error.crosses

0th

Percentile

Plot x and y error bars

Given two vectors of data (X and Y), plot the means and show standard errors in both X and Y directions.

Keywords
multivariate, hplot
Usage
error.crosses(x,y,labels=NULL,main=NULL,xlim=NULL,ylim= NULL,
colors=NULL,col.arrows=NULL,col.text=NULL,...)
Arguments
x

A vector of data or summary statistics (from Describe)

y

A second vector of data or summary statistics (also from Describe)

labels

the names of each pair -- defaults to rownames of x

main

The title for the graph

xlim

xlim values if desired-- defaults to min and max mean(x) +/- 2 se

ylim

ylim values if desired -- defaults to min and max mean(y) +/- 2 se

xlab

label for x axis -- grouping variable 1

ylab

label for y axis -- grouping variable 2

pos

Labels are located where with respect to the mean?

offset

Labels are then offset from this location

arrow.len

Arrow length

alpha

alpha level of error bars

sd

if sd is TRUE, then draw means +/- 1 sd)

if TRUE, overlay the values with a prior plot

colors

What color(s) should be used for the plot character? Defaults to black

col.arrows

What color(s) should be used for the arrows -- defaults to colors

col.text

What color(s) should be used for the text -- defaults to colors

Other parameters for plot

Details

For an example of two way error bars describing the effects of mood manipulations upon positive and negative affect, see https://personality-project.org/revelle/publications/happy-sad-appendix/FIG.A-6.pdf

The second example shows how error crosses can be done for multiple variables where the grouping variable is found dynamically. The errorCircles example shows how to do this in one step.

To draw error bars for single variables error.bars, or by groups error.bars.by, or to find descriptive statistics describe or descriptive statistics by a grouping variable describeBy and statsBy.

A much improved version is now called errorCircles.

Aliases
• error.crosses
Examples
# NOT RUN {
#just draw one pair of variables
desc <- describe(attitude)
x <- desc[1,]
y <- desc[2,]
error.crosses(x,y,xlab=rownames(x),ylab=rownames(y))

#now for a bit more complicated plotting
data(bfi)
desc <- describeBy(bfi[1:25],bfi$gender) #select a high and low group error.crosses(desc$'1',desc$'2',ylab="female scores",xlab="male scores",main="BFI scores by gender") abline(a=0,b=1) #do it from summary statistics (using standard errors) g1.stats <- data.frame(n=c(10,20,30),mean=c(10,12,18),se=c(2,3,5)) g2.stats <- data.frame(n=c(15,20,25),mean=c(6,14,15),se =c(1,2,3)) error.crosses(g1.stats,g2.stats) #Or, if you prefer to draw +/- 1 sd. instead of 95% confidence g1.stats <- data.frame(n=c(10,20,30),mean=c(10,12,18),sd=c(2,3,5)) g2.stats <- data.frame(n=c(15,20,25),mean=c(6,14,15),sd =c(1,2,3)) error.crosses(g1.stats,g2.stats,sd=TRUE) #and seem even fancy plotting: This is taken from a study of mood #four films were given (sad, horror, neutral, happy) #with a pre and post test data(affect) colors <- c("black","red","green","blue") films <- c("Sad","Horror","Neutral","Happy") affect.mat <- describeBy(affect[10:17],affect$Film,mat=TRUE)
error.crosses(affect.mat[c(1:4,17:20),],affect.mat[c(5:8,21:24),],
labels=films[affect.mat$group1],xlab="Energetic Arousal", ylab="Tense Arousal",colors = colors[affect.mat$group1],pch=16,cex=2)

# }

Documentation reproduced from package psych, version 1.8.12, License: GPL (>= 2)

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