20 Output posteriors
The posteriors may be output to file for inspection with the -output-posteriors.
The options are as follows:
Option | Description | Default |
-output-posteriors | do a task to output the posteriors of a network to file | |
-output-posteriors-name name | label the task with a name | Task-n |
-output-posteriors-network-name network | output posteriors for this network | previous network (or the default model given by a node for each data variable and no edges if there is no previous network) |
-output-posteriors-file posts.dat | output the posteriors to file posts.dat | posteriors.dat |
The following is an example parameter file to output the posteriors of a network.
#input continuous data
-input-data
-input-data-file example-cts.dat
-input-data-cts
#input discrete data
-input-data
-input-data-file example-discrete.dat
-input-data-discrete
#input SNP data as discrete data
-input-data
-input-data-file example.bed
-input-data-discrete-snp
#input the example network in format 1
-input-network
-input-network-file example-network-format1.dat
#calculate the posterior of the network
-calc-posterior
#output the posteriors to file
-output-posteriors
-output-posteriors-file example-posteriors.dat
This parameter file, paras-output-post.txt, can be found in example.zip and can be used as follows:
./bayesnetty paras-output-post.txt
Which should produce output that looks like something as follows:
BayesNetty: Bayesian Network software, v1.00
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Copyright 2015-present Richard Howey, GNU General Public License, v3
Institute of Genetic Medicine, Newcastle University
Random seed: 1551958097
--------------------------------------------------
Task name: Task-1
Loading data
Continuous data file: example-cts.dat
Number of ID columns: 2
Including (all) 2 variables in analysis
Each variable has 1500 data entries
Missing value: not set
--------------------------------------------------
--------------------------------------------------
Task name: Task-2
Loading data
Discrete data file: example-discrete.dat
Number of ID columns: 2
Including the 1 and only variable in analysis
Each variable has 1500 data entries
Missing value: NA
--------------------------------------------------
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Task name: Task-3
Loading data
SNP binary data file: example.bed
SNP data treated as discrete data
Total number of SNPs: 2
Total number of subjects: 1500
Number of ID columns: 2
Including (all) 2 variables in analysis
Each variable has 1500 data entries
--------------------------------------------------
--------------------------------------------------
Task name: Task-4
Loading network
Network file: example-network-format1.dat
Network type: bnlearn
Network score type: BIC
Total number of nodes: 5 (Discrete: 3 | Factor: 0 | Continuous: 2)
Total number of edges: 4
Network Structure: [mood][rs1][rs2][pheno|rs1:rs2][express|pheno:mood]
Total data at each node: 1495
Missing data at each node: 5
--------------------------------------------------
--------------------------------------------------
Task name: Task-5
Calculating posterior
Network: Task-4
Network Structure: [mood][rs1][rs2][pheno|rs1:rs2][express|pheno:mood]
--------------------------------------------------
--------------------------------------------------
Task name: Task-6
Outputting posteriors
Network: Task-4
Network Structure: [mood][rs1][rs2][pheno|rs1:rs2][express|pheno:mood]
Output posteriors to file: example-posteriors.dat
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Run time: less than one second
The data is loaded, the network input, the posterior is calculated and then output to a file.