calcSig: Performs inference on data.
In Sage-Bionetworks/snm: Supervised Normalization of Microarrays
Description
Usage
Arguments
Value
Author(s)
Examples
Description
Calculates p and q values for a given model fit. Accepts both biological and adjustment variables.
Usage
1
Arguments
Y
Matrix of data
bio.var
Model matrix of biological variables
adj.var
Model matrix of adjustment variables
Value
An object of class EDGE.
Author(s)
Brig Mecham <brig.mecham@sagebase.org> and John D. Storey <jstorey@princeton.edu>
Examples
1
2
3
4
seed <- 1234
sim.d1 <- sim.preProcessed(seed=seed,0.5,0.3,0.1)
sig <- calcSig(sim.d1$raw.data, sim.d1$bio.var, sim.d1$adj.var)
ks.test(sig$pval[sim.d1$true.nulls],"punif")$p
Sage-Bionetworks/snm documentation built on May 9, 2019, 12:14 p.m.
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Description Usage Arguments Value Author(s) Examples
Description
Calculates p and q values for a given model fit. Accepts both biological and adjustment variables.
Usage
1 |
Arguments
Y |
Matrix of data |
bio.var |
Model matrix of biological variables |
adj.var |
Model matrix of adjustment variables |
Value
An object of class EDGE.
Author(s)
Brig Mecham <brig.mecham@sagebase.org> and John D. Storey <jstorey@princeton.edu>
Examples
1 2 3 4 | seed <- 1234
sim.d1 <- sim.preProcessed(seed=seed,0.5,0.3,0.1)
sig <- calcSig(sim.d1$raw.data, sim.d1$bio.var, sim.d1$adj.var)
ks.test(sig$pval[sim.d1$true.nulls],"punif")$p
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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