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p-value from McNemar test simulation

Source: R/p_mcnemar.test.R
p_mcnemar.test.Rd

Generates two-dimensional sample data for McNemar test and return a p-value. Uses mcnemar.test.

Usage

p_mcnemar.test(
  n,
  prop,
  OR = NULL,
  prop.disc = NULL,
  two.tailed = TRUE,
  correct = TRUE,
  gen_fun = gen_mcnemar.test,
  return_analysis = FALSE,
  ...
)

gen_mcnemar.test(n, prop, ...)

Arguments

n

total sample size

prop

two-dimensional matrix of proportions/probabilities

OR

instead of supplying the prop table, the odds ratio can be specified instead \(\pi_{12}/\pi_{21}\). Also requires proportion of discordant pairings to be specified

prop.disc

proportion of discordant pairings, \(\pi_{12} + \pi_{21}\)

two.tailed

logical; should a two-tailed or one-tailed test be used?

correct

logical; use continuity correction? Only applicable for 2x2 tables

gen_fun

function used to generate the required discrete data. Object returned must be a matrix with k rows and k columns of counts. Default uses gen_mcnemar.test. User defined version of this function must include the argument ...

return_analysis

logical; return the analysis object for further extraction and customization?

...

additional arguments to be passed to gen_fun. Not used unless a customized gen_fun is defined

Value

a single p-value

See also

gen_mcnemar.test

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


# from ?mcnemar.test
Performance <- matrix(c(794, 86, 150, 570),
       nrow = 2,
       dimnames = list("1st Survey" = c("Approve", "Disapprove"),
                   "2nd Survey" = c("Approve", "Disapprove")))
(prop <- prop.table(Performance))
#>             2nd Survey
#> 1st Survey   Approve Disapprove
#>   Approve    0.49625    0.09375
#>   Disapprove 0.05375    0.35625

# one sample + test and resulting p-value
p_mcnemar.test(n=sum(Performance), prop=prop)
#> [1] 9.417676e-05

# return analysis model
p_mcnemar.test(n=sum(Performance), prop=prop, return_analysis=TRUE)
#> 
#> 	McNemar's Chi-squared test with continuity correction
#> 
#> data:  dat
#> McNemar's chi-squared = 21.147, df = 1, p-value = 4.254e-06
#> 

# \donttest{

# post-hoc power (not recommended)
Spower(p_mcnemar.test(n=sum(Performance), prop=prop))
#> Error in eval(x, parent.frame()): object 'Performance' not found

# odds ratio + discordant proportions supplied instead
OR <- prop[1,2] / prop[2,1]
disc <- prop[1,2] + prop[2,1]
p_mcnemar.test(n=50, OR=.25, prop.disc=disc, two.tailed=FALSE) |>
  Spower(replications=30000)
#> 
#> Execution time (H:M:S): 00:00:05
#> Design conditions: 
#> 
#> # A tibble: 1 × 5
#>       n    OR two.tailed sig.level power
#>   <dbl> <dbl> <lgl>          <dbl> <lgl>
#> 1    50  0.25 FALSE           0.05 NA   
#> 
#> Estimate of power: 0.336
#> 95% Confidence Interval: [0.331, 0.341]

# }