Apply both generics::tidy() and generics::glance() to an object and return a single tibble with both sets of information.

tidy_glance(x, ..., tidy_args = list(), glance_args = list())

Arguments

x

An object to be converted into a tidy tibble.

...

Additional arguments passed to generics::tidy() and generics::glance().

Arguments are passed to both methods, but should be ignored by the inapplicable method. For example, if called on an lm object, conf.int will affect generics::tidy() but not generics::glance().

tidy_args

A list of additional arguments passed only to generics::tidy().

glance_args

A list of additional arguments passed only to generics::glance().

Value

A tibble with columns and rows from generics::tidy() and columns of repeated rows from generics::glance().

Column names that appear in both the tidy data and glance data will be disambiguated by appending "model." to the glance column names.

Examples

mod <- lm(mpg ~ wt + qsec, data = mtcars)
tidy_glance(mod)
#> # A tibble: 3 × 17
#>   term   estim…¹ std.e…² stati…³  p.value r.squ…⁴ adj.r…⁵ sigma model…⁶ model.…⁷
#>   <chr>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl> <dbl>   <dbl>    <dbl>
#> 1 (Inte…  19.7     5.25     3.76 7.65e- 4   0.826   0.814  2.60    69.0 9.39e-12
#> 2 wt      -5.05    0.484  -10.4  2.52e-11   0.826   0.814  2.60    69.0 9.39e-12
#> 3 qsec     0.929   0.265    3.51 1.50e- 3   0.826   0.814  2.60    69.0 9.39e-12
#> # … with 7 more variables: df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> #   deviance <dbl>, df.residual <int>, nobs <int>, and abbreviated variable
#> #   names ¹​estimate, ²​std.error, ³​statistic, ⁴​r.squared, ⁵​adj.r.squared,
#> #   ⁶​model.statistic, ⁷​model.p.value
#> # ℹ Use `colnames()` to see all variable names
tidy_glance(mod, conf.int = TRUE)
#> # A tibble: 3 × 19
#>   term    estim…¹ std.e…² stati…³  p.value conf.…⁴ conf.…⁵ r.squ…⁶ adj.r…⁷ sigma
#>   <chr>     <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#> 1 (Inter…  19.7     5.25     3.76 7.65e- 4   9.00    30.5    0.826   0.814  2.60
#> 2 wt       -5.05    0.484  -10.4  2.52e-11  -6.04    -4.06   0.826   0.814  2.60
#> 3 qsec      0.929   0.265    3.51 1.50e- 3   0.387    1.47   0.826   0.814  2.60
#> # … with 9 more variables: model.statistic <dbl>, model.p.value <dbl>,
#> #   df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> #   df.residual <int>, nobs <int>, and abbreviated variable names ¹​estimate,
#> #   ²​std.error, ³​statistic, ⁴​conf.low, ⁵​conf.high, ⁶​r.squared, ⁷​adj.r.squared
#> # ℹ Use `colnames()` to see all variable names
tidy_glance(mod, tidy_args = list(conf.int = TRUE))
#> # A tibble: 3 × 19
#>   term    estim…¹ std.e…² stati…³  p.value conf.…⁴ conf.…⁵ r.squ…⁶ adj.r…⁷ sigma
#>   <chr>     <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#> 1 (Inter…  19.7     5.25     3.76 7.65e- 4   9.00    30.5    0.826   0.814  2.60
#> 2 wt       -5.05    0.484  -10.4  2.52e-11  -6.04    -4.06   0.826   0.814  2.60
#> 3 qsec      0.929   0.265    3.51 1.50e- 3   0.387    1.47   0.826   0.814  2.60
#> # … with 9 more variables: model.statistic <dbl>, model.p.value <dbl>,
#> #   df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> #   df.residual <int>, nobs <int>, and abbreviated variable names ¹​estimate,
#> #   ²​std.error, ³​statistic, ⁴​conf.low, ⁵​conf.high, ⁶​r.squared, ⁷​adj.r.squared
#> # ℹ Use `colnames()` to see all variable names