mean_test {quest}

Test for Sample Mean Against Mu (one-sample t-test)

Description

mean_test computes the sample mean and compares it against a specified population mu value. This is sometimes referred to as a one-sample t-test. It provides the same results as t.test, but provides the confidence interval for the mean difference from mu rather than the mean itself. The function also calculates the descriptive statistics and the standardized mean difference (i.e., Cohen's d) based on the sample standard deviation.

Usage

mean_test(x, mu = 0, d.ci.type = "tdist", ci.level = 0.95, check = TRUE)

Arguments

x

numeric vector.

mu

numeric vector of length 1 specifying the population mean value to compare the sample mean against.

d.ci.type

character vector with length 1 specifying the type of confidence interval to compute for the standardized mean difference (i.e., Cohen's d). There are currently two options: 1. "tdist" which calculates the confidence intervals based on the t-distribution using the function cohen.d.ci. No standard error is calculated for this option and NA is returned for "d_se" in the return object. 2. "classic" which calculates the confidence interval of Cohen's d based on the confidence interval of the mean difference itself. The lower and upper confidence bounds are divided by the sample standard deviation. Technically, this confidence interval is biased due to not taking into account the uncertainty of the standard deviations. No standard error is calculated for this option and NA is returned for "d_se" in the return object.

ci.level

numeric vector of length 1 specifying the confidence level. It must be between 0 and 1.

check

logical vector of length 1 specifying whether the input arguments should be checked for errors. For example, checking whether x is a numeric vector. This is a tradeoff between computational efficiency (FALSE) and more useful error messages (TRUE).

Value

list of numeric vectors containing statistical information about the sample mean: 1) nhst = one-sample t-test stat info in a numeric vector, 2) desc = descriptive statistics stat info in a numeric vector, 3) std = standardized mean difference stat info in a numeric vector

1) nhst = one-sample t-test stat info in a numeric vector

est

mean - mu estimate

se

standard error

t

t-value

df

degrees of freedom

p

two-sided p-value

lwr

lower bound of the confidence interval

upr

upper bound of the confidence interval

2) desc = descriptive statistics stat info in a numeric vector

mean

mean of x

mu

population value of comparison

sd

standard deviation of x

n

sample size of x

3) std = standardized mean difference stat info in a numeric vector

d_est

Cohen's d estimate

d_se

Cohen's d standard error

d_lwr

Cohen's d lower bound of the confidence interval

d_upr

Cohen's d upper bound of the confidence interval

See Also

means_test one-sample t-tests for multiple variables, t.test same results, mean_diff independent two-sample t-test, mean_change dependent two-sample t-test,

Examples

# one-sample t-test
mean_test(x = mtcars$"mpg")
mean_test(x = attitude$"rating", mu = 50)
mean_test(x = attitude$"rating", mu = 50, d.ci.type = "classic")

# compare to t.test()
mean_test(x = attitude$"rating", mu = 50, ci.level = .99)
t.test(attitude$"rating", mu = 50, conf.level = .99)

# same as intercept-only regression when mu = 0
mean_test(x = mtcars$"mpg")
lm_obj <- lm(mpg ~ 1, data = mtcars)
coef(summary(lm_obj))