Extension and Customization

library(modelsummary)

Unsupported models: `modelsummary_list`

The simplest way to summarize an unsupported model is to create a modelsummary_list object. This approach is super flexible, but it requires manual intervention, and it can become tedious if you need to summarize many models. The next section shows how to add formal support for an unsupported model type.

A modelsummary_list is a list with two element that conform to the broom package specification: tidy and glance. tidy is a data.frame with at least three columns: term, estimate, and std.error. glance is a data.frame with only a single row, and where each column will be displayed at the bottom of the table in the goodness-of-fit section. Finally, we wrap those two elements in a list and assign it a modelsummary_list class:

ti <- data.frame(
  term = c("coef1", "coef2", "coef3"),
  estimate = 1:3,
  std.error = c(pi, exp(1), sqrt(2)))

gl <- data.frame(
  stat1 = "blah",
  stat2 = "blah blah")

mod <- list(
  tidy = ti,
  glance = gl)
class(mod) <- "modelsummary_list"

modelsummary(mod)

tinytable_dsgsrq3mkudzub622rjf

	(1)
coef1	1.000
	(3.142)
coef2	2.000
	(2.718)
coef3	3.000
	(1.414)
stat1	blah
stat2	blah blah

Unsupported models: `glance` and `tidy`

modelsummary relies on two functions from the broom package to extract model information: tidy and glance. If broom doesn’t support the type of model you are trying to summarize, modelsummary won’t support it out of the box. Thankfully, it is extremely easy to add support for most models using custom methods.

For example, models produced by the MCMCglmm package are not currently supported by broom. To add support, you simply need to create a tidy and a glance method:

# load packages and data
library(modelsummary)
library(MCMCglmm)
data(PlodiaPO)

# add custom functions to extract estimates (tidy) and goodness-of-fit (glance) information
tidy.MCMCglmm <- function(x, ...) {
    s <- summary(x, ...)
    ret <- data.frame(
      term      = row.names(s$solutions),
      estimate  = s$solutions[, 1],
      conf.low  = s$solutions[, 2],
      conf.high = s$solutions[, 3])
    ret
}

glance.MCMCglmm <- function(x, ...) {
    ret <- data.frame(
      dic = x$DIC,
      n   = nrow(x$X))
    ret
}

# estimate a simple model
model <- MCMCglmm(PO ~ 1 + plate, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE, pr=TRUE)

# summarize the model
modelsummary(model, statistic = 'conf.int')

Three important things to note.

First, the methods are named tidy.MCMCglmm and glance.MCMCglmm because the model object I am trying to summarize is of class MCMCglmm. You can find the class of a model by running: class(model).

Second, both of the methods include the ellipsis ... argument.

Third, in the example above we used the statistic = 'conf.int' argument. This is because the tidy method produces conf.low and conf.high columns. In most cases, users will define std.error column in their custom tidy methods, so the statistic argument will need to be adjusted.

If you create new tidy and glance methods, please consider contributing them to broom so that the rest of the community can benefit from your work: https://github.com/tidymodels/broom

Modifying information: `tidy_custom` and `glance_custom`

Users may want to include more information than is made available by the default extractor function. For example, models produced by the MASS::polr do not produce p values by default, which means that we cannot use the stars=TRUE argument in modelsummary. However, it is possible to extract this information by using the lmtest::coeftest function. To include such custom information, we will define new glance_custom and tidy_custom methods.

We begin by estimating a model with the MASS::polr:

library(MASS)

mod_ordinal <- polr(as.ordered(gear) ~ mpg + drat, data = mtcars)

get_estimates(mod_ordinal)

  term     estimate  std.error conf.level   conf.low  conf.high   statistic df.error      p.value component s.value group
1  3|4 13.962948761 4.04107300       0.95  5.6851860 22.2407116  3.45525774       28 0.0017706303     alpha     9.1      
2  4|5 16.898937342 4.39497069       0.95  7.8962480 25.9016267  3.84506258       28 0.0006356348     alpha    10.6      
3  mpg -0.008646682 0.09034201       0.95 -0.1916706  0.1708667 -0.09571053       28 0.9244322098      beta     0.1      
4 drat  3.949431923 1.30665144       0.95  1.6191505  6.8457246  3.02255965       28 0.0053120619      beta     7.6

The get_estimates function shows that our default extractor does not produce a p.value column. As a result, setting stars=TRUE in modelsummary will produce an error.

We know that the MASS::polr produces an object of class polr:

class(mod_ordinal)

[1] "polr"

To extract more (custom) information from a model of this class, we thus define a method called tidy_custom.polr which returns a data.frame with two columns: term and p.value:

tidy_custom.polr <- function(x, ...) {
  s <- lmtest::coeftest(x)
  out <- data.frame(
    term = row.names(s),
    p.value = s[, "Pr(>|t|)"])
  out
}

When this method is defined, modelsummary can automatically extract p values from all models of this class, and will now work properly with stars=TRUE:

modelsummary(mod_ordinal, stars = TRUE)

tinytable_vlrqqyaumrqg25mewn9j

	(1)
+ p
3\|4	13.963**
	(4.041)
4\|5	16.899***
	(4.395)
mpg	-0.009
	(0.090)
drat	3.949**
	(1.307)
Num.Obs.	32
AIC	51.1
BIC	57.0
RMSE	3.44

New information: `tidy_custom` and `glance_custom`

Sometimes users will want to include information that is not supplied by those functions. A pretty easy way to include extra information is to define new glance_custom and tidy_custom methods. To illustrate, we estimate two linear regression models using the lm function:

library(modelsummary)

mod <- list()
mod[[1]] <- lm(hp ~ mpg + drat, mtcars)
mod[[2]] <- lm(wt ~ mpg + drat + am, mtcars)

In R, the lm function produces models of class “lm”:

class(mod[[1]])

[1] "lm"

Let’s say you would like to print the dependent variable for each model of this particular class. All you need to do is define a new method called glance_custom.lm. This method should return a data.frame (or tibble) with 1 row, and 1 column per piece of information you want to display. For example:

glance_custom.lm <- function(x, ...) {
    dv <- as.character(formula(x)[2])
    out <- data.frame("DV" = dv)
    return(out)
}

Now, let’s customize the body of the table. The vcov argument already allows users to customize uncertainty estimates. But imagine you want to override the coefficient estimates of your “lm” models. Easy! All you need to do is define a tidy_custom.lm method which returns a data.frame (or tibble) with one column called “term” and one column called “estimate”.

Here, we’ll substitute estimates by an up/down-pointing triangles which represents their signs:

tidy_custom.lm <- function(x, ...) {
    s <- summary(x)$coefficients
    out <- data.frame(
      term = row.names(s),
      estimate = ifelse(s[,1] > 0, '▲', '▼'))
    return(out)
}

After you define the glance_custom and tidy_custom methods, modelsummary will automatically display your customized model information:

modelsummary(mod)

tinytable_wbvo8h9eemnldayf666e

	(1)	(2)
(Intercept)	▲	▲
	(55.415)	(0.728)
mpg	▼	▼
	(1.792)	(0.019)
drat	▲	▼
	(20.198)	(0.245)
am		▼
		(0.240)
Num.Obs.	32	32
R2	0.614	0.803
R2 Adj.	0.588	0.782
AIC	337.9	46.4
BIC	343.7	53.7
Log.Lik.	-164.940	-18.201
F	23.100	38.066
RMSE	41.91	0.43
DV	hp	wt

Note that you can define a std.error column in tidy_custom.lm to replace the uncertainty estimates instead of the coefficients.

Customization: New model class

An even more fundamental way to customize the output would be to completely bypass modelsummary’s extractor functions by assigning a new class name to your model. For example,

# estimate a linear model
mod_custom <- lm(hp ~ mpg + drat, mtcars)

# assign it a new class
class(mod_custom) <- "custom"

# define tidy and glance methods
tidy.custom <- function(x, ...) {
  data.frame(
    term = names(coef(x)),
    estimate = letters[1:length(coef(x))],
    std.error = seq_along(coef(x))
  )
}

glance.custom <- function(x, ...) {
  data.frame(
    "Model" = "Custom",
    "nobs" = stats:::nobs.lm(x)
  )
}

# summarize
modelsummary(mod_custom)

tinytable_f07k5cn16hvi9nyr2h9u

	(1)
(Intercept)	a
	(1.000)
mpg	b
	(2.000)
drat	c
	(3.000)
Num.Obs.	32
Model	Custom

Warning: When defining new tidy and glance methods, it is important to include an ellipsis argument (...).

Note that in the glance.custom() method, we called stats:::nobs.lm() instead of the default stats::nobs() method, because the latter know does not know where to dispatch models of our new “custom” class. Being more explicit solves the problem.

An alternative would be to set a new class that inherits from the previous one, and to use a global option to set broom as the default extractor function (otherwise modelsummary will use its standard lm extractors by inheritance):

options(modelsummary_get = "broom")
class(mod_custom) <- c("custom", "lm")

Customization: `modelsummary_list`

Another flexible way to customize model output is to use output = "modelsummary_list". With this output option, modelsummary() returns a list with two elements: tidy contains parameter estimates, standard errors, etc., and glance contains model statistics such as the AIC. For example,

mod <- lm(hp ~ mpg + drat, mtcars)
mod_list <- modelsummary(mod, output = "modelsummary_list")
mod_list$tidy

         term   estimate std.error  statistic df.error      p.value s.value group conf.low conf.high
1 (Intercept) 278.515455 55.414866  5.0260061       29 2.359726e-05    15.4             NA        NA
2         mpg  -9.985499  1.791837 -5.5727709       29 5.172030e-06    17.6             NA        NA
3        drat  19.125752 20.197756  0.9469246       29 3.515013e-01     1.5             NA        NA

mod_list$glance

       aic      bic r.squared adj.r.squared     rmse nobs        F    logLik
1 337.8809 343.7438 0.6143611     0.5877653 41.90687   32 23.09994 -164.9404

Both tidy and glance can now be customized, and the updated model can be passed back to modelsummary using modelsummary(mod_list). All information that is displayed in the table is contained in mod_list, so this pattern allows for very flexible adjustments of output tables.

A useful example for this pattern concerns mixed models using lme4. Assume we want to compare the effect of using different degrees-of-freedom adjustments on the significance of the coefficients. The models have identical parameter estimates, standard errors, and model fit statistics - we only want to change the p-values. We use the parameters package to compute the adjusted p-values.

library("lme4")
mod <- lmer(mpg ~ drat + (1 | am), data = mtcars)
mod_list <- modelsummary(mod, output = "modelsummary_list", effects = "fixed")
# create a copy, where we'll change the p-values
mod_list_kenward <- as.list(mod_list)
mod_list_kenward$tidy$p.value <- parameters::p_value_kenward(mod)$p

modelsummary(list("Wald" = mod_list, "Kenward" = mod_list_kenward), 
            statistic = "{std.error} ({p.value}) {stars}")

tinytable_8c1pjyy1vy07jjp8ayed

	Wald	Kenward
(Intercept)	-5.159	-5.159
	6.409 (0.428)	6.409 (0.680)
drat	7.045	7.045
	1.736 (	1.736 (0.086) +
Num.Obs.	32	32
R2 Marg.	0.402	0.402
R2 Cond.	0.440	0.440
AIC	188.7	188.7
BIC	194.6	194.6
ICC	0.1	0.1
RMSE	4.28	4.28

Unsupported models: modelsummary_list

Unsupported models: glance and tidy

Modifying information: tidy_custom and glance_custom

New information: tidy_custom and glance_custom