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Add p-values

Source: R/add_p.tbl_svysummary.R
add_p.tbl_svysummary.Rd

Adds p-values to tables created by tbl_svysummary() by comparing values across groups.

Usage

# S3 method for class 'tbl_svysummary'
add_p(
  x,
  test = list(all_continuous() ~ "svy.wilcox.test", all_categorical() ~ "svy.chisq.test"),
  pvalue_fun = label_style_pvalue(digits = 1),
  include = everything(),
  test.args = NULL,
  ...
)

Arguments

x

(tbl_svysummary)
table created with tbl_svysummary()

test

(formula-list-selector)
List of formulas specifying statistical tests to perform. Default is list(all_continuous() ~ "svy.wilcox.test", all_categorical() ~ "svy.chisq.test").

See below for details on default tests and ?tests for details on available tests and creating custom tests.

pvalue_fun

(function)
Function to round and format p-values. Default is label_style_pvalue(). The function must have a numeric vector input, and return a string that is the rounded/formatted p-value (e.g. pvalue_fun = label_style_pvalue(digits = 2)).

include

(tidy-select)
Variables to include in output. Default is everything().

test.args

(formula-list-selector)
Containing additional arguments to pass to tests that accept arguments. For example, add an argument for all t-tests, use test.args = all_tests("t.test") ~ list(var.equal = TRUE).

...

These dots are for future extensions and must be empty.

Value

a gtsummary table of class "tbl_svysummary"

Examples

# Example 1 ----------------------------------
# A simple weighted dataset
survey::svydesign(~1, data = as.data.frame(Titanic), weights = ~Freq) |>
  tbl_svysummary(by = Survived, include = c(Sex, Age)) |>
  add_p()
#> The following warnings were returned during `add_p()`:
#> ! For variable `Age` (`Survived`) and "statistic" and "p.value" statistics:
#>   Chi-squared approximation may be incorrect
#> ! For variable `Sex` (`Survived`) and "statistic" and "p.value" statistics:
#>   Chi-squared approximation may be incorrect



  
Characteristic
      No

N = 1,4901		
      Yes

N = 7111		
      p-value2
    
Sex




0.048
    Male
1,364 (92%)
367 (52%)

    Female
126 (8.5%)
344 (48%)

Age




0.4
    Child
52 (3.5%)
57 (8.0%)

    Adult
1,438 (97%)
654 (92%)

1 n (%)
    
2 Pearson’s X^2: Rao & Scott adjustment
    

# A dataset with a complex design
data(api, package = "survey")
d_clust <- survey::svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)

# Example 2 ----------------------------------
tbl_svysummary(d_clust, by = both, include = c(api00, api99)) |>
  add_p()



  
Characteristic
      No

N = 1,6921		
      Yes

N = 4,5021		
      p-value2
    
api00
631 (559, 710)
655 (551, 723)
0.4
api99
632 (550, 701)
613 (499, 687)
0.2
1 Median (Q1, Q3)
    
2 Design-based KruskalWallis test
    

# Example 3 ----------------------------------
# change tests to svy t-test and Wald test
tbl_svysummary(d_clust, by = both, include = c(api00, api99, stype)) |>
  add_p(
    test = list(
      all_continuous() ~ "svy.t.test",
      all_categorical() ~ "svy.wald.test"
    )
  )



  
Characteristic
      No

N = 1,6921		
      Yes

N = 4,5021		
      p-value2
    
api00
631 (559, 710)
655 (551, 723)
0.4
api99
632 (550, 701)
613 (499, 687)
0.2
stype




0.049
    E
1,083 (64%)
3,791 (84%)

    H
237 (14%)
237 (5.3%)

    M
372 (22%)
474 (11%)

1 Median (Q1, Q3); n (%)
    
2 Design-based t-test; Design-based Wald test of association