version 0.1.0

DESCRIPTION

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+Package: SQI
+Type: Package
+Title: Soil Quality Index
+Version: 0.1.0
+Authors@R: c(person(" Dr. Owais Ali", "Wani", role = c("aut","cre"), email = "owaisaliwani@skuastkashmir.ac.in"),
+    person("Dr. Faaique", "Nazir", role = "aut", email = "faaiquenazir@gmail.com"),
+    person("Dr. Syed Sheraz", "Mahdi", role = "aut", email = "syedapbau@gmail.com"),
+    person("Dr. Shabir", "Bangroo", role = "aut", email = "shabir@skuastkashmir.ac.in"),
+    person("Dr. A Raouf", "Malik", role = "aut", email = "roufmalik@gmail.com"),
+    person("Dr. Shahnawaz Rasool", "Dar", role = "aut", email = "srdarskuastk@gmail.com"),
+    person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
+Author: Dr. Owais Ali Wani [aut, cre],
+  Dr. Faaique Nazir [aut],
+  Dr. Syed Sheraz Mahdi [aut],
+  Dr. Shabir Bangroo [aut],
+  Dr. A Raouf Malik [aut],
+  Dr. Shahnawaz Rasool Dar [aut],
+  Dr. Md Yeasin [aut]
+Maintainer: Dr. Owais Ali Wani <owaisaliwani@skuastkashmir.ac.in>
+Description: The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.  
+License: GPL-3
+Encoding: UTF-8
+RoxygenNote: 7.2.1
+Imports: readxl, dplyr, stats, matrixStats, olsrr, FactoMineR
+LazyData: true
+Depends: R (>= 3.5.0)
+NeedsCompilation: no
+Packaged: 2023-04-07 17:49:07 UTC; YEASIN
+Repository: CRAN
+Date/Publication: 2023-04-10 14:20:03 UTC

MD5

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+b4d63a3e8f057fc21ed24cbb3a509566 *DESCRIPTION
+043b84e4cbedbf89b5c3852cc14fbf9a *NAMESPACE
+b8b7ed75d7b51043e76772845afebcea *R/SQI.R
+eab87763d774ad335992c041ccc96d60 *data/Data.rda
+255192d37a20f538c25cff9389cf4346 *inst/extdata/SQI.xlsx
+3ab7b8d1b5425f3b55baa48d3f18fbd6 *inst/extdata/example.Rdata
+c1fb946377fc16fb5dc551cd38d1f09d *man/Data.Rd
+8a37e78decc7161342dcac85372b23a6 *man/PCAIndex.Rd
+496dfcdc943052b33af9033a19095719 *man/RegIndex.Rd
+8de142566ebf50a0812e2955e83a7726 *man/ScoingIndex.Rd

NAMESPACE

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+
+export(PCAIndex)
+export(RegIndex)
+export(ScoingIndex)
+import(FactoMineR)
+import("dplyr", except=c(count,filter,lag))
+import(matrixStats)
+import(olsrr)
+import(readxl)
+import(stats)

R/SQI.R

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+#' @title Soil Quality Index Based on Linear Scoring
+#' @param DataFrame Data set with first column as factors
+#' @param OptimumValue Optimum value of each variable; Minimum and maximum coded as "1111" and "9999" respectively.
+#' @import readxl dplyr stats matrixStats olsrr FactoMineR
+#' @return
+#' \itemize{
+#'   \item Raw_mean: Raw score
+#'   \item Index: Final index
+#' }
+#' @export
+#'
+#' @examples
+#' library("SQI")
+#' OP<-c(7,1111,9999,9999,9999,9999,9999,9999,9999,9999,1111)
+#' ScoreIndex<-ScoingIndex(DataFrame = Data,OptimumValue = OP)
+#' @references
+#' \itemize{
+#'\item Bastida, F., Zsolnay, A., Hernández, T., & García, C. (2008). Past, present and future of soil quality indices: a biological perspective. Geoderma, 147(3-4), 159-171.
+
+#' \item Doran, J. W., & Parkin, T. B. (1994). Defining and assessing soil quality. Defining soil quality for a sustainable environment, 35, 1-21.
+
+#' \item Mukherjee, A., & Lal, R. (2014). Comparison of soil quality index using three methods. PloS one, 9(8), e105981.
+#' }
+
+ScoingIndex<-function(DataFrame,OptimumValue){
+  optimum_value<-OptimumValue
+  data<-DataFrame
+  n_opt<-length(optimum_value)
+  a<-as.matrix(unique(data[,1]))
+  n_group<-length(a)
+  data_indv<-NULL
+  mean<-NULL
+  for (g in c(1:n_group)) {
+    name<-paste("data","_",g)
+    name1<-paste("mean","_",g)
+    name1<-NULL
+    name<-subset(data, data[,1]==a[g])
+    name<-as.data.frame(name)
+    n_var<-ncol(name)
+    if(n_var !=(n_opt+1)){
+      stop("Number of optimum values is not matching with number of variables ")
+    }
+    VAR<-NULL
+    for (v in c(1:n_opt)){
+      var_mean<-paste("var","_",v)
+      if(is.numeric(optimum_value[v]==1111)){
+        var_mean<-mean(min(name[,(v+1)])/name[,(v+1)])
+      }else if(is.numeric(optimum_value[v]==9999)){
+        var_mean<-mean(name[,(v+1)]/max(name[,(v+1)]))
+      }else{
+        var_mean<-mean(name[,(v+1)]/as.vector(optimum_value[v]))
+      }
+      VAR<-cbind(VAR,var_mean)
+    }
+    mean<-rbind(mean,VAR)
+  }
+  rownames(mean)<-a
+  colnames(mean)<- colnames(name[,-1])
+
+  min_max_norm <- function(x) {
+    (x - min(x)) / (max(x) - min(x))
+  }
+  index<- min_max_norm(as.matrix(rowMeans(mean)))
+  colnames(index)<-"Index"
+  return(list(Raw_mean=mean,Index=index))
+}
+
+#' @title Soil Quality Index Based on Regression
+#' @param DataFrame Data set with first column as factors
+#' @param OptimumValue Optimum value of each variable; Minimum and maximum coded as "1111" and "9999" respectively.
+#' @param Dep_col Dependent variable column number
+#' @import readxl dplyr stats matrixStats olsrr FactoMineR
+#' @return
+#' \itemize{
+#'   \item RegIndex: Final index
+#' }
+#' @export
+#'
+#' @examples
+#' library("SQI")
+#' OP<-c(7,1111,9999,9999,9999,9999,9999,9999,9999,9999,1111)
+#' RIndex<-RegIndex(DataFrame = Data,Dep_col=7,OptimumValue = OP)
+#' @references
+#' \itemize{
+#'\item Bastida, F., Zsolnay, A., Hernández, T., & García, C. (2008). Past, present and future of soil quality indices: a biological perspective. Geoderma, 147(3-4), 159-171.
+
+#' \item Doran, J. W., & Parkin, T. B. (1994). Defining and assessing soil quality. Defining soil quality for a sustainable environment, 35, 1-21.
+
+#' \item Mukherjee, A., & Lal, R. (2014). Comparison of soil quality index using three methods. PloS one, 9(8), e105981.
+#' }
+
+RegIndex<-function(DataFrame, Dep_col,OptimumValue){
+  Data<-DataFrame
+  C_Number<-Dep_col
+  colnames(Data)[C_Number] <- "Dep"
+  model <- lm(Dep~.-1, data=Data[,-1])
+  both <- step(model, direction='both', trace = 0)
+  coefficient<-t(as.data.frame(both$coefficients))
+  sel_var<-colnames(coefficient)
+  RawIndex<-ScoingIndex(DataFrame = DataFrame,OptimumValue = OptimumValue)$Raw_mean
+  Sel_RawIndex<-as.data.frame(RawIndex[,c(sel_var)])
+  RI<-NULL
+  for (q in c(1:nrow(Sel_RawIndex))) {
+    row_pred<-sum(Sel_RawIndex[q,]*coefficient)/length(sel_var)
+    RI<-rbind(RI,row_pred)
+  }
+  colnames(RI)<-"RI"
+  row.names(RI)<-row.names(Sel_RawIndex)
+  min_max_norm <- function(x) {
+    (x - min(x)) / (max(x) - min(x))
+  }
+  Index<- apply(RI,2,min_max_norm)
+  colnames(Index)<-"Index"
+  return(RegIndex=Index)
+}
+
+#' @title Soil Quality Index Based on Regression
+#' @param DataFrame Data set with first column as factors
+#' @param OptimumValue Optimum value of each variable; Minimum and maximum coded as "1111" and "9999" respectively.
+#' @import readxl dplyr stats matrixStats olsrr FactoMineR
+#' @return
+#' \itemize{
+#'   \item PCAIndex: Final index
+#' }
+#' @export
+#'
+#' @examples
+#'library("SQI")
+#' OP<-c(7,1111,9999,9999,9999,9999,9999,9999,9999,9999,1111)
+#' PIndex<-PCAIndex(DataFrame = Data,OptimumValue = OP)
+#' @references
+#' \itemize{
+#'\item Bastida, F., Zsolnay, A., Hernández, T., & García, C. (2008). Past, present and future of soil quality indices: a biological perspective. Geoderma, 147(3-4), 159-171.
+
+#' \item Doran, J. W., & Parkin, T. B. (1994). Defining and assessing soil quality. Defining soil quality for a sustainable environment, 35, 1-21.
+
+#' \item Mukherjee, A., & Lal, R. (2014). Comparison of soil quality index using three methods. PloS one, 9(8), e105981.
+#' }
+PCAIndex<-function(DataFrame, OptimumValue){
+  Data<-DataFrame
+  data <- scale((Data[,-1]))
+  pca_res <- PCA(data)
+  sel_pca<-names(subset(pca_res$eig[,1],pca_res$eig[,1]>1))
+  no_pca<-length(sel_pca)
+  variance<-pca_res$eig[,2]
+  factor_lod<-pca_res$svd$V[,1:length(sel_pca)]
+  row.names(factor_lod)<-colnames(data)
+  sel_variance<-variance[1:no_pca]
+  weight<-sel_variance/sum(sel_variance)
+  Sel_Var<-NULL
+  for (p in 1:no_pca) {
+    sel_pvar<-names(sort(factor_lod[,1], decreasing = TRUE))[c(1:4)]
+    data_cor<-data[,c(sel_pvar)]
+    Cor<- apply(cor(data_cor),1,sum)
+    sel_var_l<- names(sort(Cor, decreasing = TRUE))[1]
+    Sel_Var<-c(Sel_Var,sel_var_l)
+  }
+  sel_var<-Sel_Var
+  RawIndex<-ScoingIndex(DataFrame = DataFrame,OptimumValue = OptimumValue)$Raw_mean
+  Sel_RawIndex<-as.data.frame(RawIndex[,c(sel_var)])
+  RI<-NULL
+  for (q in c(1:nrow(Sel_RawIndex))) {
+    row_pred<-sum(Sel_RawIndex[q,]*as.vector(weight))/no_pca
+    RI<-rbind(RI,row_pred)
+  }
+  colnames(RI)<-"RI"
+  row.names(RI)<-row.names(Sel_RawIndex)
+  min_max_norm <- function(x) {
+    (x - min(x)) / (max(x) - min(x))
+  }
+  Index<- apply(RI,2,min_max_norm)
+  colnames(Index)<-"Index"
+  return(PCAIndex=Index)
+}
+
+#' This is data to be included in my package
+#' @name Data
+#' @docType data
+#' @keywords datasets
+#' @usage data(Data)
+#' @format A data frame with 60 rows and 12 column
+NULL