Use tidy_override and tidy_replace to provide your own p values, confidence intervals etc. for a model.

tidy_override(x, ..., glance = list(), extend = FALSE)

tidy_replace(x, tidied, glance = list())

# S3 method for tidy_override
tidy(x, ...)

# S3 method for tidy_override
glance(x, ...)

# S3 method for tidy_override
nobs(object, ...)

Arguments

x

A model with methods defined for generics::tidy() and/or generics::glance().

...

In tidy_override, columns of statistics to replace tidy output. In tidy and glance methods, arguments passed on to the underlying model.

glance

A list of summary statistics for glance.

extend

Logical: allow adding new columns to tidy(x)?

tidied

Data frame to replace the result of tidy(x).

object

A tidy_override object.

Value

An object that can be passed in to huxreg.

Details

tidy_override allows you to replace some columns of tidy(x) with your own data.

tidy_replace allows you to replace the result of tidy(x) entirely.

Examples

if (! requireNamespace("broom", quietly = TRUE)) { stop("Please install 'broom' to run this example.") } lm1 <- lm(mpg ~ cyl, mtcars) fixed_lm1 <- tidy_override(lm1, p.value = c(.04, .12), glance = list(r.squared = 0.99)) huxreg(lm1, fixed_lm1)
#> ──────────────────────────────────────────────────── #> (1) (2) #> ─────────────────────────────────── #> (Intercept) 37.885 *** 37.885 * #> (2.074)    (2.074)  #> cyl -2.876 *** -2.876   #> (0.322)    (0.322)  #> ─────────────────────────────────── #> N 32         32       #> R2 0.726     0.990   #> logLik -81.653     -81.653   #> AIC 169.306     169.306   #> ──────────────────────────────────────────────────── #> *** p < 0.001; ** p < 0.01; * p < 0.05. #> #> Column names: names, model1, model2
if (requireNamespace("nnet", quietly = TRUE)) { mnl <- nnet::multinom(gear ~ mpg, mtcars) tidied <- broom::tidy(mnl) mnl4 <- tidy_replace(mnl, tidied[tidied$y.level == 4, ]) mnl5 <- tidy_replace(mnl, tidied[tidied$y.level == 5, ]) huxreg(mnl4, mnl5, statistics = "nobs") }
#> # weights: 9 (4 variable) #> initial value 35.155593 #> iter 10 value 23.131901 #> final value 23.129234 #> converged
#> ──────────────────────────────────────────────────── #> (1) (2) #> ─────────────────────────────────── #> (Intercept) -9.502 ** -7.691 * #> (3.262)   (3.232)  #> mpg 0.475 ** 0.358 * #> (0.168)   (0.168)  #> ─────────────────────────────────── #> nobs                #> ──────────────────────────────────────────────────── #> *** p < 0.001; ** p < 0.01; * p < 0.05. #> #> Column names: names, model1, model2