Nothing
PhenoPlot <-
function(data, metrics, add=FALSE, show.uncert=TRUE, ...) {
if (class(metrics)=='list') metrics <- metrics[[1]]
## recognise series and understand wheter uncert exists
## data is output from [fitname]Fit functions
## metrics is output from PhenoExtract function
if (is.null(dim(metrics))) uncert=FALSE else uncert=TRUE
if (!add) plot(data$fit$predicted, type='n', ...)
if (show.uncert & !is.null(data$uncertainty)) for (a in 1:dim(data$uncertainty$predicted)[2]) lines(data$uncertainty$predicted[,a], col='grey')
lines(data$fit$predicted, lwd=2)
mean.y <- mean(data$fit$predicted, na.rm=T)
if (length(metrics)!=10) the.seq <- c(1:4) else the.seq <- c(1,2,4)
if (length(the.seq)==3) {
ylons <- c(min(data$fit$predicted, na.rm=T)*1.01,
mean(data$fit$predicted, na.rm=T),
max(data$fit$predicted, na.rm=T)*0.95)
colors <- palette()[2:4]
} else {
ylons <- c(min(data$fit$predicted, na.rm=T)*1.01,
mean(data$fit$predicted, na.rm=T),
max(data$fit$predicted, na.rm=T)*0.95,
min(data$fit$predicted, na.rm=T)*1.01)
colors <- palette()[2:5]
}
if (!uncert) {
abline(v=metrics[the.seq], col=colors)
text(metrics[the.seq], y=ylons, labels=names(metrics)[the.seq], col=colors)
} else {
abline(v=metrics[2,the.seq], col=colors)
text(metrics[2, the.seq], y=ylons*1.02, labels=names(metrics)[the.seq], col=colors)
for (a in the.seq) arrows(metrics[1,a], ylons[a], metrics[3,a], ylons[a], code=3, angle=90, length=0.1,
lwd=2, col=colors[a])
}
}
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