Description Usage Arguments Details Value
Estimate functional q-values based on p-values and realizations of the informative variable z, where z may affect either the power of a statistical test or the likelihood of a true null hypothesis.
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p.value |
A vector of p-values |
z0 |
A vector of observations from the informative variable, of the same length as |
pi0.method |
Method for estimating the functional proportion pi0(z); either "gam" (default), "glm", "kernel" or "bin" |
lambda |
The parameter Lambda or its values to use in estimating pi0(z) |
fixed.pi0 |
Whether pi0(z) is believed to be independent of z |
monotone.window |
Parameter used to force estimated densities to decrease with increasing p-values; higher value of this parameter means densities are smoothed more aggressively. If NULL, perform no such smoothing |
... |
Extra arguments to be passed to kernelUnitInterval for estimating the density |
Assume the random variable z0 may affect the power of a statistical test (that induces the p-values) or the likelihood of a true null hypothesis. The m observations z0_i, i=1,...,m of z0 are quantile transformed into z_i, i=1,...,m such that z_i = rank(z0_i) / m, where rank(z0_i) is the rank of z0_i among z0_i, i=1,...,m. Consequently, z_i, i=1,...,m are approximately uniformly distributed on the interval [0,1]. When z_i, i=1,...,m are regarded as observations from the random variable z, then z is approximately uniformly distributed on [0,1]. Namely, z0 has been quantile transformed into z, and they are equivalent. Further, z or z0 is referred to as the informative variable.
The likelihood of a true null hypothesis is regarded as a function of z, referrred to as the functional proportion, and denoted by pi0(z), and the fFDR methodology is applied to the m paired observations (p_i,z_i), i=1,...,m of the p-value p and z.
An object of S3 class "fqvalue"
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