version 0.1.0

DESCRIPTION

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+Package: ImHD
+Type: Package
+Title: Artificial Intelligence Based Machine Learning Algorithms for
+        Height Diameter Relationships of Conifer Trees
+Version: 0.1.0
+Authors@R: c(person("Dr. M. Iqbal", "Jeelani", role = c("aut","cre"), email = "jeelani.miqbal@gmail.com"),
+    person("Dr. Fehim", "Jeelani", role = "aut", email = "faheemwani@skuastkashmir.ac.in"),
+    person(" Dr. Shakeel Ahmad", "Mir", role = "aut"),
+    person(" Dr. Syed Naseem", "Geelani", role = "aut"),
+    person(" Dr. Mushtaq Ahmad", "Lone", role = "aut"),
+    person(" Dr. Asif", "Ali", role = "aut"),
+    person(" Dr. Tahir", "Mushtaq", role = "aut"),
+    person(" Dr. Amir", "Bhat", role = "aut"),
+    person("Dr. Md", "Yeasin", role = "aut"))
+Author: Dr. M. Iqbal Jeelani [aut, cre],
+  Dr. Fehim Jeelani [aut],
+  Dr. Shakeel Ahmad Mir [aut],
+  Dr. Syed Naseem Geelani [aut],
+  Dr. Mushtaq Ahmad Lone [aut],
+  Dr. Asif Ali [aut],
+  Dr. Tahir Mushtaq [aut],
+  Dr. Amir Bhat [aut],
+  Dr. Md Yeasin [aut]
+Maintainer: Dr. M. Iqbal Jeelani <jeelani.miqbal@gmail.com>
+Description: Estimating height of forest plant is one of the key challenges of recent times. This package will help to fit and validate AI (Artificial Intelligence) based machine learning algorithms for estimation of height of conifer trees based on diameter at breast height as explanatory variable using algorithm of Paul et al. (2022) <doi:10.1371/journal.pone.0270553>..
+License: GPL-3
+Encoding: UTF-8
+Imports: stats, randomForest, e1071, xgboost, ggplot2, reshape2, rpart
+RoxygenNote: 7.2.1
+Depends: R (>= 2.10)
+NeedsCompilation: no
+Packaged: 2023-09-11 12:42:17 UTC; YEASIN
+Repository: CRAN
+Date/Publication: 2023-09-12 06:12:44 UTC

MD5

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+1c559b9d2fcdbeb622ccbbc5d4f504f4 *DESCRIPTION
+c3e1fb751eb3392a61e7afb64dbb275b *NAMESPACE
+6a8f942e731b8f651c8bc3ea30b29964 *R/ImHD.R
+0dce2c2b467ff765cf4902d7795cff93 *inst/extdata/data_test.RData
+5fec3530f50fc2377942e4f33e00587e *inst/extdata/data_test.csv
+aefeaf512cf8c2be7f66cd15756a6abf *man/ImHD.Rd

NAMESPACE

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+
+export(ImHD)
+import(e1071)
+import(ggplot2)
+import(randomForest, except=margin)
+import(reshape2)
+import(rpart)
+import(stats)
+import(xgboost)

R/ImHD.R

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+#'@title Artificial Intelligence Based Machine Learning Algorithms for Height Diameter Relationships of Conifer Trees
+#' @param data Datasets
+#' @param splitratio Train-Test split ratio
+#' @import stats randomForest e1071 xgboost ggplot2 reshape2 rpart
+#' @return
+#' \itemize{
+#'   \item Prediction: Prediction of all ML models
+#'   \item Accuracy:  Accuracy metrics
+#' }
+#' @export
+#'
+#' @examples
+#' \donttest{
+#' library("ImHD")
+#' data <- system.file("extdata", "data_test.csv", package = "ImHD")
+#' data_test <- read.csv(data)
+#' Model<-ImHD(data =data_test)
+#' }
+#' @references
+#' \itemize{
+#'\item Jeelani, M.I., Tabassum, A., Rather, K and Gul,M.2023. Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169–178.  <doi:10.1002/9781118032985>
+#' }
+
+ImHD <- function(data, splitratio=0.7) {
+  Model<-NULL
+  value<-NULL
+  Metric<-NULL
+  Diameter<-NULL
+  SplitRatio<-splitratio
+  train_index <- sample(1:nrow(data), 0.7 * nrow(data))
+  train_data <- data[train_index, ]
+  test_data <- data[-train_index, ]
+
+  # Define predictors and target variable
+  x_train <- train_data$Diameter
+  y_train <- train_data$Height
+  x_test <- test_data$Diameter
+
+  # Decision Tree
+  dt_model <- rpart(Height ~ Diameter, data = train_data)
+  dt_predictions <- predict(dt_model, newdata = data.frame(Diameter = x_test))
+
+  # Random Forest
+  rf_model <- randomForest(Height ~ Diameter, data = train_data)
+  rf_predictions <- predict(rf_model, newdata = data.frame(Diameter = x_test))
+
+  # Support Vector Machine
+  svm_model <- svm(Height ~ Diameter, data = train_data)
+  svm_predictions <- predict(svm_model, newdata = data.frame(Diameter = x_test))
+
+  # Gradient Boosting
+  gb_model <- xgboost(data = matrix(x_train), label = y_train, nrounds = 100)
+  gb_predictions <- predict(gb_model, newdata = matrix(x_test))
+
+    # Calculate RMSE ,  MAE and PER
+
+  # Calculate RMSE and MAE
+  rmse <- function(predictions, actual) {
+    sqrt(mean((predictions - actual)^2))
+  }
+
+  mae <- function(predictions, actual) {
+    mean(abs(predictions - actual))
+  }
+
+  # Calculate PER (Prediction Error Rate)
+  per <- function(predictions, actual, threshold) {
+    sum(abs(predictions - actual) > threshold) / length(actual)
+  }
+
+  # Actual height values for the test data
+  y_test <- test_data$Height
+
+  # Calculate RMSE and MAE for each model
+  dt_rmse <- rmse(dt_predictions, y_test)
+  dt_mae <- mae(dt_predictions, y_test)
+
+  rf_rmse <- rmse(rf_predictions, y_test)
+  rf_mae <- mae(rf_predictions, y_test)
+
+  svm_rmse <- rmse(svm_predictions, y_test)
+  svm_mae <- mae(svm_predictions, y_test)
+
+  gb_rmse <- rmse(gb_predictions, y_test)
+  gb_mae <- mae(gb_predictions, y_test)
+
+  # Set threshold for PER calculation
+  threshold <- 0.1
+
+  # Calculate PER for each model
+  dt_per <- per(dt_predictions, y_test, threshold)
+  rf_per <- per(rf_predictions, y_test, threshold)
+  svm_per <- per(svm_predictions, y_test, threshold)
+  gb_per <- per(gb_predictions, y_test, threshold)
+
+  # Print the results
+  cat("Decision Tree:\n", "RMSE:", dt_rmse, "MAE:", dt_mae, "PER:", dt_per, "\n")
+  cat("Random Forest:\n", "RMSE:", rf_rmse, "MAE:", rf_mae, "PER:", rf_per, "\n")
+  cat("Support Vector Machine:\n", "RMSE:", svm_rmse, "MAE:", svm_mae, "PER:", svm_per, "\n")
+  cat("Gradient Boosting:\n", "RMSE:", gb_rmse, "MAE:", gb_mae, "PER:", gb_per, "\n")
+
+
+  # Comparison of Algothirms in tems of metrices
+  # Create a data frame for the metrics
+  metrics_df <- data.frame(
+    Model = c("Decision Tree", "Random Forest", "Support Vector Machine", "Gradient Boosting"),
+    RMSE = c(dt_rmse, rf_rmse, svm_rmse, gb_rmse),
+    MAE = c(dt_mae, rf_mae, svm_mae, gb_mae),
+    PER = c(dt_per, rf_per, svm_per, gb_per)
+  )
+
+  # Melt the data for plotting
+  metrics_melted <- melt(metrics_df, id.vars = "Model", variable.name = "Metric")
+
+  # Create the plot using ggplot2
+  ggplot(metrics_melted, aes(x = Model, y = value, fill = Metric)) +
+    geom_bar(stat = "identity", position = "dodge") +
+    labs(title = "Model Comparison: RMSE, MAE, and PER",
+         x = "Model",
+         y = "Value") +
+    scale_fill_manual(values = c("RMSE" = "blue", "MAE" = "green", "PER" = "red")) +
+    theme_minimal()
+
+
+  # Prediction plot
+  # Create a data frame with actual and predicted values
+  prediction_df <- data.frame(
+    Diameter = x_test,
+    Actual = y_test,
+    Decision_Tree = dt_predictions,
+    Random_Forest = rf_predictions,
+    SVM = svm_predictions,
+    Gradient_Boosting = gb_predictions
+  )
+
+  # Melt the data for plotting
+  prediction_melted <- melt(prediction_df, id.vars = "Diameter", variable.name = "Model")
+
+  # Create the prediction plot using ggplot2
+  ggplot(prediction_melted, aes(x = Diameter, y = value, color = Model)) +
+    geom_point(size = 2) +
+    geom_smooth(method = "loess", se = FALSE, size = 1.5, aes(group = Model)) +
+    labs(title = "Height Prediction Comparison with Smooth Lines",
+         x = "Diameter",
+         y = "Height") +
+    scale_color_manual(values = c("Actual" = "black",
+                                  "Decision_Tree" = "blue",
+                                  "Random_Forest" = "green",
+                                  "SVM" = "red",
+                                  "Gradient_Boosting" = "purple")) +
+    theme_minimal() +
+    theme(legend.position = "top")
+  results<- list(Predictions=prediction_df, Accuracy=metrics_df)
+  return(results)
+}