This vignette can be cited as:

`citation("correlation")`

```
##
## To cite 'correlation' in publications use:
##
## Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2019).
## Methods and Algorithms for Correlation Analysis in R. Journal of Open
## Source Software, 5(51), 2306. doi:10.21105/joss.02306
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Methods and Algorithms for Correlation Analysis in R.},
## author = {Dominique Makowski and Mattan S. Ben-Shachar and Indrajeet Patil and Daniel Lüdecke},
## doi = {10.21105/joss.02306},
## year = {2020},
## journal = {Journal of Open Source Software},
## number = {51},
## volume = {5},
## pages = {2306},
## url = {https://joss.theoj.org/papers/10.21105/joss.02306},
## }
```

Imagine we have an experiment in which **10 individuals** completed a task with **100 trials**. For each trial - there will 1000 trials (10 * 1000) in total - we measured two things, **V1** and **V2**, and we are interested in **investigating the link between these two variables**.

We will generate data using the `simulate_simpson()`

function from this package and look at its summary:

```
library(correlation)
<- simulate_simpson(n = 100, groups = 10)
data
summary(data)
```

```
## V1 V2 Group
## Min. :-1.6 Min. :-12.3 Length:1000
## 1st Qu.: 3.1 1st Qu.: -8.0 Class :character
## Median : 5.4 Median : -5.6 Mode :character
## Mean : 5.5 Mean : -5.5
## 3rd Qu.: 8.1 3rd Qu.: -3.0
## Max. :12.3 Max. : 1.3
```

Now let’s visualize the two variables:

```
library(ggplot2)
ggplot(data, aes(x = V1, y = V2)) +
geom_point() +
geom_smooth(colour = "black", method = "lm", se = FALSE) +
theme_classic()
```

`## `geom_smooth()` using formula 'y ~ x'`

That seems pretty straightforward! It seems like there is a **negative correlation** between V1 and V2. Let’s test this.

`correlation(data)`

```
## Registered S3 methods overwritten by 'parameters':
## method from
## as.double.parameters_kurtosis datawizard
## as.double.parameters_skewness datawizard
## as.double.parameters_smoothness datawizard
## as.numeric.parameters_kurtosis datawizard
## as.numeric.parameters_skewness datawizard
## as.numeric.parameters_smoothness datawizard
## print.parameters_distribution datawizard
## print.parameters_kurtosis datawizard
## print.parameters_skewness datawizard
## summary.parameters_kurtosis datawizard
## summary.parameters_skewness datawizard
```

```
## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t(998) | p
## ---------------------------------------------------------------------
## V1 | V2 | -0.84 | [-0.86, -0.82] | -48.77 | < .001***
##
## p-value adjustment method: Holm (1979)
## Observations: 1000
```

Indeed, there is a **strong, negative and significant correlation** between V1 and V2.

Great, can we go ahead and **publish these results in PNAS**?

Not so fast! Ever heard of the **Simpson’s Paradox**?

Let’s colour our datapoints by group (by individuals):

```
library(ggplot2)
ggplot(data, aes(x = V1, y = V2)) +
geom_point(aes(colour = Group)) +
geom_smooth(aes(colour = Group), method = "lm", se = FALSE) +
geom_smooth(colour = "black", method = "lm", se = FALSE) +
theme_classic()
```

```
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
```

Mmh, interesting. It seems like, for each subject, the relationship is different. The (global) negative trend seems to be an artifact of **differences between the groups** and could be spurious!

**Multilevel (as in multi-group) ** correlations allow us to account for

You can compute them with the **correlations** package by setting the `multilevel`

argument to `TRUE`

.

`correlation(data, multilevel = TRUE)`

```
## Parameter1 | Parameter2 | r | CI | t(998) | p
## ------------------------------------------------------------------
## V1 | V2 | 0.50 | [0.45, 0.55] | 18.24 | < .001***
##
## Observations: 1000
```

For completeness, let’s also see if its Bayesian cousin agrees with it:

`correlation(data, multilevel = TRUE, bayesian = TRUE)`

```
## Parameter1 | Parameter2 | r | CI | t(998) | p
## ------------------------------------------------------------------
## V1 | V2 | 0.50 | [0.45, 0.54] | 18.14 | < .001***
##
## Observations: 1000
```

**Dayum!** We were too hasty in our conclusions! Taking the group into account seems to be super important.

*Note*: In this simple case where only two variables are of interest, it would be of course best to directly proceed using a mixed regression model instead of correlations. That being said, the latter can be useful for exploratory analysis, when multiple variables are of interest, or in combination with a network or structural approach.