R/spatial_val_accuracy_Simple_Random.R

In hamun001/STEF: Space-time change analysis of earth observation data for forest cover change monitoring and detection

#'@title Calculates accuracy of land/forest cover maps based on random sampling
#'
#'@description This function calculates accuracy of land/forest cover and its change mapping. For sample collected using simple random sampling. Accuracy estimates such as overall accuracies, class accuracies are calculated while correcting
#'area bias in the sample representation that is recommended when the sample sites do not have equal inclusion probability. Area estimation of land/forest cover classes are also corrected for map error (Card 1982 and Olofsson et al 2014, Tsendbazar et al 2016).
#'Area weighted or error adjusted accuracy and area estimation uses a normal confusion matrix based on sample counting and proportion of area of land/forest cover classes. Accuracy calculations
#'by Card 1982 is implemented.
#'
#'@param cm    Input confusion matrix where reference classes are represented in the columns and mapped classes are in the rows. It can be data.frame or matrix with equal number of rows and columns.
#'@param area    A list containing the area proportions (between o to 1) of each classes within the mapped area.
#'@param alpha   Value (1-confidence level) used for calculating z score for upper and lower limits of the accuracy estimates.
#'@import stats
#'
#'@references 1. Card, D.H."Using known map category marginal frequencies to improve estimates of thematic map accuracy." Review of. Photogrammetric Engineering and Remote Sensing 48 (3):431-9.
#'
#'2. Tsendbazar, N. E., S. de Bruin, B. Mora, L. Schouten, and M. Herold."Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data." International Journal of Applied Earth Observation and Geoinformation 44:124-35.
#'  \url{http://dx.doi.org/10.1016/j.jag.2015.08.009}
#'
#'3. Olofsson, Pontus, Giles M. Foody, Martin Herold, Stephen V. Stehman, Curtis E. Woodcock, and Michael A. Wulder."Good practices for estimating area and assessing accuracy of land change." Remote Sensing of Environment 148:42-57.
#'  \url{http://dx.doi.org/10.1016/j.rse.2014.02.015}
#'
#'@details  ADD extra details about the function and methods here Nandika, if any ......
#'@return Returns a \code{list} object containing:
#'@section :
#'{\code{overall_accuracy} {:} {Matrix of class \code{"numeric"}, containing overall accuracies (sample count and area adjusted), their variences and confidence intervals.}}
#'  \describe{
#'    \itemize{
#'    \item{\code{sample n} {-} {Total number of sample used for the analysis}}
#'    \item{\code{overall accuracy} {-} {Overall accuracies based on sample-count based and area adjusted confusion matrices}}
#'    \item{\code{overall accuracy variance} {-} {Variance of the overall accuracies based on sample-count based and area adjusted confusion matrices}}
#'    \item{\code{o.a.upper limit} {-} {Upper limit of the overall accuracies based on the confidence level of 1-Alpha e.g.95 percent condence level: 1-0.05}}
#'    \item{\code{o.a.lower limit} {-} {Lower limit of the overall accuracies based on the confidence level of 1-Alpha e.g.95 percent condence level: 1-0.05}}
#'    }
#'    {\code{class_area_accuracy} : {Matrix of class \code{"numeric"}, containing class specific user's and producer's accuracies as well as area estimation of each classes adjusted for area bias for the sample selection and map error.}}
#'    \itemize{
#'    \item{\code{tp} {-} {True proportion: Error adjusted map proportion for each class.}}
#'    \item{\code{tpc} {-} {Confidence interval for the error adjusted map proportions. Upper limit tp+tpc; Lower limit tp-tpc}}
#'    \item{\code{uaw} {-} {User's accucary for each class based after adjusting area bias of the sample selection}}
#'    \item{\code{uac} {-} {Confidence intervals for the user accucaries. Upper limit uaw+uac; lower limit uaw-uac}}
#'    \item{\code{paw} {-} {Producer's accucary for each class based after adjusting area bias of the sample selection}}
#'    \item{\code{pac} {-} {Confidence intervals for the producer accucaries. Upper limit paw+pac; lower limit uaw-uac}}
#'    }
#'    {\code{adjusted_conf_matrix} : {A confusion matrix that is corrected for adjusted for area bias for the sample selection.}}
#'  }
#'
#'
#'@examples
#'\dontrun{
#'
#'
#' #preparing matrix
#' cm<-c(48, 0, 2, 5, 1, 49, 0, 4, 1, 0, 47, 3, 0, 1, 1, 34)
#' cm1<-matrix(cm, 4, 4, byrow=T)
#' rownames(cm1)<-c( "class1", "class2", "class3", "class4")
#' colnames(cm1)<-c( "class1", "class2", "class3", "class4")
#' #proportions of the classes
#' area<-c(0.4, 0.4, 0.14, 0.06)
#' test.acc<-accuracy.random(cm1, area, alpha=0.05)
#'}
#' @author Nandika Tsendbazar
#'
#'
accuracy.random<-function(cm, area,  alpha=0.05) {
  #convert both data frames and vectors to matrices
  cmx<-as.matrix(cm)
  #try to convert a vector to a square matrix
  if (ncol(cmx) == 1)
    cmx<-matrix(cmx, byrow=TRUE, nrow=sqrt(nrow(cmx)))
  nr<-nrow(cmx); nc<-ncol(cmx)
  if (nr != nc)
  { print("Error: matrix is not square"); break }

  #descriptive statistics
  n<-sum(cmx)
  d<-diag(cmx); dsum<-sum(d); oa<-dsum/n
  oav<-((oa*(1-oa))/n)
  oul<-oa+(2*(sqrt(oav)))
  oll<-oa-(2*(sqrt(oav)))
  csum<-apply(cmx,2,sum); rsum<-apply(cmx,1,sum)


  #area  weighted matrix
  cmx_area<-matrix(0, nrow(cmx),ncol(cmx))
  for (i in 1:nrow(cmx)) {
    for (j in 1: ncol(cmx)){
      cmx_area[i,j]<-cmx[i,j]/rsum[i]*area[i]
    }
  }

  d_area<-diag(cmx_area)

  #accuracy metrics for simple random sample
  oaw<-sum(d_area) # overall area weighted accuracy
  oawv<-sum(d_area*(area-d_area)/(area*n))# variance for random sample
  tp<-apply(cmx_area,2,sum) # true(error adjusted) proportion
  tpv <- numeric(ncol(cmx_area)) #true (error adjusted) map proportion variance
  for (j in 1:ncol(cmx_area)) {
    tpv[j]<-sum(cmx_area[,j]*(area[j]-cmx_area[,j]))/(rsum[j]*area[j])
  }
  paw<-d_area/tp #producers accuracy area weighted
  #for variance of paw, random sample
  fake_m<-cmx_area
  diag(fake_m)=0
  er<-numeric(ncol(cmx_area))
  for (j in 1:ncol(cmx_area)) {
    er[j]<-sum(fake_m[,j]*(area[j]-fake_m[,j]))/(area[j]*n)
  }
  pawv<-(d_area*(tp^(-4))*((d_area*er)+((area-d_area)*((tp-d_area)^2)/(area*n))))

  # users accuracy area weighted eq18, card 1982
  uaw<-d/rsum
  uawv<-(d_area*(area-d_area))/((area^3)*n) #eq 29 for simple random sample

  #confidence interval
  oac<-qnorm(1-(alpha/2))*sqrt(oawv)
  oawul<-oaw+oac
  oawll<-oaw-oac
  tpc<-qnorm(1-(alpha/2))*sqrt(tpv)
  uac<-qnorm(1-(alpha/2))*sqrt(uawv)
  pac<-qnorm(1-(alpha/2))*(sqrt(pawv))

  #true(error adjusted) proportion statistics
  tps<-data.frame(tp)
  row.names(tps)<-row.names(cmx)
  tps$tpc<-tpc

  #class specific accuracy statistics
  uas<-data.frame(uaw)
  row.names(uas)<-row.names(cmx)
  uas$uac<-uac

  pas<-data.frame(paw)
  row.names(pas)<-row.names(cmx)
  pas$pac<-pac

  # all overall accuracies
  overall.area.weighted<-as.data.frame(rbind(n, oaw, oawv, oawul, oawll))
  overall.sample.count<-as.data.frame(rbind(n, oa, oav, oul, oll))
  overall<-cbind(overall.sample.count, overall.area.weighted)
  row.names(overall)<-c("sample n","overall accuracy","overall accuracy variance","o.a.upper limit","o.a.lower limit")
  colnames(overall)<-c("sample count", "area weighted")
  #writing class specific results
  class.acc<-(tps);class.acc[3:4]<-uas; class.acc[5:6]<-pas
  #area adjusted (proportional confusion matrix)
  p.conf.m<-as.data.frame(cmx_area)
  colnames(p.conf.m)<-colnames(cmx)
  row.names(p.conf.m)<-row.names(cmx)
  #returning the results
  output <- list(overall, class.acc, p.conf.m)
  names(output)<-c("overall_accuracy", "class_area_accuracy ","adjusted_conf_matrix")
  output
  }