-#' @title plot curves
+#' plot curves
#'
-#' @description Plot a range of curves in data
+#' Plot a range of curves in data
#'
#' @param data Object of class Data
#' @param indices Range of indices (integers or dates)
}
}
-#' @title plot measured / predicted
+#' plot measured / predicted
#'
-#' @description Plot measured curve (in black) and predicted curve (in red)
+#' Plot measured curve (in black) and predicted curve (in red)
#'
#' @param data Object return by \code{getData}
#' @param pred Object as returned by \code{computeForecast}
plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
}
-#' @title Compute filaments
+#' Compute filaments
#'
-#' @description Get similar days in the past + "past tomorrow", as black as distances are small
+#' Get similar days in the past + "past tomorrow", as black as distances are small
#'
#' @param data Object as returned by \code{getData}
#' @param index Index in data
list("indices"=c(fdays[ indices[plot_order] ],index), "colors"=colors)
}
-#' @title Plot similarities
+#' Plot similarities
#'
-#' @description Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights)
#'
#' @param pred Object as returned by \code{computeForecast}
#' @param index Index in forecasts (not in data)
hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
}
-#' @title Plot error
+#' Plot error
#'
-#' @description Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast}
#'
#' @param err Error as returned by \code{computeError}
#' @param cols Colors for each error (default: 1,2,3,...)
#'
-#' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
-#' \code{\link{plotFbox}}
+#' @seealso \code{\link{plotPredReal}},\code{\link{plotFilaments}}
+#' \code{\link{plotSimils}},\code{\link{plotFbox}},\code{\link{plotRelativeVariability}}
#'
#' @export
plotError <- function(err, cols=seq_along(err))
}
}
-#' @title Functional boxplot
+#' Functional boxplot
#'
-#' @description Draw the functional boxplot on the left, and bivariate plot on the right
+#' Draw the functional boxplot on the left, and bivariate plot on the right
#'
#' @param data Object return by \code{getData}
#' @param fiter Optional filter: return TRUE on indices to process
rep(NA,L)
})
# TODO: merge with previous step: only one pass should be required
- no_NAs_indices = sapply( 1:ncol(series_matrix), function(i) all(!is.na(series_matrix[,i])) )
+ no_NAs_indices = sapply( 1:ncol(series_matrix),
+ function(i) all(!is.na(series_matrix[,i])) )
series_matrix = series_matrix[,no_NAs_indices]
series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
}
-#' @title Functional boxplot on filaments
+#' Functional boxplot on filaments
#'
-#' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
}
-#' @title Plot relative conditional variability / absolute variability
+#' Plot relative conditional variability / absolute variability
#'
-#' @description Draw the relative conditional variability / absolute variability based on on
-#' filaments obtained by \code{computeFilaments}
+#' Draw the relative conditional variability / absolute variability based on filaments
+#' obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
#' @export
plotRelativeVariability = function(data, indices, ...)
{
- #plot left / right separated by vertical line brown dotted
- #median of 3 runs for random length(indices) series
ref_series = t( sapply(indices, function(i) {
c( data$getSerie(i), data$getSerie(i+1) )
}) )
if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
fdays = c(fdays, i)
}
+ global_var = c( apply(data$getSerie(fdays),2,sd), apply(data$getSerie(fdays+1),2,sd) )
- # TODO: 3 == magic number
- random_var = matrix(nrow=3, ncol=48)
- for (mc in seq_len(nrow(random_var)))
- {
- random_indices = sample(fdays, length(indices))
- random_series = t( sapply(random_indices, function(i) {
- c( data$getSerie(i), data$getSerie(i+1) )
- }) )
- random_var[mc,] = apply(random_series, 2, sd)
- }
- random_var = apply(random_var, 2, median)
-
- yrange = range(ref_var, random_var)
+ yrange = range(ref_var, global_var)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- plot(ref_var, type="l", col=1, lwd=3, ylim=yrange, xlab="Temps (heures)", ylab="Écart-type")
+ plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
+ xlab="Temps (heures)", ylab="Écart-type")
par(new=TRUE)
plot(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
abline(v=24, lty=2, col=colors()[56])