Multivariate tests

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Hotelling’s test

# HypothesisTests.OneSampleHotellingT2Test — Type

OneSampleHotellingT2Test(X::AbstractMatrix, μ₀=<zero vector>)

Perform a one sample Hotelling’s test of the hypothesis that the vector of column means of X is equal to μ₀.

OneSampleHotellingT2Test(X::AbstractMatrix, Y::AbstractMatrix, μ₀=<zero vector>)

Perform a paired Hotelling’s test of the hypothesis that the vector of mean column differences between X and Y is equal to μ₀.

# HypothesisTests.EqualCovHotellingT2Test — Type

EqualCovHotellingT2Test(X::AbstractMatrix, Y::AbstractMatrix)

Perform a two sample Hotelling’s test of the hypothesis that the difference in the mean vectors of X and Y is zero, assuming that X and Y have equal covariance matrices.

# HypothesisTests.UnequalCovHotellingT2Test — Type

UnequalCovHotellingT2Test(X::AbstractMatrix, Y::AbstractMatrix)

Perform a two sample Hotelling’s test of the hypothesis that the difference in the mean vectors of X and Y is zero, without assuming that X and Y have equal covariance matrices.

Equality of covariance matrices

Bartlett’s test for equality of two covariance matrices is provided. This is equivalent to Box’s -test for two groups.

# HypothesisTests.BartlettTest — Type

BartlettTest(X::AbstractMatrix, Y::AbstractMatrix)

Perform Bartlett’s test of the hypothesis that the covariance matrices of X and Y are equal.

Bartlett’s test is sensitive to departures from multivariate normality.

Correlation and partial correlation test

# HypothesisTests.CorrelationTest — Type

CorrelationTest(x, y)

Perform a t-test for the hypothesis that , i.e. the correlation of vectors x and y is zero.

CorrelationTest(x, y, Z)

Perform a t-test for the hypothesis that , i.e. the partial correlation of vectors x and y given the matrix Z is zero.

Implements pvalue for the t-test. Implements confint using an approximate confidence interval based on Fisher’s -transform.