R: Posterior class probabilities, local, and adjusted IDRs.
get.IDR
R Documentation
Posterior class probabilities, local, and adjusted IDRs.
Description
Functions for computing posterior cluster probabilities (get.prob)
in the general GMCM as well as local and
adjusted irreproducibility discovery rates (get.IDR) in the
special GMCM.
A matrix of observations where rows corresponds to features
and columns to studies.
par
A vector of length 4 where par[1] is mixture proportion of
the irreproducible component, par[2] is the mean value,
par[3] is the standard deviation, and par[4] is the
correlation of the reproducible component.
threshold
The threshold level of the IDR rate.
theta
A list of parameters for the full model as described in
rtheta.
...
Arguments passed to qgmm.marginal.
Value
get.IDR returns a list of length 5 with elements:
idr
A vector of the local idr values. I.e. the posterior
probability that x[i, ] belongs to the irreproducible component.
IDR
A vector of the adjusted IDR values.
l
The number of reproducible features at the specified
threshold.
threshold
The IDR threshold at which features are deemed
reproducible.
Khat
A vector signifying whether the corresponding feature is
reproducible or not.
get.prob returns a matrix where entry (i,j) is the
posterior probability that the observation x[i, ] belongs to cluster
j.
Note
get.idr returns a vector where the i'th entry is the
posterior probability that observation i is irreproducible.
It is a simple wrapper for get.prob.
From GMCM version 1.1 it is an internal. Use get.prop or
get.IDR instead. However, the function can still be accessed with
GMCM:::get.idr.
Author(s)
Anders Ellern Bilgrau <anders.ellern.bilgrau@gmail.com>
References
Li, Q., Brown, J. B. J. B., Huang, H., & Bickel, P. J. (2011).
Measuring reproducibility of high-throughput experiments. The Annals of
Applied Statistics, 5(3), 1752-1779. doi:10.1214/11-AOAS466
Tewari, A., Giering, M., & Raghunathan, A. (2011). Parametric
Characterization of Multimodal Distributions with Non-gaussian Modes. IEEE
11th International Conference on Data Mining Workshops, 2011, 286-292.
doi:10.1109/ICDMW.2011.135
Examples
set.seed(1123)
# True parameters
true.par <- c(0.9, 2, 0.7, 0.6)
# Simulation of data from the GMCM model
data <- SimulateGMCMData(n = 1000, par = true.par, d = 2)
# Initial parameters
init.par <- c(0.5, 1, 0.5, 0.9)
# Nelder-Mead optimization
nm.par <- fit.meta.GMCM(data$u, init.par = init.par, method = "NM")
# Get IDR values
res <- get.IDR(data$u, nm.par, threshold = 0.05)
# Plot results
plot(data$u, col = res$Khat, pch = c(3,16)[data$K])
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(GMCM)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GMCM/get.IDR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: get.IDR
> ### Title: Posterior class probabilities, local, and adjusted IDRs.
> ### Aliases: get.IDR get.idr get.prob
>
> ### ** Examples
>
> set.seed(1123)
>
> # True parameters
> true.par <- c(0.9, 2, 0.7, 0.6)
>
> # Simulation of data from the GMCM model
> data <- SimulateGMCMData(n = 1000, par = true.par, d = 2)
>
> # Initial parameters
> init.par <- c(0.5, 1, 0.5, 0.9)
>
> # Nelder-Mead optimization
> nm.par <- fit.meta.GMCM(data$u, init.par = init.par, method = "NM")
Nelder-Mead direct search function minimizer
function value for initial parameters = 171.783273
Scaled convergence tolerance is 2.55977e-06
Stepsize computed as 0.219722
BUILD 5 227.072307 113.692422
EXTENSION 7 203.422655 80.536529
EXTENSION 9 171.783273 0.969765
LO-REDUCTION 11 125.403851 0.969765
EXTENSION 13 113.692422 -36.695436
EXTENSION 15 80.536529 -59.774697
REFLECTION 17 2.445024 -73.341823
LO-REDUCTION 19 0.969765 -73.341823
HI-REDUCTION 21 -36.695436 -73.341823
LO-REDUCTION 23 -44.519612 -73.341823
LO-REDUCTION 25 -59.774697 -73.341823
REFLECTION 27 -65.323185 -75.050504
REFLECTION 29 -67.858104 -75.735675
HI-REDUCTION 31 -69.377201 -75.735675
EXTENSION 33 -72.263424 -85.343583
LO-REDUCTION 35 -73.341823 -85.343583
EXTENSION 37 -75.050504 -90.646223
EXTENSION 39 -75.735675 -95.177646
HI-REDUCTION 41 -82.140512 -95.177646
LO-REDUCTION 43 -83.887908 -95.177646
HI-REDUCTION 45 -85.343583 -95.177646
REFLECTION 47 -89.413691 -96.366569
REFLECTION 49 -90.646223 -96.897874
LO-REDUCTION 51 -94.934992 -97.221745
REFLECTION 53 -95.177646 -97.685118
EXTENSION 55 -96.366569 -100.599712
HI-REDUCTION 57 -96.897874 -100.599712
REFLECTION 59 -97.221745 -100.606776
LO-REDUCTION 61 -97.685118 -100.606776
HI-REDUCTION 63 -98.649070 -100.606776
REFLECTION 65 -100.128410 -101.684845
LO-REDUCTION 67 -100.537253 -101.684845
EXTENSION 69 -100.599712 -101.993131
EXTENSION 71 -100.606776 -102.553812
HI-REDUCTION 73 -100.868522 -102.553812
LO-REDUCTION 75 -101.559228 -102.553812
HI-REDUCTION 77 -101.684845 -102.553812
LO-REDUCTION 79 -101.993131 -102.553812
EXTENSION 81 -102.018479 -102.717295
REFLECTION 83 -102.078186 -102.809142
LO-REDUCTION 85 -102.373072 -102.809142
REFLECTION 87 -102.553812 -102.982773
LO-REDUCTION 89 -102.705807 -102.982773
LO-REDUCTION 91 -102.717295 -102.982773
LO-REDUCTION 93 -102.809142 -102.982773
HI-REDUCTION 95 -102.858857 -102.982773
REFLECTION 97 -102.872474 -102.988904
REFLECTION 99 -102.914137 -103.035337
EXTENSION 101 -102.966506 -103.134041
LO-REDUCTION 103 -102.982773 -103.134041
LO-REDUCTION 105 -102.988904 -103.134041
LO-REDUCTION 107 -103.035337 -103.138305
REFLECTION 109 -103.108410 -103.175415
HI-REDUCTION 111 -103.122287 -103.175415
LO-REDUCTION 113 -103.134041 -103.175415
REFLECTION 115 -103.138305 -103.177946
EXTENSION 117 -103.145600 -103.202221
LO-REDUCTION 119 -103.165248 -103.202221
REFLECTION 121 -103.175415 -103.212280
EXTENSION 123 -103.177946 -103.233265
HI-REDUCTION 125 -103.192753 -103.233265
LO-REDUCTION 127 -103.202221 -103.233265
LO-REDUCTION 129 -103.207320 -103.233265
LO-REDUCTION 131 -103.212280 -103.233847
HI-REDUCTION 133 -103.226681 -103.233847
LO-REDUCTION 135 -103.228143 -103.233847
LO-REDUCTION 137 -103.229832 -103.233847
REFLECTION 139 -103.232052 -103.234894
REFLECTION 141 -103.233265 -103.234996
HI-REDUCTION 143 -103.233451 -103.235265
REFLECTION 145 -103.233847 -103.235912
LO-REDUCTION 147 -103.234894 -103.235912
HI-REDUCTION 149 -103.234996 -103.236298
HI-REDUCTION 151 -103.235265 -103.236298
LO-REDUCTION 153 -103.235825 -103.236298
LO-REDUCTION 155 -103.235832 -103.236298
LO-REDUCTION 157 -103.235912 -103.236298
LO-REDUCTION 159 -103.235974 -103.236298
REFLECTION 161 -103.235979 -103.236437
HI-REDUCTION 163 -103.236126 -103.236437
LO-REDUCTION 165 -103.236211 -103.236437
LO-REDUCTION 167 -103.236249 -103.236437
HI-REDUCTION 169 -103.236298 -103.236437
EXTENSION 171 -103.236342 -103.236483
REFLECTION 173 -103.236347 -103.236486
HI-REDUCTION 175 -103.236367 -103.236486
LO-REDUCTION 177 -103.236433 -103.236486
HI-REDUCTION 179 -103.236437 -103.236486
LO-REDUCTION 181 -103.236453 -103.236493
LO-REDUCTION 183 -103.236474 -103.236494
LO-REDUCTION 185 -103.236483 -103.236498
LO-REDUCTION 187 -103.236486 -103.236498
LO-REDUCTION 189 -103.236493 -103.236501
LO-REDUCTION 191 -103.236494 -103.236501
HI-REDUCTION 193 -103.236495 -103.236501
HI-REDUCTION 195 -103.236498 -103.236501
HI-REDUCTION 197 -103.236498 -103.236502
REFLECTION 199 -103.236499 -103.236503
LO-REDUCTION 201 -103.236500 -103.236503
HI-REDUCTION 203 -103.236501 -103.236503
Exiting from Nelder Mead minimizer
205 function evaluations used
>
> # Get IDR values
> res <- get.IDR(data$u, nm.par, threshold = 0.05)
>
> # Plot results
> plot(data$u, col = res$Khat, pch = c(3,16)[data$K])
>
>
>
>
>
> dev.off()
null device
1
>