plot.seas.sum: Plot boxplots of normalized seasonal sums

Description

Plots normalized seasonal sums using boxplots.

Usage

# S3 method for seas.sum
plot(x, var, norm = "days", year.filter, ylim,
    start = 1, rep = 0, col = "lightgrey", main, ylab, …)

Arguments

a seas.sum object created by seas.sum

var

name of seasonal variable in x

norm

a variable to normalize by, either "days" (to produce unit/day) or "active" (unit/day, when active); it may also be a matrix with the same dimensions as x$days

year.filter

use only these years for analysis

ylim

either a single value for c(0, ylim), or a range of c(min, max) for the y-axis limits

start

starting bin at left-hand side of plot

rep

repeat bins on right-hand side of plot

col

colour for boxplot, default is "lightgrey"

main

title for plot; if it is missing, then it will automatically be generated

ylab

y-axis label; if it is missing, then it will automatically be generated

…

ignored

Warning

The appearance of the boxplots are sensitive to the width parameter specified in the seas.sum function on strongly discontinuous variables. Small bin widths capture the discontinuities better than wider bins, and changes the skew of the distribution.

For instance, the median will appear to decrease as width decreases.

Details

This function is a boxplot interpretation of a seas.sum object. This is not the same as treating var as a continuous variable and using seas.var.plot, since a seas.sum object has been smoothed. Daily extreme values are not well represented here as a result.

Examples

Run this code

# NOT RUN {
data(mscdata)
par.orig <- par(no.readonly=TRUE)
on.exit(par.orig)

dat <- mksub(mscdata, id=1108447)
dat.ss <- seas.sum(dat)

# Normalized by the number of days in each bin
plot(dat.ss)

# Normalized by the number of active days in each bin
plot(dat.ss, norm="active")

# Snow, using a different start day, and a better y-axis:
dat2.ss <- seas.sum(dat, var="snow", width="mon",
                    start.day=as.Date("2000-08-01"))
par(yaxs="i")
plot(dat2.ss, var="snow")
plot(dat2.ss, var="snow", norm="active")
# }

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