random.StudentsT: Draw a random sample from a StudentsT distribution

In distributions3: Probability Distributions as S3 Objects

Draw a random sample from a StudentsT distribution

Description

Please see the documentation of StudentsT() for some properties of the T distribution, as well as extensive examples showing to how calculate p-values and confidence intervals.

Usage

## S3 method for class 'StudentsT'
random(x, n = 1L, drop = TRUE, ...)

Arguments

x	A StudentsT object created by a call to StudentsT().
n	The number of samples to draw. Defaults to 1L.
drop	logical. Should the result be simplified to a vector if possible?
...	Unused. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

In case of a single distribution object or n = 1, either a numeric vector of length n (if drop = TRUE, default) or a matrix with n columns (if drop = FALSE).

See Also

Other StudentsT distribution: cdf.StudentsT(), pdf.StudentsT(), quantile.StudentsT()

Examples

set.seed(27)

X <- StudentsT(3)
X

random(X, 10)

pdf(X, 2)
log_pdf(X, 2)

cdf(X, 4)
quantile(X, 0.7)

### example: calculating p-values for two-sided T-test

# here the null hypothesis is H_0: mu = 3

# data to test
x <- c(3, 7, 11, 0, 7, 0, 4, 5, 6, 2)
nx <- length(x)

# calculate the T-statistic
t_stat <- (mean(x) - 3) / (sd(x) / sqrt(nx))
t_stat

# null distribution of statistic depends on sample size!
T <- StudentsT(df = nx - 1)

# calculate the two-sided p-value
1 - cdf(T, abs(t_stat)) + cdf(T, -abs(t_stat))

# exactly equivalent to the above
2 * cdf(T, -abs(t_stat))

# p-value for one-sided test
# H_0: mu <= 3   vs   H_A: mu > 3
1 - cdf(T, t_stat)

# p-value for one-sided test
# H_0: mu >= 3   vs   H_A: mu < 3
cdf(T, t_stat)

### example: calculating a 88 percent T CI for a mean

# lower-bound
mean(x) - quantile(T, 1 - 0.12 / 2) * sd(x) / sqrt(nx)

# upper-bound
mean(x) + quantile(T, 1 - 0.12 / 2) * sd(x) / sqrt(nx)

# equivalent to
mean(x) + c(-1, 1) * quantile(T, 1 - 0.12 / 2) * sd(x) / sqrt(nx)

# also equivalent to
mean(x) + quantile(T, 0.12 / 2) * sd(x) / sqrt(nx)
mean(x) + quantile(T, 1 - 0.12 / 2) * sd(x) / sqrt(nx)

distributions3 documentation built on Sept. 30, 2024, 9:37 a.m.