error.bars.by {psych} R Documentation

Plot means and confidence intervals for multiple groups

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 for each group are calculated using describe.by.

Usage

error.bars.by(x,group,by.var=FALSE,x.cat=TRUE,ylab =NULL, xlab=NULL, main=NULL,
ylim= NULL,xlim=NULL, eyes=TRUE, alpha=.05,sd=FALSE, labels=NULL, v.labels=NULL,
colors=c("black","blue","red"), lty=NULL,lines=TRUE, legend=0,...)

Arguments

 x A data frame or matrix group A grouping variable by.var A different line for each group (default) or each variable x.cat Is the grouping variable categorical (TRUE) or continuous (FALSE ylab y label xlab x label main title for figure ylim if specified, the y limits for the plot, otherwise based upon the data xlim if specified, the x limits for the plot, otherwise based upon the data eyes Should 'cats eyes' be drawn' alpha alpha level of confidence interval. Default is 1- alpha =95% confidence interval sd sd=TRUE will plot Standard Deviations instead of standard errors labels X axis label v.labels For a bar plot legend, these are the variable labels 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 bars Draw a barplot with error bars rather than a simple plot of the means within Should the s.e. be corrected by the correlation with the other variables? colors groups will be plotted in different colors (mod n.groups) lty line type may be specified in the case of not plotting by variables lines By default, when plotting different groups, connect the groups with a line of type = lty. If lines is FALSE, then do not connect the groups legend Where should the legend be drawn: 0 (do not draw it), 1= lower right corner, 2 = bottom, 3 ... 8 continue clockwise, 9 is the center ... other parameters to pass to the plot function e.g., lty="dashed" to draw dashed lines

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 (adjusted for the t-distribution).

This function was originally just a wrapper for error.bars but has been written to allow groups to be organized either as the x axis or as separate lines.

If desired, a barplot with error bars can be shown. Many find this type of plot to be uninformative (e.g., http://biostat.mc.vanderbilt.edu/DynamitePlots ) and recommend the more standard dot plot.

Note in particular, if choosing to draw barplots, the starting value is 0.0 and setting the ylim parameter can lead to some awkward results if 0 is not included in the ylim range. Did you really mean to draw a bar plot in this case?

Value

Graphic output showing the means + x% confidence intervals for each group. For ci=1.96, and normal data, this will be the 95% confidence region. For ci=1, the 68% confidence region.

These confidence regions are based upon normal theory and do not take into account any skew in the variables. More accurate confidence intervals could be found by resampling.

Examples

data(sat.act)
#The generic plot of variables by group
error.bars.by(sat.act[1:4],sat.act\$gender,legend=7)
#a bar plot
error.bars.by(sat.act[5:6],sat.act\$gender,bars=TRUE,labels=c("male","female"),
main="SAT V and SAT Q by gender",ylim=c(0,800),colors=c("red","blue"),
legend=5,v.labels=c("SATV","SATQ"))  #draw a barplot
#a bar plot of SAT by age -- not recommended, see the next plot
error.bars.by(sat.act[5:6],sat.act\$education,bars=TRUE,xlab="Education",
main="95 percent confidence limits of Sat V and Sat Q", ylim=c(0,800),
v.labels=c("SATV","SATQ"),legend=5,colors=c("red","blue") )
#a better graph uses points not bars
error.bars.by(sat.act[5:6],sat.act\$education,TRUE, xlab="Education",
legend=5,labels=colnames(sat.act[5:6]),ylim=c(525,700),
main="self reported SAT scores by education")  #plot SAT V and SAT Q by education

#now for a more complicated examples using 25 big 5 items scored into 5 scales
#and showing age trends by decade
#this shows how to convert many levels of a grouping variable (age) into more manageable levels.
data(bfi)   #The Big 5 data
#first create the keys
keys.list <- list(Agree=c(-1,2:5),Conscientious=c(6:8,-9,-10),
Extraversion=c(-11,-12,13:15),Neuroticism=c(16:20),Openness = c(21,-22,23,24,-25))
keys <- make.keys(28,keys.list,item.labels=colnames(bfi))
#then create the scores for those oler than 10 and less than 80
bfis <- subset(bfi,((bfi\$age > 10) & (bfi\$age < 80)))

scores <- scoreItems(keys,bfis,min=1,max=6) #set the right limits for item reversals
#now draw the results by age

error.bars.by(scores\$scores,round(bfis\$age/10)*10,by.var=TRUE,
main="BFI age trends",legend=3,labels=colnames(scores\$scores),
xlab="Age",ylab="Mean item score")

[Package psych version 1.4.5 Index]
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