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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,236 @@ | ||
| # # data(LyonIris) | ||
| # # AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img", | ||
| # # "TxChom1564","Pct_brevet","NivVieMed") | ||
| # # dataset <- sf::st_drop_geometry(LyonIris[AnalysisFields]) | ||
| # # queen <- spdep::poly2nb(LyonIris,queen=TRUE) | ||
| # # Wqueen <- spdep::nb2listw(queen,style="W") | ||
| # # result <- SFCMeans(dataset, Wqueen,k = 5, m = 1.5, alpha = 1.5, standardize = TRUE) | ||
| # # | ||
| # # library(compositions) | ||
| # # | ||
| # # x <- result$Belongings | ||
| # # listw <- Wqueen | ||
| # # | ||
| # # | ||
| # # | ||
| # # cmp_geary <- function(x, listw){ | ||
| # # | ||
| # # cmp <- acomp(x) | ||
| # # x_clr <- as.matrix(clr(cmp)) | ||
| # # | ||
| # # sums_ij <- sapply(1:nrow(x_clr), function(i){ | ||
| # # ri <- x_clr[i,] | ||
| # # diffs <- sapply(listw$neighbours[[i]], function(j){ | ||
| # # rj <- x_clr[j,] | ||
| # # sum((ri - rj) ** 2) | ||
| # # }) | ||
| # # sub_sum <- sum(listw$weights[[i]] * diffs) | ||
| # # return(sub_sum) | ||
| # # }) | ||
| # # | ||
| # # numerator <- (nrow(x_clr) - 1) * sum(sums_ij) | ||
| # # S0 <- sum(sapply(listw$weights, sum)) | ||
| # # x_bar <- colMeans(x_clr) | ||
| # # diff_mean <- sum(c(sweep(x_clr, 2, x_bar, "-")**2)) | ||
| # # | ||
| # # C <- numerator / (2*S0 * diff_mean) | ||
| # # | ||
| # # } | ||
| # | ||
| # | ||
| # spConsistency <- function(object, nblistw = NULL, window = NULL, nrep = 999, adj = FALSE, mindist = 1e-11, use_clr = FALSE) { | ||
| # | ||
| # if(inherits(object, "FCMres")){ | ||
| # belongmat <- as.matrix(object$Belongings) | ||
| # if(object$isRaster & is.null(window)){ | ||
| # window <- object$window | ||
| # if(is.null(window)){ | ||
| # stop("impossible to find a window in the given object, please | ||
| # specify one by hand.") | ||
| # } | ||
| # } | ||
| # if(object$isRaster == FALSE & is.null(nblistw)){ | ||
| # nblistw <- object$nblistw | ||
| # } | ||
| # }else{ | ||
| # belongmat <- as.matrix(object) | ||
| # } | ||
| # | ||
| # # if we are not in raster mode | ||
| # | ||
| # if(is.null(window)){ | ||
| # | ||
| # if(is.null(nblistw)){ | ||
| # stop("The nblistw must be provided if spatial vector data is used") | ||
| # } | ||
| # weights <- nblistw$weights | ||
| # neighbours <- nblistw$neighbours | ||
| # ## calcul de l'inconsistence spatiale actuelle | ||
| # | ||
| # if(use_clr){ | ||
| # belong_mat <- clr(acomp(belongmat)) | ||
| # } | ||
| # # we could aslo use the Aitchison distance (https://ima.udg.edu/~barcelo/index_archivos/Measures_of_difference__Clustering.pdf) | ||
| # # this is simply done by calculating the clr transformation on the original data | ||
| # # belongmat <- log(belongmat) | ||
| # # belongmat <- sweep(belongmat, 1, rowMeans(belongmat), "-") | ||
| # # belongmat <- as.matrix(belongmat) | ||
| # | ||
| # obsdev <- sapply(1:nrow(belongmat), function(i) { | ||
| # row <- belongmat[i, ] | ||
| # idneighbour <- neighbours[[i]] | ||
| # neighbour <- belongmat[idneighbour, ] | ||
| # if (length(idneighbour) == 1){ | ||
| # neighbour <- t(as.matrix(neighbour)) | ||
| # } | ||
| # W <- weights[[i]] | ||
| # # we are using here the euclidean distance | ||
| # diff <- (neighbour-row[col(neighbour)])**2 * W | ||
| # tot <- sum(rowSums(diff)) | ||
| # return(tot) | ||
| # }) | ||
| # | ||
| # totalcons <- sum(obsdev) | ||
| # | ||
| # ## simulation de l'inconsistance spatiale | ||
| # belongmat <- t(belongmat) | ||
| # n <- ncol(belongmat) | ||
| # simulated <- vapply(1:nrep, function(d) { | ||
| # belong2 <- belongmat[,sample(n)] | ||
| # simvalues <- vapply(1:ncol(belong2), function(i) { | ||
| # row <- belong2[,i] | ||
| # idneighbour <- neighbours[[i]] | ||
| # neighbour <- belong2[,neighbours[[i]]] | ||
| # if (length(idneighbour) == 1){ | ||
| # neighbour <- t(as.matrix(neighbour)) | ||
| # } | ||
| # W <- weights[[i]] | ||
| # diff <- (neighbour-row) | ||
| # tot <- sum(diff^2 * W) | ||
| # return(tot) | ||
| # }, FUN.VALUE = 1) | ||
| # return(sum(simvalues)) | ||
| # },FUN.VALUE = 1) | ||
| # ratio <- totalcons / simulated | ||
| # | ||
| # # if we are using a raster mode. | ||
| # }else{ | ||
| # # we must calculate for each pixel its distance to its neighbours | ||
| # # on the membership matrix. So we will calculate the distance for each | ||
| # # raster in object$rasters and then sum them all group | ||
| # rastnames <- names(object$rasters) | ||
| # ok_names <- rastnames[grepl("group",rastnames, fixed = TRUE)] | ||
| # rasters <- object$rasters[ok_names] | ||
| # matrices <- lapply(rasters, terra::as.matrix, wide = TRUE) | ||
| # mat_dim <- dim(matrices[[1]]) | ||
| # | ||
| # if(use_clr){ | ||
| # big_mat <- do.call(cbind,lapply(matrices, c)) | ||
| # big_mat <- clr(acomp(big_mat)) | ||
| # matrices <- lapply(1:ncol(big_mat), function(i){ | ||
| # mat <- big_mat[,i] | ||
| # dim(mat) <- mat_dim | ||
| # return(mat) | ||
| # }) | ||
| # } | ||
| # | ||
| # # applying the | ||
| # | ||
| # if(adj){ | ||
| # dataset <- lapply(1:ncol(object$Data), function(ic){ | ||
| # vec1 <- object$Data[,ic] | ||
| # vec2 <- rep(NA,length(object$missing)) | ||
| # vec2[object$missing] <- vec1 | ||
| # rast <- object$rasters[[1]] | ||
| # terra::values(rast) <- vec2 | ||
| # mat <- terra::as.matrix(rast, wide = TRUE) | ||
| # return(mat) | ||
| # | ||
| # }) | ||
| # totalcons <- calc_raster_spinconsistency(matrices, window, adj, dataset, mindist = mindist) | ||
| # | ||
| # }else{ | ||
| # totalcons <- calc_raster_spinconsistency(matrices,window) | ||
| # } | ||
| # | ||
| # | ||
| # # we must now do the same thing but with resampled values | ||
| # warning("Calculating the permutation for the spatial inconsistency | ||
| # when using raster can be long, depending on the raster size. | ||
| # Note that the high number of cell in a raster reduces the need of | ||
| # a great number of replications.") | ||
| # # creating a vector of ids for each cell in raster | ||
| # all_ids <- 1:terra::ncell(rasters[[1]]) | ||
| # | ||
| # # converting the matrices (columns of membership matrix) into 1d vectors | ||
| # mem_vecs <- lapply(rasters, function(rast){ | ||
| # mat <- terra::as.matrix(rast, wide = TRUE) | ||
| # dim(mat) <- NULL | ||
| # return(mat) | ||
| # }) | ||
| # | ||
| # # if necessary, doing the same with the original data | ||
| # if(adj){ | ||
| # data_vecs <- lapply(dataset, function(mat){ | ||
| # vec <- mat | ||
| # dim(vec) <- NULL | ||
| # return(vec) | ||
| # }) | ||
| # } | ||
| # # extracting the dimension of the raster | ||
| # #dim(terra::as.matrix(rasters[[1]])) | ||
| # | ||
| # # starting the simulations | ||
| # simulated <- sapply(1:nrep, function(i){ | ||
| # | ||
| # # resampling the ids | ||
| # Ids <- sample(all_ids) | ||
| # | ||
| # # resampling the matrices of memberships | ||
| # new_matrices <- lapply(mem_vecs, function(vec){ | ||
| # new_vec <- vec[Ids] | ||
| # | ||
| # # swapping the NA at their original place | ||
| # val_na <- new_vec[!object$missing] | ||
| # loc_na <- is.na(new_vec) | ||
| # new_vec[!object$missing] <- NA | ||
| # new_vec[loc_na] <- val_na | ||
| # | ||
| # dim(new_vec) <- mat_dim | ||
| # return(new_vec) | ||
| # }) | ||
| # | ||
| # if(adj){ | ||
| # # resampling the matrices of the data | ||
| # new_dataset <- lapply(data_vecs, function(vec){ | ||
| # new_vec <- vec[Ids]; | ||
| # | ||
| # # swapping the NA at their original place | ||
| # val_na <- new_vec[!object$missing] | ||
| # loc_na <- is.na(new_vec) | ||
| # new_vec[!object$missing] <- NA | ||
| # new_vec[loc_na] <- val_na | ||
| # | ||
| # dim(new_vec) <- mat_dim | ||
| # return(new_vec) | ||
| # }) | ||
| # # calculating the index value | ||
| # inconsist <- calc_raster_spinconsistency(new_matrices, window, adj, new_dataset, mindist = mindist) | ||
| # | ||
| # }else{ | ||
| # # calculating the index value | ||
| # inconsist <- calc_raster_spinconsistency(new_matrices, window) | ||
| # } | ||
| # | ||
| # | ||
| # return(inconsist) | ||
| # }) | ||
| # ratio <- totalcons / simulated | ||
| # } | ||
| # | ||
| # return(list(Mean = mean(ratio), Median = quantile(ratio, probs = c(0.5)), | ||
| # prt05 = quantile(ratio, probs = c(0.05)), | ||
| # prt95 = quantile(ratio, probs = c(0.95)), | ||
| # samples = ratio, | ||
| # sum_diff = totalcons)) | ||
| # } |
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