# Class "highTtest"

### Description

Value object returned by call to `highTtest()`

.

### Objects from the Class

This object should not be created by users.

### Slots

`CK`

:Object of class

`matrix`

or NULL. A matrix of logical values. The rows correspond to features, ordered as provided in input`dataSet1`

. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Cao-Kosorok method. The significance value associated with each column is dictated by the input`gammas`

.`pi1`

:Object of class

`numeric`

or NULL. The estimated proportion of alternative hypotheses calculated using the Cao-Kosorok method.`pvalue`

:Object of class

`numeric`

. The vector of p-values calculated using the two-sample t-statistic.`ST`

:Object of class

`matrix`

or NULL. If requested, a matrix of logical values. The rows correspond to features, ordered as provided in input`dataSet1`

. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Storey-Tibshirani (2003) method. The significance value associated with each column is dictated by the input`gammas`

.`BH`

:Object of class

`matrix`

or NULL If requested, A matrix of logical values. The rows correspond to features, ordered as provided in input`dataSet1`

. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Benjamini-Hochberg (1995) method. The significance value associated with each column is dictated by the input`gammas`

.`gammas`

:Object of class

`numeric`

. Vector of significant values provided as input for the calculation.

### Methods

- BH
`signature(x = "highTtest")`

: Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Benjamini-Hochberg (1995) method.- CK
`signature(x = "highTtest")`

: Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Cao-Kosorok (2011) method.- pi_alt
`signature(x = "highTtest")`

: Retrieves the estimated proportion of alternative hypotheses obtained by the Cao-Kosorok (2011) method.- plot
`signature(x = "highTtest")`

: Generates a plot of the number of significant features as a function of the level of significance as calculated for each method (CK,BH, and/or ST)- pvalue
`signature(x = "highTtest")`

: Retrieves the vector of p-values calculated using the two-sample t-statistic.- ST
`signature(x = "highTtest")`

: Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Storey-Tibshirani (2003) method.- vennD
`signature(x = "highTtest")`

: Generates two- and three-dimensional Venn diagrams comparing the features selected by each method. Implements methods of package colorfulVennPlot. In addition to the`highTtest`

object, the level of significance,`gamma`

, must also be provided.

### Author(s)

Authors: Hongyuan Cao, Michael R. Kosorok, and Shannon T. Holloway <sthollow@ncsu.edu> Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>

### References

Cao, H. and Kosorok, M. R. (2011). Simultaneous critical values for t-tests in very high dimensions. Bernoulli, 17, 347–394. PMCID: PMC3092179.

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.

Storey, J. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, USA, 100, 9440–9445.

### Examples

1 | ```
showClass("highTtest")
``` |