```
dcor.ttest(x, y, distance=FALSE)
dcor.t(x, y, distance=FALSE)
bcdcor(x, y, distance=FALSE)
```

x

data or distances of first sample

y

data or distances of second sample

distance

logical: TRUE if x and y are distances

- method
- description of test
- statistic
- observed value of the test statistic
- parameter
- degrees of freedom
- estimate
- (bias corrected) dCor(x,y)
- p.value
- p-value of the t-test
- data.name
- description of data

`dcor.t`

returns the t statistic, `bcdcor`

returns the
bias corrected dcor statistic, and
`dcor.ttest`

returns a list with class `htest`

containing
`dcor.ttest`

performs a nonparametric t-test of
multivariate independence in high dimension (dimension is close to
or larger than sample size). The distribution of
the test statistic is approximately Student t with $n(n-3)/2-1$
degrees of freedom and for $n \geq 10$ the statistic is approximately
distributed as standard normal.
`dcor.t`

returns the t statistic and `bcdcor`

returns
the bias corrected distance correlation statistic.
The sample sizes (number of rows) of the two samples must
agree, and samples must not contain missing values. Arguments
`x`

, `y`

can optionally be `dist`

objects
or distance matrices (in this case set `distance=TRUE`

).
The t statistic is a transformation of a bias corrected version of distance
correlation (see SR 2013 for details).
Large values (upper tail) of the t statistic are significant.
Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007),
Measuring and Testing Dependence by Correlation of Distances,
*Annals of Statistics*, Vol. 35 No. 6, pp. 2769-2794.
http://dx.doi.org/10.1214/009053607000000505

Szekely, G.J. and Rizzo, M.L. (2009),
Brownian Distance Covariance,
*Annals of Applied Statistics*,
Vol. 3, No. 4, 1236-1265.
http://dx.doi.org/10.1214/09-AOAS312

`dcov.test `

`dcor `

`DCOR`

```
x <- matrix(rnorm(100), 10, 10)
y <- matrix(runif(100), 10, 10)
dx <- dist(x)
dy <- dist(y)
dcor.t(x, y)
bcdcor(dx, dy, distance=TRUE)
dcor.ttest(x, y)
```

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