##### https://github.com/cran/bayestestR

Tip revision:

**601edcd8d1306ebea478657861c54fa9c69a52f3**authored by**Dominique Makowski**on**31 May 2021, 05:40 UTC****version 0.10.0** Tip revision:

**601edcd** print.rope.R

```
#' @importFrom insight print_color
#' @export
print.rope <- function(x, digits = 2, ...) {
orig_x <- x
# If the model is multivariate, we have have different ROPES depending on
# the outcome variable.
is_multivariate <- length(unique(x$Response)) > 1
if (isTRUE(is_multivariate)) {
insight::print_color(sprintf(
"# Proportion%s of samples inside the ROPE.\nROPE with depends on outcome variable.\n\n",
ifelse(all(x$CI[1] == x$CI), "", "s")
), "blue")
} else {
insight::print_color(sprintf(
"# Proportion%s of samples inside the ROPE [%.*f, %.*f]:\n\n",
ifelse(all(x$CI[1] == x$CI), "", "s"),
digits,
x$ROPE_low[1],
digits,
x$ROPE_high[1]
), "blue")
}
# I think this is something nobody will understand and we'll probably forget
# why we did this, so I'll comment a bit...
# These are the base columns we want to print
cols <- c(
"Parameter", "ROPE_Percentage", "Effects", "Component",
if (is_multivariate) c("ROPE_low", "ROPE_high")
)
# In case we have ropes for different CIs, we also want this information
# So we first check if values in the CI column differ, and if so, we also
# keep this column for printing
if (!all(x$CI[1] == x$CI)) {
cols <- c("CI", cols)
}
# Either way, we need to know the different CI-values, so we can
# split the data frame for printing later...
ci <- unique(x$CI)
# now we check which of the requested columns are actually in our data frame "x"
# "x" may differ, depending on if "rope()" was called with a model-object,
# or with a simple vector. So we can't hard-code this
x <- subset(x, select = intersect(cols, colnames(x)))
# This is just cosmetics, to have nicer column names and values
x$ROPE_Percentage <- sprintf("%.*f %%", digits, x$ROPE_Percentage * 100)
colnames(x)[which(colnames(x) == "ROPE_Percentage")] <- "inside ROPE"
# Add ROPE width for multivariate models
if (isTRUE(is_multivariate)) {
# This is just cosmetics, to have nicer column names and values
x$ROPE_low <- sprintf("[%.*f, %.*f]", digits, x$ROPE_low, digits, x$ROPE_high)
colnames(x)[which(colnames(x) == "ROPE_low")] <- "ROPE width"
x$ROPE_high <- NULL
}
# In case we have multiple CI values, we create a subset for each CI value.
# Else, parameter-rows would be mixed up with both CIs, which is a bit
# more difficult to read...
if (length(ci) == 1) {
# print complete data frame, because we have no different CI values here
print_data_frame(x, digits = digits)
} else {
for (i in ci) {
xsub <- x[x$CI == i, -which(colnames(x) == "CI"), drop = FALSE]
insight::print_color(sprintf("ROPE for the %s%% HDI:\n\n", 100 * i), "cyan")
print_data_frame(xsub, digits = digits)
cat("\n")
}
}
invisible(orig_x)
}
```