DescTools (version 0.99.37)

TTestA: Student's t-Test Based on Sample Statistics

Description

Performs one and two sample t-tests based on user supplied summary information instead of data as in t.test().

Usage

TTestA(mx, sx, nx, my = NULL, sy = NULL, ny = NULL,
       alternative = c("two.sided", "less", "greater"),
       mu = 0, paired = FALSE, var.equal = FALSE,
       conf.level = 0.95, ...)

Arguments

mx

a single number representing the sample mean of x.

my

an optional single number representing the sample mean of y.

sx

a single number representing the sample standard deviation of x.

sy

an optional single number representing the sample standard deviation of y.

nx

a single number representing the sample size of x.

ny

an optional single number representing the sample size of y.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

mu

a number indicating the true value of the mean (or difference in means if you are performing a two sample test).

paired

paired = TRUE is not supported here and only present for consistency of arguments. Use one-sample-test for the differences instead.

var.equal

a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

conf.level

confidence level of the interval.

…

further arguments to be passed to or from methods.

Value

A list with class "htest" containing the following components:

statistic

the value of the t-statistic.

parameter

the degrees of freedom for the t-statistic.

p.value

the p-value for the test.

conf.int

a confidence interval for the mean appropriate to the specified alternative hypothesis.

estimate

the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.

null.value

the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating what type of t-test was performed.

data.name

a character string giving the name(s) of the data.

Details

alternative = "greater" is the alternative that x has a larger mean than y.

If paired is TRUE then both mx, sx and my, sy must be specified and nx must be equal to ny. If var.equal is TRUE then the pooled estimate of the variance is used. By default, if var.equal is FALSE then the variance is estimated separately for both groups and the Welch modification to the degrees of freedom is used.

If the input data are effectively constant (compared to the larger of the two means) an error is generated.

See Also

t.test

Examples

# NOT RUN {
## Classical example: Student's sleep data
mx <- 0.75
my <- 2.33
sx <- 1.789010
sy <- 2.002249
nx <- ny <- 10
TTestA(mx=mx, my=my, sx=sx, sy=sy, nx=nx, ny=ny)

# compare to
with(sleep, t.test(extra[group == 1], extra[group == 2]))

# use the one sample test for the differences instead of paired=TRUE option
x <- with(sleep, extra[group == 1])
y <- with(sleep, extra[group == 2])

TTestA(mx=mean(x-y), sx=sd(x-y), nx=length(x-y))

# compared to 
t.test(x, y, paired = TRUE)
# }

Run the code above in your browser using DataCamp Workspace