# cwi: Curve-wise Intervals (CWI) In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

## Description

Compute the Curve-Wise Interval (CWI) of posterior distributions using `ggdist::curve_interval()`. These are particularly useful for visualisation of model's predictions.

## Usage

 ```1 2 3 4``` ```cwi(x, ...) ## S3 method for class 'data.frame' cwi(x, ci = 0.95, ...) ```

## Arguments

 `x` Vector representing a posterior distribution, or a data frame of such vectors. Can also be a Bayesian model (`stanreg`, `brmsfit`, `MCMCglmm`, `mcmc` or `bcplm`) or a `BayesFactor` model. `...` Currently not used. `ci` Value or vector of probability of the (credible) interval - CI (between 0 and 1) to be estimated. Default to `.95` (`95%`).

## Details

Unlike equal-tailed intervals (see `eti()`) that typically exclude `2.5%` from each tail of the distribution and always include the median, the HDI is not equal-tailed and therefore always includes the mode(s) of posterior distributions.

The `95%` or `89%` Credible Intervals (CI) are two reasonable ranges to characterize the uncertainty related to the estimation (see here for a discussion about the differences between these two values).
The `89%` intervals (`ci = 0.89`) are deemed to be more stable than, for instance, `95%` intervals (Kruschke, 2014). An effective sample size of at least 10.000 is recommended if one wants to estimate `95%` intervals with high precision (Kruschke, 2014, p. 183ff). Unfortunately, the default number of posterior samples for most Bayes packages (e.g., `rstanarm` or `brms`) is only 4.000 (thus, you might want to increase it when fitting your model). Moreover, 89 indicates the arbitrariness of interval limits - its only remarkable property is being the highest prime number that does not exceed the already unstable `95%` threshold (McElreath, 2015).
However, `95%` has some advantages too. For instance, it shares (in the case of a normal posterior distribution) an intuitive relationship with the standard deviation and it conveys a more accurate image of the (artificial) bounds of the distribution. Also, because it is wider, it makes analyses more conservative (i.e., the probability of covering 0 is larger for the `95%` CI than for lower ranges such as `89%`), which is a good thing in the context of the reproducibility crisis.

A `95%` equal-tailed interval (ETI) has `2.5%` of the distribution on either side of its limits. It indicates the 2.5th percentile and the 97.5h percentile. In symmetric distributions, the two methods of computing credible intervals, the ETI and the HDI, return similar results.

This is not the case for skewed distributions. Indeed, it is possible that parameter values in the ETI have lower credibility (are less probable) than parameter values outside the ETI. This property seems undesirable as a summary of the credible values in a distribution.

On the other hand, the ETI range does change when transformations are applied to the distribution (for instance, for a log odds scale to probabilities): the lower and higher bounds of the transformed distribution will correspond to the transformed lower and higher bounds of the original distribution. On the contrary, applying transformations to the distribution will change the resulting HDI.

## Value

A data frame with following columns:

• `Parameter` The model parameter(s), if `x` is a model-object. If `x` is a vector, this column is missing.

• `CI` The probability of the credible interval.

• `CI_low`, `CI_high` The lower and upper credible interval limits for the parameters.

Other ci: `bci()`, `ci()`, `eti()`, `hdi()`, `si()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```library(bayestestR) if (require("ggplot2") && require("rstanarm") && require("ggdist")) { # Generate data ============================================= k = 11 # number of curves (iterations) n = 201 # number of rows data <- data.frame(x = seq(-15,15,length.out = n)) # Simulate iterations as new columns for(i in 1:k) { data[paste0("iter_", i)] <- dnorm(data\$x, seq(-5,5, length.out = k)[i], 3) } # Note: first, we need to transpose the data to have iters as rows iters <- datawizard::data_transpose(data[paste0("iter_", 1:k)]) # Compute Median data\$Median <- point_estimate(iters)[["Median"]] # Compute Credible Intervals ================================ # Compute ETI (default type of CI) data[c("ETI_low", "ETI_high")] <- eti(iters, ci = 0.5)[c("CI_low", "CI_high")] # Compute CWI # ggdist::curve_interval(reshape_iterations(data), iter_value .width = c(.5)) # Visualization ============================================= ggplot(data, aes(x = x, y = Median)) + geom_ribbon(aes(ymin = ETI_low, ymax = ETI_high), fill = "red", alpha = 0.3) + geom_line(size = 1) + geom_line(data = reshape_iterations(data), aes(y = iter_value, group = iter_group), alpha = 0.3) } ```