Change a model's tidy output

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) and glance(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