I am conducting a power calculation for a brm model that has an ordered factor as the outcome variable and therefore uses family ‘cumulative’. I am using the WebPower function wp.regression (https://www.rdocumentation.org/packages/WebPower/versions/0.5.2/topics/wp.regression) which has been very helpful so far, except for this particular model because I need to compute the model’s R^2 using bayes_R2() and this function does not accept model input with cumulative family/ordinal outcome variable. If anyone has experience computing bayesian R^2 for this type of data I would appreciate any advice.

The `bayes_R2`

function in brms should work with ordinal regressions; e.g., this works for me:

```
fit <- brm(rating ~ period + carry,
data = inhaler, family = cumulative("logit"),
prior = set_prior("normal(0,5)"), chains = 2)
summary(fit)
bayes_R2(fit)
```

Note that R2s for logistic (Bernoulli) and cumulative (ordinal) regressions are typically lower than you would expect to see in a standard linear regression. For such models, you might want to consider a Bayesian version of the McKelvey-Zavoina R2, which provides an approximation of the R2 that would have been obtained if a linear model had have been run on observations of the continuous latent variable underlying the discrete responses (Veall and Zimmermann, 1992; Hagle and Mitchell, 1992; Veall and Zimmermann, 1994).

If of interest, I can provide a function to calculate a Bayesian version of the McKelvey-Zavoina R2 from a fitted brms model.

Hi @andymilne thank you for your advice! Yes I would appreciate the function for the Bayesian version of the McKelvey-Zavoina R2, if possible. Much appreciated.

Here it is. Apologies for probably clumsy R code but it should do the trick …

```
# Bayesian McKelvey-Zavoina R2 ------------------------------------------------
# Bayesian version of McKelvey and Zavoina's pseudo-R2 for binary and ordinal
# brms models (McKelvey and Zavoina, 1975). See also Gelman et al. (2018). This
# pseudo-R2 closely approximates the R2 that would have been obtained if a
# linear model had have been run on observations of the continuous latent
# variable underlying the discrete responses (Veall and Zimmermann, 1992; Hagle
# and Mitchell, 1992; Veall and Zimmermann, 1994).
Bayes_R2_MZ <- function(fit, ...) {
y_pred <- fitted(fit, scale = "linear", summary = FALSE, ...)
var_fit <- apply(y_pred, 1, var)
if (fit$formula$family$family == "cumulative" ||
fit$formula$family$family == "bernoulli") {
if (fit$formula$family$link == "probit" ||
fit$formula$family$link == "probit_approx") {
var_res <- 1
}
else if (fit$formula$family$link == "logit") {
var_res <- pi^2 / 3
}
}
else {
sum_fit <- summary(fit)
sig_res <- sum_fit$spec_pars["sigma", "Estimate"]
var_res <- sig_res^2
}
R2_MZ <- var_fit / (var_fit + var_res)
print(
data.frame(
Estimate = mean(R2_MZ),
Est.Error = sd(R2_MZ),
"l-95% CI" = quantile(R2_MZ, 0.025),
"u-95% CI" = quantile(R2_MZ, 0.975),
row.names = "Bayes_R2_MZ",
check.names = FALSE),
digits = 2)
}
```

This is very useful thanks so much