_4_int_matrix(A_int) +prob_extinction_sp +``` + +Finally, given an initial vector of intrinsic growing rates, denoted by center, we can compute the intrinsic growing rates and abundances of the species that is/are closest to exclusion, by running: + +```R +center <- I + runif(nrow(A_int)) +closect_sp_to_exclusion <- closest_to_exclusion(center, A_int) +closect_sp_to_exclusion +``` + + + + + diff --git a/anisoFun.Rproj b/anisoFun.Rproj new file mode 100644 index 0000000..8d82f1f --- /dev/null +++ b/anisoFun.Rproj @@ -0,0 +1,17 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/example.R b/example.R new file mode 100644 index 0000000..9c00ebd --- /dev/null +++ b/example.R @@ -0,0 +1,240 @@ +#load several functions +library(matlib) # to multiply matrices +library(tidyverse) +library(dplyr) +library(nleqslv) # to solve non-linear equations +library(zipfR) # beta incomplete function to estimate the area of d-dimensional spherical caps +library(pracma) # to solve n-dimensional cross products +library(parallel) +library(foreach) +library(doParallel) +library(boot) + +#This is to load all functions from AnisoFUN package +pathnames <- list.files(pattern="[.]R$", path="R", full.names=TRUE); +sapply(pathnames, FUN=source) + +#The first step is to provide a matrix of species interactions + +# Define symmetric interaction matrix-------------------------------------------- +A_int <- matrix(rep(0,9), ncol=3) +A_int[,1] <- -c(1, 0, 0) +A_int[,2] <- -c(0.3406884710289558, 0.9328944522580684, 0.11678744219579273) +A_int[,3] <- -c(0.3406884710289558, 0.12410827817548943, 0.9319487652208257) + +species_names <- paste0("sp_",1:ncol(A_int)) +colnames(A_int) <- species_names +rownames(A_int) <- species_names +A_int + +numCores <- detectCores() +cl <- makeCluster(numCores -1) +registerDoParallel(cl) # register the cluster for using foreach + +prob_excl_AnisoFun_symmetric <- prob_extinction_4_int_matrix(A_int) +prob_excl_AnisoFun_symmetric + +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +incenter_inradius_isoprob + +I <- incenter_inradius_isoprob[[1]] #the incenter of the feasibility domain. +theta <- incenter_inradius_isoprob[[2]] #the great circle distance between the incerter and the feasibility domain's border. +Xi <- incenter_inradius_isoprob[[3]] #the maximum isotropic cap that can be placed in the feasibility domain. + + +omega_iso <- small_omega_iso(A_int) # the S root of the maximum isotropic cap, Xi +omega_iso + +numCores <- detectCores() +numCores_selected <- numCores-4 +registerDoParallel(numCores_selected) + + +anisotropy_metrics_A_int <- anisotropy_metrics_4_int_matrix(A_int) +anisotropy_metrics_A_int + +#Here +# small omega is the proportion of the feasible parameter space inside the unit sphere +# The mean value of the anisotropy index J, + +prob_extinction_sp <- prob_extinction_4_int_matrix(A_int) +prob_extinction_sp + +source("R/anisotropy_index.R") +probability_vector <- as.numeric(as.matrix(prob_extinction_sp[2])) +anisotropy_index<-anisotropy_index(probability_vector) +anisotropy_index + +source("R/mean_pairwise_similarity.R") +mean_pairwise_similarity<-mean_pairwise_similarity(probability_vector) +mean_pairwise_similarity + +center <- I + runif(nrow(A_int)) +closest_sp_to_exclusion <- closest_to_exclusion(center, A_int) +closest_sp_to_exclusion + +# Exclusion prob from ODE to compare results and speed. Careful, it takes time! +number_random_points <- 5e4 + +number_species <- ncol(A_int) + +set.seed(1234) +random_feasible_growth_rates <- generate_random_feasible_growth_rates(A_int, random_points=number_random_points) + +first_sp_excluded_results_symmetric <- foreach (i=1:nrow(random_feasible_growth_rates), .combine=c, + .packages = c("tidyverse","matlib", + "zipfR","pracma","deSolve")) %dopar% { + growth_rates_vector <- random_feasible_growth_rates[i,1:number_species] %>% as.numeric() %>% unname() + initial_abundances <- random_feasible_growth_rates[i,(number_species+1):(2*number_species)] %>% as.numeric() %>% unname() + species_excluded_i <- first_species_excluded_ODE(A_int, growth_rates_vector,initial_abundances, delta_t = 0.1, tmax = 1000) + } + + +random_feasible_growth_rates_results <- random_feasible_growth_rates +random_feasible_growth_rates_results$LV_first_sp_excluded <- first_sp_excluded_results_symmetric + +prob_excl_ODE_symmetric <- table(first_sp_excluded_results_symmetric)/number_random_points +prob_excl_ODE_symmetric + +stopImplicitCluster() + + + + +# Define asymmetric interaction matrix-------------------------------------------- +A_int2 <- matrix(rep(0,9), ncol=3) +A_int2[,1] <- -c(1,0,0) +A_int2[,2] <- -c(0.3995691276168492, 0.7476891904854146, -0.5303823023903146) +A_int2[,3] <- -c(0.39956912762129915, -0.19648768029759267, 0.8953977349474517) + +species_names <- paste0("sp_",1:ncol(A_int2)) +colnames(A_int2) <- species_names +rownames(A_int2) <- species_names + +# Exclusion prob from Anisofun +prob_excl_AnisoFun_asymmetric <- prob_extinction_4_int_matrix(A_int2) +prob_excl_AnisoFun_asymmetric + +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int2) +incenter_inradius_isoprob + +I <- incenter_inradius_isoprob[[1]] #the incenter of the feasibility domain. +theta <- incenter_inradius_isoprob[[2]] #the great circle distance between the incerter and the feasibility domain's border. +Xi <- incenter_inradius_isoprob[[3]] #the maximum isotropic cap that can be placed in the feasibility domain. + + +omega_iso <- small_omega_iso(A_int2) # the S root of the maximum isotropic cap, Xi +omega_iso + +numCores <- detectCores() +numCores_selected <- numCores-4 +registerDoParallel(numCores_selected) + + +anisotropy_metrics_A_int <- anisotropy_metrics_4_int_matrix(A_int2) +anisotropy_metrics_A_int + +#Here +# small omega is the proportion of the feasible parameter space inside the unit sphere +# The mean value of the anisotropy index J, + +prob_extinction_sp <- prob_extinction_4_int_matrix(A_int2) +prob_extinction_sp + +source("R/anisotropy_index.R") +probability_vector <- as.numeric(as.matrix(prob_extinction_sp[2])) +anisotropy_index<-anisotropy_index(probability_vector) +anisotropy_index + +mean_pairwise_similarity<-mean_pairwise_similarity(probability_vector) +mean_pairwise_similarity + +center <- I + runif(nrow(A_int2)) +closest_sp_to_exclusion <- closest_to_exclusion(center, A_int2) +closest_sp_to_exclusion + + +# Increase the number of species to 6 and 12 species +#6 species +A_int2 <- matrix(rnorm(36),nrow=6) +diag(A_int2)<- -4 +colnames(A_int2) <- paste0("sp_",1:nrow(A_int2)) +rownames(A_int2) <- paste0("sp_",1:nrow(A_int2)) + +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int2) +incenter_inradius_isoprob + +I <- incenter_inradius_isoprob[[1]] #the incenter of the feasibility domain. +theta <- incenter_inradius_isoprob[[2]] #the great circle distance between the incerter and the feasibility domain's border. +Xi <- incenter_inradius_isoprob[[3]] #the maximum isotropic cap that can be placed in the feasibility domain. + + +omega_iso <- small_omega_iso(A_int2) # the S root of the maximum isotropic cap, Xi +omega_iso + +numCores <- detectCores() +numCores_selected <- numCores-4 +registerDoParallel(numCores_selected) + + +anisotropy_metrics_A_int <- anisotropy_metrics_4_int_matrix(A_int2) +anisotropy_metrics_A_int + +#Here +# small omega is the proportion of the feasible parameter space inside the unit sphere +# The mean value of the anisotropy index J, + +prob_extinction_sp <- prob_extinction_4_int_matrix(A_int2) +prob_extinction_sp + +source("R/anisotropy_index.R") +probability_vector <- as.numeric(as.matrix(prob_extinction_sp[2])) +anisotropy_index<-anisotropy_index(probability_vector) +anisotropy_index + +mean_pairwise_similarity<-mean_pairwise_similarity(probability_vector) +mean_pairwise_similarity + +center <- I + runif(nrow(A_int2)) +closest_sp_to_exclusion <- closest_to_exclusion(center, A_int2) +closest_sp_to_exclusion + +#12 species +A_int3 <- matrix(rnorm(144),nrow=12) +diag(A_int3)<- -3 +colnames(A_int3) <- paste0("sp_",1:nrow(A_int3)) +rownames(A_int3) <- paste0("sp_",1:nrow(A_int3)) + +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int3) +incenter_inradius_isoprob +I <- incenter_inradius_isoprob[[1]] +theta <- incenter_inradius_isoprob[[2]] +Xi <- incenter_inradius_isoprob[[3]] + + +omega_iso <- small_omega_iso(A_int3) +omega_iso + +numCores <- detectCores() +numCores_selected <- numCores-4 +registerDoParallel(numCores_selected) + +source("R/anisotropy_index.R") +anisotropy_metrics_A_int <- anisotropy_metrics_4_int_matrix(A_int3) +anisotropy_metrics_A_int + +prob_extinction_sp <- prob_extinction_4_int_matrix(A_int3) +prob_extinction_sp + +probability_vector <- as.numeric(as.matrix(prob_extinction_sp[2])) +anisotropy_index<-anisotropy_index(probability_vector) +anisotropy_index + +source("R/mean_pairwise_similarity.R") +mean_pairwise_similarity<-mean_pairwise_similarity(probability_vector) +mean_pairwise_similarity + +center <- I + runif(nrow(A_int3)) +closest_sp_to_exclusion <- closest_to_exclusion(center, A_int3) +closest_sp_to_exclusion +### diff --git a/man/Omega_bootstrap.Rd b/man/Omega_bootstrap.Rd new file mode 100644 index 0000000..9fb008c --- /dev/null +++ b/man/Omega_bootstrap.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Omega_bootstrap.R +\name{Omega_bootstrap} +\alias{Omega_bootstrap} +\title{Random estimations of capital Omega} +\usage{ +Omega_bootstrap(A_int, replicates = 1000, use_chol_decomp = F) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} + +\item{replicates}{a number that specifies how many estimations of Omega +will be calculated. By default, this parameter is set to 1,000 replicates.} + +\item{use_chol_decomp}{boolean that specifies if the QR decomposition should +be used to compute Omega.} +} +\value{ +A vector whose length is equal to the parameter replicate, where each + element represents an estimation of capital Omega. +} +\description{ +Given an interaction matrix, this function generates a vector with +estimations of capital Omega (i.e., the proportion of the feasible parameter +space inside the unit sphere). Those estimations are computed via a +quasi-Monte Carlo method for even relatively large communities. +} +\examples{ +numCores <- detectCores() +numCores +registerDoParallel(2) +A_int <- -1*diag(c(3,5,7)) +Omega_bootstrap(A_int, replicates = 1e3) +stopImplicitCluster() + +} +\references{ +\url{https://doi.org/10.1016/j.jtbi.2018.04.030} +} diff --git a/man/anisotropy_index.Rd b/man/anisotropy_index.Rd new file mode 100644 index 0000000..55b0b57 --- /dev/null +++ b/man/anisotropy_index.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/anisotropy_index.R +\name{anisotropy_index} +\alias{anisotropy_index} +\title{Anisotropy index} +\usage{ +anisotropy_index(probability_vector) +} +\arguments{ +\item{probability_vector}{vector with length S whose elements are the +average probabilities of extinction of the species in a given community, +where S is the number of species.} +} +\value{ +A non-negative number between 0 and 1 (isotropic community). +} +\description{ +Given a vector whose elements are the average probabilities of extinction of +the species in a given community, this function calculates its anisotropy +index (i.e., the relative Shannon diversity index of such probabilities). +} +\examples{ +probability_vector <- c(.25,.15,.60) +anisotropy_index(probability_vector) + +} diff --git a/man/anisotropy_metrics_4_int_matrix.Rd b/man/anisotropy_metrics_4_int_matrix.Rd new file mode 100644 index 0000000..256ee8b --- /dev/null +++ b/man/anisotropy_metrics_4_int_matrix.Rd @@ -0,0 +1,62 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/anisotropy_metrics_4_int_matrix.R +\name{anisotropy_metrics_4_int_matrix} +\alias{anisotropy_metrics_4_int_matrix} +\title{Anisotropy metrics for a given interaction matrix} +\usage{ +anisotropy_metrics_4_int_matrix( + A_int, + number_Omega_replicates = 1000, + number_boot_replicates = 1000, + use_chol_decomp = F +) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} + +\item{number_Omega_replicates}{a number that specifies how many estimations +of Omega will be calculated by the quasi-Monte Carlo method in function +boot_prob_excl_Omega_raw. By default, this parameter is set to 1,000 +replicates.} + +\item{number_boot_replicates}{a number that specifies how many bootstrap +estimations of the probability of exclusion and Omega will be calculated +for each species in function boot_prob_excl_Omega_raw. By default, this +parameter is set to 1,000 replicates.} + +\item{use_chol_decomp}{boolean that specifies if the QR decomposition should +be used to compute Omega.} +} +\value{ +A tibble with 1 row and 6 columns, that contain the following data: + \itemize{ + \item The mean value of small Omega. + \item The lower bound of the 95% confidence interval for small Omega. + \item The upper bound of the 95% confidence interval for small Omega. + \item The mean value of the anisotropy index. + \item The lower bound of the 95% confidence interval for the anisotropy + index. + \item The upper bound of the 95% confidence interval for the anisotropy + index. + } +} +\description{ +Given an interaction matrix, this function uses the output of the function +boot_prob_excl_Omega_raw to estimate the mean values and 95% confidence +intervals of the following metrics: small omega (i.e., the S root of capital +Omega, that is, the proportion of the feasible parameter space inside the +unit sphere) and the anisotropy index (i.e., the relative Shannon diversity +index of the species' probabilities of exclusion). +} +\examples{ +numCores <- detectCores() +numCores +registerDoParallel(2) +A_int <- -1*diag(c(3,5,7)) +anisotropy_metrics_4_int_matrix(A_int) +stopImplicitCluster() + +} +\references{ +\url{https://doi.org/10.1016/j.jtbi.2018.04.030} +} diff --git a/man/boot_prob_excl_Omega_raw.Rd b/man/boot_prob_excl_Omega_raw.Rd new file mode 100644 index 0000000..9ce03a0 --- /dev/null +++ b/man/boot_prob_excl_Omega_raw.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/boot_prob_excl_Omega_raw.R +\name{boot_prob_excl_Omega_raw} +\alias{boot_prob_excl_Omega_raw} +\title{Bootstrap estimation of the probabilities of exclusion and capital Omega} +\usage{ +boot_prob_excl_Omega_raw( + A_int, + number_Omega_replicates = 1000, + number_boot_replicates = 1000, + use_chol_decomp = F +) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} + +\item{number_Omega_replicates}{a number that specify how many estimations +of Omega will be calculated by the quasi-Monte Carlo method. By default, +this parameter is set to 1,000 replicates.} + +\item{number_boot_replicates}{a number that specifies how many bootstrap +estimations of the probability of exclusion and Omega will be calculated +for each species. By default, this parameter is set to 1,000 replicates.} + +\item{use_chol_decomp}{boolean that specifies if the QR decomposition should +be used to compute Omega.} +} +\value{ +A tibble with S rows and 2 x number_boot_replicates columns. Each row +contains the estimated probabilities of exclusion and values of Omega for +each species, when considering the intersection of the feasibility domain +and the unit ball. Specifically, each row contains: + \itemize{ + \item Resamples of the estimated probabilities of exclusion for species i + (prob_excl_rep_x), where x takes values from 1 to number_boot_replicates. + \item The corresponding value of capital Omega for species i and + a given prob_excl_rep_x (Omega_rep_x), where x takes values from 1 to + number_boot_replicates. + } +} +\description{ +Given an interaction matrix, this function generates a tibble with S rows, +where S is the number of species in the matrix. Each row contains the +following information for a given: replicates of its probability of exclusion +and the corresponding value of capital Omega for each replicate (i.e., the +proportion of the feasible parameter #' space inside the unit sphere). Those +estimations are computed via a quasi-Monte Carlo method and by +bootstrapping the quasi-Mote Carlo results. +} +\examples{ +numCores <- detectCores() +numCores +registerDoParallel(2) +A_int <- -1*diag(c(3,5,7)) +boot_prob_excl_Omega_raw(A_int) +stopImplicitCluster() + +} +\references{ +\url{https://doi.org/10.1016/j.jtbi.2018.04.030} +} diff --git a/man/closest_to_exclusion.Rd b/man/closest_to_exclusion.Rd new file mode 100644 index 0000000..35bb557 --- /dev/null +++ b/man/closest_to_exclusion.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/closest_to_exclusion.R +\name{closest_to_exclusion} +\alias{closest_to_exclusion} +\title{Parameters of the species closest to exclusion} +\usage{ +closest_to_exclusion(center, A_int) +} +\arguments{ +\item{center}{an S vector that contains the initial intrinsic growth +rates that are used to computed the minimal isotropic variation that +excludes one or more species from the community.} + +\item{A_int}{SxS interaction matrix, where S is the number of species.} +} +\value{ +A matrix with 2xS columns. The number of rows can take values +between 1 and S. Each row contains the information of one of species that +is closest to exclusion. Specifically, it shows: + \itemize{ + \item The intrinsic growth rate of the species when it is excluded + (r_T_i). + \item The abundance of species when it is excluded (N_T_i). + } +} +\description{ +Given an interaction matrix and an initial vector of intrinsic growing rates, +denoted by center, this function computes the intrinsic growing +rates and abundances of the species that is/are closest to exclusion. +} +\examples{ +A_int <- (-1) * diag(c(3,5,7)) +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +I <- incenter_inradius_isoprob[[1]] +center <- I + runif(nrow(A_int)) +closest_to_exclusion(center, A_int) + +} diff --git a/man/cone_vertices_director_vertices.Rd b/man/cone_vertices_director_vertices.Rd new file mode 100644 index 0000000..6499733 --- /dev/null +++ b/man/cone_vertices_director_vertices.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cone_vertices_director_vertices.R +\name{cone_vertices_director_vertices} +\alias{cone_vertices_director_vertices} +\title{Vertices of the feasibility domain} +\usage{ +cone_vertices_director_vertices(A_int) +} +\arguments{ +\item{A_int}{SxS interaction matrix, where S is the number of species.} +} +\value{ +Dataframe with S rows and 2xS+2 columns. Each row contains the vertex + information for each species, when considering the intersection + of the feasibility domain and the unit ball. Specifically, each row + shows: + \itemize{ + \item The intrinsic growth rate of species i at the given vertex (r_i). + \item The vertex ID. + \item The vertex name. + \item the normal vector of the great circle that contains all the S-1 + remaining vertices and the origin of the unit ball. + } +} +\description{ +This function computes the vertices of the simplicial cone of the +feasibility domain, when it intersects the unit ball, as well as the normal +vector of the great circle that contains all the S-1 remaining vertices and +the origin of the unit ball (i.e., a point where all the intrinsic growth +rates are equal to zero). +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +cone_vertices_director_vertices(A_int) + +} diff --git a/man/furthest_from_exclusion.Rd b/man/furthest_from_exclusion.Rd new file mode 100644 index 0000000..6fc49d5 --- /dev/null +++ b/man/furthest_from_exclusion.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/furthest_from_exclusion.R +\name{furthest_from_exclusion} +\alias{furthest_from_exclusion} +\title{Parameters of the species furthest away from exclusion} +\usage{ +furthest_from_exclusion(center, A_int) +} +\arguments{ +\item{center}{a S vector that contains the initial intrinsic growth +rates that are used to computed the minimal isotropic variation that +excludes one or more species from the community.} + +\item{A_int}{a SxS interaction matrix, where S is the number of species.} +} +\value{ +A vector whose length varies between 1 and S. It element contains the +column index of the species that is/are furthest away from exclusion. +} +\description{ +Given an interaction matrix and an initial vector of intrinsic growing rates, +denoted by center, this function returns the column number/s of the +interaction matrix with the species that is/are further away from exclusion. +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +I <- incenter_inradius_isoprob[[1]] +center <- I + runif(nrow(A_int)) +furthest_from_exclusion(center, A_int) + +} diff --git a/man/incenter_inradius_isoprob_calculation.Rd b/man/incenter_inradius_isoprob_calculation.Rd new file mode 100644 index 0000000..dad2706 --- /dev/null +++ b/man/incenter_inradius_isoprob_calculation.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/incenter_inradius_isoprob_calculation.R +\name{incenter_inradius_isoprob_calculation} +\alias{incenter_inradius_isoprob_calculation} +\title{Main parameters of the maximum isotropic cap} +\usage{ +incenter_inradius_isoprob_calculation(A_int) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} +} +\value{ +A list with 3 elements: +\itemize{ + \item The first element is a vector that contains the intrinsic growth + rates of the feasibility domain's incenter, I. + \item The second element is a number that represents the great circle + distance from I to the border of the maximum isotropic cap, theta. + \item The third element is a number that shows area of the maximum + isotropic cap, Xi. + } +} +\description{ +Given an interaction matrix, this function computes the main parameters of +its maximum isotropic cap. +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +incenter_inradius_isoprob_calculation(A_int) + +} diff --git a/man/max_min_intrinsic_growth_rates.Rd b/man/max_min_intrinsic_growth_rates.Rd new file mode 100644 index 0000000..a03bf5d --- /dev/null +++ b/man/max_min_intrinsic_growth_rates.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/max_min_intrinsic_growth_rates.R +\name{max_min_intrinsic_growth_rates} +\alias{max_min_intrinsic_growth_rates} +\title{Maximum and minimum (feasible) intrinsic growth rates for the community's +species} +\usage{ +max_min_intrinsic_growth_rates(A_int, number_points_per_species = 1000) +} +\arguments{ +\item{A_int}{SxS interaction matrix, where S is the number of species.} + +\item{number_points_per_species}{number of intrinsic growth rates vectors to +be randomly sampled in the border of the feasibility domain where a given +species goes excluded.} +} +\value{ +Dataframe with S rows and 3 columns. Each row contains the +information for each species. Specifically, each row shows: + \itemize{ + \item The species name. + \item The minimum intrinsic growth rate estimated for such species. + \item The miaximum intrinsic growth rate estimated for such species. + } +} +\description{ +This function computes a rough estimation of the species range of feasible +intrinsic growth rates in a community with S species, under Lotka-Volterra +dynamics. To do so, for each species, we randomly select 1,000 intrinsic +growth rates vectors in the border where a given species goes extinct, and +then we extract the the minimum and maximum values for each component of the +1,000xS selected vectors. +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +max_min_intrinsic_growth_rates(A_int, number_points_per_species = 1000) + +} diff --git a/man/mean_pairwise_similarity.Rd b/man/mean_pairwise_similarity.Rd new file mode 100644 index 0000000..d6c3632 --- /dev/null +++ b/man/mean_pairwise_similarity.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mean_pairwise_similarity.R +\name{mean_pairwise_similarity} +\alias{mean_pairwise_similarity} +\title{Mean pairwise similarity} +\usage{ +mean_pairwise_similarity(probability_vector) +} +\arguments{ +\item{probability_vector}{a vector with length S whose elements are the +average probabilities of extinction of the species in a given community, +where S is the number of species.} +} +\value{ +A non-negative number between 0 and 1 (isotropic community). +} +\description{ +Given a vector whose elements are the average probabilities of extinction of +the species in a given community, this function calculates the mean pairwise +similarity of such probabilities. +} +\examples{ +probability_vector <- c(.25,.15,.60) +mean_pairwise_similarity(probability_vector) + +} diff --git a/man/prob_extinction_4_int_matrix.Rd b/man/prob_extinction_4_int_matrix.Rd new file mode 100644 index 0000000..6654488 --- /dev/null +++ b/man/prob_extinction_4_int_matrix.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prob_extinction_4_int_matrix.R +\name{prob_extinction_4_int_matrix} +\alias{prob_extinction_4_int_matrix} +\title{Probabilities of exclusion} +\usage{ +prob_extinction_4_int_matrix( + A_int, + number_Omega_replicates = 1000, + number_boot_replicates = 1000, + use_chol_decomp = F +) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} + +\item{number_Omega_replicates}{a number that specifies how many estimations +of Omega will be calculated by the quasi-Monte Carlo method in function +boot_prob_excl_Omega_raw. By default, this parameter is set to 1,000 +replicates.} + +\item{number_boot_replicates}{a number that specifies how many bootstrap +estimations of the probability of exclusion and Omega will be calculated +for each species in function boot_prob_excl_Omega_raw. By default, this +parameter is set to 1,000 replicates.} + +\item{use_chol_decomp}{boolean that specifies if the QR decomposition should +be used to compute Omega.} +} +\value{ +A tibble with S rows and 4 columns, that contain the following data: + \itemize{ + \item The species ID. + \item The mean value of the species' probability of exclusion. + \item The lower bound of the 95% confidence interval for the species' + probability of exclusion. + \item The upper bound of the 95% confidence interval for the species' + probability of exclusion. + } +} +\description{ +Given an interaction matrix, this function uses the output of the function +boot_prob_excl_Omega_raw to estimate the mean values and 95% confidence +intervals of the species' probabilities of exclusion. +} +\examples{ +numCores <- detectCores() +numCores +registerDoParallel(2) +A_int <- -1*diag(c(3,5,7)) +prob_extinction_4_int_matrix(A_int) +stopImplicitCluster() + +} +\references{ +\url{https://doi.org/10.1016/j.jtbi.2018.04.030} +} diff --git a/man/small_omega_iso.Rd b/man/small_omega_iso.Rd new file mode 100644 index 0000000..030ad53 --- /dev/null +++ b/man/small_omega_iso.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/small_omega_iso.R +\name{small_omega_iso} +\alias{small_omega_iso} +\title{Small Omega for the maximum isotropic cap} +\usage{ +small_omega_iso(A_int) +} +\arguments{ +\item{A_int}{a SxS interaction matrix, where S is the number of species.} +} +\value{ +A number that shows the value of value of small Omega, the S root of +the volume of the maximum isotropic cap, Xi. +} +\description{ +Given an SxS interaction matrix, this function computes the value of small +Omega for maximum isotropic cap (i.e., the S root of the maximum isotropic +cap, Xi). +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +small_omega_iso(A_int) + +} diff --git a/man/sp_distance_to_exclusion.Rd b/man/sp_distance_to_exclusion.Rd new file mode 100644 index 0000000..508d6fe --- /dev/null +++ b/man/sp_distance_to_exclusion.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sp_distance_to_exclusion.R +\name{sp_distance_to_exclusion} +\alias{sp_distance_to_exclusion} +\title{Parameters of the species closest to exclusion} +\usage{ +sp_distance_to_exclusion(center, A_int) +} +\arguments{ +\item{center}{an S vector that contains the initial intrinsic growth +rates that are used to computed the minimal isotropic variation that +excludes one or more species from the community.} + +\item{A_int}{SxS interaction matrix, where S is the number of species.} +} +\value{ +A tibble data frame with S rows and 2 columns. Each row contains the +species name and its great circle distance to the border where it goes +excluded (i.e, that distance is the absolute value of the angle between +the center and the tangent point in a given "edge" of the feasibility domain). +} +\description{ +Given an interaction matrix and an initial vector of intrinsic growing rates, +denoted by center, this function computes the arc distance from each species +to the feasibility domain's border where only such species is excluded. +} +\examples{ +A_int <- (-1) * diag(c(3,5,7)) +incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) +I <- incenter_inradius_isoprob[[1]] +center <- I + runif(nrow(A_int)) +sp_distance_to_exclusion(center, A_int) + +} diff --git a/man/vertices_unit_ball.Rd b/man/vertices_unit_ball.Rd new file mode 100644 index 0000000..86a7fc3 --- /dev/null +++ b/man/vertices_unit_ball.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/vertices_unit_ball.R +\name{vertices_unit_ball} +\alias{vertices_unit_ball} +\title{Vertices of the feasibility domain} +\usage{ +vertices_unit_ball(A_int) +} +\arguments{ +\item{A_int}{SxS interaction matrix, where S is the number of species.} +} +\value{ +Dataframe with S rows and 2xS+1 columns. Each row contains the vertex + information for each species, when considering the intersection + of the feasibility domain and the unit ball. Specifically, each row + shows: + \itemize{ + \item The abundance of species i at the given vertex (N_i). + \item The intrinsic growth rate of species i at the given vertex (r_i). + \item The vertex ID. + } +} +\description{ +This function computes the vertices of the simplicial cone of the +feasibility domain when it intersects the unit ball. +} +\examples{ +A_int <- -1*diag(c(3,5,7)) +vertices_unit_ball(A_int) + +} diff --git a/plotting.R b/plotting.R new file mode 100644 index 0000000..807dfa8 --- /dev/null +++ b/plotting.R @@ -0,0 +1,196 @@ + +library(matlib) # to multiply matrices +library(tidyverse) +library(gg3D) +library(nleqslv) # to solve non-linear equations +library(zipfR) # beta incomplete function to estimate the area of d-dimensional spherical caps +library(RColorBrewer) + + +# This function get the coordinates of npoints in a circle with radius "radius_circle", centered at +# the point C (located by "Center_vector") and in the plane that contain the vectors "unit_vector1", +# and "unit_vector2", where unit_vector1 and unit_vector2 are normal vectors +# (i.e., unit_vector1 . unit_vector2 = 0) + +circle3DFun <- function(center_vector,radius_circle, unit_vector1,unit_vector2,npoints = 100){ + + tt <- seq(0,2*pi,length.out = npoints) + xx <- as.numeric(center_vector[1]) + radius_circle * cos(tt) * unit_vector1[1] + + radius_circle * sin(tt) * unit_vector2[1] + yy <- as.numeric(center_vector[2]) + radius_circle * cos(tt) * unit_vector1[2] + + radius_circle * sin(tt) * unit_vector2[2] + zz <- as.numeric(center_vector[3]) + radius_circle * cos(tt) * unit_vector1[3] + + radius_circle * sin(tt) * unit_vector2[3] + return(data.frame(x = xx, y = yy, z = zz)) +} + +# To plot circles +circleFun <- function(center = c(0,0),diameter = 1, npoints = 100){ + r = diameter / 2 + tt <- seq(0,2*pi,length.out = npoints) + xx <- center[1] + r * cos(tt) + yy <- center[2] + r * sin(tt) + return(data.frame(x = xx, y = yy)) +} + +# To plot the FD's limits +limits_feasibility_domain <- function(A_int, species_limiting, number_points_domain=1000){ + + num_species <- 1:(ncol(A_int)) + + r_values <- data.frame(matrix(ncol = (2*ncol(A_int)), nrow = number_points_domain)) + r_names <- c(paste0("N_", num_species),paste0("r_", num_species)) + colnames(r_values) <- r_names + + for(j in 1:ncol(A_int)){ + + r_values[,j] <- runif(number_points_domain) # uniform on [-1, 1] + + } + + r_values[,species_limiting] <- 0 + + for(i in 1:nrow(r_values)){ + + N_column <- matrix(as.numeric(r_values[i,1:ncol(A_int)]), + ncol = 1, nrow = ncol(A_int) ) + + r_column <- (-1)*(A_int %*% as.matrix(N_column)) + + sq_sum <- sum(r_column*r_column) + + r_values[i,c((ncol(A_int)+1):(2*ncol(A_int)))] <- r_column/sqrt(sq_sum) + + } + + return(r_values) + +} + +# Create an interaction matrix +S = 3 # number of species + +use_intencer <- TRUE #FALSE es un random point. + + +alphas <- runif(S*S) + +set.seed(1234) +A_int <- -1*matrix(alphas, ncol=S, nrow=S) + +# Get main curves data: vertices and normal vectors for their hyperplanes +dimensions <- ncol(A_int) +curves_data <- cone_vertices_director_vertices(A_int) + +# Estimate the vector of initial intrinsic growth rates + +if(use_intencer){ + incenter_inradius_isoprob <- incenter_inradius_isoprob_calculation(A_int) + center_unit_ball <- incenter_inradius_isoprob[[1]] +}else{ + + # We choose a random point inside de feasibility domain (N_i^*>0) and estimate its growth rates + set.seed(123) + feasible_N <- runif(3) + feasible_N_mat <- matrix(feasible_N,nrow = dimensions,ncol = 1) + center_mat <- (-1)*A_int %*% feasible_N_mat + + center <- as.numeric(center_mat) + center_unit_ball <- center/sqrt(sum(center*center)) + +} + + + +FD_point_closest_to_exclusion <- closest_to_exclusion(center_unit_ball,A_int) +min_great_circle_dist_to_limits <- acos(sum(center_unit_ball*as.numeric(FD_point_closest_to_exclusion[1,1:S]))) + + +# Plot results--------- +# 1) Data to plot the limits of the feasibility domain +r_N1 <- limits_feasibility_domain(A_int, species_limiting=1, number_points_domain=1000) +r_N2 <- limits_feasibility_domain(A_int, species_limiting=2, number_points_domain=1000) +r_N3 <- limits_feasibility_domain(A_int, species_limiting=3, number_points_domain=1000) + +# 2) Data to plot the vertices of the simplicial cone, the incenter and the tangent points +important_points_feasibility_domain_aux <- vertices_unit_ball(A_int) +important_points_feasibility_domain_aux$color_sol <- "Vertex" +random_center_coordinates <- tibble(r_1 = center_unit_ball[1], + r_2 = center_unit_ball[2], + r_3 = center_unit_ball[3], + color_sol = "Random Center") + +important_points_feasibility_domain <- bind_rows(important_points_feasibility_domain_aux, + random_center_coordinates) +# 3) Data to plot a 2d Circle centered at the origin, i.e., the 2-D projection of the unit ball +circ_points <- circleFun(c(0,0),2,npoints = 100) + +# 4) Data to plot a the incircle that is tangent to the feasibility domain +cos_theta <- cos(min_great_circle_dist_to_limits) +center_vector <- as.numeric(center_unit_ball*cos_theta) +radius_circle <- sin(min_great_circle_dist_to_limits) + +point_1 <- c(0,0,cos_theta/center_vector[3]) + +unit_vector1 <- center_vector-point_1 +unit_vector1 <- unit_vector1 / sqrt(sum(unit_vector1*unit_vector1)) + +# Cross product +unit_vector2 <- pracma::cross(unit_vector1,center_vector) +unit_vector2 <- unit_vector2 / sqrt(sum(unit_vector2*unit_vector2)) + +circ_3D_points <- circle3DFun(center_vector,radius_circle, + unit_vector1,unit_vector2,npoints = 100) %>% + rename(r_1=x,r_2=y,r_3=z) %>% mutate(color_sol = "Tangent circle", + unit_ball=r_1^2+r_2^2+r_3^2) + +# 2D Plots +ggplot()+ + geom_path(data=circ_points,aes(x=x,y=y))+ + geom_path(data=circ_3D_points,aes(x=r_1,y=r_2))+ + geom_point(data=r_N1,aes(x=r_1,y=r_2),color="red")+ + geom_point(data=r_N2,aes(x=r_1,y=r_2),color="black")+ + geom_point(data=r_N3,aes(x=r_1,y=r_2),color="blue")+ + geom_point(data=important_points_feasibility_domain,aes(x=r_1,y=r_2),color="purple",size=2)+ + labs(x="Intrinsic growth rate sp 1", y="Intrinsic growth rate sp 2")+ + theme_bw()+ + theme(aspect.ratio=1) + +ggplot()+ + geom_path(data=circ_points,aes(x=x,y=y))+ + geom_path(data=circ_3D_points,aes(x=r_1,y=r_3))+ + geom_point(data=r_N1,aes(x=r_1,y=r_3),color="red")+ + geom_point(data=r_N2,aes(x=r_1,y=r_3),color="black")+ + geom_point(data=r_N3,aes(x=r_1,y=r_3),color="blue")+ + geom_point(data=important_points_feasibility_domain,aes(x=r_1,y=r_3),color="purple",size=2)+ + labs(x="Intrinsic growth rate sp 1", y="Intrinsic growth rate sp 3")+ + theme_bw()+ + theme(aspect.ratio=1) + +ggplot()+ + geom_path(data=circ_points,aes(x=x,y=y))+ + geom_path(data=circ_3D_points,aes(x=r_2,y=r_3))+ + geom_point(data=r_N1,aes(x=r_2,y=r_3),color="red")+ + geom_point(data=r_N2,aes(x=r_2,y=r_3),color="black")+ + geom_point(data=r_N3,aes(x=r_2,y=r_3),color="blue")+ + geom_point(data=important_points_feasibility_domain,aes(x=r_2,y=r_3),color="purple",size=2)+ + labs(x="Intrinsic growth rate sp 2", y="Intrinsic growth rate sp 3")+ + theme_bw()+ + theme(aspect.ratio=1) + + +# 3D Plots + +r_N1$color_sol <- "N1=0" +r_N2$color_sol <- "N2=0" +r_N3$color_sol <- "N3=0" + +feas_limits <- bind_rows(important_points_feasibility_domain,r_N1,r_N2,r_N3,circ_3D_points) + +ggplot(data = feas_limits, aes(x=r_1,y=r_2, z=r_3,color=color_sol))+ + theme_void() + + scale_color_brewer(palette = "Set1")+ + axes_3D() + + stat_3D()+ + theme(aspect.ratio=1)+ + labs(color="Description",title="Feasibility domain (3 species)") diff --git a/real example.R b/real example.R new file mode 100644 index 0000000..dc53906 --- /dev/null +++ b/real example.R @@ -0,0 +1,95 @@ +library(matlib) # to multiply matrices +library(tidyverse) +library(nleqslv) # to solve non-linear equations +library(zipfR) # beta incomplete function to estimate the area of d-dimensional spherical caps +library(pracma) # to solve n-dimensional cross products +library(MultitrophicFun) +library(parallel) +library(foreach) +library(doParallel) +library(boot) + + +# Functions to run calculations about the isotropic area +pathnames <- list.files(pattern="[.]R$", path="R", full.names=TRUE); +sapply(pathnames, FUN=source) + +# We create the metaweb for each year + +matrix_entries_raw <- read_csv2("Data/caracoles_raw_data/alpha_heterogeneous_time.csv") %>% + group_by(year,focal,neighbour) %>% summarise(magnitude = mean(magnitude, na.rm =T)) + + +number_Omega_replicates <- 200 +number_boot_replicates <- number_Omega_replicates + +years_included <- matrix_entries_raw$year %>% unique() + +prob_excl_caracoles <- NULL + +# Config parallelization +numCores <- detectCores() +registerDoParallel(numCores-1) + + +# Estimate prob_excl_caracoles +set.seed(123) + +for(year_i in years_included){ + + cat(year_i, "\n") + + A_int <- matrix_year_i(matrix_entries_raw, year_i) + prob_excl_A_int <- prob_extinction_4_int_matrix(A_int, + number_Omega_replicates, + number_boot_replicates) + + prob_excl_A_int$year <- year_i + prob_excl_caracoles <- bind_rows(prob_excl_caracoles, prob_excl_A_int) + +} + +stopImplicitCluster() # + +number_plant_sp_year <- prob_excl_caracoles %>% group_by(year) %>% count() %>% + rename(number_plant_sp = n) + +# Add number of species per year, isoprobability of exclusion and mean exclusion ratio + +caracoles_plot_year <- prob_excl_caracoles %>% + left_join(number_plant_sp_year, by = c("year")) %>% + mutate(iso_prob_exclusion = 1/number_plant_sp, + exclusion_ratio_mean = prob_excl_mean/iso_prob_exclusion, + exclusion_ratio_upper = prob_excl_upperCI/iso_prob_exclusion, + exclusion_ratio_lower = prob_excl_lowerCI/iso_prob_exclusion) + +# Plot mean exclusion ratio + +species_names <- c( + `BEMA` = "Beta\nmacrocarpa", + `CETE` = "Centaurium\ntenuiflorum", + `HOMA` = "Hordeum\nmarinum", + `LEMA` = "Leontodon\nmaroccanus", + `PAIN` = "Parapholis\nincurva", + `POMA` = "Polypogon\nmaritimus", + `SASO` = "Salsola\nsoda", + `SCLA` = "Scorzonera\nlaciniata" +) + + +ggplot(caracoles_plot_year %>% filter(species %in% c("BEMA","CETE", "HOMA", + "LEMA","PAIN","POMA", + "SASO")), + aes(x = as.factor(year),y = exclusion_ratio_mean))+ + geom_errorbar(aes(ymin=exclusion_ratio_lower, ymax=exclusion_ratio_upper), width=.2)+ + stat_summary(fun = mean, color = "red", geom = "line", aes(group = 1),lwd=1.3)+ + stat_summary(fun = mean, geom="point", shape=23, size=3,fill="red")+ + geom_hline(yintercept = 1, color = "deepskyblue", linetype = "dashed", size = 1.3)+ + facet_wrap(~species, ncol = 4, labeller = as_labeller(species_names))+ #, scales = "free")+ + # scale_y_continuous(labels = scientific)+ + # scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), + # labels = trans_format("log10", math_format(10^.x))) + + ylab("Exclusion ratio")+ + labs(x=NULL)+ + theme_bw()+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+ + theme(strip.text = element_text(face = "italic")) diff --git a/tests/test-all.R b/tests/test-all.R new file mode 100644 index 0000000..a1d0d97 --- /dev/null +++ b/tests/test-all.R @@ -0,0 +1,3 @@ +library(testthat) +library("anisoFun") +test_check("anisoFun") diff --git a/tests/testthat/test-functions.R b/tests/testthat/test-functions.R new file mode 100644 index 0000000..e8d2864 --- /dev/null +++ b/tests/testthat/test-functions.R @@ -0,0 +1,176 @@ +context("parameter fitting functions") + +# set data ---------------------------------------------------------------- + +S = 3 +A_int_symm <- (-1)*diag(rep(1,S)) +incenter_inradius_isoprob_sym <- + incenter_inradius_isoprob_calculation(A_int_symm) +I_sym <- incenter_inradius_isoprob_sym[[1]] +Xi_sym <- incenter_inradius_isoprob_sym[[3]] + +center_rand_aux <- runif(S) +center_rand <- center_rand_aux/sqrt(sum(center_rand_aux^2)) + +# Interaction matrices with the same value of capital Omega + + +A_int_1 <- matrix(rep(0,9), ncol=3) +A_int_1[,1] <- -c(1, 0, 0) + +A_int_2 <- A_int_1 + +A_int_1[,2] <- -c(0.3406884710289558, 0.9328944522580684, 0.11678744219579273) +A_int_1[,3] <- -c(0.3406884710289558, 0.12410827817548943, 0.9319487652208257) + +A_int_2[,2] <- -c(0.3995691276168492, 0.7476891904854146, -0.5303823023903146) +A_int_2[,3] <- -c(0.39956912762129915, -0.19648768029759267, 0.8953977349474517) + +incenter_inradius_isoprob_mat_1 <- + incenter_inradius_isoprob_calculation(A_int_1) +I_1 <- incenter_inradius_isoprob_mat_1[[1]] +theta_1 <- incenter_inradius_isoprob_mat_1[[2]] +Xi_1 <- incenter_inradius_isoprob_mat_1[[3]] + +incenter_inradius_isoprob_mat_2 <- + incenter_inradius_isoprob_calculation(A_int_2) +I_2 <- incenter_inradius_isoprob_mat_2[[1]] +theta_2 <- incenter_inradius_isoprob_mat_2[[2]] +Xi_2 <- incenter_inradius_isoprob_mat_2[[3]] + + +# when run with nonsensical parameters, the function returns NULL + +test_that("invalid arguments return NULL",{ + + wrong_A_int_1 <- c(2,"b",1,"x") + + wrong_anisotropy_index_1 <- anisotropy_index(wrong_A_int_1) + wrong_mean_pairwise_sim_1 <- mean_pairwise_similarity(wrong_A_int_1) + + wrong_anisotropy_metrics_1 <- anisotropy_metrics_4_int_matrix(wrong_A_int_1) + wrong_boot_prob_excl_Omega_1 <- boot_prob_excl_Omega_raw(wrong_A_int_1) + wrong_cone_vertices_director_vertices_1 <- cone_vertices_director_vertices(wrong_A_int_1) + wrong_incenter_inradius_isoprob_1 <- incenter_inradius_isoprob_calculation(wrong_A_int_1) + wrong_Omega_bootstrap_1 <- Omega_bootstrap(wrong_A_int_1) + wrong_prob_extinction_1 <- prob_extinction_4_int_matrix(wrong_A_int_1) + wrong_small_omega_iso_1 <- small_omega_iso(wrong_A_int_1) + wrong_vertices_1 <- vertices_unit_ball(wrong_A_int_1) + wrong_closest_to_excl_1 <- closest_to_exclusion(center_rand, wrong_A_int_1) + wrong_furthest_from_excl_1 <- furthest_from_exclusion(center_rand, wrong_A_int_1) + + + wrong_A_int_2 <- matrix(c(1,2,3,"x"),ncol = 2) + + wrong_anisotropy_index_2 <- anisotropy_index(wrong_A_int_2) + wrong_mean_pairwise_sim_2 <- mean_pairwise_similarity(wrong_A_int_2) + wrong_anisotropy_metrics_2 <- anisotropy_metrics_4_int_matrix(wrong_A_int_2) + wrong_boot_prob_excl_Omega_2 <- boot_prob_excl_Omega_raw(wrong_A_int_2) + wrong_cone_vertices_director_vertices_2 <- cone_vertices_director_vertices(wrong_A_int_2) + wrong_incenter_inradius_isoprob_2 <- incenter_inradius_isoprob_calculation(wrong_A_int_2) + wrong_closest_to_excl_2 <- closest_to_exclusion(center_rand, wrong_A_int_2) + wrong_furthest_from_excl_2 <- furthest_from_exclusion(center_rand, wrong_A_int_2) + wrong_Omega_bootstrap_2 <- Omega_bootstrap(wrong_A_int_2) + wrong_prob_extinction_2 <- prob_extinction_4_int_matrix(wrong_A_int_2) + wrong_small_omega_iso_2 <- small_omega_iso(wrong_A_int_2) + wrong_vertices_2 <- vertices_unit_ball(wrong_A_int_2) + + + wrong_probability_1 <- c(-.25,.75) + wrong_anisotropy_index_3 <- anisotropy_index(wrong_probability_1) + wrong_mean_pairwise_sim_3 <- mean_pairwise_similarity(wrong_probability_1) + + wrong_probability_2 <- c(.25,1) + wrong_anisotropy_index_4 <- anisotropy_index(wrong_probability_2) + wrong_mean_pairwise_sim_4 <- mean_pairwise_similarity(wrong_probability_2) + + + expect_null(wrong_anisotropy_index_1) + expect_null(wrong_mean_pairwise_sim_1) + expect_null(wrong_closest_to_excl_1) + expect_null(wrong_furthest_from_excl_1) + expect_null(wrong_anisotropy_metrics_1) + expect_null(wrong_boot_prob_excl_Omega_1) + expect_null(wrong_cone_vertices_director_vertices_1) + expect_null(wrong_incenter_inradius_isoprob_1) + expect_null(wrong_Omega_bootstrap_1) + expect_null(wrong_prob_extinction_1) + expect_null(wrong_small_omega_iso_1) + expect_null(wrong_vertices_1) + + expect_null(wrong_anisotropy_index_2) + expect_null(wrong_mean_pairwise_sim_2) + expect_null(wrong_closest_to_excl_2) + expect_null(wrong_furthest_from_excl_2) + expect_null(wrong_anisotropy_metrics_2) + expect_null(wrong_boot_prob_excl_Omega_2) + expect_null(wrong_cone_vertices_director_vertices_2) + expect_null(wrong_incenter_inradius_isoprob_2) + expect_null(wrong_Omega_bootstrap_2) + expect_null(wrong_prob_extinction_2) + expect_null(wrong_small_omega_iso_2) + expect_null(wrong_vertices_2) + + + expect_null(wrong_anisotropy_index_3) + expect_null(wrong_mean_pairwise_sim_3) + expect_null(wrong_anisotropy_index_4) + expect_null(wrong_mean_pairwise_sim_4) + + +}) + +test_that("isotropic communities return expected values",{ + + iso_prob <- rep(1/S,S) + + iso_anisotropy_index <- anisotropy_index(iso_prob) + iso_mean_pairwise_sim <- mean_pairwise_similarity(iso_prob) + iso_small_omega_iso <- small_omega_iso(A_int_symm) + iso_closest_to_exclusion <- closest_to_exclusion(I_sym, A_int_symm) + iso_furthest_from_exclusion <- furthest_from_exclusion(I_sym, A_int_symm) + + + expect_equal(iso_anisotropy_index,1) + expect_equal(iso_mean_pairwise_sim,1) + expect_equal(iso_small_omega_iso,Xi_sym^(1/S)) + expect_equal(nrow(iso_closest_to_exclusion),S) + expect_equal(length(iso_furthest_from_exclusion),S) + + + skip_on_cran() + + registerDoParallel(2) + iso_prob_extinction <- prob_extinction_4_int_matrix(A_int_symm) + iso_Omega_bootstrap <- Omega_bootstrap(A_int_symm) + stopImplicitCluster() + + expect_equal(round(dplyr::pull(iso_prob_extinction[,2]),3),round(iso_prob,3)) + expect_equal(round(dplyr::pull(iso_prob_extinction[,3]),3),round(iso_prob,3)) + expect_equal(round(dplyr::pull(iso_prob_extinction[,4]),3),round(iso_prob,3)) + expect_equal(length(iso_Omega_bootstrap),1e3) + + +}) + +test_that("given two comunities with the same Omega, the more isotropic has larger theta and Xi",{ + + + expect_lt(theta_2, theta_1) + expect_lt(Xi_2, Xi_1) + + small_omega_iso_1 <- small_omega_iso(A_int_1) + small_omega_iso_2 <- small_omega_iso(A_int_2) + + expect_lt(small_omega_iso_2, small_omega_iso_1) + + skip_on_cran() + registerDoParallel(2) + anisotropy_metrics_1 <- anisotropy_metrics_4_int_matrix(A_int_1) + anisotropy_metrics_2 <- anisotropy_metrics_4_int_matrix(A_int_2) + stopImplicitCluster() + + expect_equal(round(dplyr::pull(anisotropy_metrics_2[1,1:3]),3),round(dplyr::pull(anisotropy_metrics_1[1,1:3]),3)) # Same capital Omega + expect_lt(round(dplyr::pull(anisotropy_metrics_2[1,4:6]),3),round(dplyr::pull(anisotropy_metrics_1[1,4:6]),3)) # Different shape + +}) \ No newline at end of file