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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 65ab5b6fe84871db0fe18244d805cea19a44e830"
| author | ecology |
|---|---|
| date | Sat, 26 Jun 2021 07:20:29 +0000 |
| parents | c12897ba5f83 |
| children |
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#Rscript ################################################################################################################################## ####################### PAMPA Galaxy tools functions : Calculate metrics, compute GLM and plot ################################# ################################################################################################################################## #### Based on Yves Reecht R script #### Modified by Coline ROYAUX for integrating within Galaxy-E ######################################### start of the function fact.def.f called by FunctExeCalcCommIndexesGalaxy.r and FunctExeCalcPresAbsGalaxy.r ####### Define the finest aggregation with the observation table fact_det_f <- function(obs, size_class = "size.class", code_species = "species.code", unitobs = "observation.unit") { if (any(is.element(c(size_class), colnames(obs))) && all(! is.na(obs[, size_class]))) { factors <- c(unitobs, code_species, size_class) }else{ factors <- c(unitobs, code_species) } return(factors) } ######################################### end of the function fact.def.f ######################################### start of the function def_typeobs_f called by FunctExeCalcCommIndexesGalaxy.r and FunctExeCalcPresAbsGalaxy.r ####### Define observation type from colnames def_typeobs_f <- function(obs) { if (any(is.element(c("rotation", "rot", "rotate"), colnames(obs)))) { obs_type <- "SVR" }else{ obs_type <- "other" } return(obs_type) } ######################################### end of the function fact.def.f ######################################### start of the function create_unitobs called by FunctExeCalcCommIndexesGalaxy.r and FunctExeCalcPresAbsGalaxy.r ####### Create unitobs column when inexistant create_unitobs <- function(data, year = "year", location = "location", unitobs = "observation.unit") { if (is.element(paste(unitobs), colnames(data))) { unitab <- data }else{ unitab <- tidyr::unite(data, col = "observation.unit", c(year, location)) } return(unitab) } ######################################### start of the function create_unitobs ######################################### start of the function create_year_location called by FunctExeCalcCommIndexesGalaxy.r and FunctExeCalcPresAbsGalaxy.r ####### separate unitobs column when existant create_year_location <- function(data, year = "year", location = "location", unitobs = "observation.unit") { if (all(grepl("[1-2][0|8|9][0-9]{2}_.*", data[, unitobs])) == TRUE) { tab <- tidyr::separate(data, col = unitobs, into = c(year, location), sep = "_") }else{ if (all(grepl("[A-Z]{2}[0-9]{2}.*", data[, unitobs]) == TRUE)) { tab <- tidyr::separate(data, col = unitobs, into = c("site1", year, "obs"), sep = c(2, 4)) tab <- tidyr::unite(tab, col = location, c("site1", "obs")) }else{ tab <- data } } tab <- cbind(tab, observation.unit = data[, unitobs]) return(tab) } ######################################### start of the function create_year_location ######################################### start of the function check_file called by every Galaxy Rscripts check_file <- function(dataset, err_msg, vars, nb_vars) { ## Purpose: General function to check integrity of input file. Will ## check numbers and contents of variables(colnames). ## return an error message and exit if mismatch detected ## ---------------------------------------------------------------------- ## Arguments: dataset : dataset name ## err_msg : output error ## vars : expected name of variables ## nb_vars : expected number of variables ## ---------------------------------------------------------------------- ## Author: Alan Amosse, Benjamin Yguel if (ncol(dataset) < nb_vars) { #checking for right number of columns in the file if not = error message cat("\nerr nb var\n") stop(err_msg, call. = FALSE) } for (i in vars) { if (!(i %in% names(dataset))) { #checking colnames stop(err_msg, call. = FALSE) } } } ######################################### end of the function check_file ######################################### start of the function stat_rotations_nb_f called by calc_numbers_f stat_rotations_nb_f <- function(factors, obs) { ## Purpose: Computing abundance statistics by rotation (max, sd) ## on SVR data ## ---------------------------------------------------------------------- ## Arguments: factors : Names of aggregation factors ## obs : observation data ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 29 oct. 2012, 16:01 modified by Coline ROYAUX 04 june 2020 ## Identification of valid rotations : if (is.element("observation.unit", factors)) { ## valid rotations (empty must be there as well) : rotations <- tapply(obs$rotation, as.list(obs[, c("observation.unit", "rotation"), drop = FALSE]), function(x)length(x) > 0) ## Changing NA rotations in FALSE : rotations[is.na(rotations)] <- FALSE } ## ########################################################### ## Abundance per rotation at chosen aggregation factors : nombres_rot <- tapply(obs$number, as.list(obs[, c(factors, "rotation"), drop = FALSE]), function(x, ...) { ifelse(all(is.na(x)), NA, sum(x, ...)) }, na.rm = TRUE) ## If valid rotation NA are considered 0 : nombres_rot <- sweep(nombres_rot, match(names(dimnames(rotations)), names(dimnames(nombres_rot)), nomatch = NULL), rotations, # Tableau des secteurs valides (booléens). function(x, y) { x[is.na(x) & y] <- 0 # Lorsque NA et secteur valide => 0. return(x) }) ## ################################################## ## Statistics : ## Means : nb_mean <- apply(nombres_rot, which(is.element(names(dimnames(nombres_rot)), factors)), function(x, ...) { ifelse(all(is.na(x)), NA, mean(x, ...)) }, na.rm = TRUE) ## Maxima : nb_max <- apply(nombres_rot, which(is.element(names(dimnames(nombres_rot)), factors)), function(x, ...) { ifelse(all(is.na(x)), NA, max(x, ...)) }, na.rm = TRUE) ## SD : nb_sd <- apply(nombres_rot, which(is.element(names(dimnames(nombres_rot)), factors)), function(x, ...) { ifelse(all(is.na(x)), NA, sd(x, ...)) }, na.rm = TRUE) ## Valid rotations count : nombres_rotations <- apply(rotations, 1, sum, na.rm = TRUE) ## Results returned as list : return(list(nb_mean = nb_mean, nb_max = nb_max, nb_sd = nb_sd, nombres_rotations = nombres_rotations, nombresTot = nombres_rot)) } ######################################### end of the function stat_rotations_nb_f ######################################### start of the function calc_nb_default_f called by calc_numbers_f calc_nb_default_f <- function(obs, factors = c("observation.unit", "species.code", "size.class"), nb_name = "number") { ## Purpose : Compute abundances at finest aggregation ## --------------------------------------------------------------------- ## Arguments: obs : observation table ## factors : aggregation factors ## nb_name : name of abundance column. ## ## Output: array with ndimensions = nfactors. ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 19 déc. 2011, 13:38 modified by Coline ROYAUX 04 june 2020 ## Sum individuals number : nbr <- tapply(obs[, nb_name], as.list(obs[, factors]), sum, na.rm = TRUE) ## Absences as "true zero" : nbr[is.na(nbr)] <- 0 return(nbr) } ######################################### end of the function calc_nb_default_f ######################################### start of the function calc_numbers_f calc_numbers_f <- function(obs, obs_type = "", factors = c("observation.unit", "species.code", "size.class"), nb_name = "number") { ## Purpose: Produce data.frame used as table from output of calc_nb_default_f(). ## ---------------------------------------------------------------------- ## Arguments: obs : observation table ## obs_type : Type of observation (SVR, LIT, ...) ## factors : aggregation factors ## nb_name : name of abundance column ## ## Output: data.frame with (N aggregation factors + 1) columns ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 19 déc. 2011, 13:46 modified by Coline ROYAUX 04 june 2020 if (obs_type == "SVR") { ## Compute SVR abundances statistics : stat_rotations <- stat_rotations_nb_f(factors = factors, obs = obs) ## Mean for rotating videos (3 rotations at most times) : nbr <- stat_rotations[["nb_mean"]] }else{ nbr <- calc_nb_default_f(obs, factors, nb_name) } res <- as.data.frame(as.table(nbr), responseName = nb_name) if (is.element("size.class", colnames(res))) { res$size.class[res$size.class == ""] <- NA } ## If integer abundances : if (isTRUE(all.equal(res[, nb_name], as.integer(res[, nb_name])))) { res[, nb_name] <- as.integer(res[, nb_name]) } if (obs_type == "SVR") { ## statistics on abundances : res[, "number.max"] <- as.vector(stat_rotations[["nb_max"]]) res[, "number.sd"] <- as.vector(stat_rotations[["nb_sd"]]) } return(res) } ######################################### end of the function calc_numbers_f ######################################### start of the function pres_abs_f called by calc_biodiv_f pres_abs_f <- function(nombres, logical = FALSE) { ## Purpose: Compute presence absence from abundances ## ---------------------------------------------------------------------- ## Arguments: nombres : vector of individuals count. ## logical : (boolean) results as boolean or 0/1 ? ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 29 oct. 2010, 10:20 modified by Coline ROYAUX 04 june 2020 if (any(nombres < 0, na.rm = TRUE)) { stop("Negative abundances!") } if (logical) { return(nombres > 0) }else{ nombres[nombres > 0] <- 1 return(nombres) } } ######################################### end of the function pres_abs_f ######################################### start of the function bettercbind called by agregations_generic_f bettercbind <- function(..., df_list = NULL, deparse.level = 1) { ## Purpose: Apply cbind to data frame with mathcing columns but without ## redundancies. ## ---------------------------------------------------------------------- ## Arguments: same as cbind... ## df_list : data.frames list ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 17 janv. 2012, 21:10 modified by Coline ROYAUX 04 june 2020 if (is.null(df_list)) { df_list <- list(...) } return(do.call(cbind, c(list(df_list[[1]][, c(tail(colnames(df_list[[1]]), -1), head(colnames(df_list[[1]]), 1))]), lapply(df_list[- 1], function(x, coldel) { return(x[, !is.element(colnames(x), coldel), drop = FALSE]) }, coldel = colnames(df_list[[1]])), deparse.level = deparse.level))) } ######################################### end of the function bettercbind ######################################### start of the function agregation_f called by agregations_generic_f agregation_f <- function(metric, d_ata, factors, cas_metric, nb_name = "number") { ## Purpose: metric aggregation ## ---------------------------------------------------------------------- ## Arguments: metric: colnames of chosen metric ## d_ata: Unaggregated data table ## factors: aggregation factors vector ## cas_metric: named vector of observation types depending ## on chosen metric ## nb_name : abundance column name ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 20 déc. 2011, 14:29 modified by Coline ROYAUX 04 june 2020 switch(cas_metric[metric], "sum" = { res <- tapply(d_ata[, metric], as.list(d_ata[, factors, drop = FALSE]), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) }, "w.mean" = { res <- tapply(seq_len(nrow(d_ata)), as.list(d_ata[, factors, drop = FALSE]), function(ii) { ifelse(all(is.na(d_ata[ii, metric])), NA, weighted.mean(d_ata[ii, metric], d_ata[ii, nb_name], na.rm = TRUE)) }) }, "w.mean.colonies" = { res <- tapply(seq_len(nrow(d_ata)), as.list(d_ata[, factors, drop = FALSE]), function(ii) { ifelse(all(is.na(d_ata[ii, metric])), NA, weighted.mean(d_ata[ii, metric], d_ata[ii, "colonies"], na.rm = TRUE)) }) }, "w.mean.prop" = { res <- tapply(seq_len(nrow(d_ata)), as.list(d_ata[, factors, drop = FALSE]), function(ii) { ifelse(all(is.na(d_ata[ii, metric])) || sum(d_ata[ii, "nombre.tot"], na.rm = TRUE) == 0, NA, ifelse(all(na.omit(d_ata[ii, metric]) == 0), 0, (sum(d_ata[ii, nb_name][!is.na(d_ata[ii, metric])], na.rm = TRUE) / sum(d_ata[ii, "nombre.tot"], na.rm = TRUE)) * ## Correction if size class isn't an aggregation factor ## (otherwise value divided by number of present classes) : ifelse(is.element("size.class", factors), 100, 100 * length(unique(d_ata$size.class))))) }) }, "w.mean.prop.bio" = { res <- tapply(seq_len(nrow(d_ata)), as.list(d_ata[, factors, drop = FALSE]), function(ii) { ifelse(all(is.na(d_ata[ii, metric])) || sum(d_ata[ii, "tot.biomass"], na.rm = TRUE) == 0, NA, ifelse(all(na.omit(d_ata[ii, metric]) == 0), 0, (sum(d_ata[ii, "biomass"][!is.na(d_ata[ii, metric])], na.rm = TRUE) / sum(d_ata[ii, "tot.biomass"], na.rm = TRUE)) * ## Correction if size class isn't an aggregation factor ## (otherwise value divided by number of present classes) : ifelse(is.element("size.class", factors), 100, 100 * length(unique(d_ata$size.class))))) }) }, "pres" = { res <- tapply(d_ata[, metric], as.list(d_ata[, factors, drop = FALSE]), function(x) { ifelse(all(is.na(x)), # When only NAs. NA, ifelse(any(x > 0, na.rm = TRUE), # Otherwise... 1, # ... presence if at least one observation in the group. 0)) }) }, "nbMax" = { ## Sum by factor cross / rotation : nb_tmp2 <- apply(nb_tmp, which(is.element(names(dimnames(nb_tmp)), c(factors, "rotation"))), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) ## Sum by factor cross : res <- as.array(apply(nb_tmp2, which(is.element(names(dimnames(nb_tmp)), factors)), function(x) { ifelse(all(is.na(x)), NA, max(x, na.rm = TRUE)) })) }, "nbSD" = { ## Sum by factor cross / rotation : nb_tmp2 <- apply(nb_tmp, which(is.element(names(dimnames(nb_tmp)), c(factors, "rotation"))), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) ## Sum by factor cross : res <- as.array(apply(nb_tmp2, which(is.element(names(dimnames(nb_tmp)), factors)), function(x) { ifelse(all(is.na(x)), NA, sd(x, na.rm = TRUE)) })) }, "densMax" = { ## Sum by factor cross / rotation : dens_tmp2 <- apply(dens_tmp, which(is.element(names(dimnames(dens_tmp)), c(factors, "rotation"))), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) ## Sum by factor cross : res <- as.array(apply(dens_tmp2, which(is.element(names(dimnames(dens_tmp)), factors)), function(x) { ifelse(all(is.na(x)), NA, max(x, na.rm = TRUE)) })) }, "densSD" = { ## Sum by factor cross / rotation : dens_tmp2 <- apply(dens_tmp, which(is.element(names(dimnames(dens_tmp)), c(factors, "rotation"))), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) ## Sum by factor cross : res <- as.array(apply(dens_tmp2, which(is.element(names(dimnames(dens_tmp)), factors)), function(x) { ifelse(all(is.na(x)), NA, sd(x, na.rm = TRUE)) })) }, "%.nesting" = { res <- tapply(seq_len(nrow(d_ata)), as.list(d_ata[, factors, drop = FALSE]), function(ii) { ifelse(all(is.na(d_ata[ii, metric])), NA, weighted.mean(d_ata[ii, metric], d_ata[ii, "readable.tracks"], na.rm = TRUE)) }) }, stop("Not implemented!") ) ## dimension names names(dimnames(res)) <- c(factors) ## Transformation to long format : reslong <- as.data.frame(as.table(res), responseName = metric) reslong <- reslong[, c(tail(colnames(reslong), 1), head(colnames(reslong), -1))] # metric first return(reslong) } ######################################### end of the function agregation_f ######################################### start of the function agregations_generic_f called y calc_biodiv_f in FucntExeCalcCommIndexesGalaxy.r agregations_generic_f <- function(d_ata, metrics, factors, list_fact = NULL, unit_sp_sz = NULL, unit_sp = NULL, nb_name = "number") { ## Purpose: Aggregate data ## ---------------------------------------------------------------------- ## Arguments: d_ata : data set ## metrics : aggregated metric ## factors : aggregation factors ## list_fact : other factors to aggregate and add to output ## unit_sp_sz : Metrics table by unitobs/species/Size Class ## unit_sp : Metrics table by unitobs/species ## nb_name : abundance colname ## ## Output : aggregated data frame ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 18 oct. 2010, 15:47 modified by Coline ROYAUX 04 june 2020 ## trt depending on metric type : cas_metric <- c("number" = "sum", "mean.length" = "w.mean", "taille_moy" = "w.mean", "biomass" = "sum", "Biomass" = "sum", "weight" = "sum", "mean.weight" = "w.mean", "density" = "sum", "Density" = "sum", "CPUE" = "sum", "CPUE.biomass" = "sum", "presence_absence" = "pres", "abundance.prop.SC" = "w.mean.prop", # Not OK [!!!] ? "biomass.prop.SC" = "w.mean.prop.bio", # Not OK [!!!] ? ## Benthos : "colonies" = "sum", "coverage" = "sum", "mean.size.colonies" = "w.mean.colonies", ## SVR (expérimental) : "number.max" = "nbMax", "number.sd" = "nbSD", "density.max" = "densMax", "density.sd" = "densSD", "biomass.max" = "sum", "spawning.success" = "%.nesting", "spawnings" = "sum", "readable.tracks" = "sum", "tracks.number" = "sum") ## add "readable.tracks" for egg laying percentage : if (any(cas_metric[metrics] == "%.nesting")) { if (is.element("size.class", colnames(d_ata))) { if (is.null(unit_sp_sz)) stop("unit_sp_sz doit être défini") d_ata <- merge(d_ata, unit_sp_sz[, c("species.code", "observation.unit", "size.class", "readable.tracks")], by = c("species.code", "observation.unit", "size.class"), suffixes = c("", ".y")) }else{ if (is.null(unit_sp)) stop("unit_sp must be defined") d_ata <- merge(d_ata, unit_sp[, c("species.code", "observation.unit", "readable.tracks")], by = c("species.code", "observation.unit"), suffixes = c("", ".y")) } } ## Add "number" field for computing ponderate means if absent : if (any(cas_metric[metrics] == "w.mean" | cas_metric[metrics] == "w.mean.prop")) { if (is.element("size.class", colnames(d_ata))) { if (is.null(unit_sp_sz)) stop("unit_sp_sz must be defined") d_ata <- merge(d_ata, unit_sp_sz[, c("species.code", "observation.unit", "size.class", nb_name)], by = c("species.code", "observation.unit", "size.class"), suffixes = c("", ".y")) ## add tot abundance / species / observation unit : nb_tot <- tapply(unit_sp_sz[, nb_name], as.list(unit_sp_sz[, c("species.code", "observation.unit")]), sum, na.rm = TRUE) d_ata <- merge(d_ata, as.data.frame(as.table(nb_tot), responseName = "nombre.tot")) }else{ if (is.null(unit_sp)) stop("unit_sp must be defined") d_ata <- merge(d_ata, unit_sp[, c("species.code", "observation.unit", nb_name)], # [!!!] unit_sp_sz ? by = c("species.code", "observation.unit"), suffixes = c("", ".y")) } } ## Add biomass field of biomass proportion by size class : if (any(cas_metric[metrics] == "w.mean.prop.bio")) { if (is.null(unit_sp_sz)) stop("unit_sp_sz doit être défini") d_ata <- merge(d_ata, unit_sp_sz[, c("species.code", "observation.unit", "size.class", "biomass")], by = c("species.code", "observation.unit", "size.class"), suffixes = c("", ".y")) ## add tot biomass / species / observation unit : biom_tot <- tapply(unit_sp_sz$biomass, as.list(unit_sp_sz[, c("species.code", "observation.unit")]), function(x) { ifelse(all(is.na(x)), NA, sum(x, na.rm = TRUE)) }) d_ata <- merge(d_ata, as.data.frame(as.table(biom_tot), responseName = "tot.biomass")) } ## add colony field for ponderate means pondérées if absent : if (any(cas_metric[metrics] == "w.mean.colonies" & ! is.element("colonies", colnames(d_ata)))) { d_ata$colonies <- unit_sp[match(apply(d_ata[, c("species.code", "observation.unit")], 1, paste, collapse = "*"), apply(unit_sp[, c("species.code", "observation.unit")], 1, paste, collapse = "*")), "colonies"] } ## Aggregation of metric depending on factors : reslong <- bettercbind(df_list = lapply(metrics, # sapply used to have names agregation_f, d_ata = d_ata, factors = factors, cas_metric = cas_metric, nb_name = nb_name)) ## Aggregation and add other factors : if (! (is.null(list_fact) || length(list_fact) == 0)) { reslong <- cbind(reslong, sapply(d_ata[, list_fact, drop = FALSE], function(fact) { tapply(fact, as.list(d_ata[, factors, drop = FALSE]), function(x) { if (length(x) > 1 && length(unique(x)) > 1) { # must be one modality return(NULL) # otherwise it is NULL }else{ unique(as.character(x)) } }) })) } ## If some factors aren't at the right class : if (any(tmp <- sapply(reslong[, list_fact, drop = FALSE], class) != sapply(d_ata[, list_fact, drop = FALSE], class))) { for (i in which(tmp)) { switch(sapply(d_ata[, list_fact, drop = FALSE], class)[i], "integer" = { reslong[, list_fact[i]] <- as.integer(as.character(reslong[, list_fact[i]])) }, "numeric" = { reslong[, list_fact[i]] <- as.numeric(as.character(reslong[, list_fact[i]])) }, reslong[, list_fact[i]] <- eval(call(paste("as", sapply(d_ata[, list_fact, drop = FALSE], class)[i], sep = "."), reslong[, list_fact[i]])) ) } } ## Initial order of factors levels : reslong <- as.data.frame(sapply(colnames(reslong), function(x) { if (is.factor(reslong[, x])) { return(factor(reslong[, x], levels = levels(d_ata[, x]))) }else{ return(reslong[, x]) } }, simplify = FALSE)) ## Check of other aggregated factors supplémentaires. There must be no NULL elements : if (any(sapply(reslong[, list_fact], function(x) { any(is.null(unlist(x))) }))) { warning(paste("One of the suppl. factors is probably a subset", " of the observations grouping factor(s).", sep = "")) return(NULL) }else{ return(reslong) } } ######################################### end of the function agregations_generic_f ######################################### start of the function drop_levels_f called y calc_biodiv_f in FucntExeCalcCommIndexesGalaxy.r and glm_community in FunctExeCalcGLMGalaxy.r drop_levels_f <- function(df, which = NULL) { ## Purpose: Suppress unused levels of factors ## ---------------------------------------------------------------------- ## Arguments: df : a data.frame ## which : included columns index (all by default) ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 10 août 2010, 13:29 modified by Coline ROYAUX 04 june 2020 if (class(df) != "data.frame") { stop("'df' must be a data.frame") }else{ if (is.null(which)) { x <- as.data.frame(sapply(df, function(x) { return(x[, drop = TRUE]) }, simplify = FALSE), stringsAsFactors = FALSE) }else{ # Only some columns used x <- df x[, which] <- as.data.frame(sapply(df[, which, drop = FALSE], function(x) { return(x[, drop = TRUE]) }, simplify = FALSE), stringsAsFactors = FALSE) } return(x) } } ######################################### end of the function drop_levels_f ######################################### start of the function subset_all_tables_f called by glm_community in FunctExeCalcGLMGalaxy.r subset_all_tables_f <- function(metrique, tab_metrics, facteurs, selections, tab_unitobs, refesp, tab_metrique = "", nb_name = "number", obs_type = "", exclude = NULL, add = c("species.code", "observation.unit")) { ## Purpose: Extract useful data only from chosen metrics and factors ## ---------------------------------------------------------------------- ## Arguments: metrique : chosen metric ## facteurs : all chosen factors ## selections : corresponding modality selected ## tab_metrique : metrics table name ## exclude : factors levels to exclude ## add : field to add to data table ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 6 août 2010, 16:46 modified by Coline ROYAUX 04 june 2020 ## If no metrics table available : if (is.element(tab_metrique, c("", "TableOccurrences", "TablePresAbs"))) { tab_metrique <- "unit_sp" } cas_tables <- c("unit_sp" = "unit_sp", "TablePresAbs" = "unit_sp", "unit_sp_sz" = "unit_sp_sz") ## Recuperation of metrics table : data_metric <- tab_metrics unitobs <- tab_unitobs refesp <- refesp ## If no metrics available or already computed : if (is.element(metrique, c("", "occurrence.frequency"))) { metrique <- "tmp" data_metric$tmp <- 0 data_metric$tmp[data_metric[, nb_name] > 0] <- 1 } if (!is.null(add)) { metriques <- c(metrique, add[is.element(add, colnames(data_metric))]) }else{ metriques <- metrique } ## Subset depending on metrics table switch(cas_tables[tab_metrique], ## Observation table by unitobs and species : unit_sp = { restmp <- cbind(data_metric[!is.na(data_metric[, metrique]), metriques, drop = FALSE], unitobs[match(data_metric$observation.unit[!is.na(data_metric[, metrique])], unitobs$observation.unit), # ajout des colonnes sélectionnées d'unitobs facteurs[is.element(facteurs, colnames(unitobs))], drop = FALSE], refesp[match(data_metric$species.code[!is.na(data_metric[, metrique])], refesp$species.code), # ajout des colonnes sélectionnées d'especes facteurs[is.element(facteurs, colnames(refesp))], drop = FALSE]) }, ## Observation table by unitobs, species and size class : unit_sp_sz = { restmp <- cbind(data_metric[!is.na(data_metric[, metrique]), c(metriques, "size.class"), drop = FALSE], unitobs[match(data_metric$observation.unit[!is.na(data_metric[, metrique])], unitobs$observation.unit), # ajout des colonnes sélectionnées d'unitobs facteurs[is.element(facteurs, colnames(unitobs))], drop = FALSE], refesp[match(data_metric$species.code[!is.na(data_metric[, metrique])], refesp$species.code), # ajout des colonnes sélectionnées d'especes facteurs[is.element(facteurs, colnames(refesp))], drop = FALSE]) }, ## Other cases : restmp <- cbind(data_metric[!is.na(data_metric[, metrique]), metriques, drop = FALSE], unitobs[match(data_metric$observation.unit[!is.na(data_metric[, metrique])], unitobs$observation.unit), # ajout des colonnes sélectionnées d'unitobs. facteurs[is.element(facteurs, colnames(unitobs))], drop = FALSE]) ) sel_col <- which(!is.na(selections)) if (!is.null(exclude)) { sel_col <- sel_col[sel_col != exclude] } ## Particular case of size classes : if (is.element("size.class", colnames(restmp))) { if (length(grep("^[[:digit:]]*[-_][[:digit:]]*$", unique(as.character(restmp$size.class)), perl = TRUE)) == length(unique(as.character(restmp$size.class)))) { restmp[, "size.class"] <- factor(as.character(restmp$size.class), levels = unique(as.character(restmp$size.class))[ order(as.numeric(sub("^([[:digit:]]*)[-_][[:digit:]]*$", "\\1", unique(as.character(restmp$size.class)), perl = TRUE)), na.last = FALSE)]) }else{ restmp[, "size.class"] <- factor(restmp$size.class) } } ## Biomass and density conversion -> /100m² : if (any(is.element(colnames(restmp), c("biomass", "density", "biomass.max", "density.max", "biomass.sd", "density.sd"))) && obs_type != "fishing") { restmp[, is.element(colnames(restmp), c("biomass", "density", "biomass.max", "density.max", "biomass.sd", "density.sd"))] <- 100 * restmp[, is.element(colnames(restmp), c("biomass", "density", "biomass.max", "density.max", "biomass.sd", "density.sd"))] } return(restmp) } ######################################### end of the function subset_all_tables_f ######################################### start of the function organise_fact called by modeleLineaireWP2.xxx.f in FunctExeCalcGLMxxGalaxy.r organise_fact <- function(list_rand, list_fact) { ## Purpose: organise response factors ## ---------------------------------------------------------------------- ## Arguments: list_rand : Analysis random factors list ## list_fact : Analysis factors list ## ---------------------------------------------------------------------- ## Author: Coline ROYAUX 14 november 2020 if (list_rand[1] != "None") { if (all(is.element(list_fact, list_rand)) || list_fact[1] == "None") { resp_fact <- paste("(1|", paste(list_rand, collapse = ") + (1|"), ")") list_f <- NULL list_fact <- list_rand }else{ list_f <- list_fact[!is.element(list_fact, list_rand)] resp_fact <- paste(paste(list_f, collapse = " + "), " + (1|", paste(list_rand, collapse = ") + (1|"), ")") list_fact <- c(list_f, list_rand) } }else{ list_f <- list_fact resp_fact <- paste(list_fact, collapse = " + ") } return(list(resp_fact, list_f, list_fact)) } ######################################### end of the function organise_fact ######################################### start of the function organise_fact called by modeleLineaireWP2.xxx.f in FunctExeCalcGLMxxGalaxy.r distrib_choice <- function(distrib = distrib, metrique = metrique, data = tmpd_ata) { ## Purpose: choose the right distribution ## ---------------------------------------------------------------------- ## Arguments: data : data used for analysis ## metrique : Chosen metric ## distrib : distribution law selected by user ## ---------------------------------------------------------------------- ## Author: Coline ROYAUX 14 november 2020 if (distrib == "None") { if (metrique == "presence_absence") { chose_distrib <- "binomial" }else{ switch(class(data[, metrique]), "integer" = { chose_distrib <- "poisson" }, "numeric" = { chose_distrib <- "gaussian" }, stop("Selected metric class doesn't fit, you should select an integer or a numeric variable")) } }else{ chose_distrib <- distrib } return(chose_distrib) } ######################################### end of the function organise_fact ######################################### start of the function create_res_table called by modeleLineaireWP2.xxx.f in FunctExeCalcGLMxxGalaxy.r create_res_table <- function(list_rand, list_fact, row, lev, distrib) { ## Purpose: create results table ## ---------------------------------------------------------------------- ## Arguments: list_rand : Analysis random factors list ## list_fact : Analysis factors list ## row : rows of results table = species or separation factor ## lev : Levels of analysis factors list ## distrib : distribution law ## ---------------------------------------------------------------------- ## Author: Coline ROYAUX 04 october 2020 if (list_rand[1] != "None") { ## if random effects tab_sum <- data.frame(analysis = row, Interest.var = NA, distribution = NA, AIC = NA, BIC = NA, logLik = NA, deviance = NA, df.resid = NA) colrand <- unlist(lapply(list_rand, FUN = function(x) { lapply(c("Std.Dev", "NbObservation", "NbLevels"), FUN = function(y) { paste(x, y, collapse = ":") }) })) tab_sum[, colrand] <- NA if (! is.null(lev)) { ## if fixed effects + random effects colcoef <- unlist(lapply(c("(Intercept)", lev), FUN = function(x) { lapply(c("Estimate", "Std.Err", "Zvalue", "Pvalue", "IC_up", "IC_inf", "signif"), FUN = function(y) { paste(x, y, collapse = ":") }) })) }else{ ## if no fixed effects colcoef <- NULL } }else{ ## if no random effects tab_sum <- data.frame(analysis = row, Interest.var = NA, distribution = NA, AIC = NA, Resid.deviance = NA, df.resid = NA, Null.deviance = NA, df.null = NA) switch(distrib, "gaussian" = { colcoef <- unlist(lapply(c("(Intercept)", lev), FUN = function(x) { lapply(c("Estimate", "Std.Err", "Tvalue", "Pvalue", "IC_up", "IC_inf", "signif"), FUN = function(y) { paste(x, y, collapse = ":") }) })) }, "quasipoisson" = { colcoef <- unlist(lapply(c("(Intercept)", lev), FUN = function(x) { lapply(c("Estimate", "Std.Err", "Tvalue", "Pvalue", "IC_up", "IC_inf", "signif"), FUN = function(y) { paste(x, y, collapse = ":") }) })) } , { colcoef <- unlist(lapply(c("(Intercept)", lev), FUN = function(x) { lapply(c("Estimate", "Std.Err", "Zvalue", "Pvalue", "IC_up", "IC_inf", "signif"), FUN = function(y) { paste(x, y, collapse = ":") }) })) }) } tab_sum[, colcoef] <- NA return(tab_sum) } ######################################### end of the function create_res_table ######################################### start of the function sorties_lm_f called by glm_community in FunctExeCalcGLMGalaxy.r sorties_lm_f <- function(obj_lm, obj_lmy, tab_sum, #formule, metrique, fact_ana, cut, col_ana, list_fact, list_rand, lev = NULL, d_ata, log = FALSE, sufixe = NULL) { ## Purpose: Form GLM and LM results ## ---------------------------------------------------------------------- ## Arguments: obj_lm : lm object ## obj_lmy : lm object with year as continuous ## tab_sum : output summary table ## formule : LM formula ## metrique : Chosen metric ## fact_ana : separation factor ## cut : level of separation factor ## col_ana : colname for separation factor in output summary table ## list_fact : Analysis factors list ## list_rand : Analysis random factors list ## levels : Levels of analysis factors list ## d_ata : d_ata used for analysis ## log : put log on metric ? (boolean) ## sufixe : sufix for file name ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 25 août 2010, 16:19 modified by Coline ROYAUX 04 june 2020 tab_sum[, "Interest.var"] <- as.character(metrique) sum_lm <- summary(obj_lm) tab_sum[, "distribution"] <- as.character(sum_lm$family[1]) if (length(grep("^glmmTMB", obj_lm$call)) > 0) { #if random effects tab_sum[tab_sum[, col_ana] == cut, "AIC"] <- sum_lm$AICtab[1] tab_sum[tab_sum[, col_ana] == cut, "BIC"] <- sum_lm$AICtab[2] tab_sum[tab_sum[, col_ana] == cut, "logLik"] <- sum_lm$AICtab[3] tab_sum[tab_sum[, col_ana] == cut, "deviance"] <- sum_lm$AICtab[4] tab_sum[tab_sum[, col_ana] == cut, "df.resid"] <- sum_lm$AICtab[5] if (! is.null(lev)) { ## if fixed effects + random effects tab_coef <- as.data.frame(sum_lm$coefficients$cond) tab_coef$signif <- lapply(tab_coef[, "Pr(>|z|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Zvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "z value"] tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Pvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "Pr(>|z|)"] tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Zvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "z value"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Pvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "Pr(>|z|)"] }else{ NA } })) if (any(obj_lmy != "")) { sum_lmy <- summary(obj_lmy) tab_coefy <- as.data.frame(sum_lmy$coefficients$cond) tab_coefy$signif <- lapply(tab_coefy[, "Pr(>|z|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, "year Zvalue"] <- ifelse(length(tab_coefy["year", "z value"]) > 0, tab_coefy["year", "z value"], NA) tab_sum[tab_sum[, col_ana] == cut, "year Pvalue"] <- ifelse(length(tab_coefy["year", "Pr(>|z|)"]) > 0, tab_coefy["year", "Pr(>|z|)"], NA) } } switch(as.character(length(sum_lm$varcor$cond)), "1" = { std_d <- c(sum_lm$varcor$cond[[1]]) }, "2" = { std_d <- c(sum_lm$varcor$cond[[1]], sum_lm$varcor$cond[[2]]) }, std_d <- NULL) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(list_rand, "Std.Dev", collapse = "|"), colnames(tab_sum))] <- std_d tab_sum[tab_sum[, col_ana] == cut, grepl(paste(list_rand, "NbObservation", collapse = "|"), colnames(tab_sum))] <- sum_lm$nobs tab_sum[tab_sum[, col_ana] == cut, grepl(paste(list_rand, "NbLevels", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(list_rand, FUN = function(x) { nlevels(d_ata[, x]) })) }else{ ## if fixed effects only tab_sum[tab_sum[, col_ana] == cut, "AIC"] <- sum_lm$aic tab_sum[tab_sum[, col_ana] == cut, "Resid.deviance"] <- sum_lm$deviance tab_sum[tab_sum[, col_ana] == cut, "df.resid"] <- sum_lm$df.residual tab_sum[tab_sum[, col_ana] == cut, "Null.deviance"] <- sum_lm$null.deviance tab_sum[tab_sum[, col_ana] == cut, "df.null"] <- sum_lm$df.null tab_coef <- as.data.frame(sum_lm$coefficients) if (any(obj_lmy != "")) { sum_lmy <- summary(obj_lmy) tab_coefy <- as.data.frame(sum_lmy$coefficients) } if (sum_lm$family[1] == "gaussian" || sum_lm$family[1] == "quasipoisson") { tab_coef$signif <- lapply(tab_coef[, "Pr(>|t|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Tvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "t value"] tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Pvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "Pr(>|t|)"] tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Tvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "t value"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Pvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "Pr(>|t|)"] }else{ NA } })) if (any(obj_lmy != "")) { tab_coefy$signif <- lapply(tab_coefy[, "Pr(>|t|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, "year Tvalue"] <- ifelse(length(tab_coefy["year", "t value"]) > 0, tab_coefy["year", "t value"], NA) tab_sum[tab_sum[, col_ana] == cut, "year Pvalue"] <- ifelse(length(tab_coefy["year", "Pr(>|z|)"]) > 0, tab_coefy["year", "Pr(>|t|)"], NA) } }else{ tab_coef$signif <- lapply(tab_coef[, "Pr(>|z|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Zvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "z value"] tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Pvalue", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "Pr(>|z|)"] tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Zvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "z value"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Pvalue", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "Pr(>|z|)"] }else{ NA } })) if (any(obj_lmy != "")) { tab_coefy$signif <- lapply(tab_coefy[, "Pr(>|z|)"], FUN = function(x) { if (!is.na(x) && x < 0.05) { "yes" }else{ "no" } }) tab_sum[tab_sum[, col_ana] == cut, "year Zvalue"] <- ifelse(length(tab_coefy["year", "z value"]) > 0, tab_coefy["year", "z value"], NA) tab_sum[tab_sum[, col_ana] == cut, "year Pvalue"] <- ifelse(length(tab_coefy["year", "Pr(>|z|)"]) > 0, tab_coefy["year", "Pr(>|z|)"], NA) } } } if (! is.null(lev)) { ## if fixed effects tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Estimate", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "Estimate"] tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*Std.Err", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "Std. Error"] tab_sum[tab_sum[, col_ana] == cut, grepl("Intercept.*signif", colnames(tab_sum))] <- tab_coef[grepl("Intercept", rownames(tab_coef)), "signif"] tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Estimate", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "Estimate"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "Std.Err", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "Std. Error"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "signif", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(tab_coef))) > 0) { tab_coef[grepl(x, rownames(tab_coef)), "signif"] }else{ NA } })) if (any(obj_lmy != "")) { tab_sum[tab_sum[, col_ana] == cut, "year Estimate"] <- ifelse(length(tab_coefy["year", "Estimate"]) > 0, tab_coefy["year", "Estimate"], NA) tab_sum[tab_sum[, col_ana] == cut, "year Std.Err"] <- ifelse(length(tab_coefy["year", "Std. Error"]) > 0, tab_coefy["year", "Std. Error"], NA) tab_sum[tab_sum[, col_ana] == cut, "year signif"] <- ifelse(length(tab_coefy["year", "signif"]) > 0, tab_coefy["year", "signif"], NA) } } ic <- tryCatch(as.data.frame(confint(obj_lm)), error = function(e) { }) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "IC_up", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(ic))) > 0) { ic[grepl(x, rownames(ic)), "97.5 %"] }else{ NA } })) tab_sum[tab_sum[, col_ana] == cut, grepl(paste(lev, "IC_inf", collapse = "|"), colnames(tab_sum))] <- unlist(lapply(lev, FUN = function(x) { if (length(grep(x, rownames(ic))) > 0) { ic[grepl(x, rownames(ic)), "2.5 %"] }else{ NA } })) return(tab_sum) } ######################################### end of the function sorties_lm_f ######################################### start of the function note_glm_f called by glm_species and glm_community note_glm_f <- function(data, obj_lm, metric, list_fact, details = FALSE) { ## Purpose: Note your GLM analysis ## ---------------------------------------------------------------------- ## Arguments: data : d_ataframe used for analysis ## obj_lm : GLM assessed ## metric : selected metric ## list_fact : Analysis factors list ## details : detailed output ? ## ---------------------------------------------------------------------- ## Author: Coline ROYAUX, 26 june 2020 rate <- 0 detres <- list(complete_plan = NA, balanced_plan = NA, NA_proportion_OK = NA, no_residual_dispersion = NA, uniform_residuals = NA, outliers_proportion_OK = NA, no_zero_inflation = NA, observation_factor_ratio_OK = NA, enough_levels_random_effect = NA, rate = NA) #### d_ata criterions #### ## Plan plan <- as.data.frame(table(data[, list_fact])) if (nrow(plan[plan$Freq == 0, ]) < nrow(plan) * 0.1) { # +0.5 if less than 10% of possible factor's level combinations aren't represented in the sampling scheme rate <- rate + 0.5 detres$complete_plan <- TRUE if (summary(as.factor(plan$Freq))[1] > nrow(plan) * 0.9) { # +0.5 if the frequency of the most represented frequency of possible factor's levels combinations is superior to 90% of the total number of possible factor's levels combinations rate <- rate + 0.5 detres$balanced_plan <- TRUE } }else{ detres$complete_plan <- FALSE detres$balanced_plan <- FALSE } if (nrow(data) - nrow(na.omit(data)) < nrow(data) * 0.1) { # +1 if less than 10% of the lines in the dataframe bares a NA rate <- rate + 1 detres["NA_proportion_OK"] <- TRUE }else{ detres["NA_proportion_OK"] <- FALSE } #### Model criterions #### if (length(grep("quasi", obj_lm$family)) == 0) { #DHARMa doesn't work with quasi distributions residuals <- DHARMa::simulateResiduals(obj_lm) capture.output(test_res <- DHARMa::testResiduals(residuals)) test_zero <- DHARMa::testZeroInflation(residuals) ## dispersion of residuals if (test_res$dispersion$p.value > 0.05) { # +1.5 if dispersion tests not significative rate <- rate + 1.5 detres$no_residual_dispersion <- TRUE }else{ detres$no_residual_dispersion <- FALSE } ## uniformity of residuals if (test_res$uniformity$p.value > 0.05) { # +1 if uniformity tests not significative rate <- rate + 1 detres$uniform_residuals <- TRUE }else{ detres$uniform_residuals <- FALSE } ## residuals outliers if (test_res$outliers$p.value > 0.05) { # +0.5 if outliers tests not significative rate <- rate + 0.5 detres["outliers_proportion_OK"] <- TRUE }else{ detres["outliers_proportion_OK"] <- FALSE } ## Zero inflation test if (test_zero$p.value > 0.05) { # +1 if zero inflation tests not significative rate <- rate + 1 detres$no_zero_inflation <- TRUE }else{ detres$no_zero_inflation <- FALSE } ## Factors/observations ratio if (length(list_fact) / nrow(na.omit(data)) < 0.1) { # +1 if quantity of factors is less than 10% of the quantity of observations rate <- rate + 1 detres["observation_factor_ratio_OK"] <- TRUE }else{ detres["observation_factor_ratio_OK"] <- FALSE } ## less than 10 factors' level on random effect if (length(grep("^glmmTMB", obj_lm$call)) > 0) { nlev_rand <- c() for (fact in names(summary(obj_lm)$varcor$cond)) { nlev_rand <- c(nlev_rand, length(unlist(unique(data[, fact])))) } if (all(nlev_rand > 10)) { # +1 if more than 10 levels in one random effect rate <- rate + 1 detres$enough_levels_random_effect <- TRUE }else{ detres$enough_levels_random_effect <- FALSE } } detres$rate <- rate if (details) { return(detres) }else{ return(rate) } }else{ return(NA) cat("Models with quasi distributions can't be rated for now") } } ######################################### end of the function note_glm_f ######################################### start of the function note_glms_f called by glm_species and glm_community note_glms_f <- function(tab_rate, expr_lm, obj_lm, file_out = FALSE) { ## Purpose: Note your GLM analysis ## ---------------------------------------------------------------------- ## Arguments: tab_rate : rates table from note_glm_f ## expr_lm : GLM expression assessed ## obj_lm : GLM object ## file_out : Output as file ? else global rate only ## ---------------------------------------------------------------------- ## Author: Coline ROYAUX, 26 june 2020 namefile <- "RatingGLM.txt" if (length(grep("quasi", obj_lm$family)) == 0) { #DHARMa doesn't work with quasi distributions rate_m <- median(na.omit(tab_rate[, "rate"])) sum <- summary(obj_lm) if (length(grep("^glmmTMB", obj_lm$call)) > 0) { if (median(na.omit(tab_rate[, "rate"])) >= 6) { # if 50% has a rate superior or equal to 6 +1 rate_m <- rate_m + 1 } if (quantile(na.omit(tab_rate[, "rate"]), probs = 0.9) >= 6) { # if 90% has a rate superior or equal to 6 +1 rate_m <- rate_m + 1 } }else{ if (median(na.omit(tab_rate[, "rate"])) >= 5) { # if 50% has a rate superior or equal to 5 +1 rate_m <- rate_m + 1 } if (quantile(na.omit(tab_rate[, "rate"]), probs = 0.9) >= 5) { # if 90% has a rate superior or equal to 5 +1 rate_m <- rate_m + 1 } } if (file_out) { cat("###########################################################################", "\n########################### Analysis evaluation ###########################", "\n###########################################################################", file = namefile, fill = 1, append = TRUE) ## Informations on model : cat("\n\n######################################### \nFitted model:", file = namefile, fill = 1, append = TRUE) cat("\t", deparse(expr_lm), "\n\n", file = namefile, sep = "", append = TRUE) cat("Family: ", sum$family[[1]], file = namefile, append = TRUE) cat("\n\nNumber of analysis: ", nrow(tab_rate), file = namefile, append = TRUE) ## Global rate : cat("\n\n######################################### \nGlobal rate for all analysis:", "\n\n", rate_m, "out of 10", file = namefile, append = TRUE) ## details on every GLM : cat("\n\n######################################### \nDetails on every analysis:\n\n", file = namefile, append = TRUE) cat("Analysis\tC1\tC2\tC3\tC4\tC5\tC6\tC7\tC8\tC9\tFinal rate", file = namefile, append = TRUE) apply(tab_rate, 1, FUN = function(x) { if (!is.na(x["complete_plan"]) && x["complete_plan"] == TRUE) { cat("\n", x[1], "\tyes", file = namefile, append = TRUE) }else{ cat("\n", x[1], "\tno", file = namefile, append = TRUE) } for (i in c("balanced_plan", "NA_proportion_OK", "no_residual_dispersion", "uniform_residuals", "outliers_proportion_OK", "no_zero_inflation", "observation_factor_ratio_OK", "enough_levels_random_effect")) { if (!is.na(x[i]) && x[i] == TRUE) { cat("\tyes", file = namefile, append = TRUE) }else{ cat("\tno", file = namefile, append = TRUE) } } cat("\t", x["rate"], "/ 8", file = namefile, append = TRUE) }) cat("\n\nC1: Complete plan?\nC2: Balanced plan?\nC3: Few NA?\nC4: Regular dispersion?\nC5: Uniform residuals?\nC6: Regular outliers proportion?\nC7: No zero-inflation?\nC8: Good observation/factor ratio?\nC9: Enough levels on random effect?", file = namefile, append = TRUE) ## Red flags - advice : cat("\n\n######################################### \nRed flags - advice:\n\n", file = namefile, append = TRUE) if (all(na.omit(tab_rate["NA_proportion_OK"]) == FALSE)) { cat("\n", "\t- More than 10% of lines of your dataset contains NAs", file = namefile, append = TRUE) } if (length(grep("FALSE", tab_rate["no_residual_dispersion"])) / length(na.omit(tab_rate["no_residual_dispersion"])) > 0.5) { cat("\n", "\t- More than 50% of your analyses are over- or under- dispersed : Try with another distribution family", file = namefile, append = TRUE) } if (length(grep("FALSE", tab_rate["uniform_residuals"])) / length(na.omit(tab_rate["uniform_residuals"])) > 0.5) { cat("\n", "\t- More than 50% of your analyses haven't an uniform distribution of residuals : Try with another distribution family", file = namefile, append = TRUE) } if (length(grep("FALSE", tab_rate["outliers_proportion_OK"])) / length(na.omit(tab_rate["outliers_proportion_OK"])) > 0.5) { cat("\n", "\t- More than 50% of your analyses have too much outliers : Try with another distribution family or try to select or filter your data", file = namefile, append = TRUE) } if (length(grep("FALSE", tab_rate["no_zero_inflation"])) / length(na.omit(tab_rate["no_zero_inflation"])) > 0.5) { cat("\n", "\t- More than 50% of your analyses have zero inflation : Try to select or filter your data", file = namefile, append = TRUE) } if (length(grep("FALSE", tab_rate["observation_factor_ratio_OK"])) / length(na.omit(tab_rate["observation_factor_ratio_OK"])) > 0.5) { cat("\n", "\t- More than 50% of your analyses have not enough observations for the amount of factors : Try to use less factors in your analysis or try to use another separation factor", file = namefile, append = TRUE) } if (any(tab_rate["enough_levels_random_effect"] == FALSE, na.rm = TRUE) && length(grep("^glmmTMB", obj_lm$call)) > 0) { cat("\n", "\t- Random effect hasn't enough levels to be robust : If it has less than ten levels remove the random effect", file = namefile, append = TRUE) } }else{ return(rate_m) } }else{ cat("Models with quasi distributions can't be rated for now", file = namefile, append = TRUE) } } ######################################### end of the function note_glm_f ######################################### start of the function info_stats_f called by glm_species and glm_community info_stats_f <- function(filename, d_ata, agreg_level = c("species", "unitobs"), type = c("graph", "stat"), metrique, fact_graph, fact_graph_sel, list_fact, list_fact_sel) { ## Purpose: informations and simple statistics ## ---------------------------------------------------------------------- ## Arguments: filename : name of file ## d_ata : input data ## agreg_level : aggregation level ## type : type of function calling ## metrique : selected metric ## fact_graph : selection factor ## fact_graph_sel : list of factors levels selected for this factor ## list_fact : list of grouping factors ## list_fact_sel : list of factors levels selected for these factors ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 10 sept. 2012, 15:26 modified by Coline ROYAUX 04 june 2020 ## Open file : f_ile <- file(description = filename, open = "w", encoding = "UTF-8") ## if error : on.exit(if (exists("filename") && tryCatch(isOpen(f_ile), error = function(e)return(FALSE))) close(f_ile)) ## Metrics and factors infos : print_selection_info_f(metrique = metrique, #fact_graph = fact_graph, fact_graph_sel = fact_graph_sel, list_fact = list_fact, #list_fact_sel = list_fact_sel, f_ile = f_ile, agreg_level = agreg_level, type = type) ## statistics : if (class(d_ata) == "list") { cat("\n###################################################", "\nStatistics per level of splitting factor:\n", sep = "", file = f_ile, append = TRUE) invisible(sapply(seq_len(length(d_ata)), function(i) { print_stats_f(d_ata = d_ata[[i]], metrique = metrique, list_fact = list_fact, f_ile = f_ile, headline = fact_graph_sel[i]) })) }else{ print_stats_f(d_ata = d_ata, metrique = metrique, list_fact = list_fact, f_ile = f_ile, headline = NULL) } ## Close file : close(f_ile) } ######################################### end of the function info_stats_f ######################################### start of the function print_selection_info_f called by info_stats_f print_selection_info_f <- function(metrique, list_fact, f_ile, agreg_level = c("species", "unitobs"), type = c("graph", "stat")) { ## Purpose: Write data informations ## ---------------------------------------------------------------------- ## Arguments: metrique : chosen metric ## list_fact : factor's list ## f_ile : Results file name ## agreg_level : aggregation level ## type : function type ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 11 sept. 2012, 10:41 modified by Coline ROYAUX 04 june 2020 cat("\n##################################################\n", "Metrics and factors (and possible units/selections):\n", sep = "", file = f_ile, append = TRUE) ## metric info : cat("\n Metrics:", metrique, "\n", file = f_ile, append = TRUE) ## Clustering factors : if (is.element(agreg_level, c("spCL_unitobs", "spCL_espece", "spSpecies", "spEspece", "spUnitobs", "spUnitobs(CL)"))) { type <- "spatialGraph" } cat(switch(type, "graph" = "\nGrouping factor(s): \n * ", "stat" = "\nAnalyses factor(s): \n * ", "spatialGraph" = "\nSpatial aggregation factor(s): \n * "), paste(list_fact, collaspe = "\n * "), "\n", file = f_ile, append = TRUE) } ######################################### end of the function print_selection_info_f ######################################### start of the function print_stats_f called by info_stats_f print_stats_f <- function(d_ata, metrique, list_fact, f_ile, headline = NULL) { ## Purpose: Write general statistics table ## ---------------------------------------------------------------------- ## Arguments: d_ata : Analysis data ## metrique : metric's name ## list_fact : Factor's list ## f_ile : Simple statistics file name ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 11 sept. 2012, 10:09 modified by Coline ROYAUX 04 june 2020 ## Header : if (! is.null(headline)) { cat("\n", rep("#", nchar(headline) + 3), "\n", "## ", headline, "\n", sep = "", file = f_ile, append = TRUE) } cat("\n########################\nBase statistics:\n\n", file = f_ile, append = TRUE) capture.output(print(summary_fr(d_ata[, metrique])), file = f_ile, append = TRUE) if (! is.null(list_fact)) { cat("\n#########################################", "\nStatistics per combination of factor levels:\n\n", file = f_ile, sep = "", append = TRUE) ## Compute summary for each existing factor's cross : res <- with(d_ata, tapply(eval(parse(text = metrique)), INDEX = do.call(paste, c(lapply(list_fact, function(y)eval(parse(text = y))), sep = ".")), FUN = summary_fr)) ## results in table capture.output(print(do.call(rbind, res)), file = f_ile, append = TRUE) } ## empty line : cat("\n", file = f_ile, append = TRUE) } ######################################### end of the function print_stats_f ######################################### start of the function summary_fr called by print_stats_f summary_fr <- function(object, digits = max(3, getOption("digits") - 3), ...) { ## Purpose: Adding SD and N to summary ## ---------------------------------------------------------------------- ## Arguments: object : Object to summarise ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 13 sept. 2012, 15:47 modified by Coline ROYAUX 04 june 2020 if (! is.numeric(object)) stop("Programming error") ## Compute summary : res <- c(summary(object = object, digits, ...), "sd" = signif(sd(x = object), digits = digits), "N" = length(object)) return(res) } ######################################### start of the function summary_fr