#' Plot curves
#'
-#' Plot a range of curves in data
+#' Plot a range of curves in data.
#'
-#' @param data Object of class Data
+#' @inheritParams computeError
#' @param indices Range of indices (integers or dates)
#'
#' @export
series = data$getSeries(indices)
yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- for (i in seq_along(indices))
- {
- plot(series[,i], type="l", ylim=yrange,
- xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"PM10",""))
- if (i < length(indices))
- par(new=TRUE)
- }
+ matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
}
#' Plot error
#'
-#' Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast()}.
#'
-#' @param err Error as returned by \code{computeError}
+#' @param err Error as returned by \code{computeError()}
#' @param cols Colors for each error (default: 1,2,3,...)
#'
#' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
-#' \code{\link{plotSimils}}, \code{\link{plotFbox}},
-#' \code{\link{computeFilaments}, }\code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
+#' \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
+#' \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
#'
#' @export
plotError <- function(err, cols=seq_along(err))
#' Plot measured / predicted
#'
-#' Plot measured curve (in black) and predicted curve (in blue)
+#' Plot measured curve (in black) and predicted curve (in blue).
#'
-#' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
plotPredReal <- function(data, pred, index)
{
- horizon = length(pred$getSerie(1))
- measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
- prediction = pred$getSerie(index)
+ prediction = pred$getForecast(index)
+ measure = data$getSerie( pred$getIndexInData(index) )[1:length(prediction)]
yrange = range(measure, prediction)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
#' Plot similarities
#'
-#' Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights), for 'Neighbors' method.
#'
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
#' Functional boxplot
#'
-#' 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 indices integer or date indices to process
+#' @inheritParams computeError
+#' @inheritParams plotCurves
#'
#' @export
plotFbox <- function(data, indices=seq_len(data$getSize()))
#' Compute filaments
#'
-#' Get similar days in the past, as black as distances are small
+#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
+#' are small.
#'
-#' @param data Object as returned by \code{getData}
-#' @param pred Object of class Forecast
+#' @inheritParams computeError
#' @param index Index in forecast (integer or date)
#' @param limit Number of neighbors to consider
#' @param plot Should the result be plotted?
+#' @param predict_from First prediction instant
#'
#' @return A list with
#' \itemize{
#' }
#'
#' @export
-computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
+computeFilaments <- function(data, pred, index, predict_from, limit=60, plot=TRUE)
{
- ref_serie = data$getCenteredSerie( pred$getIndexInData(index) )
- if (any(is.na(ref_serie)))
+ if (is.null(pred$getParams(index)$weights) || is.na(pred$getParams(index)$weights[1]))
stop("computeFilaments requires a serie without NAs")
# Compute colors for each neighbor (from darkest to lightest)
if (plot)
{
# Complete series with (past and present) tomorrows
- ref_serie = c(ref_serie, data$getCenteredSerie( pred$getIndexInData(index)+1 ))
+ ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
+ data$getCenteredSerie( pred$getIndexInData(index) ) )
centered_series = rbind(
- data$getCenteredSeries( pred$getParams(index)$indices ),
- data$getCenteredSeries( pred$getParams(index)$indices+1 ) )
- yrange = range( ref_serie, quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
+ data$getCenteredSeries( pred$getParams(index)$indices-1 ),
+ data$getCenteredSeries( pred$getParams(index)$indices ) )
+ yrange = range( ref_serie,
+ quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
for (i in nn:1)
{
}
# Also plot ref curve, in red
plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
- abline(v=24, lty=2, col=colors()[56], lwd=1)
+ abline(v=24+predict_from-0.5, lty=2, col=colors()[56], lwd=1)
}
list(
#' Functional boxplot on filaments
#'
-#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
#'
-#' @param data Object return by \code{getData}
+#' @inheritParams computeError
#' @param fil Output of \code{computeFilaments}
#'
#' @export
-plotFilamentsBox = function(data, fil)
+plotFilamentsBox = function(data, fil, predict_from)
{
if (!requireNamespace("rainbow", quietly=TRUE))
stop("Functional boxplot requires the rainbow package")
series_matrix = rbind(
- data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
+ data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
+
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
plotlegend=FALSE, lwd=2)
- # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
+ # "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
usr <- par("usr")
yr <- (usr[4] - usr[3]) / 27
par(new=TRUE)
plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
- abline(v=24, lty=2, col=colors()[56])
+ abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}
#' Plot relative conditional variability / absolute variability
#'
#' Draw the relative conditional variability / absolute variability based on filaments
-#' obtained by \code{computeFilaments}
+#' obtained by \code{computeFilaments()}.
#'
-#' @param data Object return by \code{getData}
-#' @param fil Output of \code{computeFilaments}
+#' @inheritParams computeError
+#' @inheritParams plotFilamentsBox
#'
#' @export
-plotRelVar = function(data, fil)
+plotRelVar = function(data, fil, predict_from)
{
- ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
- apply(data$getSeries(fil$neighb_indices+1),1,sd) )
- fdays = getNoNA2(data, 1, fil$index-1)
- global_var = c( apply(data$getSeries(fdays),1,sd), apply(data$getSeries(fdays+1),1,sd) )
+ ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
+ apply(data$getSeries(fil$neighb_indices),1,sd) )
+ tdays = .getNoNA2(data, 2, fil$index)
+ global_var = c(
+ apply(data$getSeries(tdays-1),1,sd),
+ apply(data$getSeries(tdays),1,sd) )
yrange = range(ref_var, global_var)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
xlab="Time (hours)", ylab="Standard deviation")
par(new=TRUE)
plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
- abline(v=24, lty=2, col=colors()[56])
+ abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}