plotCurves <- function(data, indices=seq_len(data$getSize()))
{
series = data$getSeries(indices)
- yrange = quantile(series, probs=c(0.05,0.95), na.rm=TRUE)
+ 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))
{
# Complete series with (past and present) tomorrows
ref_serie = c(ref_serie,data$getCenteredSerie(index+1))
centered_series = rbind( centered_series, data$getCenteredSeries(fdays+1) )
- yrange = quantile(cbind(ref_serie,centered_series), probs=c(0.05,0.95), na.rm=TRUE)
+ yrange = quantile(cbind(ref_serie,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])
+ abline(v=24, lty=2, col=colors()[56], lwd=1)
}
list("index"=index,"neighb_indices"=fdays[sorted_distances$ix[1:nn]],"colors"=colors)
usr <- par("usr")
yr <- (usr[4] - usr[3]) / 27
par(new=TRUE)
- plot(data$getSerie(fil$index), type="l", lwd=2, lty=2,
+ plot(c(data$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])
}
#' Plot relative conditional variability / absolute variability