Description Usage Arguments Details Value References Examples
Given a list of p-values, this function conducts multiple testing and outputs the indices of the rejected hypothesis. Uses an adaptive Benjamini-Hochberg (BH) procedure where the proportion of true nulls π_0 is estimated. This estimation is done based on the pi0est function in the qvalue package. See Storey(2015).
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pvalue |
a vector of p-values obtained from multiple testing |
alpha |
an optional significance level for testing (in decimals). Default is 0.05. Must be in (0,1). |
type |
an optional character string specifying the type of test. The default is the modified BH procedure (type = "mBH"). The usual BH procedure is also available (type = "BH"). See Benjamini and Hochberg (1995). |
lambda |
an optional threshold for estimating the proportion of true null hypotheses π_0. Must be in [0,1). |
pi0.method |
optional, either "smoother" or "bootstrap"; the method for automatically choosing tuning parameter in the estimation of π_0, the proportion of true null hypotheses. |
smooth.df |
an optional number of degrees-of-freedom to use when estimating π_0 with a smoother. |
smooth.log.pi0 |
an optional TRUE/FALSE. If TRUE and pi0.method = "smoother", π_0 will be estimated by applying a smoother to a scatterplot of \log(π_0) estimates against the tuning parameter lambda. Default is FALSE. |
The "mBH" procedure is simply the regular Benjamini-Hochberg pocedure, but in the rejection threshold the denominator p is replaced by π_0 * p. This is a less conservative approach. See Storey (2002).
rejected |
the indices of rejected hypotheses, along with their corresponding p values, and adjusted p values, ordered from most significant to least significant |
alldata |
all the indices of the tested hypotheses, along with their corresponding p values, adjusted p values, and a column with 1 if declared siginificant and 0 if not |
significant |
The number of hypotheses rejected |
Benjamini, Y. and Hochberg, Y. (1995). "Controlling the False Discovery Rate: A Practical and PowerfulApproach to Multiple Testing." Journal of the Royal Statistical Society B, 51, 289–300.
Storey, J.D. (2015). "qvalue: Q-value estimation for false discovery rate control. R package version 2.8.0, https://github.com/jdstorey/qvalue.
Storey, J.D. (2002). " Direct Approach to False Discovery Rates." Journal of the Royal Statistical Society B, 64(3), 479–498.
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