ciBand {berryFunctions}

polygon confidence bands

Description

polygon for confidence interval bands, can handle NA's well

Usage

ciBand(
  yu,
  yl,
  ym = NULL,
  x = 1:length(yu),
  na = "interpolate",
  nastars = TRUE,
  singlepoints = TRUE,
  args = NULL,
  add = FALSE,
  lwd = 1,
  colm = "green3",
  colb = addAlpha(colm),
  border = NA,
  las = 1,
  ylim = range(yu, yl, finite = TRUE),
  ...
)

Arguments

yu

y values of upper confidence region boundary

yl

y values of lower confidence region boundary

ym

y values of middle/median/mean line. Only added if this argument is given. DEFAULT: NULL

x

x values (one ascending vector). DEFAULT: 1:length(yu)

na

Method used at NA points. One of "interpolate" or "remove". DEFAULT: "interpolate"

nastars

If na="interpolate", should stars be drawn at places that used to be NA? DEFAULT: TRUE

singlepoints

If na="remove", add points for places surrounded by NAs? can be a boolean (T/F) vector of length three for upper, lower, median. Code to identify isolated points is taken from wq::plotTs. DEFAULT: TRUE

args

List of arguments passed to points for the previous two arguments. DEFAULT: NULL

add

Add to existing plot? If FALSE, plot is called before adding confidence interval. DEFAULT: FALSE

lwd

Line width of middle line. DEFAULT: 1

colm

Color for median/mean line. DEFAULT: "green3"

colb

Color of the confidence region band. DEFAULT: addAlpha(colm)

border

polygon border. DEFAULT: NA

las

LabelAxisStyle (axis labels turned upright, see par). DEFAULT: 1

ylim

limits of plot. DEFAULT: range(yu,yl, finite=TRUE)

...

Further arguments passed to plot - or maybe better polygon??

Value

None, currently. Used for drawing.

Author(s)

Berry Boessenkool, [e-mail], July 2015

See Also

quantileBands, polygon, approx2

Examples

y1 <- c(1,3,4,2,1,4,6,8,7)
y2 <- c(5,6,5,6,9,8,8,9,10)
y3 <- c(4,4,5,4,4,6,7,8,9)
ciBand(yl=y1, yu=y2, ym=y3)

y1[6:7] <- NA
ciBand(yl=y1, yu=y2, ym=y3) # interpolation marked with stars if nastars=TRUE
ciBand(yl=y1, yu=y2, ym=y3, na="remove")
lines(y1, col=3, type="o")
lines(y2, col=3, type="o")

y2[1] <- NA
ciBand(yl=y1, yu=y2, ym=y3) # next observation carried backwards (NAs at begin)
# LOCF (last observation carried forwards if NAs at end)
# See ?approx2 for median/mean imputation in these cases
ciBand(yl=y1, yu=y2, ym=y3, na="remove")
y2[9] <- NA
ciBand(yl=y1, yu=y2, ym=y3)
ciBand(yl=y1, yu=y2, ym=y3, na="remove")  # NAs at both ends
y2[1] <- 5
ciBand(yl=y1, yu=y2, ym=y3)
ciBand(yl=y1, yu=y2, ym=y3, na="remove")  # NA only at end

# Actual usefull stuff:  sample size dependency of max and mean
ssdep_max <- function(n) quantile(  replicate(n=200, expr=max(rnorm(n)) )  )
ssdep_mean<- function(n) quantile(  replicate(n=200,expr=mean(rnorm(n)) )  )
x <- 1:100
res_max <- sapply(x, ssdep_max)
res_mean <- sapply(x, ssdep_mean)
ciBand(yl=res_max[2,], yu=res_max[4,], ym=res_max[3,], x=x, ylim=c(-0.5, 3))
ciBand(res_mean[2,], res_mean[4,], res_mean[3,], x=x, add=TRUE, colm="purple")