View source: R/approx.hotelling.diff.test.R
approx.hotelling.diff.test | R Documentation |
A multivariate hypothesis test for a single population mean or a difference between them. This version attempts to adjust for multivariate autocorrelation in the samples.
approx.hotelling.diff.test(
x,
y = NULL,
mu0 = 0,
assume.indep = FALSE,
var.equal = FALSE,
...
)
x |
a numeric matrix of data values with cases in rows and variables in columns. |
y |
an optinal matrix of data values with cases in rows and variables in columns for a 2-sample test. |
mu0 |
an optional numeric vector: for a 1-sample test, the poulation mean under the null hypothesis; and for a 2-sample test, the difference between population means under the null hypothesis; defaults to a vector of 0s. |
assume.indep |
if |
var.equal |
for a 2-sample test, perform the pooled test: assume population variance-covariance matrices of the two variables are equal. |
... |
additional arguments, passed on to |
An object of class htest
with the following information:
statistic |
The |
parameter |
Degrees of freedom. |
p.value |
P-value. |
method |
Method specifics. |
null.value |
Null hypothesis mean or mean difference. |
alternative |
Always |
estimate |
Sample difference. |
covariance |
Estimated variance-covariance matrix of the estimate of the difference. |
covariance.x |
Estimated variance-covariance matrix of the estimate of the mean of |
covariance.y |
Estimated variance-covariance matrix of the estimate of the mean of |
It has a print method print.htest()
.
For mcmc.list
input, the variance for this test is
estimated with unpooled means. This is not strictly correct.
Hotelling, H. (1947). Multivariate Quality Control. In C. Eisenhart, M. W. Hastay, and W. A. Wallis, eds. Techniques of Statistical Analysis. New York: McGraw-Hill.
t.test()
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