For each X-value I calculated the average Y-value and the standard deviation (sd) of each Y-value
x = 1:5
y = c(1.1, 1.5, 2.9, 3.8, 5.2)
sd = c(0.1, 0.3, 0.2, 0.2, 0.4)
plot (x, y)
How can I use the standard deviation to add error bars to each datapoint of my plot?
This question is related to
r
plot
statistics
standard-deviation
errorbar
The answer is
A Problem with csgillespie solution appears, when You have an logarithmic X axis. The you will have a different length of the small bars on the right an the left side (the epsilon follows the x-values).
You should better use the errbar function from the Hmisc package:
d = data.frame(
x = c(1:5)
, y = c(1.1, 1.5, 2.9, 3.8, 5.2)
, sd = c(0.2, 0.3, 0.2, 0.0, 0.4)
)
##install.packages("Hmisc", dependencies=T)
library("Hmisc")
# add error bars (without adjusting yrange)
plot(d$x, d$y, type="n")
with (
data = d
, expr = errbar(x, y, y+sd, y-sd, add=T, pch=1, cap=.1)
)
# new plot (adjusts Yrange automatically)
with (
data = d
, expr = errbar(x, y, y+sd, y-sd, add=F, pch=1, cap=.015, log="x")
)
In addition to @csgillespie's answer, segments is also vectorised to help with this sort of thing:
plot (x, y, ylim=c(0,6))
segments(x,y-sd,x,y+sd)
epsilon <- 0.02
segments(x-epsilon,y-sd,x+epsilon,y-sd)
segments(x-epsilon,y+sd,x+epsilon,y+sd)
A solution with ggplot2 :
qplot(x,y)+geom_errorbar(aes(x=x, ymin=y-sd, ymax=y+sd), width=0.25)
You can use segments to add the bars in base graphics. Here epsilon controls the line across the top and bottom of the line.
plot (x, y, ylim=c(0, 6))
epsilon = 0.02
for(i in 1:5) {
up = y[i] + sd[i]
low = y[i] - sd[i]
segments(x[i],low , x[i], up)
segments(x[i]-epsilon, up , x[i]+epsilon, up)
segments(x[i]-epsilon, low , x[i]+epsilon, low)
}
As @thelatemail points out, I should really have used vectorised function calls:
segments(x, y-sd,x, y+sd)
epsilon = 0.02
segments(x-epsilon,y-sd,x+epsilon,y-sd)
segments(x-epsilon,y+sd,x+epsilon,y+sd)
You can use arrows:
arrows(x,y-sd,x,y+sd, code=3, length=0.02, angle = 90)
Source: Stackoverflow.com