Include accuracy assessment

DESCRIPTION

@@ -56,6 +56,7 @@ LazyData: true
 RoxygenNote: 5.0.1
 Collate:
     'class-crossValidation.R'
+    'class-twdtwAccuracy.R'
     'class-twdtwTimeSeries.R'
     'class-twdtwMatches.R'
     'class-twdtwRaster.R'
@@ -86,6 +87,8 @@ Collate:
     'subset.R'
     'twdtw.R'
     'twdtwApply.R'
+    'twdtwAssessment.R'
     'twdtwClassify.R'
+    'twdtwCrossValidation.R'
     'zzz.R'
 VignetteBuilder: knitr

NAMESPACE

@@ -60,6 +60,7 @@ exportMethods(show)
 exportMethods(subset)
 exportMethods(summary)
 exportMethods(twdtwApply)
+exportMethods(twdtwAssessment)
 exportMethods(twdtwClassify)
 exportMethods(twdtwCrossValidation)
 exportMethods(twdtwMatches)

R/class-crossValidation.R

@@ -80,87 +80,3 @@ setMethod("initialize",
             }
 )
 
-setGeneric("twdtwCrossValidation", 
-           def = function(object, ...) standardGeneric("twdtwCrossValidation")
-)
-
-#' @inheritParams twdtwCrossValidation-class
-#' @aliases twdtwCrossValidation-create
-#' 
-#' @describeIn twdtwCrossValidation Splits the set of time 
-#' series into training and validation. The function uses stratified 
-#' sampling and a simple random sampling for each stratum. For each data partition 
-#' this function performs a TWDTW analysis and returns the Overall Accuracy, 
-#' User's Accuracy, Produce's Accuracy, error matrix (confusion matrix), and a 
-#' \code{\link[base]{data.frame}} with the classification (Predicted), the 
-#' reference classes (Reference), and some TWDTW information.
-#'
-#' @examples 
-#' \dontrun{
-#' # Data folder 
-#' data_folder = system.file("lucc_MT/data", package = "dtwSat")
-#' 
-#' # Read dates 
-#' dates = scan(paste(data_folder,"timeline", sep = "/"), what = "dates")
-#' 
-#' # Read raster time series 
-#' evi = brick(paste(data_folder,"evi.tif", sep = "/"))
-#' raster_timeseries = twdtwRaster(evi, timeline = dates)
-#' 
-#' # Read field samples 
-#' field_samples = read.csv(paste(data_folder,"samples.csv", sep = "/")) 
-#' table(field_samples[["label"]])
-#' 
-#' # Read field samples projection 
-#' proj_str = scan(paste(data_folder,"samples_projection", sep = "/"), 
-#'      what = "character")
-#' 
-#' # Get sample time series from raster time series 
-#' field_samples_ts = getTimeSeries(raster_timeseries, 
-#'      y = field_samples, proj4string = proj_str)
-#' field_samples_ts
-#' 
-#' # Run cross validation
-#' set.seed(1)
-#' # Define TWDTW weight function 
-#' log_fun = logisticWeight(alpha=-0.1, beta=50) 
-#' cross_validation = twdtwCrossValidation(field_samples_ts, times=3, p=0.1, 
-#'                           freq = 8, formula = y ~ s(x, bs="cc"), weight.fun = log_fun)
-#' cross_validation
-#' 
-#' summary(cross_validation)
-#' 
-#' }
-#' @export
-setMethod(f = "twdtwCrossValidation", 
-          definition = function(object, times, p, ...) twdtwCrossValidation.twdtwTimeSeries(object, times, p, ...))
-
-twdtwCrossValidation.twdtwTimeSeries = function(object, times, p, ...){
-
-  partitions = createDataPartition(y = labels(object), times, p, list = TRUE)
-
-  res = lapply(partitions, function(I){
-    training_ts = subset(object, I)
-    validation_ts = subset(object, -I)
-    patt = createPatterns(training_ts, ...)
-    twdtw_res = twdtwApply(x = validation_ts, y = patt, n=1, ...)
-    df = do.call("rbind", lapply(twdtw_res[], function(xx) xx[which.min(xx$distance),]) )
-    ref = labels(twdtw_res)$timeseries
-    pred = df$label
-    data = data.frame(.adjustFactores(ref, pred, levels=NULL, labels=NULL), df[,!names(df)%in%"labels"])
-    error.matrix = table(Predicted=data$Predicted, Reference=data$Reference)
-    UA = diag(error.matrix) / rowSums(error.matrix)
-    PA = diag(error.matrix) / colSums(error.matrix)
-    O  = sum(diag(error.matrix)) / sum(rowSums(error.matrix))
-    list(OverallAccuracy=O, UsersAccuracy=UA, ProducersAccuracy=PA, ErrorMatrix=error.matrix, data=data)
-  })
-
-  new("twdtwCrossValidation", partitions=partitions, accuracy=res)
-
-}
-
-
-
-
-
-

R/class-twdtwAccuracy.R

@@ -0,0 +1,87 @@
+###############################################################
+#                                                             #
+#   (c) Victor Maus <[email protected]>                       #
+#       Institute for Geoinformatics (IFGI)                   #
+#       University of Muenster (WWU), Germany                 #
+#                                                             #
+#       Earth System Science Center (CCST)                    #
+#       National Institute for Space Research (INPE), Brazil  #
+#                                                             #
+#                                                             #
+#   R Package dtwSat - 2017-01-18                             #
+#                                                             #
+###############################################################
+
+
+#' @title class "twdtwAssessment" 
+#' @name twdtwAssessment-class
+#' @aliases twdtwAssessment
+#' @author Victor Maus, \email{vwmaus1@@gmail.com}
+#' 
+#' @description This class stores the map assessment.  
+#' 
+#' @param object an object of class \code{\link[dtwSat]{twdtwRaster}} resulting from 
+#' the classification, i.e. \code{\link[dtwSat]{twdtwClassify}}.
+#' 
+#' @param y a \code{\link[base]{data.frame}} whose attributes are: longitude, 
+#' latitude, the start ''from'' and the end ''to'' of the time interval 
+#' for each sample. This can also be a \code{\link[sp]{SpatialPointsDataFrame}} 
+#' whose attributes are the start ''from'' and the end ''to'' of the time interval.
+#' If missing ''from'' and/or ''to'', they are set to the time range of the 
+#' \code{object}. 
+#' 
+#' @param id.labels a numeric or character with an column name from \code{y} to 
+#' be used as samples labels. Optional.
+#' 
+#' @param proj4string projection string, see \code{\link[sp]{CRS-class}}. Used 
+#' if \code{y} is a \code{\link[base]{data.frame}}.
+#' 
+#' @param conf.int specifies the confidence level.
+#' 
+#' @seealso 
+#' \code{\link[dtwSat]{twdtwRaster-class}}, and 
+#' \code{\link[dtwSat]{twdtwClassify}}.
+#'
+#' @section Slots :
+#' \describe{
+#'  \item{\code{accuracy}:}{A list with the accuracy for each classified time period.}
+#'  \item{\code{data}:}{A \code{\link[base]{data.frame}} with reference labels, predicted labels, 
+#'  and other TWDTW information.}
+#' }
+#'
+#' @examples
+#' \dontrun{
+#' 
+#' }
+NULL
+setClass(
+  Class = "twdtwAssessment",
+  slots = c(accuracy = "list", data = "list"),
+  validity = function(object){
+    if(!is(object@partitions, "list")){
+      stop("[twdtwTimeSeries: validation] Invalid partitions, class different from list.")
+    }else{}
+    if(!is(object@accuracy, "list")){
+      stop("[twdtwTimeSeries: validation] Invalid accuracy, class different from list.")
+    }else{}
+    return(TRUE)
+  }
+)
+
+setMethod("initialize",
+          signature = "twdtwAssessment",
+          definition = 
+            function(.Object, partitions, accuracy){
+              .Object@partitions = list(Resample1=NULL)
+              .Object@accuracy = list(OverallAccuracy=NULL, UsersAccuracy=NULL, ProducersAccuracy=NULL, 
+                                      error.matrix=table(NULL), data=data.frame(NULL))
+              if(!missing(partitions))
+                .Object@partitions = partitions
+              if(!missing(accuracy))
+                .Object@accuracy = accuracy
+              validObject(.Object)
+              return(.Object)
+            }
+)
+
+