MRPC (version 2.0.0)

SimulatedData: Generating Simulated Data

Description

Function for simulating data.

Usage

SimulatedData(N, p, model, b0.1, b1.1, b1.2, b1.3, sd.1)

Arguments

N

The number of observations.

p

Population frequency of the reference allele. Real number between 0 to 1, which is the number of a particular allele is present.

model

The model for which data will be simulated. For example, if you want to generate data for model 0 you would type 'model0' into the function.

b0.1

Intercept of b0.1 + b1.1*P1 + b1.2*P2 + b1.3*P3, where P1, P2, and P3 are the parents of the corresponding gene.

b1.1

Slope of P1 for b0.1 + b1.1*P1 + b1.2*P2 + b1.3*P3, where P1, P2, and P3 are the parents of the corresponding gene.

b1.2

Slope of P2 for b0.1 + b1.1*P1 + b1.2*P2 + b1.3*P3, where P1, P2, and P3 are the parents of the corresponding gene.

b1.3

Slope of P3 for b0.1 + b1.1*P1 + b1.2*P2 + b1.3*P3, where P1, P2, and P3 are the parents of the corresponding gene.

sd.1

Standard deviation for corresponding data generated gene.

Value

Matrix

Details

The first column of the input matrix is the genotype of the expression quantitative trait loci (eQTL)/Copy number variation (CNVs) and the remaining columns are the gene expression data.

See Also

MRPC for estimating a DAG using the modified pc (MRPC) algorithm; ModiSkeleton for estimating a undirected graph using modified skeleton function; EdgeOrientation for orientation rules for edges in MRPC algorithm; Case_NP the gene to have data generated has no parent; Case_1P the gene to have data generated has one parent; Case_2P the gene to have data generated has two parents.

Examples

# NOT RUN {
# Data pre-processing

# If you use only one genotype of the expression quantitative trait loci
# (eQTL)/Copy number variation (CNV), the 1st column of
# the input matrix will be #eQTL/CNV and the remaining
# columns are the gene expression data.


## Model 0
simu.data_M0 <- SimulatedData(N = 10^3, p = 0.45,
                              'model0', b0.1 = 0,
                              b1.1 = 1, b1.2 = 1,
                              b1.3 = 1, sd.1 = 1)

## Model 1
simu.data_M1 <- SimulatedData(N = 10^3, p = 0.45,
                              'model1', b0.1 = 0,
                              b1.1 = 1, b1.2 = 1,
                              b1.3 = 1, sd.1 = 1)

## Model 2
simu.data_M2 <- SimulatedData(N = 10^3, p = 0.45,
                              'model2', b0.1 = 0,
                              b1.1 = 1, b1.2 = 1,
                              b1.3 = 1, sd.1 = 1)

## Model 3
simu.data_M3 <- SimulatedData(N = 10^3, p = 0.45,
                              'model3', b0.1 = 0,
                              b1.1 = 1, b1.2 = 1,
                              b1.3 = 1, sd.1 = 1)

## Model 4
simu.data_M4 <- SimulatedData(N = 10^3, p = 0.45,
                              'model4', b0.1 = 0,
                              b1.1 = 1, b1.2 = 1,
                              b1.3 = 1, sd.1 = 1)

## Multiple Parent Model
simu.data_multiparent <- SimulatedData(N = 10^3, p = 0.45,
                                       'multiparent', b0.1 = 0,
                                       b1.1 = 1, b1.2 = 1,
                                       b1.3 = 1, sd.1 = 1)

## Star Model
simu.data_starshaped <- SimulatedData(N = 10^3, p = 0.45,
                                      'starshaped', b0.1 = 0,
                                      b1.1 = 1, b1.2 = 1,
                                      b1.3 = 1, sd.1 = 1)

## Layered Model
simu.data_layered <- SimulatedData(N = 10^3, p = 0.45,
                                   'layered', b0.1 = 0,
                                   b1.1 = 1, b1.2 = 1,
                                   b1.3 = 1, sd.1 = 1)

# }