cwi {bayestestR} R Documentation

Curvewise Intervals (CWI)

Description

Compute the Curvewise interval (CWI) (also called the "simultaneous interval" or "joint interval") of posterior distributions using ggdist::curve_interval(). Whereas the more typical "pointwise intervals" contain xx% of the posterior for a single parameter, joint/curvewise intervals contain xx% of the posterior distribution for all parameters.

Usage

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

Applied model predictions, pointwise intervals contain xx% of the predicted response values conditional on specific predictor values. In contrast, curvewise intervals contain xx% of the predicted response values across all predictor values. Put another way, curvewise intervals contain xx% of the full prediction lines from the model.

For more details, see the ggdist documentation on curvewise intervals.

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()

Examples

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)
}

[Package bayestestR version 0.11.5 Index]