proportions.oneprop.RdCalculates power or sample size (only one can be NULL at a time) for test of a proportion against a constant using normal approximation or exact method.
Formulas are validated using PASS documentation.
NOTE: The pwrss.z.prop() function is deprecated, but it will remain available as a wrapper for the power.z.oneprop() function during the transition period.
power.z.oneprop(prob, null.prob = 0.50,
n = NULL, power = NULL, alpha = 0.05,
alternative = c("two.sided", "one.sided", "two.one.sided"),
std.error = c("null", "alternative"),
arcsine = FALSE, correct = FALSE,
ceiling = TRUE, verbose = TRUE, pretty = FALSE)
power.exact.oneprop(prob, null.prob = 0.50,
n = NULL, power = NULL, alpha = 0.05,
alternative = c("two.sided", "one.sided", "two.one.sided"),
verbose = TRUE, pretty = FALSE)probability of success under alternative.
probability of success under null.
integer; sample size.
statistical power, defined as the probability of correctly rejecting a false null hypothesis, denoted as \(1 - \beta\).
type 1 error rate, defined as the probability of incorrectly rejecting a true null hypothesis, denoted as \(\alpha\).
character; whether to calculate standard error using "null" or "alternative" value. "null" by default.
logical; whether arcsine transformation should be applied. FALSE by default. Note that when arcsine = TRUE, any specification to correct and std.error will be ignored.
logical; whether Yate's continuity correction should be applied.
character; the direction or type of the hypothesis test: "two.sided", "one.sided", or "two.one.sided". For non-inferiority or superiority tests, add margin to the null hypothesis value and use alternative = "one.sided".
logical; whether sample size should be rounded up. TRUE by default.
logical; whether the output should be printed on the console. TRUE by default.
logical; whether the output should show Unicode characters (if encoding allows for it). FALSE by default.
list of parameters used in calculation.
type of the statistical test ("exact").
mean of the alternative distribution.
standard deviation of the alternative distribution.
mean of the null distribution.
standard deviation of the null distribution.
critical value(s).
statistical power \((1-\beta)\).
sample size.
Bulus, M., & Polat, C. (2023). pwrss R paketi ile istatistiksel guc analizi [Statistical power analysis with pwrss R package]. Ahi Evran Universitesi Kirsehir Egitim Fakultesi Dergisi, 24(3), 2207-2328. doi:10.29299/kefad.1209913
# power
power.z.oneprop(prob = 0.45, null.prob = 0.50,
alpha = 0.05, n = 500,
alternative = "one.sided")
#> +--------------------------------------------------+
#> | POWER CALCULATION |
#> +--------------------------------------------------+
#>
#> One Proportion
#>
#> Method : Normal Approximation
#> Continuity Correction : FALSE
#> Arcsine Transformation : FALSE
#> Standard Error : Calculated From Null
#>
#> ---------------------------------------------------
#> Hypotheses
#> ---------------------------------------------------
#> H0 (Null Claim) : prob - null.prob >= 0
#> H1 (Alt. Claim) : prob - null.prob < 0
#>
#> ---------------------------------------------------
#> Results
#> ---------------------------------------------------
#> Sample Size = 500
#> Type 1 Error (alpha) = 0.050
#> Type 2 Error (beta) = 0.276
#> Statistical Power = 0.724 <<
#>
power.exact.oneprop(prob = 0.45, null.prob = 0.50,
alpha = 0.05, n = 500,
alternative = "one.sided")
#> +--------------------------------------------------+
#> | POWER CALCULATION |
#> +--------------------------------------------------+
#>
#> One Proportion
#>
#> Method : Exact
#>
#> ---------------------------------------------------
#> Hypotheses
#> ---------------------------------------------------
#> H0 (Null Claim) : prob - null.prob >= 0
#> H1 (Alt. Claim) : prob - null.prob < 0
#>
#> ---------------------------------------------------
#> Results
#> ---------------------------------------------------
#> Sample Size = 500
#> Type 1 Error (alpha) = 0.050
#> Type 2 Error (beta) = 0.279
#> Statistical Power = 0.721 <<
#>
# sample size
power.z.oneprop(prob = 0.45, null.prob = 0.50,
alpha = 0.05, power = 0.80,
alternative = "one.sided")
#> +--------------------------------------------------+
#> | SAMPLE SIZE CALCULATION |
#> +--------------------------------------------------+
#>
#> One Proportion
#>
#> Method : Normal Approximation
#> Continuity Correction : FALSE
#> Arcsine Transformation : FALSE
#> Standard Error : Calculated From Null
#>
#> ---------------------------------------------------
#> Hypotheses
#> ---------------------------------------------------
#> H0 (Null Claim) : prob - null.prob >= 0
#> H1 (Alt. Claim) : prob - null.prob < 0
#>
#> ---------------------------------------------------
#> Results
#> ---------------------------------------------------
#> Sample Size = 617 <<
#> Type 1 Error (alpha) = 0.050
#> Type 2 Error (beta) = 0.200
#> Statistical Power = 0.8
#>
power.exact.oneprop(prob = 0.45, null.prob = 0.50,
alpha = 0.05, power = 0.80,
alternative = "one.sided")
#> +--------------------------------------------------+
#> | SAMPLE SIZE CALCULATION |
#> +--------------------------------------------------+
#>
#> One Proportion
#>
#> Method : Exact
#>
#> ---------------------------------------------------
#> Hypotheses
#> ---------------------------------------------------
#> H0 (Null Claim) : prob - null.prob >= 0
#> H1 (Alt. Claim) : prob - null.prob < 0
#>
#> ---------------------------------------------------
#> Results
#> ---------------------------------------------------
#> Sample Size = 633 <<
#> Type 1 Error (alpha) = 0.050
#> Type 2 Error (beta) = 0.197
#> Statistical Power = 0.803
#>