Include twdtwXtable to create latex tables from accuracy metrics

DESCRIPTION

@@ -89,7 +89,7 @@ Collate:
     'twdtwApply.R'
     'twdtwAssess.R'
     'twdtwClassify.R'
-    'twdtwCrossValidation.R'
-    'xtable.R'
+    'twdtwCrossValidate.R'
+    'twdtwXtable.R'
     'zzz.R'
 VignetteBuilder: knitr

NAMESPACE

@@ -63,11 +63,11 @@ exportMethods(summary)
 exportMethods(twdtwApply)
 exportMethods(twdtwAssess)
 exportMethods(twdtwClassify)
-exportMethods(twdtwCrossValidation)
+exportMethods(twdtwCrossValidate)
 exportMethods(twdtwMatches)
 exportMethods(twdtwRaster)
 exportMethods(twdtwTimeSeries)
-exportMethods(xtable)
+exportMethods(twdtwXtable)
 import(ggplot2)
 import(methods)
 import(raster)

R/class-crossValidation.R

@@ -20,18 +20,12 @@
 #' 
 #' @description This class stores the results of the cross-validation. 
 #' 
-#' @param object an object of class \code{\link[dtwSat]{twdtwTimeSeries}}.
-#' 
-#' @param times Number of partitions to create.
-#' 
-#' @param p the percentage of data that goes to training. 
-#' See \code{\link[caret]{createDataPartition}} for details.
+#' @param object an object of class twdtwCrossValidation. 
 #' 
 #' @param conf.int specifies the confidence level (0-1) for interval estimation of the 
 #' population mean. For more details see \code{\link[ggplot2]{mean_cl_boot}}.
 #' 
-#' @param ... Other arguments to be passed to \code{\link[dtwSat]{createPatterns}} and 
-#' to \code{\link[dtwSat]{twdtwApply}}.
+#' @param ... Other arguments. Not used. 
 #' 
 #' @seealso 
 #' \code{\link[dtwSat]{twdtwMatches-class}},
@@ -45,8 +39,42 @@
 #'  data partitions.}
 #' }
 #'
-#' @examples
+#' @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 = twdtwCrossValidate(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)
+#' 
+#' plot(cross_validation)
 #' 
 #' }
 NULL

R/class-twdtwAssessment.R

@@ -18,34 +18,13 @@
 #' @aliases twdtwAssessment
 #' @author Victor Maus, \email{vwmaus1@@gmail.com}
 #' 
-#' @description This class stores the map assessment.  
+#' @description This class stores the map assessment metrics.  
 #' 
-#' @param object an object of class \code{\link[dtwSat]{twdtwRaster}} resulting from 
-#' the classification, i.e. \code{\link[dtwSat]{twdtwClassify}}.
+#' @param object an object of class twdtwAssessment.
 #' 
-#' @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 labels character vector with time series labels. For signature 
-#' \code{\link[dtwSat]{twdtwRaster}} this argument can be used to set the 
-#' labels for each sample in \code{y}, or it can be combined with \code{id.labels} 
-#' to select samples with a specific label.
-#' 
-#' @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 (0-1).
-#' 
-#' @seealso 
-#' \code{\link[dtwSat]{twdtwRaster-class}}, and 
-#' \code{\link[dtwSat]{twdtwClassify}}.
+#' @seealso \code{\link[dtwSat]{twdtwClassify}},  
+#' \code{\link[dtwSat]{twdtwAssess}}, and
+#' \code{\link[dtwSat]{twdtwXtable}}.
 #'
 #' @section Slots :
 #' \describe{
@@ -63,10 +42,6 @@
 #' surface area in km2 of each cell (pixel). If the twdtwRaster is projected the estimated area is calculated 
 #' using the the pixel resolution in the map unit.
 #'
-#' @examples
-#' \dontrun{
-#' 
-#' }
 NULL
 setClass(
   Class = "twdtwAssessment",

R/methods.R

@@ -449,13 +449,22 @@ show.twdtwAssessment = function(object){
   cat("Number of classification intervals:",length(object@accuracyByPeriod),"\n")
   cat("Accuracy metrics summary\n")
   cat("\nOverall\n")
-  print(object@accuracySummary$OverallAccuracy, digits=2)
-  cat("\nUsers\n")
-  print(object@accuracySummary$UsersAccuracy, digits=2)
-  cat("\nProducers\n")
-  print(object@accuracySummary$ProducersAccuracy, digits=2)
+  aux = object@accuracySummary$OverallAccuracy
+  names(aux) = gsub("ci", "ci*", names(aux))
+  print(aux, digits=2)
+  cat("\nUser's\n")
+  aux = object@accuracySummary$UsersAccuracy
+  colnames(aux) = gsub("ci", "ci*", colnames(aux))
+  print(aux, digits=2)
+  cat("\nProducer's\n")
+  aux = object@accuracySummary$ProducersAccuracy
+  colnames(aux) = gsub("ci", "ci*", colnames(aux))
+  print(aux, digits=2)
   cat("\nArea and uncertainty\n")
-  print(object@accuracySummary$AreaUncertainty, digits=2)
+  aux = object@accuracySummary$AreaUncertainty
+  colnames(aux) = gsub("ci", "ci*", colnames(aux))
+  print(aux, digits=2)
+  cat("\n*",100*object@accuracySummary$conf.int,"% confidence interval\n")
   invisible(NULL)
 }
 

R/twdtwApply.R

@@ -79,8 +79,8 @@
 #' @references 
 #' [1] Maus  V,  Camara  G,  Cartaxo  R,  Sanchez  A,  Ramos  FM,  de Queiroz, GR.
 #' (2016). A Time-Weighted Dynamic Time Warping method for land use and land cover 
-#' mapping. Selected Topics in Applied Earth Observations and Remote Sensing, 
-#' IEEE Journal of, vol.PP, no.99, pp.1-11.
+#' mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote 
+#' Sensing, vol.9, no.8, pp.3729-3739.
 #' @references 
 #' [2] Giorgino, T. (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: 
 #' The dtw Package. Journal of Statistical Software, 31, 1-24.

R/twdtwAssess.R

@@ -3,15 +3,54 @@ setGeneric("twdtwAssess",
            def = function(object, ...) standardGeneric("twdtwAssess")
 )
 
-#' @inheritParams twdtwAssessment-class
-#' @aliases twdtwAssess
-#' 
-#' @describeIn twdtwAssessment This function performs an accuracy assessment 
+#' @title Assess TWDTW classification 
+#' @name twdtwAssess
+#' @author Victor Maus, \email{vwmaus1@@gmail.com}
+#'  
+#' @description Performs an accuracy assessment 
 #' of the classified maps. The function returns Overall Accuracy, 
 #' User's Accuracy, Produce's Accuracy, error matrix (confusion matrix),
-#' and estimated area according to [1]. The function returns the metrics 
+#' and estimated area according to [1-2]. The function returns the metrics 
 #' for each time interval and a summary considering all classified intervals. 
+#' 
+#' @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 labels character vector with time series labels. For signature 
+#' \code{\link[dtwSat]{twdtwRaster}} this argument can be used to set the 
+#' labels for each sample in \code{y}, or it can be combined with \code{id.labels} 
+#' to select samples with a specific label.
+#' 
+#' @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 (0-1).
+#' 
+#' @references 
+#' [1] Olofsson, P., Foody, G.M., Stehman, S.V., Woodcock, C.E. (2013). 
+#' Making better use of accuracy data in land change studies: Estimating 
+#' accuracy and area and quantifying uncertainty using stratified estimation. 
+#' Remote Sensing of Environment, 129, pp.122-131.
+#' 
+#' @references 
+#' [2] Olofsson, P., Foody G.M., Herold M., Stehman, S.V., Woodcock, C.E., Wulder, M.A. (2014)
+#' Good practices for estimating area and assessing accuracy of land change. Remote Sensing of 
+#' Environment, 148, pp. 42-57.
 #'
+#' @seealso \code{\link[dtwSat]{twdtwClassify}},  
+#' \code{\link[dtwSat]{twdtwAssessment}}, and
+#' \code{\link[dtwSat]{twdtwXtable}}.
+#' 
 #' @examples 
 #' \dontrun{
 #' 
@@ -52,15 +91,21 @@ setGeneric("twdtwAssess",
 #' plot(r_lucc)
 #' 
 #' # Assess classification 
-#' twdtw_assess = twdtwAssess(r_lucc, validation_samples, proj4string=proj_str) 
+#' twdtw_assess = twdtwAssess(object = r_lucc, y = validation_samples, 
+#'                            proj4string = proj_str, conf.int=.95) 
 #' twdtw_assess
 #' 
-#' 
-#' xtable(twdtw_assess, type="matrix")
-#' xtable(twdtw_assess, type="accuracy")
-#' xtable(twdtw_assess, type="area")
+#' # Create latex tables 
+#' xtable(twdtw_assess, table.type="matrix")
+#' xtable(twdtw_assess, table.type="accuracy")
+#' xtable(twdtw_assess, table.type="area")
 #' 
 #' }
+NULL
+
+#' @aliases twdtwAssess-twdtwRaster
+#' @inheritParams twdtwAssess
+#' @rdname twdtwAssess 
 #' @export
 setMethod(f = "twdtwAssess", signature = "twdtwRaster",
           definition = function(object, y, labels=NULL, id.labels=NULL, proj4string=NULL, conf.int=.95) 
@@ -111,6 +156,7 @@ twdtwAssess.twdtwRaster = function(object, y, labels, id.labels, proj4string, co
 
   # Compute accuracy assessment 
   accuracy_by_period = lapply(seq_along(error_matrix_by_period), function(i) .twdtwAssess(x = error_matrix_by_period[[i]], a_by_interval[[i]], conf.int=conf.int))
+  names(accuracy_by_period) = index(object)
   accuracy_summary = .twdtwAssess(error_matrix_summary, area_by_class, conf.int=conf.int)
 
   new("twdtwAssessment", accuracySummary=accuracy_summary, accuracyByPeriod=accuracy_by_period, data=samples_all)
@@ -214,13 +260,28 @@ twdtwAssess.twdtwRaster = function(object, y, labels, id.labels, proj4string, co
 }
 
 .getPredRefClasses = function(i, r_intervals, pred, pred_distance, y, rlevels, rnames){
-  I = which((r_intervals$to[i] - as.Date(y$from) > 30) & (as.Date(y$to) - r_intervals$from[i] > 30) )
-  if(length(I)<1)
+  i_leng = as.numeric(r_intervals$to[i] - r_intervals$from[i])
+  from   = as.Date(y$from)
+  to     = as.Date(y$to)
+  # Select overlapping alignments 
+  J      = which(from <= r_intervals$to[i] & to >= r_intervals$from[i])
+  # Adjust overlapping 
+  from   = sapply(from[J], function(x) ifelse(x < r_intervals$from[i], r_intervals$from[i], x))
+  to     = sapply(to[J], function(x) ifelse(x > r_intervals$to[i], r_intervals$to[i], x))
+  # Compute overlapping proportion 
+  i_over = to - from 
+  print(i_leng)
+  print(i_over)
+  prop_over = abs(i_over / i_leng)
+  # Select alignments 
+  I = which(prop_over > .5)
+  # I = which((r_intervals$to[i] - as.Date(y$from) > 30) & (as.Date(y$to) - r_intervals$from[i] > 30) )
+  if(length(J[I])<1)
     return(NULL)
-  J = match(pred[I,i], rlevels)
-  Predicted = factor(as.character(rnames[J]), levels = rnames, labels = rnames)
-  Reference = factor(as.character(y$label[I]), levels = rnames, labels = rnames)
-  #d = pred_distance[J]
+  K = match(pred[J[I],i], rlevels)
+  Predicted = factor(as.character(rnames[K]), levels = rnames, labels = rnames)
+  Reference = factor(as.character(y$label[J[I]]), levels = rnames, labels = rnames)
+  #d = pred_distance[K]
   data.frame(Period=i, from=r_intervals$from[i], to=r_intervals$to[i], Predicted, Reference)
 }
 

R/twdtwCrossValidation.R → R/twdtwCrossValidate.R

@@ -1,20 +1,30 @@
 
-setGeneric("twdtwCrossValidation", 
-           def = function(object, ...) standardGeneric("twdtwCrossValidation")
+setGeneric("twdtwCrossValidate", 
+           def = function(object, ...) standardGeneric("twdtwCrossValidate")
 )
 
-#' @inheritParams twdtwCrossValidation-class
-#' @aliases twdtwCrossValidation
+#' @title Cross Validate temporal patterns  
+#' @name twdtwCrossValidate
+#' @author Victor Maus, \email{vwmaus1@@gmail.com}
 #' 
-#' @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), 
+#' @description Splits the set of time series into training and validation and 
+#' compute accuracy metrics. 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 the results of the TWDTW analysis.
 #'
+#' @param object an object of class \code{\link[dtwSat]{twdtwTimeSeries}}.
+#' 
+#' @param times Number of partitions to create.
+#' 
+#' @param p the percentage of data that goes to training. 
+#' See \code{\link[caret]{createDataPartition}} for details.
+#' 
+#' @param ... Other arguments to be passed to \code{\link[dtwSat]{createPatterns}} and 
+#' to \code{\link[dtwSat]{twdtwApply}}.
+#'
 #' @examples 
 #' \dontrun{
 #' # Data folder 
@@ -44,7 +54,7 @@ setGeneric("twdtwCrossValidation",
 #' 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, 
+#' cross_validation = twdtwCrossValidate(field_samples_ts, times=3, p=0.1, 
 #'                           freq = 8, formula = y ~ s(x, bs="cc"), weight.fun = log_fun)
 #' cross_validation
 #' 
@@ -53,11 +63,16 @@ setGeneric("twdtwCrossValidation",
 #' plot(cross_validation)
 #' 
 #' }
+NULL
+
+#' @aliases twdtwCrossValidate-twdtwTimeSeries
+#' @inheritParams twdtwCrossValidate
+#' @rdname twdtwCrossValidate 
 #' @export
-setMethod(f = "twdtwCrossValidation", 
-          definition = function(object, times, p, ...) twdtwCrossValidation.twdtwTimeSeries(object, times, p, ...))
+setMethod(f = "twdtwCrossValidate", signature = "twdtwTimeSeries",
+          definition = function(object, times, p, ...) twdtwCrossValidate.twdtwTimeSeries(object, times, p, ...))
 
-twdtwCrossValidation.twdtwTimeSeries = function(object, times, p, ...){
+twdtwCrossValidate.twdtwTimeSeries = function(object, times, p, ...){
 
   partitions = createDataPartition(y = labels(object), times, p, list = TRUE)