From 02a4f5f05450d8cd108ec89e591cf78a5d17d185 Mon Sep 17 00:00:00 2001 From: Marjean Pobuda Date: Tue, 5 Sep 2017 14:56:11 -0700 Subject: [PATCH 1/5] Logistic Regression R Script Tool Wrappings The script tool used during the Live Training Seminar: Go Deeper with Data Analytics Using ArcGIS Pro and R --- scripts/Logistic_Regression_Wrappings.R | 144 ++++++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 scripts/Logistic_Regression_Wrappings.R diff --git a/scripts/Logistic_Regression_Wrappings.R b/scripts/Logistic_Regression_Wrappings.R new file mode 100644 index 0000000..4851c1d --- /dev/null +++ b/scripts/Logistic_Regression_Wrappings.R @@ -0,0 +1,144 @@ +#Live Training Seminar: Go Deeper with Data Analytics Using ArcGIS Pro and R +#Logistic Regression R Script Wrappings +tool_exec<- function(in_params, out_params){ + + ##################################################################################################### + ### Check/Load Required Packages + ##################################################################################################### + arc.progress_label("Loading packages...") + arc.progress_pos(20) + + if(!requireNamespace("MKmisc", quietly = TRUE)) + install.packages("MKmisc", quiet = TRUE) + if(!requireNamespace("ROCR", quietly = TRUE)) + install.packages("ROCR", quiet = TRUE) + if(!requireNamespace("survey", quietly = TRUE)) + install.packages("survey", quiet = TRUE) + if(!requireNamespace("pROC", quietly = TRUE)) + install.packages("pROC", quiet = TRUE) + if(!requireNamespace("ROCR", quietly = TRUE)) + install.packages("ROCR", quiet = TRUE) + if(!requireNamespace("caret", quietly = TRUE)) + install.packages("caret", quiet = TRUE) + + require(MKmisc) + require(ROCR) + require(survey) + require(pROC) + require(ROCR) + require(caret) + + ##################################################################################################### + ### Define input/output parameters + ##################################################################################################### + input_data <- in_params[[1]] + train_percentage_size <- (in_params[[2]])/100 + dependent_variable <- in_params[[3]] + independent_variables <- in_params[[4]] + + output_prediction_data <- out_params[[1]] + + ##################################################################################################### + ### Load Data and Create Dataframe R Object + ##################################################################################################### + arc.progress_label("Loading data...") + arc.progress_pos(40) + + d <- arc.open(input_data) + fields_list <- append(c(dependent_variable), independent_variables) + d_df_full <- arc.select(d) + d_df <- arc.select(d, fields = fields_list) + + ##################################################################################################### + ### Create Training and Testing Datasets + ##################################################################################################### + arc.progress_label("Creating training and testing datasets...") + arc.progress_pos(60) + smp_size <- floor(train_percentage_size * nrow(d_df)) + + set.seed(1234) + train_ind <- sample(seq_len(nrow(d_df)), size = smp_size) + + d_df_train <- d_df[train_ind, ] + d_df_test <- d_df[-train_ind, ] + + ##################################################################################################### + ### Fit Logistic Regression Model + ##################################################################################################### + arc.progress_label("Creating training and testing datasets...") + arc.progress_pos(80) + + response <- d_df_train[, 1] + predictors <- d_df_train[, -1] + + d_df_train.log <- glm(response ~ ., family = binomial(link = 'logit'), data = predictors) + + d_df_full$Seagrass_Prediction <- predict(d_df_train.log, newdata = d_df_full, type = "response") + + ##################################################################################################### + ### Run Diagnostics on Logistic Regression Model + ##################################################################################################### + arc.progress_label("Running diagnostics on fitted model...") + arc.progress_pos(80) + + #Summary of model fit + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + cat(paste0("\n", "Summary of Fitted Logistic Regression Model", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + print(summary(d_df_train.log)) + + #Hosmer-Lemeshow Test + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + cat(paste0("\n", "Hosmer-Lemeshow Goodness of Fit Test Results", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + HL <- HLgof.test(fit = fitted(d_df_train.log), obs = d_df_train$Seagrass) + print(HL) + + #Prediction Accuracy + d_df_test.log.pred <- predict(d_df_train.log, newdata = d_df_test, type = 'response') + d_df_test.log.pred <- ifelse(d_df_test.log.pred > 0.5, 1, 0) + misClassificError <- mean(d_df_test.log.pred != d_df_test$Seagrass) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + cat(paste0("\n", "Prediction Accuracy for Test Dataset", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + print(paste('Accuracy Percentage:', round((1-misClassificError)*100, 2))) + cat(paste0("\n")) + + #ROC Curve + d_df_test.log.pred <- predict(d_df_train.log, newdata = d_df_test, type = 'response') + pred <- prediction(d_df_test.log.pred, d_df_test$Seagrass) + perf <- performance(pred, measure = "tpr", x.measure = "fpr") + plot(perf) + auc <- performance(pred, measure = "auc") + auc <- auc@y.values[[1]] + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + cat(paste0("\n", "Area Under the ROC Curve", "\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + print(paste('Area:', auc)) + cat(paste0("\n")) + cat(paste0("\n", "............................................", "\n")) + cat(paste0("\n")) + + ##################################################################################################### + ### Write Output + ##################################################################################################### + arc.progress_label("Writing output...") + arc.progress_pos(80) + + if(!is.null(output_prediction_data) && output_prediction_data != "NA") + arc.write(output_prediction_data, d_df_full, shape_info = arc.shapeinfo(d)) + + arc.progress_pos(100) +} From 6f28dd6248ba5d6a4a5fe3c969dcb47082a39e61 Mon Sep 17 00:00:00 2001 From: Marjean Pobuda Date: Tue, 5 Sep 2017 15:08:05 -0700 Subject: [PATCH 2/5] EMUs Florida Subset Data with Seagrass Indicator The data set used during the Live Training Seminar: Go Deeper with Data Analytics Using ArcGIS Pro and R --- Seagrass.gdb/a00000001.TablesByName.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000001.gdbindexes | Bin 0 -> 110 bytes Seagrass.gdb/a00000001.gdbtable | Bin 0 -> 330 bytes Seagrass.gdb/a00000001.gdbtablx | Bin 0 -> 5152 bytes Seagrass.gdb/a00000002.gdbtable | Bin 0 -> 2055 bytes Seagrass.gdb/a00000002.gdbtablx | Bin 0 -> 5152 bytes Seagrass.gdb/a00000003.gdbindexes | Bin 0 -> 42 bytes Seagrass.gdb/a00000003.gdbtable | Bin 0 -> 1084 bytes Seagrass.gdb/a00000003.gdbtablx | Bin 0 -> 5152 bytes .../a00000004.CatItemsByPhysicalName.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000004.CatItemsByType.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000004.FDO_UUID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000004.gdbindexes | Bin 0 -> 310 bytes Seagrass.gdb/a00000004.gdbtable | Bin 0 -> 15513 bytes Seagrass.gdb/a00000004.gdbtablx | Bin 0 -> 5152 bytes Seagrass.gdb/a00000004.spx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000005.CatItemTypesByName.atx | Bin 0 -> 12310 bytes .../a00000005.CatItemTypesByParentTypeID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000005.CatItemTypesByUUID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000005.gdbindexes | Bin 0 -> 296 bytes Seagrass.gdb/a00000005.gdbtable | Bin 0 -> 2063 bytes Seagrass.gdb/a00000005.gdbtablx | Bin 0 -> 5152 bytes .../a00000006.CatRelsByDestinationID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000006.CatRelsByOriginID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000006.CatRelsByType.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000006.FDO_UUID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000006.gdbindexes | Bin 0 -> 318 bytes Seagrass.gdb/a00000006.gdbtable | Bin 0 -> 263 bytes Seagrass.gdb/a00000006.gdbtablx | Bin 0 -> 5152 bytes .../a00000007.CatRelTypesByBackwardLabel.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByDestItemTypeID.atx | Bin 0 -> 4118 bytes .../a00000007.CatRelTypesByForwardLabel.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000007.CatRelTypesByName.atx | Bin 0 -> 4118 bytes ...a00000007.CatRelTypesByOriginItemTypeID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000007.CatRelTypesByUUID.atx | Bin 0 -> 4118 bytes Seagrass.gdb/a00000007.gdbindexes | Bin 0 -> 602 bytes Seagrass.gdb/a00000007.gdbtable | Bin 0 -> 3158 bytes Seagrass.gdb/a00000007.gdbtablx | Bin 0 -> 5152 bytes Seagrass.gdb/a00000009.gdbindexes | Bin 0 -> 116 bytes Seagrass.gdb/a00000009.gdbtable | Bin 0 -> 531012 bytes Seagrass.gdb/a00000009.gdbtablx | Bin 0 -> 30752 bytes Seagrass.gdb/a00000009.spx | Bin 0 -> 73750 bytes Seagrass.gdb/gdb | Bin 0 -> 4 bytes Seagrass.gdb/timestamps | Bin 0 -> 400 bytes 44 files 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