Code
.get_add_p_test_fun(class = "tbl_summary", test = "t.test") %>%
.run_add_p_test_fun(data = trial, variable = "age", by = "trt", type = "continuous",
group = NULL) %>% purrr::pluck("df_result")
Output
# A tibble: 1 x 10
estimate statistic parameter conf.low conf.high p.value method estimate1
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 -0.438 -0.209 184. -4.57 3.69 0.834 Welch Two S~ 47.0
# i 2 more variables: estimate2 <dbl>, alternative <chr>
Code
.get_add_p_test_fun(class = "tbl_summary", test = "lme4") %>%
.run_add_p_test_fun(data = trial, variable = "age", by = "trt", type = "continuous",
group = "stage") %>% purrr::pluck("df_result")
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
# A tibble: 1 x 2
p.value method
<dbl> <chr>
1 0.833 random intercept logistic regression
Code
.get_add_p_test_fun(class = "tbl_summary", test = "lme4") %>%
.run_add_p_test_fun(data = trial, variable = "response", by = "trt", type = "categorical",
group = "stage") %>% purrr::pluck("df_result")
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
# A tibble: 1 x 2
p.value method
<dbl> <chr>
1 0.530 random intercept logistic regression
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