ARD ANOVA — ard_stats_anova • cardx

Prepare ANOVA results from the stats::anova() function. Users may pass a pre-calculated stats::anova() object or a list of formulas. In the latter case, the models will be constructed using the information passed and models will be passed to stats::anova().

Usage

ard_stats_anova(x, ...)

# S3 method for class 'anova'
ard_stats_anova(x, method_text = "ANOVA results from `stats::anova()`", ...)

# S3 method for class 'data.frame'
ard_stats_anova(
  x,
  formulas,
  method,
  method.args = list(),
  package = "base",
  method_text = "ANOVA results from `stats::anova()`",
  ...
)

Arguments

x

(anova or data.frame)
an object of class 'anova' created with stats::anova() or a data frame

...

These dots are for future extensions and must be empty.

method_text

(string)
string of the method used. Default is "ANOVA results from stats::anova()". We provide the option to change this as stats::anova() can produce results from many types of models that may warrant a more precise description.

formulas

(list)
a list of formulas

method

(string)
string of function naming the function to be called, e.g. "glm". If function belongs to a library that is not attached, the package name must be specified in the package argument.

method.args

(named list)
named list of arguments that will be passed to method.

Note that this list may contain non-standard evaluation components. If you are wrapping this function in other functions, the argument must be passed in a way that does not evaluate the list, e.g. using rlang's embrace operator {{ . }}.

package

(string)
string of package name that will be temporarily loaded when function specified in method is executed.

Value

ARD data frame

Details

When a list of formulas is supplied to ard_stats_anova(), these formulas along with information from other arguments, are used to construct models and pass those models to stats::anova().

The models are constructed using rlang::exec(), which is similar to do.call().

rlang::exec(.fn = method, formula = formula, data = data, !!!method.args)

The above function is executed in withr::with_namespace(package), which allows for the use of ard_stats_anova(method) from packages, e.g. package = 'lme4' must be specified when method = 'glmer'. See example below.

Examples

anova(
  lm(mpg ~ am, mtcars),
  lm(mpg ~ am + hp, mtcars)
) |>
  ard_stats_anova()
#> {cards} data frame: 11 x 8
#>    variable   context   stat_name stat_label      stat fmt_fn
#> 1   model_1 stats_an…        term       term  mpg ~ am   NULL
#> 2   model_1 stats_an… df.residual  df for r…        30      1
#> 3   model_1 stats_an…         rss  Residual…   720.897      1
#> 4   model_2 stats_an…        term       term mpg ~ am…   NULL
#> 5   model_2 stats_an… df.residual  df for r…        29      1
#> 6   model_2 stats_an…         rss  Residual…   245.439      1
#> 7   model_2 stats_an…          df  Degrees …         1      1
#> 8   model_2 stats_an…       sumsq  Sum of S…   475.457      1
#> 9   model_2 stats_an…   statistic  statistic    56.178      1
#> 10  model_2 stats_an…     p.value    p-value         0      1
#> 11  model_2 stats_an…      method     method ANOVA re…   NULL
#> ℹ 2 more variables: warning, error

ard_stats_anova(
  x = mtcars,
  formulas = list(am ~ mpg, am ~ mpg + hp),
  method = "glm",
  method.args = list(family = binomial)
)
#> {cards} data frame: 10 x 8
#>    variable   context         stat_name stat_label      stat fmt_fn
#> 1   model_1 stats_an…              term       term  am ~ mpg   NULL
#> 2   model_1 stats_an…       df.residual  df for r…        30      1
#> 3   model_1 stats_an… residual.deviance  residual…    29.675      1
#> 4   model_2 stats_an…              term       term am ~ mpg…   NULL
#> 5   model_2 stats_an…       df.residual  df for r…        29      1
#> 6   model_2 stats_an… residual.deviance  residual…    19.233      1
#> 7   model_2 stats_an…                df  Degrees …         1      1
#> 8   model_2 stats_an…          deviance   deviance    10.443      1
#> 9   model_2 stats_an…           p.value    p-value     0.001      1
#> 10  model_2 stats_an…            method     method ANOVA re…   NULL
#> ℹ 2 more variables: warning, error

ard_stats_anova(
  x = mtcars,
  formulas = list(am ~ 1 + (1 | vs), am ~ mpg + (1 | vs)),
  method = "glmer",
  method.args = list(family = binomial),
  package = "lme4"
)
#> {cards} data frame: 16 x 8
#>    variable   context stat_name stat_label      stat   warning
#> 1   model_1 stats_an…      term       term    MODEL1 failed t…
#> 2   model_1 stats_an…      npar       npar         2 failed t…
#> 3   model_1 stats_an…       AIC        AIC     47.23 failed t…
#> 4   model_1 stats_an…       BIC        BIC    50.161 failed t…
#> 5   model_1 stats_an…    logLik     logLik   -21.615 failed t…
#> 6   model_1 stats_an…  deviance   deviance     43.23 failed t…
#> 7   model_2 stats_an…      term       term    MODEL2 failed t…
#> 8   model_2 stats_an…      npar       npar         3 failed t…
#> 9   model_2 stats_an…       AIC        AIC     35.25 failed t…
#> 10  model_2 stats_an…       BIC        BIC    39.647 failed t…
#> 11  model_2 stats_an…    logLik     logLik   -14.625 failed t…
#> 12  model_2 stats_an…  deviance   deviance     29.25 failed t…
#> 13  model_2 stats_an… statistic  statistic    13.979 failed t…
#> 14  model_2 stats_an…        df  Degrees …         1 failed t…
#> 15  model_2 stats_an…   p.value    p-value         0 failed t…
#> 16  model_2 stats_an…    method     method ANOVA re… failed t…
#> ℹ 2 more variables: fmt_fn, error