I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book).

You can read it here.

I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book).

You can read it here.

*Posted by Thom on November 11, 2013*

https://seriousstats.wordpress.com/2013/11/11/multicollinearity-tutoral/

There are many good resources online for learning R. However, I recently discovered Try R from Code school – which is interactive, goes at a very gentle pace and also looks very pretty:

*Posted by Thom on October 22, 2013*

https://seriousstats.wordpress.com/2013/10/22/a-gentle-introduction-to-learning-r/

It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can cope with missing outcomes, time-varying covariates and can relax the sphericity assumption of conventional repeated measures ANOVA. They can also deal with other – less well known – problems such as having stimuli that are random factor (e.g., see this post on my Psychological Statistics blog). Last, but not least, multilevel generalised linear models allow you to have discrete and bounded outcomes (e.g., dichotomous, ordinal or count data) rather than be constrained by as assuming a continuous response with normal errors.

There are two main practical problems to bear in mind when switching to the multilevel approach. First, the additional complexity of the approach can be daunting at first – though it is possible to built up gently to more complex models. Recent improvements in availability of software and support (textbooks, papers and online resources) also help. The second is that as soon as a model departs markedly from a conventional repeated measures ANOVA, correct inferences (notably significance tests and interval estimates such as confidence intervals) can be difficult to obtain. If the usual ANOVA assumptions hold in a nested, balanced design then there is a known equivalence between the multilevel model inferences using *t* or *F* tests and the familiar ANOVA tests (and this case the expected output of the tests is the same). The main culprits are boundary effects (which effect inferences about variances and hence most tests of random effects) and working out the correct degrees of freedom (*df*) to use for your test statistic. Both these problems are discussed in Chapter 18 of the book. If you have very large samples an asymptotic approach (using Wald *z* or chi-square statistics) is probably just fine. However, the further you depart from conventional repeated measures ANOVA assumptions the harder it is to know how large a sample news to be before the asymptotics kick in. In other words, the more attractive the multilevel approach the less you can rely on the Wald tests (or indeed the Wald-style *t* or *F* tests).

The solution I advocate in Serious Stats is either to use parametric bootstrapping or Markov chain Monte Carlo (MCMC) approaches. Another approach is to use some form of correction to the *df* or test statistic such as the Welch-Satterthwaite correction. For multilevel models with factorial type designs the recommended correction is generally the Kenward-Roger approximation. This is implemented in SAS, but (until recently) not available in R. Judd, Westfall and Kenny (2012) describe how to use the Kenward-Roger approximation to get more accurate significance tests from a multilevel model using R. Their examples use the newly developed pbkrtest package (Halekoh & Højsgaard, 2012) – which also has functions for parametric bootstrapping.

My purpose here is to contrast the the MCMC and Kenward-Roger correction (ignoring the parametric bootstrap for the moment). To do that I’ll go through a worked example – looking to obtain a significance test and a 95% confidence interval (CI) for a single effect.

*The pitch data example*

The example I’ll use is for the pitch data from from Chapter 18 of the book. This experiment (from a collaboration with Tim Wells and Andrew Dunn) involves looking at the at pitch of male voices making attractiveness ratings with respect to female faces. The effect of interest (for this example) is whether average pitch goes up or done for higher ratings (and if so, by how much). A conventional ANOVA is problematic because this is a design with two fully crossed random factors – each participant (*n* = 30) sees each face (*n* = 32) and any conclusions ought to generalise both to other participants and (crucially) to other faces. Furthermore, there is a time-varying covariate – the baseline pitch of the numerical rating when no face is presented. The significance tests or CIs reported by most multilevel modelling packages with also be suspect. Running the analysis in the R package lme4 gives parameter estimates and *t* statistics for the fixed effects but no *p* values or CIs. The following R code loads the pitch data, checks the first few cases, loads lme4 and runs the model of interest. (You should install lme4 using the command install.packages(‘lme4′) if you haven’t done so already).

Note the lack of df and p values. This is deliberate policy by the lme4 authors; they are not keen on giving users output that has a good chance of being very wrong.

*The Kenward-Roger approximation*

This approximation involves adjusting both the *F* statistic and its *df* so that the *p* value comes out approximately correct (see references below for further information). It won’t hurt too much to think of it as turbocharged Welch-Satterthwaite correction. To get the corrected *p* value from this approach first install the pbkrtest package and then load it. The approximation is computed using the KRmodcomp() function. This takes the model of interest (with the focal effect) and a reduced model (one without the focal effect). The code below installs and loads everything, runs the reduced model and then uses KRmodcomp() to get the corrected *p* value. Note that it may take a while to run (it took about 30 seconds on my laptop).

install.packages('pbkrtest') library(pbkrtest) pitch.red <- lmer(pitch ~ base + (1|Face) + (1|Participant), data=pitch.dat) KRmodcomp(pitch.me, pitch.red)

The corrected *p* value is .0001024. The result could reported as a Kenward-Roger corrected test with *F*(1, 118.5) = 16.17, *p* = .0001024. In this case the Wald *z* test would have given a p value of around .0000435. Here the effect is sufficiently large that the difference in approaches doesn’t matter – but that won’t always be true.

*The MCMC approach*

The MCMC approach (discussed in Chapter 18) can be run in several ways – with the lme4 functions or those in MCMCglmm being fairly easy to implement. Here I’ll stick with lme4 (but for more complex models MCMCglmm is likely to be better).

First you need to obtain a large number of Monte Carlo simulations from the model of interest. I’ll use 25,000 here (but I often start with 1,000 and work up to a bigger sample). Again this may take a while (about 30 or 40 seconds on my laptop).

pitch.mcmc <- mcmcsamp(pitch.me, n = 25000)

For MCMC approaches it is useful to check the estimates from the simulations. Here I’ll take a quick look at the trace plot (though a density plot is also sensible – see chapter 18).

xyplot(pitch.mcmc)

This produces the following plot (or something close to it):

The trace for the fixed effect of attractiveness looks pretty healthy – the thich black central portion indicating that it doesn’t jump around too much. Now we can look at the 95% confidence interval (strictly a Bayesian highest posterior density or HPD interval – but for present purposes it approximates to a 95% CI).

HPDinterval(pitch.mcmc)

This gives the interval estimate [0.2227276, 0.6578456]. This excludes zero so it is statistically significant (and MCMCglmm would have given an us MCMC-derived estimate of the *p* value).

*Comparison and reccomendation*

Although the Kenward-Roger approach is well-regarded, for the moment I would reccomend the MCMC approach. The pbkrtest package is still under development and I could not always get the approximation or the parametric bootstrap to work (but the parametric bootstrap can also be obtained in other ways – see Chapter 18).

The MCMC approach is also preferable in that it should generalize safely to models where the performance of the Kenward-Roger approximation is unknown (or poor) such as for discrete or ordinal outcomes. It also provides interval estimates rather than just *p* values. The main downside is that you need to familiarize yourself with some basic MCMC diagnostics (e.g., trace and density plots at the very least) and be willing to re-run the simulations to check that the interval estimates are stable.

*References *

Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. *Journal of Personality and Social Psychology*, *103*, 54-69.

Halekoh, U., & Højsgaard, S. (2012) A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models – the R package pbkrtest. Submitted to Journal of Statistical Software.

Update

Ben Bolker pointed out that future versions of lme4 may well drop the MCMC functions (which are limited, at present, to fairly basic models). In the book I mainly used MCMCglmm – which is rather good at fitting fully crossed factorial models. Here is the R code for the pitch data. Using 50,000 simulations seems to give decent estimates of the attractiveness effect. Plotting the model object gives both MCMC trace plots and kernel density plots of the MCMC estimates (hit return in the console to see all the plots).

Last but not least, any one interested in the topic should keep an eye on the draft r-sig-mixed-modelling FAQ for a summary of the challenges and latest available solutions for multilevel inference in R (and other packages).

*Posted by Thom on April 18, 2013*

https://seriousstats.wordpress.com/2013/04/18/using-multilevel-models-to-get-accurate-inferences-for-repeated-measures-anova-designs/

One of the main attractions of R (for me) is the ability to produce high quality graphics that look just the way you want them to. The basic plot functions are generally excellent for exploratory work and for getting to know your data. Most packages have additional functions for appropriate exploratory work or for summarizing and communicating inferences. Generally the default plots are at least as good as other (e.g., commercial packages) but with the added advantage of being fairly easy to customize once you understand basic plotting functions and parameters.

Even so, getting a plot looking just right for a presentation or publication often takes a lot of work using basic plotting functions. One reason for this is that constructing a good graphic is an inherently difficult enterprise, one that balances aesthetic factors and statistical factors and that requires a good understanding of who will look at the graphic, what they know, what they want to know and how they will interpret it. It can takes hours – maybe days – to get a graphic right.

In Serious Stats I focused on exploratory plots and how to use basic plotting functions to customize them. I think this was important to include, but one of my regrets was not having enough space to cover a different approach to plotting in R. This is Hadley Wickham’s ggplot2 package (inspired by Leland Wilkinson’s grammar of graphics approach).

In this blog post I’ll quickly demonstrate a few ways that ggplot2 can be used to quickly produce amazing graphics for presentations or publication. I’ll finish by mentioning some pros and cons of the approach.

The main attraction of ggplot2 for newcomers to R is the qplot() quick plot function. Like the R plot() function it will recognize certain types and combinations of R objects and produce an appropriate plot (in most cases). Unlike the basic R plots the output tends to be both functional and pretty. Thus you may be able to generate the graph you need for your talk or paper almost instantly.

A good place to start is the vanilla scatter plot. Here is the R default:

Compare it with the ggplot2 default:

Below is the R code for comparison. (The data here are from hov.csv file used in Chapter 10 Example 10.2 of Serious Stats).

R code formatted by Pretty R at inside-R.org

The ggplot2 version is (in my view) rather prettier, but a big advantage is being able to add a range of different model fits very easily. The common choice of model fit is that of a straight line (usually the least squares regression line). Doing this in ggplot2 is easier than with basic plot functions (and you also get 95% confidence bands by default).

Here is the straight line fit from a linear model:

POST

qplot(x, y, data=hov.dat, geom=c(‘point’, ‘smooth’), method=’lm’)

The geom specifies the type of plot (one with points and a smoothed line in this case) while the method specifies the model for obtaining the smoothed line. A formula can also be added (but the formula defaults to *y* as a simple linear function of *x*).

Mind you, the linear model fit has some disadvantages. Even if you are working with a related statistical model (e.g., a Pearson’s *r* or least squares simple or multiple regression) you might want to have a more data driven plot. A good choice here is to use a local regression approach such as *loess*. This lets the data speak for themselves – effectively fitting a complex curve driven by the local properties of the data. If this is reasonably linear then your audience should be able to see the quality of the straight-line fit themselves. The local regression also gives approximate 95% confidence bands. These may support informal inference without having to make strong assumptions about the model.

Here is the *loess* plot:

Here is the code for the *loess* plot:

qplot(x, y, data=hov.dat, geom=c(‘point’, ‘smooth’), method=’loess’)

I like the *loess* approach here because its fairly obvious that the linear fit does quite well. showing the straight line fit has the appearance of imposing the pattern on the data, whereas a local regression approach illustrates the pattern while allowing departures from the straight line fit to show through.

In Serious Stats I mention *loess* only in passing (as an alternative to polynomial regression). Loess is generally superior as an exploratory tool – whereas polynomial regression (particularly quadratic and cubic fits) are more useful for inference. Here is an example of a cubic polynomial fit (followed by R code):

qplot(x, y, data=hov.dat, geom=c(‘point’, ‘smooth’), method=’lm’, formula= y ~ poly(x, 2))

Also available are fits using robust linear regression or splines. Robust linear regression (see section 10.5.2 of Serious Stats for a brief introduction) changes the loss function least squares in order to reduce impact of extreme points. Sample R code (graph not shown):

library(MASS)

qplot(x, y, data=hov.dat, geom=c(‘point’, ‘smooth’), method=’rlm’)

One slight problem here is that the approximate confidence bands assume normality and thus are probably too narrow.

Splines are an alternative to *loess* that fits sections of simpler curves together. Here is a spline with three degrees of freedom:

library(splines)

qplot(x, y, data=hov.dat, geom=c(‘point’, ‘smooth’), method=’lm’, formula=y ~ ns(x, 3))

If you want to know more the best place to start is with Hadley Wickham’s book. Chapter 2 covers qplot() and is available free online.

The immediate pros of the ggplot2 approach are fairly obvious – quick, good-looking graphs. There is, however, much more to the package and there is almost no limit to what you can produce. The output of the ggplot2 functions is itself an R object that can be stored and edited to create new graphs. You can use qplot() to create many other graphs – notably kernel density plots, bar charts, box plots and histograms. You can get these by changing the geom (or by default with certain object types an input).

The cons are less obvious. First, it takes some time investment to get to grips with the grammar of graphics approach (though this is very minimal if you stick with the quick plot function). Second, you may not like the default look of the ggplot2 output (though you can tweak it fairly easily). For instance, I prefer the default kernel density and histogram plots from the R base package to the default ggplot2 ones. I like to take a bare bones plot and build it up … trying to keep visual clutter to a minimum. I also tend to want black and white images for publication (whereas I would use grey and colour images more often in presentations). This is mostly to do with personal taste.

*Posted by Thom on September 5, 2012*

https://seriousstats.wordpress.com/2012/09/05/highqualitygraphs/

In a previous post I showed how to plot difference-adjusted CIs for between-subjects (independent measures) ANOVA designs (see here). The rationale behind this kind of graphical display is introduced in Chapter 3 of Serious stats (and summarized in my earlier blog post).

In a between-subjects – or in indeed in a within-subjects (repeated measures) – design you or your audience will not always be interested only in the differences between the means. Rarely, the main focus may even be on the individual estimates themselves. A CI for each of the individual means might be informative for several reasons.

First, it may be important to know that the interval excludes an important parameter value (e.g., zero). The example in Chapter 3 of Serious Stats involved a task in which participants had to decide which of two diagrams matched a description they had just read. Chance performance is 50% matching accuracy, so a graphical display that showed that the 95% CI for each mean excludes 50% suggests that participants in each group were performing above chance.

Second the CI for an individual mean gives you an idea of the relative precision with which that quantity is measured. This may be particularly important in an applied domain. For example, you may want to be fairly sure that performance on a task is high in some conditions as well as being sure that there are differences between conditions.

Third, the CIs for the individual means are revealing about changes in the precision between conditions. If the sample sizes are equal (or nearly equal) they are also revealing about patterns in the variances. This is because the precision of the individual means is a function of the standard error and *n*. This may be obscured when difference-adjusted CIs are plotted – though mainly for within-subjects (repeated measures) designs which have to allow for the correlation between the samples.

In any case, it may be desirable to display CIs for individual means and difference-adjusted means on the same plot. This could be accomplished in several ways but I have proposed using a two-tiered CI plot (see here for a brief summary of my BRM paper on this or see Chapter 16 of Serious stats).

A common approach (for either individual means or difference-adjusted CIs) is to adopt a pooled error term. This results in a more accurate CI if the homogeneity of variance assumption is met. For the purposes of a graphical display I would generally avoid pooled error terms (even if you use a pooled error term in your ANOVA). A graphical display of means is useful as an exploratory aid and supports informal inference. You want to be able to see any patterns in the precision (or variances) of the means. Sometimes these patterns are clear enough to be convincing without further (formal) inference or modeling. If they aren’t completely convincing it usually better to show the noisy graphic and supplement it with formal inference if necessary.

Experienced researchers understand that real data are noisy and may (indeed should!) get suspicious if data are too clean. (I’m perhaps being optimistic here – but we really ought to have more tolerance for noisy data, as this should reduce the pressure on honest researchers to ‘optimize’ their analyses – e.g., see here).

My earlier post on this blog provided functions for the single tier difference-adjusted CIs. Here is the two-tiered function (for a oneway design):

plot.bsci.tiered <- function(data.frame, group.var=1, dv.var=2, var.equal=FALSE, conf.level = 0.95, xlab = NULL, ylab = NULL, level.labels = NULL, main = NULL, pch = 19, pch.cex = 1.3, text.cex = 1.2, ylim = c(min.y, max.y), line.width= c(1.5, 1.5), tier.width=0, grid=TRUE) { data <- subset(data.frame, select=c(group.var, dv.var)) fact <- factor(data[[1]]) dv <- data[[2]] J <- nlevels(fact) ci.outer <- bsci(data.frame=data.frame , group.var=group.var, dv.var=dv.var, difference=FALSE, var.equal=var.equal, conf.level =conf.level) ci.inner <- bsci(data.frame=data.frame , group.var=group.var, dv.var=dv.var, difference=TRUE, var.equal=var.equal, conf.level =conf.level) moe.y <- max(ci.outer) - min(ci.outer) min.y <- min(ci.outer) - moe.y/3 max.y <- max(ci.outer) + moe.y/3 if (missing(xlab)) xlab <- "Groups" if (missing(ylab)) ylab <- "Confidence interval for mean" plot(0, 0, ylim = ylim, xaxt = "n", xlim = c(0.7, J + 0.3), xlab = xlab, ylab = ylab, main = main, cex.lab = text.cex) if (grid == TRUE) grid() points(ci.outer[,2], pch = pch, bg = "black", cex = pch.cex) index <- 1:J segments(index, ci.outer[, 1], index, ci.outer[, 3], lwd = line.width[1]) axis(1, index, labels = level.labels) if(tier.width==0) { segments(index - 0.025, ci.inner[, 1], index + 0.025, ci.inner[, 1], lwd = line.width[2]) segments(index - 0.025, ci.inner[, 3], index + 0.025, ci.inner[, 3], lwd = line.width[2]) } else segments(index, ci.inner[, 1], index, ci.inner[, 3], lwd = line.width[1]*(1 + abs(tier.width))) }

The following example uses the diagram data from the book:

source('http://www2.ntupsychology.net/seriousstats/SeriousStatsAllfunctions.txt') diag.dat <- read.csv('http://www2.ntupsychology.net/seriousstats/diagram.csv') plot.bsci.tiered(diag.dat, group.var=2, dv.var=4, ylab='Mean description quality', main = 'Two-tiered CIs for the Diagram data', tier.width=1)

The result is a plot that looks something like this (though I should probably have reordered the groups and labeled them):

For these data the group sizes are equal and thus the width of the outer tier reflect differences in variances between the groups. The variances are not very unequal, but neither are they particularly homogenous. The inner tier suggests group three is different from groups 2 and 4 (but not from group 1). This is a pretty decent summary of what’s going on and could be supplemented by formal inference (see Chapter 13 for a comparison of several formal approaches also using this data set).

N.B. R code formatted via Pretty R at inside-R.org

*Footnote: The aesthetics of error bar plots*

A major difference between the plot shown here and that in my BRM paper or in the book is that I have changed the method of plotting the tiers. The change is mainly aesthetic, but also reflects the desire not to emphasize the extremes of the error bar. The most plausible values of the parameter (e.g., mean) are towards the center of the interval – not at the extremes. I have discussed the reasons for my change of heart in a bit more detail elsewhere.

To this end I have also updated all my plotting functions. They still use the crossbar style from the book by default but this is controlled by a tier width argument. If tier.width=0 the crossbar style is used otherwise it used the tier.width to control the additional thickness of the difference-adjusted lines. In general, tier.width=1 seems to work well (but the crossbar style may be necessary for some unusual within-subject CIs where the difference-adjusted CI is wider than the CI for the individual means).

*Posted by Thom on June 21, 2012*

https://seriousstats.wordpress.com/2012/06/21/confidence-intervals-with-tiers/

*UPDATE*: Some problems arose with my previous host so I have now updated the links here and elsewhere on the blog.

The companion web site for Serious Stats has a zip file with R scripts for each chapter. This contains examples of R code and and all my functions from the book (and a few extras). This is a convenient form for working through the examples. However, if you just want to access the functions it is more convenient to load them all in at once.

The functions can be downloaded as a text file from:

http://www2.ntupsychology.net/seriousstats/SeriousStatsAllfunctions.txt

More conveniently, you can load them directly into R with the following call:

source('http://www2.ntupsychology.net/seriousstats/SeriousStatsAllfunctions.txt')

In addition to the Serious Stats functions, a number of other functions are contained in the text file. These include functions published on this blog for comparing correlations or confidence intervals for independent measures ANOVA and functions my paper on confidence intervals for repeated measures ANOVA.

*Posted by Thom on March 26, 2012*

https://seriousstats.wordpress.com/2012/03/26/r-functions-for-serious-stats/

The companion web site for Serious stats is now live:

http://www.palgrave.com/psychology/Baguley/

It includes a sample chapter (Chapter 15: Contrasts), data sets, R scripts for all the examples and supplementary material.

*Posted by Thom on March 23, 2012*

https://seriousstats.wordpress.com/2012/03/23/serious-stats-companion-web-site-now-live/

In Chapter 2 (Confidence Intervals) of Serious stats I consider the problem of displaying confidence intervals (CIs) of a set of means (which I illustrate with the simple case of two independent means). Later, in Chapter 16 (Repeated Measures ANOVA), I consider the trickier problem of displaying of two or more means from paired or repeated measures. The example in Chapter 16 uses R functions from my recent paper reviewing different methods for displaying means for repeated measures (within-subjects) ANOVA designs (Baguley, 2012b). For further details and links see a brief summary on my psychological statistics blog. The R functions included a version for independent measures (between-subject) designs, but this was a rather limited designed for comparison purposes (and not for actual use).

The independent measures case is relatively straight-forward to implement and I hadn’t originally planned to write functions for it. Since then, however, I have decided that it is worth doing. Setting up the plots can be quite fiddly and it may be useful to go over the key points for the independent case before you move on to the repeated measures case. This post therefore adapts my code for independent measures (between-subjects) designs.

The approach I propose is inspired by Goldstein and Healy (1995) – though other authors have made similar suggestions over the years (see Baguley, 2012b). Their aim was to provide a simple method for displaying a large collection of independent means (or other independent statistics). At its simplest the method reduces to plotting each statistic with error bars equal to ±1.39 standard errors of the mean. This result is a normal approximation that can be refined in various ways (e.g., by using the *t* distribution or by extending it to take account of correlations between conditions). Using a Goldstein-Healy plot two means are considered different with 95% confidence if their two intervals do not overlap. In other words non-overlapping CIs are (in this form of plot) approximately equivalent to a statistically significant difference between the two means with α = .05. For convenience I will refer to CIs that have this property as *difference-adjusted* CIs (to distinguish them from conventional CIs).

It is important to realize that conventional 95% CIs constructed around each mean won’t have this property. For independent means they are usually around 40% too wide and thus will often overlap even if the usual *t* test of their difference is statistically significant at *p* < .05. This happens because the variance of a difference is (in independent samples) equal to the sum of the variances of the individual samples. Thus the standard error of the difference is around times too large (assuming equal variances). For a more comprehensive explanation see Chapter 3 of Serious stats or Baguley (2012b).

If you have only two means there are at least three basic options:

1) plot the individual means with conventional 95% CIs around each mean

2) plot the difference between means and a 95% CI for the difference

3) plot some form of difference-adjusted CI

Which option is best? It depends on what you are trying to do. A good place to start is with your reasons for constructing a graphical display in the first place. Graphs are not particularly good for formal inference and other options (e.g., significance tests, reporting point estimates CIs in text, likelihood ratios, Bayes factors and so forth) exist for reporting the outcome of formal hypothesis tests. Graphs are appropriate for informal inference. This includes exploratory data analysis, to aid the interpretation of complex patterns or to summarize a number of simple patterns in a single display. If the patterns are very clear, informal inference might be sufficient. In other cases it can be supplemented with formal inference.

What patterns do the three basic options above reveal? Option 1) shows the precision around individual means. This readily supports inference about the individual means (but not their difference). For example, a true population outside the 95% CI is considered implausible (and the observed mean would be different from that hypothesized value with p < .05 using a one sample *t* test).

Option 2) makes for a rather dull plot because it just involves a single point estimate for the difference in means and the 95% CI for the difference. If this is the only quantity of interest you’d be better off just reporting the mean and 95% CI in the text. This has advantage of being more compact and more accurate than trying to read the numbers off a graph. [This is one reason that graphs aren’t optimal for formal inference; it can be hard, for instance, to tell whether a line includes zero or excludes zero when the difference is just statistically significant or just statistically non-significant. With informal inference you shouldn’t care where *p* = .049 or *p* = .051, but whether there are any clear patterns in the data]

Option 3) shows you the individual means but calibrates the CIs so that you can tell if it is plausible that the sample means differ (using 95% confidence in the difference as a standard). Thus it seems like a good choice for graphical display if you are primarily interested in the differences between means. For formal inference it can be supplemented by reporting a hypothesis test in the text (or possibly a Figure caption).

It is worth noting that option 3) becomes even more attractive if you have more than two means to plot. It allows you to see patterns that emerge over the set of means (e.g., linear or non-linear trends or – if *n* per sample is similar – changes in variances) and to compare pairs of means to see whether it is plausible that they are different.

In contrast, option 2) is rather unattractive with more than two means. First, with *J* means there are *J*(*J*-1)/2 differences and thus an unnecessarily cluttered graphical display (e.g., with *J* = 5 means there are 10 Cis to plot). Second, plotting only the differences can obscure important patterns in the data (e.g., an increasing or decreasing trend in the means or variances would be difficult to identify).

Where only a few means are to be plotted (as is common in ANOVA) it makes sense to take a slight more accurate approach than the approximation originally proposed by Goldstein and Healy for large collections of means. This approach uses the *t* distribution. A similar approach is advocated by Afshartous and Preston (2010) who also provide R code for calculating multipliers for the standard errors using the *t* distribution (and an extension for the repeated measures). My approach is similar, but involves calculating the margin of error (half width of the error bars) directly rather than computing a multiplier to apply to the standard error.

Difference-adjusted CIs for the mean of each sample from an independent measures (between-subjects) ANOVA design is given by Equation 3.31 of Serious stats:

The term is the mean of the *j*th sample (where samples are labeled *j* = 1 to *J*) and is the standard error of that sample. The term is the quantile of the *t* distribution with degrees of freedom (where is the size of *j*th sample) that includes to 100(1 – α) % of the distribution.

Thus, apart from the term, this equation is identical to that for a 95% CI around the individual means, with the proviso that the standard error here is computed separately for each sample. This differs from the usual approach to plotting CIs for independent measures ANOVA design – where it is common to use a pooled standard error computed from a pooled standard deviation ( the root mean square error of the ANOVA) . While a pooled error term is sometimes appropriate, it is generally a bad idea for graphical display of the CIs because it will obscure any patterns in the variability of the samples. [Nevertheless, where is very small it make make sense to use a pooled error term on the grounds that each sample provides an exceptionally poor estimate of its population standard deviation]

However, the most important change is the term. It creates a difference-adjusted CI by ensuring that the joint width of the margin of error around any two means is $latex \sqrt 2 $ times larger than for a single mean. The division by 2 arises merely as a consequence of dealing jointly with two error bars. Their total has to be $latex \sqrt 2 $ times larger and therefore each one needs only to be times its conventional value (for an unadjusted CI). This is discussed in more detail by Baguley (2012a; 2012b).

This equation should perform well (e.g., providing fairly accurate coverage) as long as variances are not very unequal and the samples are approximately normal. Even when these conditions are not met, remember the aim is not to support formal inference. In addition, the approach is likely to be slightly more robust than ANOVA (at least to homogeneity of variance and unequal sample sizes). So this method is likely to be a good choice whenever ANOVA is appropriate.

Two R functions for difference-adjusted CIs in independent measures ANOVA designs are provided here. The first function bsci() calculates conventional or difference-adjusted CIs for a one-way ANOVA design.

bsci <- function(data.frame, group.var=1, dv.var=2, difference=FALSE, pooled.error=FALSE, conf.level=0.95) { data <- subset(data.frame, select=c(group.var, dv.var)) fact <- factor(data[[1]]) dv <- data[[2]] J <- nlevels(fact) N <- length(dv) ci.mat <- matrix(,J,3, dimnames=list(levels(fact), c('lower', 'mean', 'upper'))) ci.mat[,2] <- tapply(dv, fact, mean) n.per.group <- tapply(dv, fact, length) if(difference==TRUE) diff.factor= 2^0.5/2 else diff.factor=1 if(pooled.error==TRUE) { for(i in 1:J) { moe <- summary(lm(dv ~ 0 + fact))$sigma/(n.per.group[[i]])^0.5 * qt(1-(1-conf.level)/2,N-J) * diff.factor ci.mat[i,1] <- ci.mat[i,2] - moe ci.mat[i,3] <- ci.mat[i,2] + moe } } if(pooled.error==FALSE) { for(i in 1:J) { group.dat <- subset(data, data[1]==levels(fact)[i])[[2]] moe <- sd(group.dat)/sqrt(n.per.group[[i]]) * qt(1-(1-conf.level)/2,n.per.group[[i]]-1) * diff.factor ci.mat[i,1] <- ci.mat[i,2] - moe ci.mat[i,3] <- ci.mat[i,2] + moe } } ci.mat } plot.bsci <- function(data.frame, group.var=1, dv.var=2, difference=TRUE, pooled.error=FALSE, conf.level=0.95, xlab=NULL, ylab=NULL, level.labels=NULL, main=NULL, pch=21, ylim=c(min.y, max.y), line.width=c(1.5, 0), grid=TRUE) { data <- subset(data.frame, select=c(group.var, dv.var)) if(missing(level.labels)) level.labels <- levels(data[[1]]) if (is.factor(data[[1]])==FALSE) data[[1]] <- factor(data[[1]]) if (is.factor(data[[1]])==TRUE) data[[1]] <- factor(data[[1]]) dv <- data[[2]] J <- nlevels(data[[1]]) ci.mat <- bsci(data.frame=data.frame, group.var=group.var, dv.var=dv.var, difference=difference, pooled.error=pooled.error, conf.level=conf.level) moe.y <- max(ci.mat) - min(ci.mat) min.y <- min(ci.mat) - moe.y/3 max.y <- max(ci.mat) + moe.y/3 if (missing(xlab)) xlab <- "Groups" if (missing(ylab)) ylab <- "Confidence interval for mean" plot(0, 0, ylim = ylim, xaxt = "n", xlim = c(0.7, J + 0.3), xlab = xlab, ylab = ylab, main = main) grid() points(ci.mat[,2], pch = pch, bg = "black") index <- 1:J segments(index, ci.mat[, 1], index, ci.mat[, 3], lwd = line.width[1]) segments(index - 0.02, ci.mat[, 1], index + 0.02, ci.mat[, 1], lwd = line.width[2]) segments(index - 0.02, ci.mat[, 3], index + 0.02, ci.mat[, 3], lwd = line.width[2]) axis(1, index, labels=level.labels) }

The default is difference=FALSE (on the basis that these are the CIs most likely to be reported in text or tables). The second function plot.bsci() uses the former function to plot the means and CIs the default here is difference=TRUE (on the basis that it the difference-adjusted CIs are likely to be more useful for graphical display). For both functions the default is a pooled error term (pooled.error=FALSE) and a 95% confidence level (conf.level=0.95). Each function also takes input as a data frame and assumes that the grouping variable is the first column and the dependent variable the second column. If the appropriate variables are in different columns, the correct columns can be specified with the arguments group.var and dv.var. The plotting function also takes some standard graphical parameters (e.g., for labels and so forth).

The following examples use the diagram data set from Serious stats. The first line loads the data set (if you have a live internet connection). The second line generated the difference-adjusted CIs. The third line plots the difference adjusted CIs. Note that the grouping variable (factor) is in the second column and the DV is in the fourth column.

diag.dat <- read.csv('http://www2.ntupsychology.net/seriousstats/diagram.csv') bsci(diag.dat, group.var=2, dv.var=4, difference=TRUE) plot.bsci(diag.dat, group.var=2, dv.var=4, ylab='Mean description quality', main = 'Difference-adjusted 95% CIs for the Diagram data')

In this case the graph looks like this:

It should be immediately clear that while the segmented diagram condition (S) tends to have higher scores than the text (T) or picture (P) conditions, but the full diagram (F) condition is somewhere in between. This matches the uncorrected pairwise comparisons where S > P = T, S = F, and F = P = T.

At some point I will also add a function to plot two-tiered error bars (combining option 1 and 3). For details of the extension to repeated measures designs see Baguley (2012b). The code and date sets are available here.

Afshartous D., & Preston R. A. (2010). Confidence intervals for dependent data: equating nonoverlap with statistical significance. *Computational Statistics and Data Analysis. 54*, 2296-2305.

Baguley, T. (2012a, in press). Serious stats: A guide to advanced statistics for the behavioral sciences. Basingstoke: Palgrave.

Baguley, T. (2012b). Calculating and graphing within-subject confidence intervals for ANOVA. Behavior Research Methods, 44, 158-175.

Goldstein, H., & Healy, M. J. R. (1995). *Journal of the Royal Statistical Society. Series A (Statistics in Society), 158*, 175-177.

Schenker, N., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. *The American Statistician, 55, *182-186.

*Posted by Thom on March 18, 2012*

https://seriousstats.wordpress.com/2012/03/18/cis-for-anova/

In Chapter 6 (correlation and covariance) I consider how to construct a confidence interval (CI) for the difference between two independent correlations. The standard approach uses the Fisher *z* transformation to deal with boundary effects (the squashing of the distribution and increasing asymmetry as *r* approaches -1 or 1). As *z _{r}* is approximately normally distributed (which

This works well for the CI around a single correlation (assuming the main assumptions – bivariate normality and homogeneity of variance – broadly hold) or for differences between means, but can perform badly when looking at the difference between two correlations. Zou (2007) proposed modification to the standard approach that uses the upper and lower bounds of the CIs for individual correlations to calculate a CI for their difference. He considered three cases: independent correlations and two types of dependent correlations (overlapping and non-overlapping). He also considered differences in *R*^{2} (not relevant here).

*Independent correlations*

In section 6.6.2 (*p*. 224) I illustrate Zou’s approach for independent correlations and provide R code in sections 6.7.5 and 6.7.6 to automate the calculations. Section 6.7.5 shows how to write a simple R function and illustrates it with a function to calculate a CI for Pearson’s *r* using the Fisher *z *transformation. Whilst writing the book I encountered several functions do do exactly this. The cor.test() function in the base package does this for raw data (along with computing the correlation and usual NHST). A number of functions compute it using the usual text book formula. My function relies on R primitive hyperbolic functions (as the Fisher *z* transformation is related to the geometry of hyperbolas), which may be useful if you need to use it intensively (e.g., for simulations):

The function is 6.7.6 uses the rz.ci() function to construct a CI for the difference between two independent correlations. See section 6.6.2 of Serious stats or Zou (2007) for further details and a worked example. My function from section 6.7.6 is reproduced here:

r.ind.ci <- function(r1, r2, n1, n2=n1, conf.level = 0.95) { L1 <- rz.ci(r1, n1, conf.level = conf.level)[1] U1 <- rz.ci(r1, n1, conf.level = conf.level)[2] L2 <- rz.ci(r2, n2, conf.level = conf.level)[1] U2 <- rz.ci(r2, n2, conf.level = conf.level)[2] lower <- r1 - r2 - ((r1 - L1)^2 + (U2 - r2)^2)^0.5 upper <- r1 - r2 + ((U1 - r1)^2 + (r2 - L2)^2)^0.5 c(lower, upper) }

The call the function use the two correlation coefficients an sample as input (the default is to assume equal *n* and a 95% CI).

*A caveat*

As I point out in chapter 6, just because you can compare two correlation coefficients doesn’t mean it is a good idea. Correlations are standardized simple linear regression coefficients and even if the two regression coefficients measure the same effect, it doesn’t follow that their standardized counterparts do. This is not merely the problem that it may be meaningless to compare, say, a correlation between height and weight with a correlation between anxiety and neuroticism. Two correlations between the same variables in different samples might not be meaningfully comparable (e.g., because of differences in reliability, range restriction and so forth).

*Dependent overlapping correlations*

In many cases the correlations you want to compare aren’t independent. One reason for this is that the correlations share a common variable. For example if you correlate *X* with *Y* and *X* with *Z* you might be interested in whether the correlation *r _{XY}* is larger than

The following functions (not in the book) compute the correlation between the correlations and use it to adjust the CI for the difference in correlations to account for overlap (a shared predictor). Note that both functions and rz.ci() must be loaded into R. Also included is a calls to the main function that reproduces the output from example 2 of Zou (2007).

rho.rxy.rxz <- function(rxy, rxz, ryz) { num <- (ryz-1/2*rxy*rxz)*(1-rxy^2-rxz^2-ryz^2)+ryz^3 den <- (1 - rxy^2) * (1 - rxz^2) num/den } r.dol.ci <- function(r12, r13, r23, n, conf.level = 0.95) { L1 <- rz.ci(r12, n, conf.level = conf.level)[1] U1 <- rz.ci(r12, n, conf.level = conf.level)[2] L2 <- rz.ci(r13, n, conf.level = conf.level)[1] U2 <- rz.ci(r13, n, conf.level = conf.level)[2] rho.r12.r13 <- rho.rxy.rxz(r12, r13, r23) lower <- r12-r13-((r12-L1)^2+(U2-r13)^2-2*rho.r12.r13*(r12-L1)*(U2- r13))^0.5 upper <- r12-r13+((U1-r12)^2+(r13-L2)^2-2*rho.r12.r13*(U1-r12)*(r13-L2))^0.5 c(lower, upper) } # input from example 2 of Zou (2007, p.409) r.dol.ci(.396, .179, .088, 66)

The r.dol.ci() function takes three correlations as input – the correlations of interest (e.g., *r _{XY}* and

*Dependent non-overlapping correlations*

Overlapping correlations are not the only cause of dependency between correlations. The samples themselves could be correlated. Zou (2007) gives the example of a correlation between two variables for a sample of mothers. The same correlation could be computed for their children. As the children and mothers have correlated scores on each variable, the correlation between the same two variables will be correlated (but not overlapping in the sense used earlier). The following functions compute the CI for the difference in correlations between dependent non-overlapping correlations. Also included is a call to the main function that reproduces Zou (2007) example 3.

rho.rab.rcd <- function(rab, rac, rad, rbc, rbd, rcd) { num <- 1/2*rab*rcd * (rac^2 + rad^2 + rbc^2 + rbd^2) + rac*rbd + rad*rbc - (rab*rac*rad + rab*rbc*rbd + rac*rbc*rcd + rad*rbd*rcd) den <- (1 - rab^2) * (1 - rcd^2) num/den } r.dnol.ci <- function(r12, r13, r14, r23, r24, r34, n12, n34=n12, conf.level=0.95) { L1 <- rz.ci(r12, n12, conf.level = conf.level)[1] U1 <- rz.ci(r12, n12, conf.level = conf.level)[2] L2 <- rz.ci(r34, n34, conf.level = conf.level)[1] U2 <- rz.ci(r34, n34, conf.level = conf.level)[2] rho.r12.r34 <- rho.rab.rcd(r12, r13, r14, r23, r24, r34) lower <- r12 - r34 - ((r12 - L1)^2 + (U2 - r34)^2 - 2 * rho.r12.r34 * (r12 - L1) * (U2 - r34))^0.5 upper <- r12 - r34 + ((U1 - r12)^2 + (r34 - L2)^2 - 2 * rho.r12.r34 * (U1 - r12) * (r34 - L2))^0.5 c(lower, upper) } # from example 3 of Zou (2007, p.409-10) r.dnol.ci(.396, .208, .143, .023, .423, .189, 66)

Although this call reproduces the final output for example 3 it produces slightly different intermediate results (0.0891 vs. 0.0917) for the correlation between correlations. Zou (personal communication) confirms that this is either a typo or rounding error (e.g., arising from hand calculation) in example 3 and that the function here produces accurate output. The input here requires the correlations from every possible correlation between the four variables being compared (and the relevant sample size for the correlations being compared). The easiest way to get the correlations is from a correlation matrix of the four variables.

*Robust alternatives*

Wilcox (2009) describes a robust alternative to these methods for independent correlations and modifications to Zou’s method that make the dependent correlation methods robust to violations of bivariate normality and (in particular) homogeneity of variance assumptions. Wilcox provides R functions for these approaches on his web pages. His functions take raw data as input and are computationally intensive. For instance the dependent correlation methods use Zou’s approach but take boostrap CIs for the individual correlations as input (rather than the simpler Fisher *z* transformed versions).

The relevant functions are twopcor() for the independent case, TWOpov() for the dependent overlapping case and TWOpNOV() for the non-overlapping case.

UPDATE

Zou’s modified asymptotic method is easy enough that you can run it in Excel. I’ve added an Excel spreadsheet to the blog resources that should implement the methods (and matches the output to R fairly closely). As it uses Excel it may not cope gracefully with some calculations (e.g., with extremely small or large values or *r *or other extreme cases) – and I have more confidence in the R code.

*References*

Baguley, T. (2012, in press). Serious stats: A guide to advanced statistics for the behavioral sciences. Basingstoke: Palgrave.

Zou, G. Y. (2007). Toward using confidence intervals to compare correlations. *Psychological Methods, 12,* 399-413.

Wilcox, R. R. (2009). Comparing Pearson correlations: Dealing with heteroscedascity and non-normality. *Communications in Statistics – Simulation & Computation, 38*, 2220-2234.

N.B. R code formatted via Pretty R at inside-R.org

*Posted by Thom on February 5, 2012*

https://seriousstats.wordpress.com/2012/02/05/comparing-correlations/