From 52e0e777871d3c3a18083534e29dfa9eef6a18bc Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 16 Dec 2022 16:26:12 +0100 Subject: [PATCH 1/3] Change Corr and Enscorr to return significance instead of conf. intervals and p-value --- .../testing_recipes/recipe_seasonal-tests.yml | 3 + modules/Skill/Skill.R | 23 +- modules/Skill/tmp/Corr.R | 463 ++++++++++++++++++ 3 files changed, 479 insertions(+), 10 deletions(-) create mode 100644 modules/Skill/tmp/Corr.R diff --git a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml index e1857ac0..61177b71 100644 --- a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml +++ b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml @@ -30,6 +30,9 @@ Analysis: Workflow: Calibration: method: mse_min + Anomalies: + compute: yes + cross_validation: yes Skill: metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS Probabilities: diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index 9a59be40..1095ed10 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -15,6 +15,7 @@ source("modules/Skill/tmp/RandomWalkTest.R") source("modules/Skill/tmp/Bias.R") source("modules/Skill/tmp/AbsBiasSS.R") source("modules/Skill/tmp/RMSSS.R") +source("modules/Skill/tmp/Corr.R") ## TODO: Implement this in the future ## Which parameter are required? @@ -210,19 +211,21 @@ compute_skill_metrics <- function(recipe, data) { } else if (metric %in% c('enscorr', 'corr')) { ## TODO: Return significance ## TODO: Implement option for Kendall and Spearman methods? - skill <- s2dv::Corr(data$hcst$data, data$obs$data, - dat_dim = 'dat', - time_dim = time_dim, - method = 'pearson', - memb_dim = memb_dim, - memb = memb, - ncores = ncores) + skill <- Corr(data$hcst$data, data$obs$data, + dat_dim = 'dat', + time_dim = time_dim, + method = 'pearson', + memb_dim = memb_dim, + memb = memb, + conf = F, + pval = F, + sign = T, + alpha = 0.05, + ncores = ncores) skill <- lapply(skill, function(x) { .drop_dims(x)}) skill_metrics[[ metric ]] <- skill$corr - skill_metrics[[ paste0(metric, "_p.value") ]] <- skill$p.val - skill_metrics[[ paste0(metric, "_conf.low") ]] <- skill$conf.lower - skill_metrics[[ paste0(metric, "_conf.up") ]] <- skill$conf.upper + skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign } else if (metric == 'rmsss') { # Compute hcst ensemble mean exp_ensmean <- MeanDims(exp$data, dims = memb_dim, drop = FALSE) diff --git a/modules/Skill/tmp/Corr.R b/modules/Skill/tmp/Corr.R new file mode 100644 index 00000000..c95b1034 --- /dev/null +++ b/modules/Skill/tmp/Corr.R @@ -0,0 +1,463 @@ +#'Compute the correlation coefficient between an array of forecast and their corresponding observation +#' +#'Calculate the correlation coefficient (Pearson, Kendall or Spearman) for +#'an array of forecast and an array of observation. The correlations are +#'computed along 'time_dim' that usually refers to the start date dimension. If +#''comp_dim' is given, the correlations are computed only if obs along comp_dim +#'dimension are complete between limits[1] and limits[2], i.e., there is no NA +#'between limits[1] and limits[2]. This option can be activated if the user +#'wants to account only for the forecasts which the corresponding observations +#'are available at all leadtimes.\cr +#'The confidence interval is computed by the Fisher transformation and the +#'significance level relies on an one-sided student-T distribution.\cr +#'The function can calculate ensemble mean before correlation by 'memb_dim' +#'specified and 'memb = F'. If ensemble mean is not calculated, correlation will +#'be calculated for each member. +#'If there is only one dataset for exp and obs, you can simply use cor() to +#'compute the correlation. +#' +#'@param exp A named numeric array of experimental data, with at least dimension +#' 'time_dim'. +#'@param obs A named numeric array of observational data, same dimensions as +#' parameter 'exp' except along 'dat_dim' and 'memb_dim'. +#'@param time_dim A character string indicating the name of dimension along +#' which the correlations are computed. The default value is 'sdate'. +#'@param dat_dim A character string indicating the name of dataset (nobs/nexp) +#' dimension. The default value is 'dataset'. If there is no dataset +#' dimension, set NULL. +#'@param comp_dim A character string indicating the name of dimension along which +#' obs is taken into account only if it is complete. The default value +#' is NULL. +#'@param limits A vector of two integers indicating the range along comp_dim to +#' be completed. The default is c(1, length(comp_dim dimension)). +#'@param method A character string indicating the type of correlation: +#' 'pearson', 'spearman', or 'kendall'. The default value is 'pearson'. +#'@param memb_dim A character string indicating the name of the member +#' dimension. It must be one dimension in 'exp' and 'obs'. If there is no +#' member dimension, set NULL. The default value is NULL. +#'@param memb A logical value indicating whether to remain 'memb_dim' dimension +#' (TRUE) or do ensemble mean over 'memb_dim' (FALSE). Only functional when +#' 'memb_dim' is not NULL. The default value is TRUE. +#'@param pval A logical value indicating whether to return or not the p-value +#' of the test Ho: Corr = 0. The default value is TRUE. +#'@param conf A logical value indicating whether to return or not the confidence +#' intervals. The default value is TRUE. +#'@param sign A logical value indicating whether to retrieve the statistical +#' significance of the test Ho: Corr = 0 based on 'alpha'. The default value is +#' FALSE. +#'@param alpha A numeric indicating the significance level for the statistical +#' significance test. The default value is 0.05. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#' +#'@return +#'A list containing the numeric arrays with dimension:\cr +#' c(nexp, nobs, exp_memb, obs_memb, all other dimensions of exp except +#' time_dim and memb_dim).\cr +#'nexp is the number of experiment (i.e., 'dat_dim' in exp), and nobs is the +#'number of observation (i.e., 'dat_dim' in obs). If dat_dim is NULL, nexp and +#'nobs are omitted. exp_memb is the number of member in experiment (i.e., +#''memb_dim' in exp) and obs_memb is the number of member in observation (i.e., +#''memb_dim' in obs). If memb = F, exp_memb and obs_memb are omitted.\cr\cr +#'\item{$corr}{ +#' The correlation coefficient. +#'} +#'\item{$p.val}{ +#' The p-value. Only present if \code{pval = TRUE}. +#'} +#'\item{$conf.lower}{ +#' The lower confidence interval. Only present if \code{conf = TRUE}. +#'} +#'\item{$conf.upper}{ +#' The upper confidence interval. Only present if \code{conf = TRUE}. +#'} +#'\item{$sign}{ +#' The statistical significance. Only present if \code{sign = TRUE}. +#'} +#' +#'@examples +#'# Case 1: Load sample data as in Load() example: +#'example(Load) +#'clim <- Clim(sampleData$mod, sampleData$obs) +#'ano_exp <- Ano(sampleData$mod, clim$clim_exp) +#'ano_obs <- Ano(sampleData$obs, clim$clim_obs) +#'runmean_months <- 12 +#' +#'# Smooth along lead-times +#'smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = runmean_months) +#'smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = runmean_months) +#'required_complete_row <- 3 # Discard start dates which contain any NA lead-times +#'leadtimes_per_startdate <- 60 +#'corr <- Corr(MeanDims(smooth_ano_exp, 'member'), +#' MeanDims(smooth_ano_obs, 'member'), +#' comp_dim = 'ftime', +#' limits = c(ceiling((runmean_months + 1) / 2), +#' leadtimes_per_startdate - floor(runmean_months / 2))) +#' +#'# Case 2: Keep member dimension +#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member') +#'# ensemble mean +#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member', memb = FALSE) +#' +#'@import multiApply +#'@importFrom ClimProjDiags Subset +#'@importFrom stats cor pt qnorm +#'@export +Corr <- function(exp, obs, time_dim = 'sdate', dat_dim = 'dataset', + comp_dim = NULL, limits = NULL, method = 'pearson', + memb_dim = NULL, memb = TRUE, + pval = TRUE, conf = TRUE, sign = FALSE, + alpha = 0.05, ncores = NULL) { + + # Check inputs + ## exp and obs (1) + if (is.null(exp) | is.null(obs)) { + stop("Parameter 'exp' and 'obs' cannot be NULL.") + } + if (!is.numeric(exp) | !is.numeric(obs)) { + stop("Parameter 'exp' and 'obs' must be a numeric array.") + } + if (is.null(dim(exp)) | is.null(dim(obs))) { + stop(paste0("Parameter 'exp' and 'obs' must be at least two dimensions ", + "containing time_dim and dat_dim.")) + } + if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) | + any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) { + stop("Parameter 'exp' and 'obs' must have dimension names.") + } + if(!all(names(dim(exp)) %in% names(dim(obs))) | + !all(names(dim(obs)) %in% names(dim(exp)))) { + stop("Parameter 'exp' and 'obs' must have same dimension name") + } + ## time_dim + if (!is.character(time_dim) | length(time_dim) > 1) { + stop("Parameter 'time_dim' must be a character string.") + } + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) { + stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.") + } + ## dat_dim + if (!is.null(dat_dim)) { + if (!is.character(dat_dim) | length(dat_dim) > 1) { + stop("Parameter 'dat_dim' must be a character string or NULL.") + } + if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) { + stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.", + " Set it as NULL if there is no dataset dimension.") + } + } + ## comp_dim + if (!is.null(comp_dim)) { + if (!is.character(comp_dim) | length(comp_dim) > 1) { + stop("Parameter 'comp_dim' must be a character string.") + } + if (!comp_dim %in% names(dim(exp)) | !comp_dim %in% names(dim(obs))) { + stop("Parameter 'comp_dim' is not found in 'exp' or 'obs' dimension.") + } + } + ## limits + if (!is.null(limits)) { + if (is.null(comp_dim)) { + stop("Paramter 'comp_dim' cannot be NULL if 'limits' is assigned.") + } + if (!is.numeric(limits) | any(limits %% 1 != 0) | any(limits < 0) | + length(limits) != 2 | any(limits > dim(exp)[comp_dim])) { + stop(paste0("Parameter 'limits' must be a vector of two positive ", + "integers smaller than the length of paramter 'comp_dim'.")) + } + } + ## method + if (!(method %in% c("kendall", "spearman", "pearson"))) { + stop("Parameter 'method' must be one of 'kendall', 'spearman' or 'pearson'.") + } + ## memb_dim + if (!is.null(memb_dim)) { + if (!is.character(memb_dim) | length(memb_dim) > 1) { + stop("Parameter 'memb_dim' must be a character string.") + } + if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(obs))) { + stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.") + } + } + ## memb + if (!is.logical(memb) | length(memb) > 1) { + stop("Parameter 'memb' must be one logical value.") + } + ## pval + if (!is.logical(pval) | length(pval) > 1) { + stop("Parameter 'pval' must be one logical value.") + } + ## conf + if (!is.logical(conf) | length(conf) > 1) { + stop("Parameter 'conf' must be one logical value.") + } + ## sign + if (!is.logical(sign) | length(sign) > 1) { + stop("Parameter 'sign' must be one logical value.") + } + ## alpha + if (!is.numeric(alpha) | alpha < 0 | alpha > 1 | length(alpha) > 1) { + stop("Parameter 'alpha' must be a numeric number between 0 and 1.") + } + ## ncores + if (!is.null(ncores)) { + if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1) { + stop("Parameter 'ncores' must be a positive integer.") + } + } + ## exp and obs (2) + name_exp <- sort(names(dim(exp))) + name_obs <- sort(names(dim(obs))) + if (!is.null(dat_dim)) { + name_exp <- name_exp[-which(name_exp == dat_dim)] + name_obs <- name_obs[-which(name_obs == dat_dim)] + } + if (!is.null(memb_dim)) { + name_exp <- name_exp[-which(name_exp == memb_dim)] + name_obs <- name_obs[-which(name_obs == memb_dim)] + } + if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) { + stop(paste0("Parameter 'exp' and 'obs' must have same length of ", + "all dimension except 'dat_dim' and 'memb_dim'.")) + } + if (dim(exp)[time_dim] < 3) { + stop("The length of time_dim must be at least 3 to compute correlation.") + } + + + ############################### + # Sort dimension + name_exp <- names(dim(exp)) + name_obs <- names(dim(obs)) + order_obs <- match(name_exp, name_obs) + obs <- Reorder(obs, order_obs) + + + ############################### + # Calculate Corr + + # Remove data along comp_dim dim if there is at least one NA between limits + if (!is.null(comp_dim)) { + pos <- which(names(dim(obs)) == comp_dim) + if (is.null(limits)) { + obs_sub <- obs + } else { + obs_sub <- ClimProjDiags::Subset(obs, pos, list(limits[1]:limits[2])) + } + outrows <- is.na(MeanDims(obs_sub, pos, na.rm = FALSE)) + outrows <- InsertDim(outrows, pos, dim(obs)[comp_dim]) + obs[which(outrows)] <- NA + rm(obs_sub, outrows) + } + + if (!is.null(memb_dim)) { + if (!memb) { #ensemble mean + exp <- MeanDims(exp, memb_dim, na.rm = TRUE) + obs <- MeanDims(obs, memb_dim, na.rm = TRUE) +# name_exp <- names(dim(exp)) +# margin_dims_ind <- c(1:length(name_exp))[-which(name_exp == memb_dim)] +# exp <- apply(exp, margin_dims_ind, mean, na.rm = TRUE) #NOTE: remove NAs here +# obs <- apply(obs, margin_dims_ind, mean, na.rm = TRUE) + memb_dim <- NULL + } + } + + res <- Apply(list(exp, obs), + target_dims = list(c(time_dim, dat_dim, memb_dim), + c(time_dim, dat_dim, memb_dim)), + fun = .Corr, + dat_dim = dat_dim, memb_dim = memb_dim, + time_dim = time_dim, method = method, + pval = pval, conf = conf, sign = sign, alpha = alpha, + ncores = ncores) + + return(res) +} + +.Corr <- function(exp, obs, dat_dim = 'dataset', memb_dim = 'member', + time_dim = 'sdate', method = 'pearson', + conf = TRUE, pval = TRUE, sign = FALSE, alpha = 0.05) { + if (is.null(memb_dim)) { + if (is.null(dat_dim)) { + # exp: [sdate] + # obs: [sdate] + nexp <- 1 + nobs <- 1 + CORR <- array(dim = c(nexp = nexp, nobs = nobs)) + if (any(!is.na(exp)) && sum(!is.na(obs)) > 2) { + CORR <- cor(exp, obs, use = "pairwise.complete.obs", method = method) + } + } else { + # exp: [sdate, dat_exp] + # obs: [sdate, dat_obs] + nexp <- as.numeric(dim(exp)[dat_dim]) + nobs <- as.numeric(dim(obs)[dat_dim]) + CORR <- array(dim = c(nexp = nexp, nobs = nobs)) + for (j in 1:nobs) { + for (y in 1:nexp) { + if (any(!is.na(exp[, y])) && sum(!is.na(obs[, j])) > 2) { + CORR[y, j] <- cor(exp[, y], obs[, j], + use = "pairwise.complete.obs", + method = method) + } + } + } +#---------------------------------------- +# Same as above calculation. +#TODO: Compare which is faster. +# CORR <- sapply(1:nobs, function(i) { +# sapply(1:nexp, function (x) { +# if (any(!is.na(exp[, x])) && sum(!is.na(obs[, i])) > 2) { +# cor(exp[, x], obs[, i], +# use = "pairwise.complete.obs", +# method = method) +# } else { +# NA +# } +# }) +# }) +#----------------------------------------- + } + + } else { # memb_dim != NULL + exp_memb <- as.numeric(dim(exp)[memb_dim]) # memb_dim + obs_memb <- as.numeric(dim(obs)[memb_dim]) + + if (is.null(dat_dim)) { + # exp: [sdate, memb_exp] + # obs: [sdate, memb_obs] + nexp <- 1 + nobs <- 1 + CORR <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb)) + + for (j in 1:obs_memb) { + for (y in 1:exp_memb) { + + if (any(!is.na(exp[,y])) && sum(!is.na(obs[, j])) > 2) { + CORR[, , y, j] <- cor(exp[, y], obs[, j], + use = "pairwise.complete.obs", + method = method) + } + + } + } + } else { + # exp: [sdate, dat_exp, memb_exp] + # obs: [sdate, dat_obs, memb_obs] + nexp <- as.numeric(dim(exp)[dat_dim]) + nobs <- as.numeric(dim(obs)[dat_dim]) + + CORR <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb)) + + for (j in 1:obs_memb) { + for (y in 1:exp_memb) { + CORR[, , y, j] <- sapply(1:nobs, function(i) { + sapply(1:nexp, function (x) { + if (any(!is.na(exp[, x, y])) && sum(!is.na(obs[, i, j])) > 2) { + cor(exp[, x, y], obs[, i, j], + use = "pairwise.complete.obs", + method = method) + } else { + NA + } + }) + }) + + } + } + } + + } + + +# if (pval) { +# for (i in 1:nobs) { +# p.val[, i] <- try(sapply(1:nexp, +# function(x) {(cor.test(exp[, x], obs[, i], +# use = "pairwise.complete.obs", +# method = method)$p.value)/2}), silent = TRUE) +# if (class(p.val[, i]) == 'character') { +# p.val[, i] <- NA +# } +# } +# } + + if (pval || conf || sign) { + if (method == "kendall" | method == "spearman") { + if (!is.null(dat_dim) | !is.null(memb_dim)) { + tmp <- apply(obs, c(1:length(dim(obs)))[-1], rank) # for memb_dim = NULL, 2; for memb_dim, c(2, 3) + names(dim(tmp))[1] <- time_dim + eno <- Eno(tmp, time_dim) + } else { + tmp <- rank(obs) + tmp <- array(tmp) + names(dim(tmp)) <- time_dim + eno <- Eno(tmp, time_dim) + } + } else if (method == "pearson") { + eno <- Eno(obs, time_dim) + } + + if (is.null(memb_dim)) { + eno_expand <- array(dim = c(nexp = nexp, nobs = nobs)) + for (i in 1:nexp) { + eno_expand[i, ] <- eno + } + } else { #member + eno_expand <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb)) + for (i in 1:nexp) { + for (j in 1:exp_memb) { + eno_expand[i, , j, ] <- eno + } + } + } + + } + +#############old################# +#This doesn't return error but it's diff from cor.test() when method is spearman and kendall + if (pval || sign) { + t <- sqrt(CORR * CORR * (eno_expand - 2) / (1 - (CORR ^ 2))) + p.val <- pt(t, eno_expand - 2, lower.tail = FALSE) + if (sign) signif <- !is.na(p.val) & p.val <= alpha + } +################################### + if (conf) { + conf.lower <- alpha / 2 + conf.upper <- 1 - conf.lower + suppressWarnings({ + conflow <- tanh(atanh(CORR) + qnorm(conf.lower) / sqrt(eno_expand - 3)) + confhigh <- tanh(atanh(CORR) + qnorm(conf.upper) / sqrt(eno_expand - 3)) + }) + } + +################################### + # Remove nexp and nobs if dat_dim = NULL + if (is.null(dat_dim) & !is.null(memb_dim)) { + dim(CORR) <- dim(CORR)[3:length(dim(CORR))] + if (pval) { + dim(p.val) <- dim(p.val)[3:length(dim(p.val))] + } + if (conf) { + dim(conflow) <- dim(conflow)[3:length(dim(conflow))] + dim(confhigh) <- dim(confhigh)[3:length(dim(confhigh))] + } + } + +################################### + + res <- list(corr = CORR) + if (pval) { + res <- c(res, list(p.val = p.val)) + } + if (conf) { + res <- c(res, list(conf.lower = conflow, conf.upper = confhigh)) + } + if (sign) { + res <- c(res, list(sign = signif)) + } + + return(res) + +} -- GitLab From 8a49117a68b40b72e6d36af5bcb6a6dc94f1d81e Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 16 Dec 2022 16:35:36 +0100 Subject: [PATCH 2/3] Update unit tests --- tests/testthat/test-decadal_monthly_3.R | 2 +- tests/testthat/test-seasonal_monthly.R | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R index 102f2f53..9f46a1fc 100644 --- a/tests/testthat/test-decadal_monthly_3.R +++ b/tests/testthat/test-decadal_monthly_3.R @@ -133,7 +133,7 @@ TRUE ) expect_equal( names(skill_metrics), -c("bss10", "bss10_significance", "corr", "corr_p.value", "corr_conf.low", "corr_conf.up") +c("bss10", "bss10_significance", "corr", "corr_significance") ) expect_equal( class(skill_metrics[[1]]), diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R index 8caa6c62..e9792df0 100644 --- a/tests/testthat/test-seasonal_monthly.R +++ b/tests/testthat/test-seasonal_monthly.R @@ -185,8 +185,7 @@ TRUE expect_equal( names(skill_metrics), c("rpss", "rpss_significance", "crpss", "crpss_significance", "enscorr", - "enscorr_p.value", "enscorr_conf.low", "enscorr_conf.up", "corr", - "corr_p.value", "corr_conf.low", "corr_conf.up", "enscorr_specs") + "enscorr_significance", "corr", "corr_significance", "enscorr_specs") ) expect_equal( class(skill_metrics$rpss), -- GitLab From 24ac68353f14619af2972be105c29cc515ef3642 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 21 Dec 2022 09:04:44 +0100 Subject: [PATCH 3/3] Fix decadal pipeline --- tests/testthat/test-decadal_monthly_3.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R index 9f46a1fc..22fd4353 100644 --- a/tests/testthat/test-decadal_monthly_3.R +++ b/tests/testthat/test-decadal_monthly_3.R @@ -144,7 +144,7 @@ all(unlist(lapply(lapply(skill_metrics, dim)[1:2], all.equal, c(time = 3, latitu TRUE ) expect_equal( -all(unlist(lapply(lapply(skill_metrics, dim)[3:6], all.equal, c(ensemble = 3, time = 3, latitude = 25, longitude = 16)))), +all(unlist(lapply(lapply(skill_metrics, dim)[3:4], all.equal, c(ensemble = 3, time = 3, latitude = 25, longitude = 16)))), TRUE ) expect_equal( -- GitLab