Performs a one-sample, two-sample, or a Welch modified two-sample t-test
based on user supplied summary information. Output is identical to that
produced with t.test.
tsum.test(
mean.x,
s.x = NULL,
n.x = NULL,
mean.y = NULL,
s.y = NULL,
n.y = NULL,
alternative = "two.sided",
mu = 0,
var.equal = FALSE,
conf.level = 0.95
)a single number representing the sample mean of x
a single number representing the sample standard deviation for
x
a single number representing the sample size for x
a single number representing the sample mean of y
a single number representing the sample standard deviation for
y
a single number representing the sample size for y
is a character string, one of "greater",
"less" or "two.sided", or just the initial letter of each,
indicating the specification of the alternative hypothesis. For one-sample
tests, alternative refers to the true mean of the parent population
in relation to the hypothesized value mu. For the standard
two-sample tests, alternative refers to the difference between the
true population mean for x and that for y, in relation to
mu. For the one-sample and paired t-tests, alternative refers
to the true mean of the parent population in relation to the hypothesized
value mu. For the standard and Welch modified two-sample t-tests,
alternative refers to the difference between the true population mean
for x and that for y, in relation to mu. For the
one-sample t-tests, alternative refers to the true mean of the parent
population in relation to the hypothesized value mu. For the standard
and Welch modified two-sample t-tests, alternative refers to the difference
between the true population mean for x and that for y, in
relation to mu.
is a single number representing the value of the mean or difference in means specified by the null hypothesis.
logical flag: if TRUE, the variances of the parent
populations of x and y are assumed equal. Argument
var.equal should be supplied only for the two-sample tests.
is the confidence level for the returned confidence interval; it must lie between zero and one.
A list of class htest, containing the following components:
the t-statistic, with names attribute "t"
is the degrees of freedom of the t-distribution associated
with statistic. Component parameters has names attribute
"df".
the p-value for the test.
is
a confidence interval (vector of length 2) for the true mean or difference
in means. The confidence level is recorded in the attribute
conf.level. When alternative is not "two.sided", the
confidence interval will be half-infinite, to reflect the interpretation of
a confidence interval as the set of all values k for which one would
not reject the null hypothesis that the true mean or difference in means is
k . Here infinity will be represented by Inf.
vector of length 1 or 2, giving the sample mean(s) or mean
of differences; these estimate the corresponding population parameters.
Component estimate has a names attribute describing its elements.
the value of the mean or difference in means specified by
the null hypothesis. This equals the input argument mu. Component
null.value has a names attribute describing its elements.
records the value of the input argument alternative:
"greater" , "less" or "two.sided".
a character string (vector of length 1) containing the names x and y for the two summarized samples.
If y is NULL, a one-sample t-test is carried out with
x. If y is not NULL, either a standard or Welch modified
two-sample t-test is performed, depending on whether var.equal is
TRUE or FALSE.
For the one-sample t-test, the null hypothesis is
that the mean of the population from which x is drawn is mu.
For the standard and Welch modified two-sample t-tests, the null hypothesis
is that the population mean for x less that for y is
mu.
The alternative hypothesis in each case indicates the direction of
divergence of the population mean for x (or difference of means for
x and y) from mu (i.e., "greater",
"less", or "two.sided").
Kitchens, L.J. (2003). Basic Statistics and Data Analysis. Duxbury.
Hogg, R. V. and Craig, A. T. (1970). Introduction to Mathematical Statistics, 3rd ed. Toronto, Canada: Macmillan.
Mood, A. M., Graybill, F. A. and Boes, D. C. (1974). Introduction to the Theory of Statistics, 3rd ed. New York: McGraw-Hill.
Snedecor, G. W. and Cochran, W. G. (1980). Statistical Methods, 7th ed. Ames, Iowa: Iowa State University Press.
tsum.test(mean.x=5.6, s.x=2.1, n.x=16, mu=4.9, alternative="greater")
#> Warning: argument 'var.equal' ignored for one-sample test.
#>
#> One-sample t-Test
#>
#> data: Summarized x
#> t = 1.3333, df = 15, p-value = 0.1012
#> alternative hypothesis: true mean is greater than 4.9
#> 95 percent confidence interval:
#> 4.679649 NA
#> sample estimates:
#> mean of x
#> 5.6
#>
# Problem 6.31 on page 324 of BSDA states: The chamber of commerce
# of a particular city claims that the mean carbon dioxide
# level of air polution is no greater than 4.9 ppm. A random
# sample of 16 readings resulted in a sample mean of 5.6 ppm,
# and s=2.1 ppm. One-sided one-sample t-test. The null
# hypothesis is that the population mean for 'x' is 4.9.
# The alternative hypothesis states that it is greater than 4.9.
x <- rnorm(12)
tsum.test(mean(x), sd(x), n.x=12)
#> Warning: argument 'var.equal' ignored for one-sample test.
#>
#> One-sample t-Test
#>
#> data: Summarized x
#> t = 1.0524, df = 11, p-value = 0.3152
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> -0.3062109 0.8673486
#> sample estimates:
#> mean of x
#> 0.2805689
#>
# Two-sided one-sample t-test. The null hypothesis is that
# the population mean for 'x' is zero. The alternative
# hypothesis states that it is either greater or less
# than zero. A confidence interval for the population mean
# will be computed. Note: above returns same answer as:
t.test(x)
#>
#> One Sample t-test
#>
#> data: x
#> t = 1.0524, df = 11, p-value = 0.3152
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> -0.3062109 0.8673486
#> sample estimates:
#> mean of x
#> 0.2805689
#>
x <- c(7.8, 6.6, 6.5, 7.4, 7.3, 7.0, 6.4, 7.1, 6.7, 7.6, 6.8)
y <- c(4.5, 5.4, 6.1, 6.1, 5.4, 5.0, 4.1, 5.5)
tsum.test(mean(x), s.x=sd(x), n.x=11 ,mean(y), s.y=sd(y), n.y=8, mu=2)
#>
#> Welch Modified Two-Sample t-Test
#>
#> data: Summarized x and y
#> t = -0.85278, df = 11.303, p-value = 0.4115
#> alternative hypothesis: true difference in means is not equal to 2
#> 95 percent confidence interval:
#> 1.127160 2.384203
#> sample estimates:
#> mean of x mean of y
#> 7.018182 5.262500
#>
# Two-sided standard two-sample t-test. The null hypothesis
# is that the population mean for 'x' less that for 'y' is 2.
# The alternative hypothesis is that this difference is not 2.
# A confidence interval for the true difference will be computed.
# Note: above returns same answer as:
t.test(x, y)
#>
#> Welch Two Sample t-test
#>
#> data: x and y
#> t = 6.1281, df = 11.303, p-value = 6.617e-05
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> 1.127160 2.384203
#> sample estimates:
#> mean of x mean of y
#> 7.018182 5.262500
#>
tsum.test(mean(x), s.x=sd(x), n.x=11, mean(y), s.y=sd(y), n.y=8, conf.level=0.90)
#>
#> Welch Modified Two-Sample t-Test
#>
#> data: Summarized x and y
#> t = 6.1281, df = 11.303, p-value = 6.617e-05
#> alternative hypothesis: true difference in means is not equal to 0
#> 90 percent confidence interval:
#> 1.242424 2.268940
#> sample estimates:
#> mean of x mean of y
#> 7.018182 5.262500
#>
# Two-sided standard two-sample t-test. The null hypothesis
# is that the population mean for 'x' less that for 'y' is zero.
# The alternative hypothesis is that this difference is not
# zero. A 90% confidence interval for the true difference will
# be computed. Note: above returns same answer as:
t.test(x, y, conf.level=0.90)
#>
#> Welch Two Sample t-test
#>
#> data: x and y
#> t = 6.1281, df = 11.303, p-value = 6.617e-05
#> alternative hypothesis: true difference in means is not equal to 0
#> 90 percent confidence interval:
#> 1.242424 2.268940
#> sample estimates:
#> mean of x mean of y
#> 7.018182 5.262500
#>