Matrix Tests

A package dedicated to running multiple statistical hypothesis tests on rows and columns of matrices.

Goals

1. Fast execution via vectorization.
2. Convenient and detailed output format.
3. Compatibility with tests implemented in base R.
4. Careful handling of missing values and edge cases.

Examples

1. Bartlett’s test on columns

Bartlett’s test on every column of iris dataset using Species as groups:

col_bartlett(iris[,-5], iris$Species)

             obs.tot obs.groups var.pooled df statistic                pvalue
Sepal.Length     150          3 0.26500816  2 16.005702 0.0003345076070163084
Sepal.Width      150          3 0.11538776  2  2.091075 0.3515028004158132768
Petal.Length     150          3 0.18518776  2 55.422503 0.0000000000009229038
Petal.Width      150          3 0.04188163  2 39.213114 0.0000000030547839322

2. Welch t-test on rows

Welch t-test performed on each row of 2 large (million row) matrices:

X <- matrix(rnorm(10000000), ncol = 10)
Y <- matrix(rnorm(10000000), ncol = 10)

row_t_welch(X, Y)  # running time: 2.4 seconds

Confidence interval computations can be turned-off for further increase in speed:

row_t_welch(X, Y, conf.level = NA)  # running time: 1 second

Available Tests

Variant	Name	Function
Location tests (1 group)	Single sample Student’s t.test	row_t_onesample
	Single sample Wilcoxon’s test	row_wilcoxon_onesample
Location tests (2 groups)	Equal variance Student’s t.test	row_t_equalvar
	Welch adjusted Student’s t.test	row_t_welch
	Two sample Wilcoxon’s test	row_wilcoxon_twosample
Location tests (paired)	Paired Student’s t.test	row_t_paired
	Paired Wilcoxon’s test	row_wilcoxon_paired
Location tests (2+ groups)	Equal variance oneway anova	row_oneway_equalvar
	Welch’s oneway anova	row_oneway_welch
	Kruskal-Wallis test	row_kruskalwallis
	van der Waerden’s test	row_waerden
Scale tests (2 groups)	F variance test	row_f_var
Scale tests (2+ groups)	Bartlett’s test	row_bartlett
	Fligner-Killeen test	row_flignerkilleen
	Levene’s test	row_levene
	Brown-Forsythe test	row_brownforsythe
Association tests	Pearson’s correlation test	row_cor_pearson
Periodicity tests	Cosinor	row_cosinor
Distribution tests	Kolmogorov-Smirnov test	row_kolmogorovsmirnov_twosample
Normality tests	Jarque-Bera test	row_jarquebera
	Anderson-Darling test	row_andersondarling

Further Information

For more information please refer to the Wiki page:

1. Installation Instructions
2. Design Decisions
3. Speed Benchmarks
4. Bug Fixes and Improvements to Base R

See Also

Literature

Computing thousands of test statistics simultaneously in R, Holger Schwender, Tina Müller.
Statistical Computing & Graphics. Volume 18, No 1, June 2007.

Packages

CRAN:

1. ttests() in the Rfast package.
2. row.ttest.stat() in the metaMA package.
3. MultiTtest() in the ClassComparison package.
4. bartlettTests() in the heplots package.
5. harmonic.regression() in the HarmonicRegression package.

BioConductor:

1. lmFit() in the limma package.
2. rowttests() in the genefilter package.
3. mt.teststat() in the multtest package.
4. row.T.test() in the HybridMTest package.
5. rowTtest() in the viper package.
6. lmPerGene() in the GSEAlm package.

GitHub:

1. rowWilcoxonTests() in the sanssouci package.
2. matrix.t.test() in the pi0 package.
3. wilcoxauc() in the presto package.