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Default arguments for calculating and displaying tests in crosstable()

Source: R/test.R
crosstable_test_args.Rd

This is the starting point for refining the testing algorithm used in crosstable. Users can provide their own functions for test.~.

Usage

crosstable_test_args(
  test_summarize = test_summarize_auto,
  test_tabular = test_tabular_auto,
  test_correlation = test_correlation_auto,
  test_survival = test_survival_logrank,
  test_display = display_test,
  plim = 4,
  show_method = TRUE
)

Arguments

test_summarize

a function of two arguments (continuous variable and grouping variable), used to compare continuous variable. Must return a list of two components: p.value and method. See test_summarize_auto or test_summarize_linear_contrasts for some examples of such functions.

test_tabular

a function of two arguments (two categorical variables), used to test association between two categorical variables. Must return a list of two components: p.value and method. See test_tabular_auto for example.

test_correlation

a function of three arguments (two continuous variables plus the correlation method), used to test association between two continuous variables. Like cor.test, it must return a list of at least estimate, p.value, and method, with also conf.int optionally. See test_correlation_auto for example.

test_survival

a function of one argument (the formula surv~by), used to compare survival estimations. Must return a list of two components: p.value and method. See test_survival_logrank for example.

test_display

function used to display the test result. See display_test.

plim

number of digits for the p value.

show_method

whether to display the test name (logical).

Value

A list with test parameters

See also

test_summarize_auto, test_tabular_auto, test_survival_logrank, test_summarize_linear_contrasts, display_test

Author

Dan Chaltiel

Examples

library(dplyr)
my_test_args=crosstable_test_args()
my_test_args$test_summarize = test_summarize_linear_contrasts
iris %>%
  mutate(Petal.Width.qt = paste0("Q", ntile(Petal.Width, 5)) %>% ordered()) %>%
  crosstable(Petal.Length ~ Petal.Width.qt, test=TRUE, test_args = my_test_args)
#> # A tibble: 4 × 9
#>   .id          label        variable   Q1          Q2    Q3    Q4    Q5    test 
#>   <chr>        <chr>        <chr>      <chr>       <chr> <chr> <chr> <chr> <chr>
#> 1 Petal.Length Petal.Length Min / Max  1.0 / 1.9   1.3 … 3.6 … 4.5 … 4.9 … "p v…
#> 2 Petal.Length Petal.Length Med [IQR]  1.4 [1.3;1… 1.6 … 4.3 … 5.0 … 5.6 … "p v…
#> 3 Petal.Length Petal.Length Mean (std) 1.4 (0.2)   2.2 … 4.4 … 5.1 … 5.7 … "p v…
#> 4 Petal.Length Petal.Length N (NA)     30 (0)      30 (… 30 (… 30 (… 30 (… "p v…