cwi {bayestestR} | R Documentation |
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.
cwi(x, ...) ## S3 method for class 'data.frame' cwi(x, ci = 0.95, ...)
x |
Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model. bayestestR supports a wide range
of models (see, for example, |
... |
Currently not used. |
ci |
Value or vector of probability of the (credible) interval - CI
(between 0 and 1) to be estimated. Default to |
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.
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()
,
spi()
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 ) }