highTtest-class | R Documentation |
"highTtest"
Value object returned by call to highTtest()
.
This object should not be created by users.
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.
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.
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.
signature(x = "highTtest")
: Retrieves the
estimated proportion of alternative hypotheses
obtained by the Cao-Kosorok (2011) method.
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)
signature(x = "highTtest")
: Retrieves the
vector of p-values calculated using the
two-sample t-statistic.
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.
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.
Authors: Hongyuan Cao, Michael R. Kosorok, and Shannon T. Holloway <shannon.t.holloway@gmail.com> Maintainer: Shannon T. Holloway <shannon.t.holloway@gmail.com>
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.
showClass("highTtest")
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