EBexpo-class | R Documentation |
EBexpo
Class
The EBexpo
object represents the results of an Empirical Bayes
approach to estimate a distribution as a mixture of a (more or less)
known exponential distribution along with a completely unknown
"interesting" distribution. The basic method was described by Efron
and Tibshirani with an application to differential expression in
microarray data.
EBexpo(edata, resn = 200)
cutoff(target, prior, object)
## S4 method for signature 'EBexpo,missing'
plot(x, prior = 1, significance = c(0.5, 0.8, 0.9),
ylim = c(-0.5, 1), xlab = "Duration",
ylab = "Probability(Interesting | Duration)", ...)
## S4 method for signature 'EBexpo'
hist(x, ...)
edata |
A numeric vector; the observed data that we think comes mainly from an exponential distribution. |
resn |
A numeric vector; the resolution used to estimate a histogream. |
x |
An |
prior |
A numeric vector of length 1; the prior probability of an observed data point coming from the known exponential distribution. |
significance |
A numeric vector with values between 0 and 1; the target posterior probabiltiites. |
ylim |
A numeric vector of length two. |
xlab |
A character vector; the label for the x-axis. |
ylab |
A character vector; the label for the y-axis. |
... |
The usual set of graphical parameters. |
target |
The target posterior probability. |
object |
An |
The EBexpo
function constructs and returns an object of the
EBexpo
class
The plot
and hist
methods return (invisibly) the EBexpo
object that was their first argument.
xvals
:Inherited from
MultiWilcoxonTest
statistics
:Inherited from
MultiWilcoxonTest
, Here, these are
the same a the edata
slot from an link{ExpoFit}
object.
pdf
:Inherited from
MultiWilcoxonTest
theoretical.pdf
:Inherited from
MultiWilcoxonTest
unravel
:Inherited from
MultiWilcoxonTest
groups
:Inherited from
MultiWilcoxonTest
, but not used
call
:Inherited from
MultiWilcoxonTest
h0
:See ExpoFit
lambda
:See ExpoFit
mu
:See ExpoFit
Produce a plot of a EBexpo
object.
Produce a histogram of the observed distibution, with overlays.
Kevin R. Coombes <krc@silicovore.com>
Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002 Jun;23(1):70-86. doi: 10.1002/gepi.1124.
data(cytof)
diag <- AML10.node287.rips[["diagram"]]
persistence <- diag[, "Death"] - diag[, "Birth"]
d1 <- persistence[diag[, "dimension"] == 1]
eb <- EBexpo(d1, 200)
hist(eb)
plot(eb, prior = 0.56)
cutoff(0.8, 0.56, eb)
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