Monthly Archives: February 2019

R functions for the hierarchical shift function

The hierarchical shift function presented in the previous post is now available in the `rogme` R package. Here is a short demo.

Get the latest version of `rogme`:

# install.packages("devtools")

Load data and compute hierarchical shift function:

df <- flp # get reaction time data - for details `help(flp)`
# Compute shift functions for all participants
out <- hsf(df, rt ~ condition + participant)


Because of the large number of participants, the confidence intervals are too narrow to be visible. So let’s subset a random sample of participants to see what can happen with a more smaller sample size:

set.seed(22) # subset random sample of participants
id <- unique(df$participant) 
df <- subset(df, flp$participant %in% sample(id, 50, replace = FALSE))
out <- hsf(df, rt ~ condition + participant) 


Want to estimate the quartiles only?

out <- hsf(df, rt ~ condition + participant, qseq = c(.25, .5, .75))


Want to reverse the comparison?

out <- hsf(df, rt ~ condition + participant, todo = c(2,1))


P values are here:


P values adjusted for multiple comparisons using Hochberg’s method:


Percentile bootstrap version:

out <- hsf_pb(df, rt ~ condition + participant)

Plot bootstrap highest density intervals – default:



Plot distributions of bootstrap samples of group differences. Bootstrap distributions are shown in orange. Black dot marks the mode. Vertical black lines mark the 50% and 90% highest density intervals.




For more examples, a vignette is available on GitHub.

Feedback would be much appreciated: don’t hesitate to leave a comment or to get in touch directly.

Hierarchical shift function: a powerful alternative to the t-test

In this post I introduce a simple yet powerful method to compare two dependent groups: the hierarchical shift function. The code is on GitHub. More details are in Rousselet & Wilcox (2019), with a reproducibility package on figshare.

Let’s consider different situations in a hierarchical setting: we’ve got trials from 2 conditions in several participants. Imagine we collected data from one participant and the results look like this:


These fake reaction time data were created by sampling from ex-Gaussian distributions. Here the two populations are shifted by a constant, so we expect a uniform shift between the two samples. Later we’ll look at examples showing  differences most strongly in early responses, late responses, and in spread.

To better understand how the distributions differ, let’s look at a shift function, in which the difference between the deciles of the two conditions are plotted as a function of the deciles in condition 1 – see details in Rousselet et al. (2017). The decile differences are all negative, showing stochastic dominance of condition 2 over condition 1. The function is not flat because of random sampling and limited sample size. 


Now, let’s say we collected 100 trials per condition from 30 participants. How do we proceed? There are a variety of approaches available to quantify distribution differences. Ideally, such data would be analysed using a multi-level model, including for instance ex-Gaussian fits, random slopes and intercepts for participants, item analyses… This can be done using the lme4 or brms R packages. However, in my experience, in neuroscience and psychology articles, the most common approach is to collapse the variability across trials into a single number per participant and condition to be able to perform a paired t-test: typically, the mean is computed across trials for each condition and participant, then the means are subtracted, and the distribution of mean differences is entered into a one-sample t-test. Obviously, this strategy throws away a huge amount of information! And the results of such second-tier t-tests are difficult to interpret: a positive test leaves us wondering exactly how the distributions differ; a negative test is ambiguous – beside avoiding the ‘absence of evidence is not evidence of absence’ classic error, we also need to check if the distributions do not differ in other aspects than the mean. So what can we do?

Depending on how conditions differ, looking at other aspects of the data than the mean can be more informative. For instance, in Rousselet & Wilcox (2019), we consider group comparisons of individual medians. Considering that the median is the second quartile, looking at the other quartiles can be of theoretical interest to investigate effects in early or later parts of distributions. This could be done in several ways, for instance by making inferences on the first quartile (Q1) or the third quartile (Q3). If the goal is to detect differences anywhere in the distributions, a more systematic approach consists in quantifying differences at multiple quantiles. Here we consider the case of the deciles, but other quantiles could be used. First, for each participant and each condition, the sample deciles are computed over trials. Second, for each participant, condition 2 deciles are subtracted from condition 1 deciles – we’re dealing with a within-subject (repeated-measure) design. Third, for each decile, the distribution of differences is subjected to a one-sample test. Fourth, a correction for multiple comparisons is applied across the 9 one-sample tests. I call this procedure a hierarchical shift function. There are many options available to implement this procedure and the example used here is not the definitive answer: the goal is simply to demonstrate that a relatively simple procedure can be much more powerful and informative than standard approaches.

In creating a hierarchical shift function we need to make three choices: a quantile estimator, a statistical test to assess quantile differences across participants, and a correction for multiple comparisons technique. The deciles were estimated using type 8 from the base R quantile() function (see justification in Rousselet & Wilcox, 2019). The group comparisons were performed using a one-sample t-test on 20% trimmed means, which performs well in many situations, including in the presence of outliers. The correction for multiple comparisons employed Hochberg’s strategy (Hochberg, 1988), which guarantees that the probability of at least one false positive will not exceed the nominal level as long as the nominal level is not exceeded for each quantile. 

In Rousselet & Wilcox (2019), we consider power curves for the hierarchical shift function (HSF) and contrast them to other approaches: by design, HSF is sensitive to more types of differences than any standard approach using the mean or a single quantile. Another advantage of HSF is that the location of the distribution difference can be interrogated, which is impossible if inferences are limited to a single value.

Here is what the hierarchical shift function looks like for our uniform shift example:


The decile differences between conditions are plotted for each participant (colour coded) and the group 20% trimmed means are superimposed in black. Differences are pretty constant across deciles, suggesting a uniform shift. Most participants have shift functions entirely negative – a case of stochastic dominance of one condition over the other. There is growing uncertainty as we consider higher deciles, which is expected from measurements of right skewed distributions.

We can add confidence intervals:


P values are available in the GitHub code.

Instead of standard parametric confidence intervals, we can also consider percentile bootstrap confidence intervals (or highest density intervals), as done here:


Distributions of bootstrap estimates can be considered cheap Bayesian posterior distributions. They also contain useful information not captured by simply reporting confidence intervals.

Here we plot them using geom_halfeyeh() from tidybayes. 


The distributions of bootstrap estimates of the group 20% trimmed means are shown in orange, one for each decile. Along the base of each distribution, the black dot marks the mode and the vertical lines mark the 50% and 90% highest density intervals.

Nice hey?! Reporting a figure like that is dramatically more informative than reporting a P value and a confidence interval from a t-test!

A bootstrap approach can also be used to perform a cluster correction for multiple comparisons – see details on GitHub. Preliminary simulations suggest that the approach can provide substantial increase in power over the Hochberg’s correction – more on that in another post.

Let’s look at 3 more examples, just for fun…

Example 2: early difference

Example participant:


Shift function:


Hierarchical shift function with confidence intervals:


Percentile bootstrap estimate densities:


Example 3: difference in spread

Example participant:


Shift function:


Hierarchical shift function with confidence intervals:


Percentile bootstrap estimate densities:


Example 4: late difference

Example participant:


Shift function:


Hierarchical shift function with confidence intervals:


Percentile bootstrap estimate densities:



The hierarchical shift function can be used to achieve two goals: 

  • to screen data for potential distribution differences using p values, without limiting the exploration to a single statistics like the mean;
  • to illustrate and quantify how distributions differ.

I think of the hierarchical shift function as the missing link between t-tests and multi-level models. I hope it will help a few people make sense of their data and maybe nudge them towards proper hierarchical modelling.

R functions for the parametric hierarchical shift function are available in the rogme package. I also plan bootstrap functions. Then I’ll tackle the case of 2 independent groups, which requires a third level quantifying differences of differences.