Description Usage Arguments Details Value Author(s) References See Also Examples
Generating discrete ordinal data based on underlying "genome-like" graph structure. The procedure of simulating data relies on a continues variable, which can be simulated from either multivariate normal distribution, or multivariate t-distribution with d
degrees of freedom.
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p |
The number of variables. The default value is 90. |
n |
The number of sample size (observations). The default value is 200. |
k |
The number of states (categories). The default value is 3. |
g |
The number of groups (chromosomes) in the graph. The default value is about p/20 if p >= 40 and 2 if p < 40. |
adjacent |
The number of adjacent variable(s) to be linked to a variable. For example, if |
alpha |
A probability that a pair of non-adjacent variables in the same group is given an edge. The default value is 0.01. |
beta |
A probability that variables in different groups are linked with an edge. The default value is 0.02. |
con.dist |
The distribution of underlying continuous variable. If |
d |
The degrees of freedom of the continuous variable, only applicable when codecon.dist = "Mt". The default value is 3. |
vis |
Visualize the graph pattern and the adjacency matrix of the true graph structure. The default value is FALSE. |
The graph pattern is generated as below:
"genome-like": The p
variables are evenly partitions variables into g
disjoint groups; the adjacent variables within each group are linked via an edge. With a probability alpha
a pair of non-adjacent variables in the same group is given an edge. Variables in different groups are linked with an edge with a probability of beta
.
An object with S3 class "episim" is returned:
data |
The generated data as an |
Theta |
A |
adj |
A |
Sigma |
A |
n.groups |
The number of groups. |
groups |
A vector that indicates each variable belongs to which group. |
sparsity |
The sparsity levels of the true graph. |
Pariya Behrouzi and Ernst C. Wit
Maintainer: Pariya Behrouzi <pariya.behrouzi@gmail.com>
P. Behrouzi and E. C. Wit. Detecting Epistatic Selection with Partially Observed Genotype Data Using Copula Graphical Models. arXiv, 2016.
epistasis
, and epistasis-package
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