Storey's (2001) q-value Procedure
Qvalue(pValues, lambda=seq(0, 0.9, 0.05), pi0.method="smoother",
fdr.level=NULL, robust=FALSE, smooth.df=3, smooth.log.pi0=FALSE,
silent=FALSE)A list containing:
A vector of the estimated q-values
An estimate of the proportion of null hypotheses
A Mutoss S4 class of type errorControl, containing the type of error controlled by the function.
HackNiklas
pValues to be used (only necessary input)
Value of the tuning parameter to be used
Method for automatically choosing tuning parameter in the estimation of pi_0. Either 'smoother' or 'bootstrap'
Level at which to control the FDR
Logical, whether to make estimate more robust for small p-values.
Number of degrees of freedom to use when estimating pi_0 with the smoother.
Logical, if TRUE and pi0.method = 'smoother', pi0 will be estimated by applying a smoother to a scatterplot of log(pi_0) estimates against the tuning parameter lambda.
logical scalar. If TRUE no output is generated.
The Qvalue procedure estimates the q-values for a given set of p-values. The q-value of a test measures the proportion of false positive incurred when that particular test is called sigificant. It gives the scientist a hypothesis testing error measure for each observed statistic with respect to the pFDR.
Note: If no options are selected, then the method used to estimate pi0 is the smoother method desribed in Storey and Tibshirani (2003). The bootstrap method is described in Storey, Taylor and Siegmund (2004).
Storey, John (2001). The Positive False Discovery Rate: A Baysian Interpretation and the Q-Value. The Annals of Statistics, Vol. 31, No. 6, 2013-2035.
pval <- c(runif(50), runif(50, 0, 0.01))
result <- Qvalue(pval)
result <- Qvalue(pval, lambda=0.5)Run the code above in your browser using DataLab