Spower (version 0.6)

p_r: p-value from correlation simulation

Description

Generates correlated X-Y data and returns a p-value to assess the null of no correlation in the population. The X-Y data are generated assuming a bivariate normal distribution.

Usage

p_r(
  n,
  r,
  rho = 0,
  method = "pearson",
  two.tailed = TRUE,
  gen_fun = gen_r,
  return_analysis = FALSE,
  ...
)
gen_r(n, r, ...)

Value

a single p-value

Arguments

n

sample size

r

correlation

rho

population coefficient to test against. Uses the Fisher's z-transformation approximation when non-zero

method

method to use to compute the correlation (see cor.test). Only used when rho = 0

two.tailed

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

gen_fun

function used to generate the required dependent bivariate data. Object returned must be a matrix with two columns and n rows. Default uses gen_r to generate conditionally dependent data from a bivariate normal distribution. User defined version of this function must include the argument ...

return_analysis

logical; return the analysis object for further extraction and customization? Note that if rho != 0 the p.value and related element will be replaced with internally computed approximation versions

...

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

Author

Phil Chalmers

See Also

gen_r

Examples

# 50 observations, .5 correlation
p_r(50, r=.5)
p_r(50, r=.5, method = 'spearman')

# test against constant other than rho = .6
p_r(50, .5, rho=.60)

# return analysis model
p_r(50, .5, return_analysis=TRUE)
p_r(50, .5, rho=.60, return_analysis=TRUE)

# \donttest{
    # compare simulated results to pwr package

    pwr::pwr.r.test(r=0.3, n=50)
    p_r(n=50, r=0.3) |> Spower()

    pwr::pwr.r.test(r=0.3, power=0.80)
    p_r(n=interval(10, 200), r=0.3) |> Spower(power=.80)

    pwr::pwr.r.test(r=0.1, power=0.80)
    p_r(n=interval(200, 1000), r=0.1) |> Spower(power=.80)

# }