R: Test for Multiple Sample Means Against Mu (one-sample...

means_test {quest}

R Documentation

Test for Multiple Sample Means Against Mu (one-sample t-tests)

Description

means_test computes sample means and compares them against specified population mu values. These are sometimes referred to as one-sample t-tests. It provides the same results as t.test, but provides the confidence intervals for the mean differences from mu rather than the mean itself. The function also calculates the descriptive statistics and the standardized mean differences (i.e., Cohen's d) based on the sample standard deviations.

Usage

means_test(
  data,
  vrb.nm,
  mu = 0,
  d.ci.type = "tdist",
  ci.level = 0.95,
  check = TRUE
)

Arguments

`data`	data.frame or data.
`vrb.nm`	character vector of colnames specifying the variables in `data` to conduct the one-sample t-tests for.
`mu`	numeric vector of length = `length(vrb.nm)` or length 1 specifying the population mean values to compare the sample means against. The order of the values should be the same as the order in `vrb.nm`. When length 1, the same population mean value is used for all the variables.
`d.ci.type`	character vector with length 1 of specifying the type of confidence intervals to compute for the standardized mean differences (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 intervals 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 `ci.level` is between 0 and 1. This is a tradeoff between computational efficiency (FALSE) and more useful error messages (TRUE).

Value

list of data.frames containing statistical information about the sample means (the rownames of the data.frames are vrb.nm): 1) nhst = one-sample t-test stat info in a data.frame, 2) desc = descriptive statistics stat info in a data.frame, 3) std = standardized mean difference stat info in a data.frame

1) nhst = one-sample t-test stat info in a data.frame

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 data.frame

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 data.frame

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

Examples


# one-sample t-tests
means_test(data = attitude, vrb.nm = names(attitude), mu = 50)
means_test(data = attitude, vrb.nm = c("rating","complaints","privileges"),
   mu = c(60, 55, 50))
means_test(data = attitude, vrb.nm = names(attitude), mu = 50, ci.level = 0.90)
means_test(airquality, vrb.nm = names(airquality)) # different df and n due to missing data

# compare to t.test
means_test(data = attitude, vrb.nm = "rating", mu = 50, ci.level = .99)
t.test(attitude$"rating", mu = 50, conf.level = .99)

# same as intercept-only regression
means_test(data = attitude, vrb.nm = "rating")
lm_obj <- lm(rating ~ 1, data = attitude)
coef(summary(lm_obj))