error.crosses | R Documentation |

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

error.crosses(x,y,labels=NULL,main=NULL,xlim=NULL,ylim= NULL, xlab=NULL,ylab=NULL,pos=NULL,offset=1,arrow.len=.2,alpha=.05,sd=FALSE,add=FALSE, colors=NULL,col.arrows=NULL,col.text=NULL,...)

`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) |

`add` |
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 |

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.

William Revelle

revelle@northwestern.edu

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`

.

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

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