error.bars: Plot means and confidence intervals

Description

One of the many functions in R to plot means and confidence intervals. Meant mainly for demonstration purposes for showing the probabilty of replication from multiple samples. Can also be combined with such functions as boxplot to summarize distributions. Means and standard errors are calculated from the raw data using describe.

Usage

error.bars(x, ylab = "Dependent Variable",xlab="Independent Variable", main=NULL, ylim = NULL, alpha=.05, labels = NULL, pos = NULL, arrow.len = 0.05, add = FALSE, ...)

Arguments

A data frame or matrix

ylab

y label

xlab

x label

main

title for figure

ylim

if specified, the limits for the plot, otherwise based upon the data

alpha

alpha level of confidence interval -- defaults to 95% confidence interval

labels

X axis label

pos

where to place text: below, left, above, right

arrow.len

How long should the top of the error bars be?

add

add=FALSE, new plot, add=TRUE, just points and error bars

...

other parameters to pass to the plot function, e.g., typ="b" to draw lines, lty="dashed" to draw dashed lines

Value

Graphic output showing the means + x

Details

Drawing the mean +/- a confidence interval is a frequently used function when reporting experimental results. By default, the confidence interval is 1.96 standard errors.

Examples

Run this code

x <- matrix(rnorm(500),ncol=20)
error.bars(x)
#now do a boxplot and then add error bars
x.df <- as.data.frame(x)
boxplot(x.df)
error.bars(x.df, add=TRUE)

error.bars(attitude) #another example

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