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100644 index b56a7b8..0000000 Binary files a/RTools.tbx and /dev/null differ diff --git a/Scripts/Rplots.pdf b/Scripts/Rplots.pdf deleted file mode 100644 index 7a7a68a..0000000 Binary files a/Scripts/Rplots.pdf and /dev/null differ diff --git a/Scripts/cluster_analysis_as_py.py b/Scripts/cluster_analysis_as_py.py deleted file mode 100644 index 93e290e..0000000 --- a/Scripts/cluster_analysis_as_py.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -# --------------------------------------------------------------------------- -# cluster_analysis_as_py.py -# Created on: 2015-06-19 16:38:15.00000 -# (generated by ArcGIS/ModelBuilder) -# Usage: cluster_analysis_as_py -# Description: -# --------------------------------------------------------------------------- - -# Set the necessary product code -# import arcinfo - - -# Import arcpy module -import arcpy - -# Load required toolboxes -arcpy.ImportToolbox("Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx") - -# Script arguments -Input_Point_Features = arcpy.GetParameterAsText(0) - -Clipping_Dataset = arcpy.GetParameterAsText(1) - -Output_Uncertancy_Dataset = arcpy.GetParameterAsText(2) - -Output_Classification_Dataset = arcpy.GetParameterAsText(3) -if Output_Classification_Dataset == '#' or not Output_Classification_Dataset: - Output_Classification_Dataset = "%scratchGDB%\\tmp2_Dissolve_Clip" # provide a default value if unspecified - -Output_Density_Raster = arcpy.GetParameterAsText(4) -if Output_Density_Raster == '#' or not Output_Density_Raster: - Output_Density_Raster = "%scratchGDB%\\dens_raster" # provide a default value if unspecified - -Output_Superimposing_Ellipses_Dataset = arcpy.GetParameterAsText(5) - -# Local variables: -tmp2 = "%scratchGDB%\\tmp2" -tmp_dis = "%scratchGDB%\\tmp2_Dissolve" -cn_density = "%scratchGDB%\\cn_density" -dens_clip = "%scratchGDB%\\dens_clip" -Output_geostatistical_layer = "" - -# Set Geoprocessing environments -arcpy.env.extent = Clipping Dataset - -# Process: Cluster Analysis -arcpy.gp.toolbox = "Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx"; -# Warning: the toolbox Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx DOES NOT have an alias. -# Please assign this toolbox an alias to avoid tool name collisions -# And replace arcpy.gp.clustering(...) with arcpy.clustering_ALIAS(...) -arcpy.gp.clustering(Input_Point_Features, "", Output_Uncertancy_Dataset, Output_Superimposing_Ellipses_Dataset, cn_density) - -# Process: Create Thiessen Polygons -arcpy.CreateThiessenPolygons_analysis(Output_Uncertancy_Dataset, tmp2, "ALL") - -# Process: Dissolve -arcpy.Dissolve_management(tmp2, tmp_dis, "cluster", "", "MULTI_PART", "DISSOLVE_LINES") - -# Process: Clip -arcpy.Clip_analysis(tmp_dis, Clipping_Dataset, Output_Classification_Dataset, "") - -# Process: Clip (2) -arcpy.Clip_analysis(cn_density, Clipping_Dataset, dens_clip, "") - -# Process: Kernel Interpolation With Barriers -tempEnvironment0 = arcpy.env.extent -arcpy.env.extent = Clipping Dataset -arcpy.KernelInterpolationWithBarriers_ga(dens_clip, "density", Output_geostatistical_layer, Output_Density_Raster, "0.263200383398682", "", "POLYNOMIAL5", "", "1", "50", "PREDICTION") -arcpy.env.extent = tempEnvironment0 - diff --git a/Scripts/clustering.R b/Scripts/clustering.R deleted file mode 100644 index 4957329..0000000 --- a/Scripts/clustering.R +++ /dev/null @@ -1,132 +0,0 @@ -# case study using mclust package -tool_exec <- function(in_params, out_params) { - message(getOption("repos")) - if (!requireNamespace("sp", quietly = TRUE)) - install.packages("sp") - if (!requireNamespace("mclust", quietly = TRUE)) - install.packages("mclust") - require(mclust) - require(sp) - - source_dataset <- in_params[[1]] - nclust <- in_params[[2]] - out_table <- out_params[[1]] - out_ellipses <- out_params[[2]] - out_dens <- out_params[[3]] - - ### read data - arc.progress_label("Loading Dataset") - data <- arc.select(arc.open(source_dataset)) - data_shp <- arc.shape(data) - - #data.xy <- data.frame(x=data_shp$x - x_min, y=data_shp$y - y_min) - # alternatively: data.frame(x=data_shp$x, y=data_shp$y) - data.xy <- cbind(data_shp$x, data_shp$y) - - ######### - arc.progress_label("BIC for Model-Based Clustering...") - treesBIC <- mclustBIC(data.xy, G = nclust) - treesModel <- summary(treesBIC, data.xy) - print(treesModel) - - ### save to shapefile - # treesModel$classification # cluster number - # treesModel$uncertainty # uncertainty of classification - # treesModel$z # conditional probabilities - n <- treesModel$G - cond_probs <- lapply(1:n, function(i) treesModel$z[,i]) - names(cond_probs) <- paste0("cond_prob", 1:n) - - q <- quantile(treesModel$uncertainty, probs = c(0.75, 0.95)) - q7595 <- sapply(treesModel$uncertainty, function(u) { - r <- if (u <= q[1]) 1 else 0 - if (u <= q[2] && r == 0) r <- 2 - if (u > q[2] && r == 0) r <- 3 - stopifnot(r != 0) - r - 1 - }) - - result <- data.frame(data, - cluster = treesModel$classification, - class_uncert = treesModel$uncertainty, - sym_ucert=q7595, - cond_probs) - - #print(head(result)) - arc.progress_label("Writing result dataset...") - arc.write(out_table, result, coords = data_shp) - - ### automatic mapping - arc.progress_label("Best model based on BIC...") - bestModel <- mclustModel(data.xy, treesBIC) - - ### create ellipses - if (!is.null(out_ellipses)) { - arc.progress_label("Writing ellipses dataset...") - polygons <- create.elipses(bestModel) - mu <- bestModel$parameters$mean - sp.df <- SpatialPolygonsDataFrame(polygons, - data=data.frame(clust_n=1:n, - mu_x=mu[1, 1:n], - mu_y=mu[2, 1:n]), - match.ID = F) - ## keep original SpatialReference - shape_info <- list(type="Polygon", WKT=arc.shapeinfo(data_shp)$WKT) - #shape_info <- arc.shapeinfo(sp.df) - arc.write(out_ellipses, sp.df, shape_info=shape_info) - } - - ### create density grid - if (!is.null(out_dens)) { - arc.progress_label("Calculating density...") - options(device="pdf") - options(onefile=FALSE) - grid.n <- 100 - grid <- surfacePlot(verbose=T, data.xy, parameters = bestModel$parameters, - type = "image", what = "density", grid = grid.n, - nlevels = 8, transformation = "none", ask=F) - dev.off() - options(device="windows") - options(onefile=NULL) - xy <- list(x = unlist(lapply(grid$x, function(x) rep(x, grid.n))), - y = rep(grid$y, grid.n)) - arc.progress_label("Writing density dataset...") - arc.write(out_dens, - data=list(density = as.vector(grid$z)), - xy, - list(type="Point", WKT=arc.shapeinfo(data_shp)$WKT)) - } - arc.progress_label("Done") - return(out_params) -} - -create.elipses <- function(bestModel) { - n <- bestModel$G - mu <- bestModel$parameters$mean - sigma <- bestModel$parameters$variance$sigma - pp <- lapply(1:n, function(i) { - xy <- make.ellipse(mu = mu[, i], sigma = sigma[, , i]) - name <- paste0("ellipse", i) - Polygons(list(Polygon(xy)), name) - }) - #p4str <- arc.fromWktToP4(arc.shapeinfo(trees_shp)$WKT) - #SpatialPolygons(pp, 1:n, proj4string=sp::CRS(p4str)) - SpatialPolygons(pp, 1:n, proj4string=sp::CRS()) -} - -make.ellipse <- function(mu, sigma, k = 60) { - p <- length(mu) - if (p != 2) - stop("only two-dimensional case is available") - if (any(unique(dim(sigma)) != p)) - stop("mu and sigma are incompatible") - ev <- eigen(sigma, symmetric = TRUE) - s <- sqrt(rev(sort(ev$values))) - V <- t(ev$vectors[, rev(order(ev$values))]) - theta <- (0:k) * (2 * pi / k) - x <- s[1] * cos(theta) - y <- s[2] * sin(theta) - xy <- cbind(x, y) - xy <- xy %*% V - cbind(xy[,1] + mu[1], xy[,2] + mu[2]) -} diff --git a/Scripts/logreg.R b/Scripts/logreg.R deleted file mode 100644 index 0954be0..0000000 --- a/Scripts/logreg.R +++ /dev/null @@ -1,39 +0,0 @@ -tool_exec <- function(in_params, out_params) -{ -### handle input gp tool paramaters - input_dataset_name <- in_params[[1]] - primary_field <- in_params[[2]] - fields <- in_params[[3]] - -# --- import dataset to dataframe --- - fc <- arc.open(input_dataset_name) - df <- arc.select(fc, c(primary_field, fields)) - -#------- begin common R script ---------- -# Make Formula - form <- as.formula(paste(primary_field, "~", paste(fields, collapse="+"))) - cat("formula = ") - print(form, showEnv=FALSE) - - m <- glm(form, data=df, family=binomial("logit")) - -# display summary as GP Messages - print(summary(m)) - -# append predicted and residual - - df["predicted"] <- fitted(m) - r <- fitted(m) - df[primary_field] - df["residual"] <- r - -#------- end - -### handle gp tool outputs - output_dataset_name <- out_params[[1]] -# export result dataframe to FeatureClass - arc.write(output_dataset_name, df) - -# update derived parameter - out_params[[2]] <- m$aic - return(out_params) -} diff --git a/Scripts/semi_par_regression.R b/Scripts/semi_par_regression.R deleted file mode 100644 index af14be1..0000000 --- a/Scripts/semi_par_regression.R +++ /dev/null @@ -1,87 +0,0 @@ -make_knots <- function(x1, x2, num.knots) { - if (missing(num.knots)) - num.knots <- max(10, min(50, round(length(x1)/4))) - X <- cbind(x1, x2) - dup.inds <- (1:nrow(X))[dup.matrix(X) == T] - if (length(dup.inds) > 0) - X <- X[-dup.inds, ] - knots <- cluster::clara(X, num.knots)$medoids -return(knots) -} - -tool_exec <- function(in_params, out_params) { - #### Load Library for Analysis #### - if (!requireNamespace("SemiPar", quietly = TRUE)) - install.packages("SemiPar") - - library(SemiPar) - - #### Get Input Parameters #### - input_features <- in_params[[1]] - input_predictions <- in_params[[2]] - dep_variable <- in_params[[3]] - nonlin_variables <- in_params[[4]] - lin_variables <- in_params[[5]] - input_knots <- in_params[[6]] - - - #### Import Dataset to Dataframe #### - fc <- arc.open(input_features) - df <- arc.select(fc, c(dep_variable, nonlin_variables, lin_variables)) - df['x'] <- arc.shape(df)$x - df['y'] <- arc.shape(df)$y - - #### Import Knots to DataFrame #### - if (is.null(input_knots)) - { - knots.est <- make_knots(df$x, df$y) - } - else - { - knots_df <- arc.select(arc.open(input_knots)) - knots.est <- make_knots(arc.shape(knots_df)$x, arc.shape(knots_df)$y) - } - - #### Create Spatial Effect #### - fxy <- 'f(x, y, knots=knots.est)' - - #### Create Non-Linear Params #### - nonlin_params <- nonlin_variables - for (i in 1:length(nonlin_params)){ - nonlin_params[i] <- paste("f(", nonlin_params[i], ")", sep = "") - } - nonlin_params <- paste(nonlin_params, collapse = " + ") - - #### Create Linear Params #### - lin_params <- paste(lin_variables, collapse = " + ") - - #### Create Formula and Fit SemiPar #### - e <- as.list(df) - e$knots.est <- knots.est - all_params <- paste(fxy, nonlin_params, lin_params, sep = " + ") - all_params <- paste(dep_variable, " ~ ", all_params) - tryCatch({ - attach(e) - form <- as.formula(all_params) - fit <- spm(form, family="binomial") - }, finally <- detach(e)) - - #### Prediction #### - pred_fc <- arc.open(input_predictions) - pred_df <- arc.select(pred_fc, c(nonlin_variables, lin_variables)) - pred_df['x'] <- arc.shape(pred_df)$x - pred_df['y'] <- arc.shape(pred_df)$y - pred <- with(pred_df, predict.spm(fit, newdata = pred_df, se = TRUE)) - - #### Write Output #### - pred_df['pred_1'] <- pred$fit - pred_df['back_pred_1'] <- 1 / (1+exp(-pred$fit)) - pred_df['stderr_1'] <- pred$se - pred_df['back_se1l'] <- 1 / (1+exp(-pred$fit+1.96*pred$se)) - pred_df['back_se1u'] <- 1/(1+exp(-pred$fit-1.96*pred$se)) - - output_features <- out_params[[1]] - arc.write(output_features, pred_df) - - return(out_params) -} diff --git a/SemiParametricRegression.aprx b/SemiParametricRegression.aprx deleted file mode 100644 index d6a70c8..0000000 Binary files a/SemiParametricRegression.aprx and /dev/null differ diff --git a/Symbology/classes_symbology.lyrx b/Symbology/classes_symbology.lyrx deleted file mode 100644 index 3aa9c09..0000000 --- a/Symbology/classes_symbology.lyrx +++ /dev/null @@ -1,890 +0,0 @@ -{ - "type" : "CIMLayerDocument", - "version" : "1.1.0", - "build" : 3183, - "layers" : [ - "CIMPATH=map/spruce_trees_clustering.xml" - ], - 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@@ -0,0 +1,160 @@ +# +# Belarus thyroid cancer in children case study +# + +tool_exec <- function(in_params, out_params) +{ +### handle input gp tool paramaters + dataset_path <- in_params[[1]] + case_field <- in_params[[2]] + pop_field <- in_params[[3]] + covar1_field <- in_params[[4]] + covar2_field <- in_params[[5]] + + df = regional_bayesian(dataset_path, case_field, pop_field, covar1_field, covar2_field) + +### handle gp tool outputs + output_dataset_name = out_params[[1]] + arc.write(output_dataset_name, df) + return(out_params) +} + +#stat analysis with WINBUGS +regional_bayesian <- function(dataset_path, case_field, pop_field, covar1_field, covar2_field) +{ + if (!requireNamespace("sp", quietly = TRUE)) + install.packages("sp") + if (!requireNamespace("spdep", quietly = TRUE)) + install.packages("spdep") + if (!requireNamespace("R2WinBUGS", quietly = TRUE)) + install.packages("R2WinBUGS") + if (!requireNamespace("rgeos", quietly = TRUE)) + install.packages("rgeos") + require(sp) + require(spdep) + + message("Loading dataset...") + ds <- arc.open(dataset_path) + df <- arc.select(ds, fields = list(case_field, pop_field, covar1_field, covar2_field)) + sp.df <- arc.data2sp(df) + + # convert shapefile data to a list of polygons and a data frame + centroids <- rgeos::gCentroid(sp.df, byid = TRUE) + centroids <- centroids@coords + + kk <- nrow(sp.df) + + # calculate the expected counts + pmap <- probmap(df[[case_field]], df[[pop_field]]) + + # make neighbours list + message("Making Neighbours...") + five.nn <- knn2nb(knearneigh(centroids, k=5), sym=TRUE) + + #plotpolys(th_polys, bbs, border="grey") + #plot(five.nn, centroids, add=TRUE) + +#####4 "GWR" + + ## data preparation + dlist <- nbdists(five.nn, centroids) + num <- sapply(five.nn, length) + + ll <- sum(num) + # equal Weights: + weights <- rep(1, ll) + + adj <- rep(0, ll) + mm <- 1 + for (i in 1:kk) + { + for (j in 1:num[i]) + { + adj[mm] <- five.nn[[i]][j] + mm <- mm + 1 + } + } + + input.data <- list(n=kk, + cases = df[[case_field]], + expected = pmap$expCount, + weights=weights, + a1 = df[[covar1_field]], + a2 = df[[covar2_field]]/min(df[[covar2_field]]), + num=num, + adj=adj) + + ### inits + b_init <- rep(0.0, kk) + model.inits <- function() {list(alpha = 0, tau.v = 1, tau.u = 1, b1 = b_init, b2 = b_init)} + # output parameters + model.parameters <- c("theta", "mu", "v", "u", "alpha", "PP", "b1", "b2", "RR_exp", "RR_het", "RR_clust") + + model_file <- tempfile("BRCM-model", fileext='.bug') + R2WinBUGS::write.model(BRCM.model, model_file) + + ### run BUGS + message("Running WinBUGS model...") + result.4 <- R2WinBUGS::bugs(input.data, model.inits, model.parameters, model_file, n.chains=1, n.iter=100000, debug=F, working.directory=tempdir()) + + unlink(model_file) + +#append results columns + df$expected <- pmap$expCount + df$rel_risk <- result.4$mean$theta + df$rel_risk_se <- result.4$sd$theta + df$extra_risk <- result.4$mean$PP + df$extra_risk_se <- result.4$sd$PP + df$mean <- result.4$mean$mu + df$mean_se <- result.4$sd$mu + df$u_cor <- result.4$mean$u + df$u_cor_se <- result.4$sd$u + df$u_heter <- result.4$mean$v + df$u_heter_se <- result.4$sd$v + df$covariates <- result.4$mean$RR_exp + df$heterogeneous <- result.4$mean$RR_het + df$residuals <- result.4$mean$RR_clust + df$r1 <- result.4$mean$b1 + df$r1_se <- result.4$sd$b1 + df$r2 <- result.4$mean$b2 + df$r2_se <- result.4$sd$b2 + + return(df) +} + +BRCM.model <- function() +{ + for (i in 1:n) + { + # Poisson likelihood for observed counts + cases[i] ~ dpois(mu[i]) + log(mu[i]) <- log(expected[i]) + alpha + u[i] + v[i] + b1[i]*a1[i] + b2[i]*a2[i] + # Relative Risk + theta[i] <- exp(alpha + u[i] + v[i] + b1[i]*a1[i] + b2[i]*a2[i]) + # Posterior probability of RR[i]>1 + PP[i] <- step(theta[i] - 1 + eps) + # Prior distribution for the uncorrelated heterogeneity + v[i] ~ dnorm(0,tau.v) + # Relative Risk decomposition + RR_exp[i]<-exp(b1[i]*a1[i] + b2[i]*a2[i]) + RR_het[i]<-exp(v[i]) + RR_clust[i]<-exp(u[i]) + } + + eps <- 1.0E-6 + + # CAR prior distribution for spatial correlated heterogeneity + u[1:n] ~ car.normal(adj[], weights[], num[], tau.u) + b1[1:n] ~ car.normal(adj[], weights[], num[], tau.b1) + b2[1:n] ~ car.normal(adj[], weights[], num[], tau.b2) + + # Improper prior distribution for the mean relative risk in the study region + alpha ~ dflat() + mean <- exp(alpha) + + # Hyperprior distributions on inverse variance parameter of random effects + tau.u ~ dgamma(0.5,0.0005) + tau.v ~ dgamma(0.5,0.0005) + tau.b1 ~ dgamma(0.5,0.0005) + tau.b2 ~ dgamma(0.5,0.0005) +} diff --git a/scripts/clustering.R b/scripts/clustering.R new file mode 100644 index 0000000..0209064 --- /dev/null +++ b/scripts/clustering.R @@ -0,0 +1,157 @@ +# case study using mclust package +tool_exec <- function(in_params, out_prams) +{ + #message(getOption("repos")) + if (!requireNamespace("sp", quietly = TRUE)) + install.packages("sp") + if (!requireNamespace("mclust", quietly = TRUE)) + install.packages("mclust") + require(mclust) + require(sp) + + source_dataset = in_params[[1]] + nclust = NULL #in_params[[2]] + out_table = out_prams[[1]] + out_ellipses = out_prams[[2]] + out_dens = out_prams[[3]] + out_sim = out_prams[[4]] + + ### read data + arc.progress_label("Loading Dataset") + data <- arc.select(arc.open(source_dataset)) + data_shp <- arc.shape(data) + + data.xy <- cbind(data_shp$x, data_shp$y) # or data.frame(x=data_shp$x, y=data_shp$y) + + ######### + arc.progress_label("Bayesian Information Criterion for Model-Based Clustering...") + patternBIC <- mclustBIC(data.xy, G = nclust) + patternModel <- summary(patternBIC, data.xy) + print(patternModel) + + ### save to shapefile + # patternModel$classification # cluster number + # patternModel$uncertainty # uncertainty of classification + # patternModel$z # conditional probabilities + n <- patternModel$G + cond_probs <- lapply(1:n, function(i) patternModel$z[,i]) + names(cond_probs) <- paste0("cond_prob", 1:n) + + q <- quantile(patternModel$uncertainty, probs = c(0.75, 0.95)) + q7595 <- sapply(patternModel$uncertainty, function(u){ + r <- if (u <= q[1]) 1 else 0 + if(u <= q[2] && r == 0) r <- 2 + if(u > q[2] && r == 0) r <- 3 + stopifnot(r != 0) + r - 1 + }) + + result <- data.frame(data, + cluster = patternModel$classification, + class_uncert = patternModel$uncertainty, + sym_ucert=q7595, + cond_probs) + + #print(head(result)) + arc.progress_label("Writing result dataset...") + if (!is.null(out_table) && out_table != "NA") + arc.write(out_table, result, coords = data_shp) + + ### automatic mapping + arc.progress_label("Best model based on Bayesian Information Criterion...") + bestModel <- mclustModel(data.xy, patternBIC) + + ### create ellipses + if (!is.null(out_ellipses) && out_ellipses != "NA") + { + arc.progress_label("Writing ellipses dataset...") + polygons <- create.ellipses(bestModel) + mu <- bestModel$parameters$mean + sp.df <- SpatialPolygonsDataFrame(polygons, data=data.frame(clast_n=1:n, mu_x=mu[1,1:n], mu_y=mu[2,1:n]), match.ID = F) + ## keep original SpatialReference + ## following is not always correct WKT -> P4 -> WKT' == WKT + shape_info <- list(type="Polygon", WKT=arc.shapeinfo(data_shp)$WKT) + #shape_info <- arc.shapeinfo(sp.df) + #shape_info$WKT <- arc.shapeinfo(data_shp)$WKT + arc.write(out_ellipses, sp.df, shape_info=shape_info) + } + + ### create density grid + if (!is.null(out_dens) && out_dens != "NA") + { + arc.progress_label("Calculating density...") + options(device="pdf") + options(onefile=FALSE) + grid.n <- 100 + grid <- surfacePlot(verbose=T, data.xy, parameters = bestModel$parameters, type = "image", what = "density", grid=grid.n, nlevels = 8, transformation = "none", ask=F) + dev.off() + options(device="windows") + options(onefile=NULL) + xy <-list(x = unlist(lapply(grid$x, function(x) rep(x, grid.n))), + y = rep(grid$y, grid.n)) + arc.progress_label("Writing density dataset...") + arc.write(out_dens, data=list(density = as.vector(grid$z)), xy, list(type='Point', WKT=arc.shapeinfo(data_shp)$WKT)) + } + + ### simulation, fitting model + if (!is.null(out_sim) && out_sim != "NA") + { + sim.xy = simulation(bestModel, nrow(data)) + #print(length(sim.xy$x)) + arc.progress_label("Saving simulated dataset...") + arc.write(out_sim, data=list(id=1:length(sim.xy$x)), sim.xy, list(type='Point', WKT=arc.shapeinfo(data_shp)$WKT)) + } + arc.progress_label("Done") + return(out_prams) +} + +simulation <- function(bestModel, n) +{ + n <- round(round(rnorm(1, mean=n, sd=sqrt(n)))) + sim <- sim(modelName = bestModel$modelName, parameters = bestModel$parameters, n, seed = round(sqrt(n+1))) + list(x=sim[,2], y=sim[,3]) +} + +create.ellipses <- function(bestModel) +{ + n <- bestModel$G + mu <- bestModel$parameters$mean + sigma <- bestModel$parameters$variance$sigma + cls.polygons <- lapply(1:n, function(i) + { + xy <- make.ellipse(mu = mu[, i], sigma = sigma[, , i]) + name<-paste0("ellipse", i) + Polygons(list(Polygon(xy)), name) + }) + #SpatialPolygons(cls.polygons, 1:n, proj4string=sp::CRS(p4str)) + SpatialPolygons(cls.polygons, 1:n, proj4string=sp::CRS()) +} + +make.ellipse <- function(mu, sigma, k = 60) +{ + p <- length(mu) + if (p != 2) + stop("only two-dimensional case is available") + if (any(unique(dim(sigma)) != p)) + stop("mu and sigma are incompatible") + ev <- eigen(sigma, symmetric = TRUE) + s <- sqrt(rev(sort(ev$values))) + V <- t(ev$vectors[, rev(order(ev$values))]) + theta <- (0:k) * (2*pi/k) + x <- s[1] * cos(theta) + y <- s[2] * sin(theta) + xy <- cbind(x, y) + xy <- xy %*% V + cbind(xy[,1] + mu[1], xy[,2] + mu[2]) +} + +## standalone R testing +foo <- function() +{ + library(arcgisbinding) + arc.check_product() + tool_exec( + list("d:\\Data\\R-staff\\samps\\data.gdb\\spruce_trees", NULL), + list(NULL, NULL, NULL, "in_memory\\sim") + ) +} \ No newline at end of file diff --git a/scripts/semi_par_regression.R b/scripts/semi_par_regression.R new file mode 100644 index 0000000..476f243 --- /dev/null +++ b/scripts/semi_par_regression.R @@ -0,0 +1,90 @@ + +make_knots <-function (x1, x2, num.knots) +{ + if (missing(num.knots)) + num.knots <- max(10, min(50, round(length(x1)/4))) + X <- cbind(x1, x2) + dup.inds <- (1:nrow(X))[dup.matrix(X) == T] + if (length(dup.inds) > 0) + X <- X[-dup.inds, ] + knots <- cluster::clara(X, num.knots)$medoids + return(knots) +} + +tool_exec <- function(in_params, out_params) +{ + #### Load Library for Analysis #### + if (!requireNamespace("SemiPar", quietly = TRUE)) + install.packages("SemiPar") + require(SemiPar) + + #### Get Input Parameters #### + input_features = in_params[[1]] + input_predictions = in_params[[2]] + dep_variable = in_params[[3]] + nonlin_variables = in_params[[4]] + lin_variables = in_params[[5]] + input_knots = in_params[[6]] + + #### Import Dataset to Dataframe #### + fc = arc.open(input_features) + df = arc.select(fc, c(dep_variable, nonlin_variables, lin_variables)) + df['x'] = arc.shape(df)$x + df['y'] = arc.shape(df)$y + + #### Import Knots to DataFrame #### + if (is.null(input_knots)) + { + knots.est = make_knots(df$x, df$y) + } + else + { + knots_df = arc.select(arc.open(input_knots)) + knots.est = make_knots(arc.shape(knots_df)$x, arc.shape(knots_df)$y) + } + + #### Create Spatial Effect #### + fxy = "f(x,y,knots=knots.est)" + + #### Create Non-Linear Params #### + nonlin_params = paste(paste0("f(", nonlin_variables, ")"), collapse = "+") + + #### Create Formula and Fit SemiPar #### + e = as.list(df) + e$knots.est = knots.est + all_params = paste0(dep_variable, " ~ ", fxy, "+", nonlin_params) + + #### Create Linear Params #### + if (!is.null(lin_variables)) + all_params = paste0(all_params, "+", paste(lin_variables, collapse = "+")) + +#print(all_params) + tryCatch( + { + attach(e) + form = as.formula(all_params) + fit = spm(form, family="binomial") + },finally = detach(e)) + + #### Prediction #### + pred_fc = arc.open(input_predictions) + pred_df = arc.select(pred_fc, c(nonlin_variables, lin_variables)) + summary(fit) + pred_df_xy <- data.frame( + x = arc.shape(pred_df)$x, + y = arc.shape(pred_df)$y, + pred_df) + + pred = with(pred_df_xy, predict.spm(fit, newdata = pred_df_xy, se = TRUE)) + + #### Write Output #### + pred_df['prediction'] = 1 / (1+exp(-pred$fit)) + pred_df['prediction_se'] = pred$se + pred_df['lower_sign'] = 1 / (1+exp(-pred$fit+1.96*pred$se)) + pred_df['upper_sign'] = 1/(1+exp(-pred$fit-1.96*pred$se)) + + output_features = out_params[[1]] + arc.write(output_features, pred_df) + + return(out_params) +} diff --git a/spruce_forest.mxd b/spruce_forest.mxd new file mode 100644 index 0000000..0efad60 Binary files /dev/null and b/spruce_forest.mxd differ diff --git a/symbology/brcm_symbology.lyr b/symbology/brcm_symbology.lyr new file mode 100644 index 0000000..b89de89 Binary files /dev/null and b/symbology/brcm_symbology.lyr differ diff --git a/symbology/clusters_polygons_10.lyr b/symbology/clusters_polygons_10.lyr new file mode 100644 index 0000000..273aa7a Binary files 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