get_p_value.md
In infer: Tidy Statistical Inference

direction is appropriate

Code
  test_df %>% get_p_value(obs_stat = 0.5, direction = "righ")
Condition
  Error in `get_p_value()`:
  ! The provided value for `direction` is not appropriate. Possible values are "less", "greater", "two-sided", "left", "right", "both", "two_sided", "two sided", or "two.sided".

theoretical p-value not supported error

Code
  gss_tbl %>% specify(hours ~ partyid) %>% hypothesize(null = "independence") %>%
    calculate(stat = "F") %>% get_p_value(obs_stat = obs_F, direction = "right")
Condition
  Error in `get_p_value()`:
  ! Theoretical p-values are not yet supported. `x` should be the result of calling `generate()`.

get_p_value warns in case of zero p-value

Code
  res_ <- get_p_value(gss_calc, obs_stat = -10, direction = "left")
Condition
  Warning:
  Please be cautious in reporting a p-value of 0. This result is an approximation based on the number of `reps` chosen in the `generate()` step. See `?get_p_value()` for more information.

get_p_value throws error in case of `NaN` stat

Code
  res_ <- get_p_value(gss_calc, 0, "both")
Condition
  Error:
  ! 1 calculated statistic was `NaN`. Simulation-based p-values are not well-defined for null distributions with non-finite values. See ?calculate for more details.

Code
  res_ <- get_p_value(gss_calc, 0, "both")
Condition
  Error:
  ! 2 calculated statistics were `NaN`. Simulation-based p-values are not well-defined for null distributions with non-finite values. See ?calculate for more details.

Code
  res_ <- get_p_value(gss_calc, 0, "both")
Condition
  Error:
  ! All calculated statistics were `NaN`. See ?calculate for more details.

get_p_value can handle fitted objects

Code
  get_p_value(null_fits, obs_fit_2, "both")
Condition
  Error in `get_p_value()`:
  ! The explanatory variables used to generate the distribution of null fits are not the same used to fit the observed data.

Code
  get_p_value(null_fits, obs_fit_3, "both")
Condition
  Error in `get_p_value()`:
  ! The response variable of the null fits (hours) is not the same as that of the observed fit (year).

get_p_value can handle bad args with fitted objects

Code
  get_p_value(null_fits, "boop", "both")
Condition
  Error in `get_p_value()`:
  ! The `obs_stat` argument should be the output of `fit()`. See the documentation with `?get_p_value`.

Code
  get_p_value(null_fits, obs_fit$estimate, "both")
Condition
  Error in `get_p_value()`:
  ! The `obs_stat` argument should be the output of `fit()`. See the documentation with `?get_p_value`.

Code
  get_p_value(obs_fit, null_fits, "both")
Condition
  Error in `get_p_value()`:
  ! The `x` argument needs to be passed to `generate()` before `fit()`.

get_p_value errors informatively when args are switched

Code
  get_p_value(obs_stat, null_dist, "both")
Condition
  Error in `get_p_value()`:
  ! It seems like the `obs_stat` argument has been passed to `get_p_value()` as the first argument when `get_p_value()` expects `x`, a distribution of statistics or coefficient estimates, as the first argument. Have you mistakenly switched the order of `obs_stat` and `x`?

get_p_value can handle theoretical distributions

Code
  old_way <- chisq_test(gss, college ~ finrela)
Condition
  Warning in `stats::chisq.test()`:
  Chi-squared approximation may be incorrect

get_p_value warns with bad theoretical distributions

Code
  res_ <- get_p_value(t_dist_30, t_obs, direction = "both")
Condition
  Warning:
  `x` and `obs_stat` were generated using different null hypotheses. This workflow is untested and results may not mean what you think they mean.