Description Usage Arguments Details Value Author(s) References See Also Examples
The data generator creates random samples from conditional Gaussian distribution with different graph structures
1 | datagen(parlist,n)
|
parlist |
The parameter list generated by |
n |
The number of observations (sample size) |
We use the exact probability rather than MCMC
methods to generate the binary variables.
We generate the probability distribution of Z
as well as the canonical parameters.
The memory requirements for the
distribution of Z
make it difficult to generate a large number of binary variables in simulations.
However, this is not a problem for real data where the variables are already observed.
The function returns a data list:
z |
Value of binary variable |
y |
Value of continous variable |
Prob |
The probability distribution of discrete variables |
cparlist |
The canonical parameter |
Mingyu Qi, Tianxi Li
Jie Cheng, Tianxi Li, Elizaveta Levina, and Ji Zhu.(2017) High-dimensional Mixed Graphical Models. Journal of Computational and Graphical Statistics 26.2: 367-378, https://arxiv.org/pdf/1304.2810.pdf
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