Description Usage Arguments Value See Also Examples

Function to get a MCMC simulation results based on the imported MVN distribution. It is commonly used for inquiring the specified conditional probability of MVN distribuiton calculated through Bayesian posteriori.

1 2 3 4 5 |

`data` |
A double level list which contains the mean vector ( |

`steps` |
A positive integer. The numbers of random vectors to be generated for MCMC step. |

`pars` |
A integer vector to declare fixed dimension(s). For example if the desired dimensions are 1st=7 and 3rd=10, set this argument as c(1,3). |

`values` |
A numeric vector to assign value(s) to declared dimension(s). For example if the desired dimensions are 1st=7 and 3rd=10, set this argument as c(7,10). |

`tol` |
Tolerance. A numeric value to control the generated vectors to be accepted or rejected. Criterion uses Euclidean distance in declared dimension(s). Default value is 0.3. |

`...` |
Other arguments to control the process in Gibbs sampling. |

return a list which contains:

`AcceptRate` |
Acceptance of declared conditions of MCMC |

`MCMCdata` |
All generated random vectors in MCMC step based on MVN distribution |

`Accept` |
Subset of accepted sampling in MCMCdata |

`Reject` |
Subset of rejected sampling in MCMCdata |

`MVN_GibbsSampler`

, `MVN_FConditional`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
library(mvtnorm)
library(plyr)
# dataset1 has three parameters: fac1, fac2 and fac3:
head(dataset1)
# Get posteriori parameters of dataset1 using prior of c(80,16,3):
BPos <- MVN_BayesianPosteriori(dataset1, pri_mean=c(80,16,3))
# If we want to know when fac1=78, how fac2 responses to fac3, run:
BPos_MCMC <- MVN_MCMC(BPos, steps=8000, pars=c(1), values=c(78), tol=0.3)
MCMC <- BPos_MCMC$MCMCdata
head(MCMC)
# Visualization using plot3d() if necessary:
library(rgl)
plot3d(MCMC[,1], MCMC[,2], z=MCMC[,3], col=MCMC[,5]+1, size=2)
# Visualization: 2d scatter plot
MCMC_2d <- BPos_MCMC$Accept
head(MCMC_2d)
plot(MCMC_2d[,3], MCMC_2d[,2], pch=20, col="red", xlab = "fac3", ylab = "fac2")
# Compared to the following scatter plot when fac1 is not fixed:
plot(BPos_MCMC$MCMCdata[,3], BPos_MCMC$MCMCdata[,2], pch=20, col="red", xlab = "fac3",
ylab = "fac2")
``` |

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