quest (version 0.2.0)

means_test: 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
)

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

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).

See Also

mean_test one-sample t-test for a single variable, t.test same results, means_diff independent two-sample t-tests for multiple variables, means_change dependent two-sample t-tests for multiple variables,

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))