CompareMethodsVStructure: Comparison of inference accuracy of different methods on data...

In MRPC: PC Algorithm with the Principle of Mendelian Randomization

Comparison of inference accuracy of different methods on data with and without a v-structure

Description

This function compares inference accuracy on graphs with and without a v-structure in terms of recall and precision by six methods MRPC, pc, pc.stable, mmpc, mmhc, and hc, across multiple data sets. See details in Badsha and Fu (2019) and Badsha et al.(2021).

Usage

CompareMethodsVStructure(N, signal, model, includeGV, ita)

Arguments

N	Number of observations.
signal	The coefficient of parent nodes in the linear model. For example, strong = 1.0, moderate = 0.5, and weak = 0.2.
model	The graph from which the data is generated. Specifically, two graphs are considered here: 'model 1' (V1->T1->T2), which does not contain a v-structure, and 'model 2' (V1->T1<-T2), which is a v-structure.
includeGV	If TRUE, include edges involving genetic variants (GVs) when comparing the true and inferred graphs. If FALSE, exclude such edges.
ita	Number of independent data sets to simulate.

Details

The output is a matrix, where the rows are the six methods: MRPC, pc, pc.stable, mmpc, mmhc, and hc, and the columns are the mean of recall, sd of recall, mean of precision, and sd of precision, respectively. Mean and sd are calculated across all the simulated data sets. For methods from the bnlearn package (pc.stable, mmpc, mmhc, and hc), we apply the blacklist argument to exclude edges pointing at the genetic variant, and therefore evaluate recall and precision including the edges involving these edges (i.e., includeGV = TRUE).

Value

Matrix

Author(s)

Md Bahadur Badsha ([e-mail])

References

1. Badsha MB and Fu AQ (2019). Learning causal biological networks with the principle of Mendelian randomization. Frontiers in Genetics, 10:460.

2. Badsha MB, Martin EA and Fu AQ (2021). MRPC: An R package for inference of causal graphs. Frontiers in Genetics, 10:651812.

3. Kalisch M, Machler M, Colombo D, Maathuis MH and Buhlmann P (2012). Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software, 47, 26.

4. Scutari M (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1-22.

See Also

RecallPrecision: Performance evaluation in terms of recall and precision.

Examples

  
 
# Load the libraries
library(MRPC)     # MRPC
library(pcalg)    # pc
library(bnlearn)  # pc.stable, mmpc, mmhc and hc

# For demonstration purposes, only 10 data sets 
# are simulated here 
# with sample size N = 100 and signal = 1

# Comparison of inference accuracy on 
# model 1 (mediation)
Result1 <- CompareMethodsVStructure(N = 100, 
                                    signal = 1.0,
                                    'model1', 
                                    includeGV = TRUE,
                                    ita = 10)

# Comparison of inference accuracy on 
# model 2 (v-structure)
Result2 <- CompareMethodsVStructure(N = 100, 
                                    signal = 1.0,
                                    'model2',
                                    includeGV = TRUE,
                                    ita = 10)


  

MRPC documentation built on April 11, 2022, 5:10 p.m.