Bayesian Statistics

7. Posteriors

Dominique Makowski
D.Makowski@sussex.ac.uk

Installing brms

  • Restart R (Session -> Restart R)
# In a NEW session:
# 1.
install.packages("brms")

# 2.
library(brms)

# 3.
brm(mpg ~ wt, data=mtcars)
  • It should prompt you with installing additional tools. Say yes. It should start downloading, and then installing automatically
  • If prompted again, say no.
  • During or after the installation, it might throw an error. Just wait until the installation is complete
  • Close R and RStudio
  • Reopen, and run đŸ€ž
# 4.
library(brms)
brm(mpg ~ wt, data=mtcars)

Installing brms (option 2 - cmdstanr backend)

# In a NEW session:
# 1.
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("paul-buerkner/brms")

# 2. 
remotes::install_github("stan-dev/cmdstanr")

# 3. 
cmdstanr::check_cmdstan_toolchain()
  • If this does not say that the C++ toolchain is setup properly, install the toolchain (Linux, Windows, Mac)
# Once the toolchain is installed and setup:  
cmdstanr::install_cmdstan()
  • Test if it works
library(brms)

options(brms.backend="cmdstanr")

brm(mpg ~ wt, data=mtcars)

Recap

  • We understood how Bayes’ Theorem can be used to update beliefs in light of new evidence
  • How it can be used to compare models using Bayes Factors (BF) and use it in basic hypothesis tests (e.g., correlation)
  • How we can set priors over parameters of interest (e.g., correlation coefficients) using distributions

Recap of Differences (1)

  • Frequentist Statistics 😱
    • Generate a sampling distribution of the test statistic assuming \(H0\)
    • Compare observed value of the test statistic with the sampling distribution
    • Reject \(H0\) if probability of observed value (or more extreme values) is less than \(\alpha\)
    • What we get: \(p(data|Null Hypothesis)\)
    • What we want to know: \(p(Hypothesis|data)\)
  • Bayesian Statistics đŸ„°ïž
    • Directly test hypotheses of interest: \(p(H|data)\)
    • Define prior over hypotheses \(p(H)\)
    • Compute likelihood of the data for each hypothesis \(p(data|H)\)
    • Use Bayes’ rule to infer the posterior over hypotheses given the data \(p(H|data)\)

Recap of Differences (2)

  • Main difference is that it doesn’t rely on complicated assumptions and algorithms to estimate parameters
  • Instead, it relies on Bayes’ theorem to compute the posterior distribution of the parameters of interest
  • Frequentist estimation: Data * Model * Optimization Algorithm -> result (best fitting parameters) -> inference relies on further assumptions
  • Bayesian estimation does not rely on assumptions, but instead, starts with initial “results” (priors), and then updates these results based on the data
  • Every statistical parameter that we want to estimate has to start with a prior distribution

History of Statistics

  • Frequentism
    • 1900: Karl Pearson develops the chi-squared and correlation test
    • 1913: Ronald Fisher introduces the concept of likelihood
    • 1920s: Fisher develops maximum likelihood estimation (MLE) and analysis of variance (ANOVA)
    • 1927: Jerzy Neyman and Egon Pearson (son of Karl Pearson) introduce hypothesis testing and confidence intervals
    • 1950s: Frequentism becomes the dominant statistical approach in most scientific disciplines
  • Bayesianism
    • 1763: Thomas Bayes’ essay “An Essay towards solving a Problem in the Doctrine of Chances” published posthumously
    • 1812: Pierre-Simon Laplace, a French mathematician and physicist, extended and formalized Bayesian probability theory
    • 
 Not much happening 

    • 1950s: Leonard Jimmie Savage lays the foundation for modern Bayesian statistics
    • 1960s: Work by Jeffrey explores the philosophical and practical aspects of Bayesian inference
    • 1980s: Bayesian statistics becomes increasingly applicable thanks to advancements in computing power and algorithms (MCMC)

Why such a gap?

  • We know the Bayes’ theorem since the 18th century (actually predates Frequentist statistics)
  • But Bayesian statistics really didn’t appear until the 1960’s, with a real boom in the 2000’s
  • Why did it take 250 years to take off?
  • Because performing Bayesian inference for real data (not just estimating Bayes factors) is hard, and could only be achieved with the advent of modern computers

Correlation

  • The BayesFactor package allows us to compute Bayes factors using a mathematical trick without performing “full” Bayesian inference
result <- BayesFactor::correlationBF(mtcars$mpg, mtcars$qsec)
result
Bayes factor analysis
--------------
[1] Alt., r=0.333 : 4.416463 ±0%

Against denominator:
  Null, rho = 0 
---
Bayes factor type: BFcorrelation, Jeffreys-beta*
  • \(BF_{10} = 4.42\) is moderate evidence for the existence of a correlation

    • But in what direction? Of what magnitude?
  • What if we want to describe the correlation coefficient?
  • In a Bayesian context, this means to analyze our beliefs about the correlation coefficient after observing the data. Bayes’ Theorem: \(\underbrace{P(H|data)}_{Posterior} = \underbrace{P(H)}_{Prior}~\underbrace{P(data|H)}_{Likelihood}\)
  • This is called the Posterior, and is the last ingredient of Bayesian inference - Priors * (model + data) = Posterior

Posterior Description

  • The results obtained by BayesFactor don’t contain the posterior, because it is not computed by default (the package focuses on estimating Bayes Factors in the most efficient way possible)
  • We can compute it using the posterior() function and specifying the number of iterations (i.e., the number of “draws”)
posterior <- BayesFactor::posterior(result, iterations=500)
head(posterior)
Markov Chain Monte Carlo (MCMC) output:
Start = 1 
End = 7 
Thinning interval = 1 
           rho      zeta
[1,] 0.4061366 0.4309760
[2,] 0.4595994 0.4968032
[3,] 0.3391690 0.3531532
[4,] 0.3391690 0.3531532
[5,] 0.5697774 0.6471931
[6,] 0.2361734 0.2407175
[7,] 0.3211367 0.3329140

MCMC

  • It performs the sampling using a technique called Markov Chain Monte Carlo (MCMC), which is a family of algorithms that allow to sample from a distribution without knowing its exact shape.
  • This is because, while we can calculate the exact Bayesian posterior of simple known distributions, it becomes very hard for empirical distributions (i.e., distributions that don’t have a formula, such as that of the data).
  • We say that the posterior is intractable (i.e., we can’t compute it analytically using formulas).
head(posterior)
Markov Chain Monte Carlo (MCMC) output:
Start = 1 
End = 7 
Thinning interval = 1 
           rho      zeta
[1,] 0.4061366 0.4309760
[2,] 0.4595994 0.4968032
[3,] 0.3391690 0.3531532
[4,] 0.3391690 0.3531532
[5,] 0.5697774 0.6471931
[6,] 0.2361734 0.2407175
[7,] 0.3211367 0.3329140
  • While the exact posterior distribution is intractable, we can approximate it by drawing n random samples from it
  • This is what MCMC does, and this is why computational power is typically needed to perform Bayesian inference

MCMC

  • MCMC is a key milestone in the history of Bayesian statistics, because it allows us to perform Bayesian inference for complex models
  • In general, “Bayesian sampling” is a hot area of research with lots of improvements and developments over the past & next couple of years
  • MCMC is a family of algorithms1, and the core idea is that we obtain a random sample from the (inaccessible) posterior distribution
  • The more samples we have, the better the approximation of the posterior distribution is
  • But sampling takes time: often a tradeoff between accuracy and speed

Quizz

  • Doing Bayesian Statistics often involves describing the posterior distribution (the updated beliefs about some statistical parameters, e.g., the correlation value)
  • Computing the posterior distribution is easy and can be done with formulas
  • In real-world problems, the exact posterior distribution is intractable (i.e., we can’t compute it analytically using formulas)
  • Not being able to compute the posterior analytically means that we cannot draw random samples from this distribution
  • Drawing many samples from the posterior allows us to get an empirical approximation of the posterior distribution
  • The process of random sampling is called York Chain Monte Altro (YMCA)
  • The process of random sampling is called Markov Chain Monte Carlo (MCMC)

MCMC Chains Inspection

  • MCMC is a process that should be inspected to ensure that it is working properly
  • It can go wrong (e.g., not converged)
head(posterior)
Markov Chain Monte Carlo (MCMC) output:
Start = 1 
End = 7 
Thinning interval = 1 
           rho      zeta
[1,] 0.4061366 0.4309760
[2,] 0.4595994 0.4968032
[3,] 0.3391690 0.3531532
[4,] 0.3391690 0.3531532
[5,] 0.5697774 0.6471931
[6,] 0.2361734 0.2407175
[7,] 0.3211367 0.3329140
  • In the example above, we have drawn 500 samples from the posterior distribution
  • The returned object shows us the values of 2 variables, the correlation coefficient rho and zeta (a transformed version of the correlation coefficient that is not important)
plot(posterior)

  • The plot on the right is the density plot of the parameter, and on the left it shows the “trace plots” of the MCMC chain
  • Trace plots should look like “Hairy Caterpillars”, i.e., stationary (overall flat) and “random” (noisy)
  • Put a pin in that: We will come back to “model diagnostic” in the future

Posterior Description

  • Once we assessed our sampling process and made sure the posterior has been correctly explored and sampled from, we can proceed to visualize and describe the posterior distribution
data <- as.data.frame(posterior)

p <- data |> 
  ggplot(aes(x=rho)) +
  geom_density(fill="purple") +
  geom_vline(xintercept=0, linetype="dashed") +
  theme_bw() +
  labs(x="Correlation Coefficient", y="Probability") +
  coord_cartesian(xlim=c(-1, 1))
p

Add Prior

rscale <- 1/3

prior <- data.frame(
  y = dbeta(seq(0, 1, length.out=100), 1/rscale, 1/rscale),
  x = seq(-1, 1, length.out=100)
)

p + 
  geom_area(data=prior, aes(x=x, y=y), fill="orange", alpha=.4) +
  labs(title="Beliefs about rho before (orange) and after (purple) seeing the data")

Impact of Prior on Posterior

  • As we have seen, priors and models specification have a strong influence on Bayes Factors
  • But they also have an impact on the posterior distribution
Code 
simulate_results <-  function(V1, V2) {
  
  # Initialize dataframes
  data_priors <- data.frame()
  data_posteriors <- data.frame()
  
  for(rscale in c(0.05, 0.1, 1/sqrt(27), 1/3, 1/sqrt(3), 1)) {
    # Prior
    prior <- data.frame(
      y = dbeta(seq(0, 1, length.out=100), 1/rscale, 1/rscale),
      x = seq(-1, 1, length.out=100),
      rscale = insight::format_value(rscale)
    )
    # prior$y <- prior$y / max(prior$y)  # Normalize y (for visualization)
    data_priors <- rbind(data_priors, prior)
    
    # Compute results
    result <- BayesFactor::correlationBF(V1, V2, rscale=rscale)
    
    # Sample posterior
    posterior <- as.data.frame(BayesFactor::posterior(result, iterations=2000))
    posterior$rscale <- insight::format_value(rscale)
    data_posteriors <- rbind(data_posteriors, posterior)
  }
  
  # Make plot
  ggplot(data_priors, aes(color=rscale)) +
    geom_line(aes(x=x, y=y), linewidth=1, linetype="dashed") +
    geom_density(data=data_posteriors, aes(x=rho), alpha=.2, linewidth=2) +
    theme_bw() +
    labs(x="Correlation Coefficient", y="Probability") +
    coord_cartesian(xlim=c(-1, 1))
}
simulate_results(mtcars$mpg, mtcars$qsec)

Impact of Prior on Posterior

  • What if we have even less data (current sample size is 32)?
simulate_results(mtcars$mpg[1:5], mtcars$qsec[1:5])

  • What if we have more data?
idx <- sample(1:32, 250, replace=TRUE)
simulate_results(mtcars$mpg[idx], mtcars$qsec[idx])

Priors and Posteriors

  • Priors have an effect, especially when evidence from data is weak (e.g., small sample size)
  • This is why we say that “Bayesian statistics are useful when we have little data”
    • This is because we can set informative priors based on our knowledge that can help us make better inferences
    • But if there is clear evidence from the data, the prior doesn’t matter that much
  • The more data, the higher our posterior precision (narrower posterior = lower uncertainty)
  • This is where Bayesian statistics shine: they allow us to quantify the uncertainty associated with our estimates by describing the posterior distribution (in particular, its width)

Priors and Posteriors

  • Bayesian inference is often intuitively conceptualized as a mixture between a prior distribution (expectations) and a “likelihood” distribution (representing the evidence, i.e., the data given a model), which result in the posterior distribution
  • This posterior is seen as a compromise between the prior and the likelihood (i.e., the data)
  • In neuroscience, this is often used in the context of the “Bayesian brain” hypothesis, which posits that the brain is a Bayesian inference machine that has prior expectations (e.g., about what it’s going to hear), that gets confronted to sensory evidence (the noisy data coming from the ears), and resolves this: what we consciously hear is the “posterior” distribution that combines the prior and the evidence

Game: beware of your intuitions! (1/4)

  • Normal prior, normal likelihood
    • \(y \sim Normal(\mu,1)\)
    • \(\mu \sim Normal(10,1)\)
  • Can you guess the shape of the posterior?

  • The classic flavor of Bayesian updating: the posterior is a compromise between the prior and likelihood

Game: beware of your intuitions! (2/4)

  • Student prior (df=2), normal likelihood
    • \(y \sim Normal(\mu, 1)\)
    • \(\mu \sim Student(2, 10, 1)\)

  • Now the likelihood dominates: it’s thin tails are very skeptical of the prior, but the prior’s thick tails are not surprised by the likelihood
  • Note: the mass in the tails can make a big difference!

Game: beware of your intuitions! (3/4)

  • Normal prior, Student likelihood (df=2)
    • \(y \sim Student(2, \mu, 1)\)
    • \(\mu \sim Normal(10, 1)\)

  • Now the prior dominates, so reason as previous example but in reverse

Game: beware of your intuitions! (4/4)

  • Student prior, student likelihood (df=2)
    • \(y \sim Student(2, \mu, 1)\)
    • \(\mu \sim Student(2, 10, 1)\)

  • The two modes persist: the extra mass in the tails means each distribution finds the other’s mode more plausible and so the average isn’t the best “compromise”

Posterior Description

  • How can we describe and report this correlation test?
  • We can report the posterior distribution of the correlation coefficient
  • We could (and should) visualize it, but we also often need to report it with numbers (e.g., in a paper)
  • Because it is “just” a distribution, we can re-use the concepts that we have seen for bootstrapping
  • We need to report various indices to convey as much information as we can about this distribution

Indices of Centrality

  • We want to describe the “center” / “typical” value of the posterior distribution
  • What indices to you know?
  • Mean
# Extract the vector of rho values
rvals <- as.data.frame(posterior)$rho

mean(rvals)
[1] 0.3477762
  • Median
median(rvals)
[1] 0.3662127
  • (Mode) MAP (Maximum A Posteriori)
as.numeric(bayestestR::map_estimate(rvals))
        x 
0.4022657
  • Which one to pick?
    • Up to you! But be mindful.
  • I recommend the median because it is less jittery and has a probabilistic meaning (the effect has 50% to be lower and 50% to be higher)

Point-estimates - Note

  • The index of centrality that we reported is often referred to as a “point-estimate” of the parameter’s central tendency (e.g., “the” correlation coefficient)
    • Note that we can also talk about “point-estimates of dispersion” etc., point-estimate just means “single value” summary
  • When re-used (in other analyses, meta-analyses, 
), it inevitably carries a loss of information (e.g., uncertainty).
    • E.g., computing the Bayesian correlation within each participant between RT and number of errors, and then using the “point-estimate” to further relate it to personality traits
  • Unfortunately, propagating uncertainty is not common practice (and is technically challenging)

Indices of Uncertainty - Deviation

  • We want to describe the “uncertainty” / “variability” of the posterior distribution
  • This informs us about the “precision” of our estimate
  • What index?
  • Standard deviation: the SD of the posterior is the “equivalent”** of the standard error (SE) in Frequentist models. But its interpretation is more straightforward: it is the average distance of the values from the mean. Whereas the Frequentist SE is the deviation of sample means if we drew infinite samples from the parent population (which is a bit more abstract).
sd(rvals)
[1] 0.1487645
  • MAD: the Median Absolute Deviation is the median of the absolute deviations from the median. It is more robust to outliers than the SD.
mad(rvals)
[1] 0.1491232

Indices of Uncertainty - Intervals (Range)

  • One can also communicate uncertainty by reporting “credibility” (i.e., certainty) intervals
  • What bounds? 95%? 90%? 89%?
ci <- bayestestR::eti(rvals, ci=c(0.50, 0.89, 0.90, 0.95))
plot(ci)

  • 95% is the most common (because it has been used in Frequentist statistics), but it is ultimately arbitrary1
  • 89% has been advocated by Richard McElreath as an iconoclast move to break the 95% tradition
  • Some people report multiple intervals (especially via plots), to help conveying the shape of the distribution
  • (I recommend 95%)

Indices of Uncertainty - Intervals (Type)

  • What type of interval?
  • Equal-tailed intervals (ETI): Quantiles are values that split the distribution into equal parts (e.g., the 2.5% quantile is the value below which 2.5% of the distribution lies). Quantile-based intervals are commonly used to report the 95% most “central” values of the posterior distribution (i.e., the 95% quantile interval). They are called “equal-tailed” because they contain the same amount of probability mass on each side of the distribution.
bayestestR::eti(rvals)
95% ETI: [0.05, 0.60]
  • Caveat: While easy to compute and to interpret, it is possible that values outside the ETI range are more probable than values inside (if the distribution is highly skewed)
x <- rgamma(10000, 1, 1000) * 10000

ci <- bayestestR::eti(x, ci=0.50)
plot(ci)

Indices of Uncertainty - Intervals (Type)

  • Highest Density Interval (HDI): the HDI is the Bayesian equivalent of the Confidence Interval (CI). It is the interval that contains the specified probability mass (e.g., 95%) and has the highest density (i.e., is the narrowest). It is the most common interval reported in Bayesian analyses.
  • The HDI is not straightforward to compute (is based on density estimation which is not a trivial issue)
  • But is has the advantage of showing the most “credible” values: the values in the range are the most probable
ci <- bayestestR::hdi(x, ci=0.50)
plot(ci)

  • It is the most “intuitive” interval, and people often interpret Freqentist CIs as HDIs, which is not correct

Indices of Effect “Significance”

  • Indices useful to make a decision about the effect (whether it is worth mentioning and discussing or not)
    • This can mean either an effect being big enough (“significant”)
      • Relates to the concept of effect size
    • Or an effect being certain enough (e.g., “a positive correlation exists for sure, even if very very small”)
  • Bayes Factor (BF)
  • Probability of Direction (pd)
  • Probability of being in the Region Of Practical Equivalence (ROPE)
  • The two latter are “posterior-based indices”, as they only require the posterior distribution to be computed (although the ROPE requires the specification of a “practical equivalence” range). They are often opposed to Bayes Factors, which requires the specification of an alternative models and cannot be computed from the posterior.

Probability of Direction (pd)

  • The probability that the effect is positive or negative
  • It is a Bayesian equivalent usually strongly correlated with the p-value
rez <- bayestestR::pd(rvals)
plot(rez)

rez
Probability of Direction

Parameter |     pd
------------------
Posterior | 98.40%
  • Interpretation rules of thumb1
    • pd <= 95% ~ p > .1: uncertain
    • pd > 95% ~ p < .1: possibly existing
    • pd > 97% ~ p < .06: likely existing
    • pd > 99% ~ p < .02: probably existing
    • pd > 99.9% ~ p < .002: certainly existing
  • The pd is a good index because of its simplicity and its intuitive interpretation

ROPE (Region Of Practical Equivalence)

  • The ROPE is a range of values that are considered as “practically equivalent” to zero (or to any other value)
  • It is used to make a decision about the effect being “big” enough (i.e., “significant”)
rez <- bayestestR::rope(rvals, rope=c(-0.1, 0.1))
rez
# Proportion of samples inside the ROPE [-0.10, 0.10]:

inside ROPE
-----------
3.59 %
  • Often we use \(P_{~in~rope}< .05\) (= \(5\%\)) as a threshold
plot(rez)

  • Be careful, by default we consider some % of CI that is in the ROPE, so your p-ROPE highly depends on the CI bounds you choose
bayestestR::rope(rvals, rope=c(-0.1, 0.1), ci=0.90)
# Proportion of samples inside the ROPE [-0.10, 0.10]:

inside ROPE
-----------
1.98 %     
bayestestR::rope(rvals, rope=c(-0.1, 0.1), ci=0.89)
# Proportion of samples inside the ROPE [-0.10, 0.10]:

inside ROPE
-----------
0.45 %

Decision Fatigue

  • “I hate Bayesian stats because there is so many things to decide / pay attention to”
  • There is even more things we should care about in Frequentist estimation
  • The things we care about in Bayesian statistics are much more transparently accessible and interpretable (e.g., priors, MCMC diagnostics, posterior description, 
)
  • Because applied Bayesian stats are a new field, there is not yet a very strong “standard” way to do things
  • It is a moving and rapidly evolving field
  • For now, best way to deal with this is to 1) understand the main concepts and indices, 2) be transparent about the choices you make
  • Rest assured: if a result is really there, details of the approach shouldn’t matter as much, the effect should show regardless of small variations in the analysis

Reporting a Bayesian Correlation

mtcars |> 
  ggplot(aes(x=mpg, y=qsec)) +
  geom_point() +
  geom_smooth(method="lm")

    • “a shifted, scaled \(Beta(3, 3)\) prior distribution was used for the correlation coefficient”
    • “Default priors from the BayesFactor package (Morey & Rouder, 2023) correlationBF() function were used (with the rscale parameter being set to medium)”
library(BayesFactor)

report::cite_packages()

Sampling Information

    • “We drew 500 samples using MCMC”
    • “The sampling algorithm (MCMC; 500 iterations) converged.”
plot(posterior)

  • Make trace plot with ggplot
    • Plot the iterations “in order” of their sampling (from 1 to i)
data.frame(r=rvals, i=1:length(rvals)) |> 
  ggplot(aes(x=i, y=r)) +
  geom_line() +
  labs(title="Trace Plot of the Posterior Distribution")

Posterior Distribution

data.frame(r=rvals) |> 
  ggplot(aes(x=r)) +
  geom_density(fill="orange") +
  labs(title="Correlation Posterior Distribution")

plot(hdi(rvals))

Centrality and Direction

    • Mean, median, MAP
    • “The median of the correlation coefficient’s posterior distribution is 0.37”
    • “We will report the median of the posterior distributions” + “The correlation was positive and significant (r = 0.37, 
)”
    • Of the centrality point-estimate
    • “The median of the correlation coefficient’s posterior distribution is positive (r = 0.37)
    • Using the Probability of Direction (pd) index
    • “The probability of the correlation coefficient being positive (pd) is 98.40%”

Precision

    • SD, MAD
    • “(MAD = 0.15)”
    • HDI, ETI
    • “95% HDI = [
, 
]”
    • “Credible intervals were computed as Highest Density Intervals (HDI)” + “95% CI = [
, 
]”

Effect significance

    • “There is a significant probability that the correlation is positive (pd > 99%)”
    • “The ROPE was set as [-0.10, 0.10] following Cohen’s (1988) effect size recommendations for correlations and we computed the portion of the 95% CI lying inside as an index of effect significance” + “(p-ROPE=
)”
    • “The proportion of the posterior falling in the region of practical null equivalence (ROPE = [-0.10, 0.10]) is 
”
    • “There is strong evidence against the null hypothesis that the correlation is null (BF10 = 
)”

Effect size

effectsize::interpret_r(median(rvals), rules="cohen1988")
[1] "moderate"
(Rules: cohen1988)
    • Mention rules: “We labelled the correlation effect sizes using Cohen’s (1988) rules of thumb set”
    • Of the point-estimate
    • “The median of the correlation coefficient’s posterior distribution is positive and moderate”
  • Of the posterior distribution
effsizes_alldraws <- effectsize::interpret_r(rvals, rules="cohen1988") 
head(effsizes_alldraws)
[1] "moderate" "moderate" "moderate" "moderate" "large"    "small"   
length(effsizes_alldraws)
[1] 500
table(effsizes_alldraws) / length(effsizes_alldraws)
effsizes_alldraws
     large   moderate      small very small 
     0.136      0.518      0.292      0.054
  • “The correlation has a 13.6% probability of being large, 51.8% of being moderate, 29.2% of being small and 5.40% of being very small”
    • Can become verbose (especially if many correlations to report): find good balance between detail and conciseness

Minimal Template Sentence

  • “The Bayesian Pearson correlation between X and Y was estimated to be r = median (95% CI [
, 
]), with a probability of pd of being negative and can be considered as significant (\(p_{-rope}\)=
)”
  • “There was moderate evidence (\(BF_{10}=~...\)) in favour of a non-null correlation between X and Y that has a probability of pd of being negative (Median = median, 89% CI [
, 
]) and can be considered as significant and moderate (\(p_{-rope}\)=
)”

Spearman Correlation

  • What are the benefits of Spearman correlations?
    • More “robust” to outliers
y <- c(0.53, 0.67, 0.70, 0.86, 0.92, 0.94, 0)
x <- c(100, 101, 102, 103, 104, 105, 115)

ggplot(data.frame(x=x, y=y), aes(x=x, y=y)) +
  geom_point() +
  geom_smooth(method="lm", se=FALSE)

cor.test(x, y, method="pearson")

    Pearson's product-moment correlation

data:  x and y
t = -2.1175, df = 5, p-value = 0.08779
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9491708  0.1357760
sample estimates:
       cor 
-0.6875858

Rank-Transformations

  • Why is it more robust? What are Spearman correlations?
  • Spearman Correlation are Pearson correlation on “rank-transformed” data
xrank <- datawizard::ranktransform(x)
xrank
[1] 1 2 3 4 5 6 7
yrank <- datawizard::ranktransform(y)

ggplot(data.frame(x=xrank, y=yrank), aes(x=x, y=y)) +
  geom_point() +
  geom_smooth(method="lm", se=FALSE)

cor.test(x, y, method="spearman")

    Spearman's rank correlation rho

data:  x and y
S = 42, p-value = 0.5948
alternative hypothesis: true rho is not equal to 0
sample estimates:
 rho 
0.25

Spearman Bayesian Correlations

cor.test(x, y, method="spearman")

    Spearman's rank correlation rho

data:  x and y
S = 42, p-value = 0.5948
alternative hypothesis: true rho is not equal to 0
sample estimates:
 rho 
0.25 
cor.test(xrank, yrank, method="pearson")

    Pearson's product-moment correlation

data:  xrank and yrank
t = 0.57735, df = 5, p-value = 0.5887
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.6197316  0.8441370
sample estimates:
 cor 
0.25
  1. Rank-transform variables
  2. Compute Bayesian Pearson correlation

Exercice

  • In the iris in-built dataset, report the Spearman correlation between Petal.Length and Petal.Width for the setosa species only
  • Desired output1:
Summary of Posterior Distribution

Parameter | Median |       95% CI |     pd |          ROPE | % in ROPE |   BF |         Prior
---------------------------------------------------------------------------------------------
rho       |   0.29 | [0.03, 0.52] | 98.62% | [-0.05, 0.05] |     0.76% | 3.86 | Beta (3 +- 3)

The End (for now)

Thank you!