Cumulative distribution function
Source:R/DataDistribution.R
, R/BinomialDistribution.R
, R/ChiSquaredDistribution.R
, and 4 more
cumulative_distribution_function.Rd
cumulative_distribution_function
evaluates the cumulative distribution
function of a specific distribution dist
at a point x
.
Usage
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'Binomial,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'ChiSquared,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'NestedModels,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'Normal,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'Student,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
# S4 method for class 'Survival,numeric,numeric,numeric'
cumulative_distribution_function(dist, x, n, theta, ...)
Arguments
- dist
a univariate
distribution
object- x
outcome
- n
sample size
- theta
distribution parameter
- ...
further optional arguments
Details
If the distribution is Binomial
,
theta denotes the rate difference between
intervention and control group.
Then, the mean is assumed to be
√ n theta.
If the distribution is Normal
, then
the mean is assumed to be
√ n theta.
Examples
cumulative_distribution_function(Binomial(.1, TRUE), 1, 50, .3)
#> [1] 0.004310344
cumulative_distribution_function(Pearson2xK(3), 1, 30, get_tau_Pearson2xK(c(0.3,0.4,0.7,0.2)))
#> [1] 0.001966853
cumulative_distribution_function(ZSquared(TRUE), 1, 35, get_tau_ZSquared(0.4, 1))
#> [1] 0.2466166
cumulative_distribution_function(ANOVA(3), 1, 30, get_tau_ANOVA(c(0.3, 0.4, 0.7, 0.2)))
#> [1] 0.2402678
cumulative_distribution_function(Normal(), 1, 50, .3)
#> [1] 0.3085375
cumulative_distribution_function(Student(two_armed = FALSE), .75, 50, .9)
#> [1] 1.062003e-08
cumulative_distribution_function(Survival(0.6,TRUE),0.75,50,0.9)
#> [1] 0.899164