Performs one and two sample t-tests based on user supplied summary information instead of data as in t.test().
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, ...)a single number representing the sample mean of x.
an optional single number representing the sample mean of y.
a single number representing the sample standard deviation of x.
an optional single number representing the sample standard deviation of y.
a single number representing the sample size of x.
an optional single number representing the sample size of y.
a character string specifying the alternative
hypothesis, must be one of "two.sided" (default),
"greater" or "less". You can specify just the initial
letter.
a number indicating the true value of the mean (or difference in means if you are performing a two sample test).
a logical indicating whether you want a paired t-test.
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.
confidence level of the interval.
further arguments to be passed to or from methods.
A list with class "htest" containing the following components:
the value of the t-statistic.
the degrees of freedom for the t-statistic.
the p-value for the test.
a confidence interval for the mean appropriate to the specified alternative hypothesis.
the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.
the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test.
a character string describing the alternative hypothesis.
a character string indicating what type of t-test was performed.
a character string giving the name(s) of the data.
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.
# 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]))
# }
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