Description Usage Arguments Details Author(s) See Also Examples
Make a plot of the marginal likelihood against the prior strength parameter, highlighting the value used to produce the network.
1 2 |
output |
The object returned from the interventionalInference function. |
xlab |
A label for the prior strength axis. |
ylab |
A label for the marginal likelihood axis. |
col.max |
The colour of the line highlighting the maximum. |
lty.max |
The line type of the highlight. |
lwd.max |
The line width of the highlight. |
... |
Other arguments, such as |
It is important to check that the Empirical Bayes calculation is doing something sensible.
Simon Spencer
interventionalDBN-package
,interventionalInference
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | library(interventionalDBN)
data(interventionalData)# loads interventionalData.
# Load your own data spreadsheet using myData<-read.csv("myDataFile.csv").
# Format the data for network inference
d<-formatData(interventionalData)
# EGFRi is active in conditions 2 and 4, AKTi is active in conditions 3 and 4.
# Each condition has 8 timepoints.
Z<-matrix(0,32,15)
Z[9:16,1]<-1 # EGFR (node 1) inhibited in condition 2
Z[25:32,1]<-1 # EGFR inhibited in condition 4
Z[17:24,8]<-1 # AKT (node 8) inhibited in condition 3
Z[25:32,8]<-1 # AKT inhibited in condition 4
# Perform network inference with Hamming Prior that prefers self-edges,
# and use Empirical Bayes to choose the priorStrength.
myNetwork4<-interventionalInference(d$y,d$X0,d$X1,Z,max.indeg=3,
perfectOut=TRUE,fixedEffectOut=TRUE,
priorType="Hamming",priorGraph=diag(rep(1,15)),priorStrength=0:10/2)
# You should always check to see if the Empirical Bayes appears to be working.
plotMaxML(myNetwork4)
|
n = 32
4 conditions:
Cell line 1 : 32 samples.
Inhibitor DMSO : 8 samples.
Inhibitor EGFRi : 8 samples.
Inhibitor AKTi : 8 samples.
Inhibitor EGFRi+AKTi : 8 samples.
Stimulus EGF : 32 samples.
Time 0 : 4 samples.
Time 1 : 4 samples.
Time 2 : 4 samples.
Time 3 : 4 samples.
Time 4 : 4 samples.
Time 5 : 4 samples.
Time 6 : 4 samples.
Time 7 : 4 samples.
n = 32 , nodes = 15
a = 2
Calculating Hamming prior distances...
Processing 576 models Sun Aug 4 22:55:26 2019
Estimated duration 0.00240937 minutes.
Actual duration 0.002281356 minutes.
Renormalising...
Calculating posterior edge probabilities...
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.