modelsummary display output from lm and brms concurrently with statistics

Question

models <- list(
"Linear" = lm(outcome ~ week * food data = df ),
"Bayesian" = brm(outcome ~ s(week, k = 4, fx = TRUE, by = food) + food, data = df, family = "zero_one_inflated_beta")
)

The following code works when I run it

modelsummary(models,
             estimate = "{estimate}[{conf.low}, {conf.high}]",
             statistic = NULL)

The problem is that when I attempt to also get the p-value, t-value and standard error of the linear model with the following code, the error comes up as Error: std.error is not available. The estimate and statistic arguments must correspond to column names in the output of this command: get_estimates(model)

modelsummary(models,
             estimate = "estimate[{conf.low}, {conf.high}]",
             statistic = c("Std.Error" = "std.error", 
                           "t-value" = "statistic", 
                           "p-value" = "p.value"))

How can I enable modelsummary() to ignore the display of statistics when there is none like in the case of the brms model instead of throwing an error?

score 2 · Accepted Answer · answered Feb 27 '23 at 13:30

This is a common use-case which is not well supported in the CRAN version of modelsummary. Instead of suggesting a complicated hack, I pushed a change to the development version which makes this much easier. You can install it now with:

remotes::install_github("vincentarelbundock/modelsummary")

Restart R completely for the changes to take effect.

Then, you can do things like:

library(brms)
library(modelsummary)

mod1 <- lm(mpg ~ hp + qsec, data = mtcars)
mod2 <- brm(mpg ~ hp + qsec, data = mtcars)
models <- list(mod1, mod2)

modelsummary(
    models,
    statistic = c("std.error", "conf.int"),
    # clean-up coefficient names
    coef_rename = \(x) gsub("b_", "", x),
    coef_omit = "Intercept")

	(1)	(2)
hp	-0.085	-0.084
	(0.014)
	[-0.113, -0.056]	[-0.112, -0.055]
qsec	-0.887	-0.867
	(0.535)
	[-1.980, 0.207]	[-1.944, 0.239]
sigma		3.815
		[3.000, 4.991]
Num.Obs.	32	32
R2	0.637	0.631
R2 Adj.	0.612	0.553
AIC	180.3
BIC	186.2
Log.Lik.	-86.170
F	25.431
ELPD		-91.1
ELPD s.e.		4.9
LOOIC		182.3
LOOIC s.e.		9.8
WAIC		181.9
RMSE	3.57	3.57

modelsummary display output from lm and brms concurrently with statistics

1 Answers1