clustMDparallel {clustMD} | R Documentation |

## Run multiple clustMD models in parallel

### Description

This function allows the user to run multiple clustMD models in parallel.
The inputs are similar to `clustMD()`

except `G`

is now a vector
containing the the numbers of components the user would like to fit and
`models`

is a vector of strings indicating the covariance models the
user would like to fit for each element of G. The user can specify the
number of cores to be used or let the function detect the number available.

### Usage

```
clustMDparallel(X, CnsIndx, OrdIndx, G, models, Nnorms, MaxIter, store.params,
scale, startCL = "hc_mclust", Ncores = NULL, autoStop = FALSE,
ma.band = 50, stop.tol = NA)
```

### Arguments

`X` |
a data matrix where the variables are ordered so that the continuous variables come first, the binary (coded 1 and 2) and ordinal variables (coded 1, 2,...) come second and the nominal variables (coded 1, 2,...) are in last position. |

`CnsIndx` |
the number of continuous variables in the data set. |

`OrdIndx` |
the sum of the number of continuous, binary and ordinal variables in the data set. |

`G` |
a vector containing the numbers of mixture components to be fitted. |

`models` |
a vector of strings indicating which clustMD models are to be
fitted. This may be one of: |

`Nnorms` |
the number of Monte Carlo samples to be used for the intractable E-step in the presence of nominal data. |

`MaxIter` |
the maximum number of iterations for which the (MC)EM algorithm should run. |

`store.params` |
a logical variable indicating if the parameter estimates
at each iteration should be saved and returned by the |

`scale` |
a logical variable indicating if the continuous variables should be standardised. |

`startCL` |
a string indicating which clustering method should be used to initialise the (MC)EM algorithm. This may be one of "kmeans" (K means clustering), "hclust" (hierarchical clustering), "mclust" (finite mixture of Gaussian distributions), "hc_mclust" (model-based hierarchical clustering) or "random" (random cluster allocation). |

`Ncores` |
the number of cores the user would like to use. Must be less than or equal to the number of cores available. |

`autoStop` |
a logical argument indicating whether the (MC)EM algorithm
should use a stopping criterion to decide if convergence has been
reached. Otherwise the algorithm will run for If only continuous variables are present the algorithm will use Aitken's
acceleration criterion with tolerance If categorical variables are present, the stopping criterion is based
on a moving average of the approximated log likelihood values. let $t$
denote the current interation. The average of the |

`ma.band` |
the number of iterations to be included in the moving average stopping criterion. |

`stop.tol` |
the tolerance of the (MC)EM stopping criterion. |

### Value

An object of class `clustMDparallel`

is returned. The output
components are as follows:

`BICarray` |
A matrix indicating the estimated BIC values for each of the models fitted. |

`results` |
A list containing the output for each of the models
fitted. Each entry of this list is a |

### References

McParland, D. and Gormley, I.C. (2016). Model based clustering for mixed data: clustMD. Advances in Data Analysis and Classification, 10 (2):155-169.

### See Also

### Examples

```
data(Byar)
# Transformation skewed variables
Byar$Size.of.primary.tumour <- sqrt(Byar$Size.of.primary.tumour)
Byar$Serum.prostatic.acid.phosphatase <-
log(Byar$Serum.prostatic.acid.phosphatase)
# Order variables (Continuous, ordinal, nominal)
Y <- as.matrix(Byar[, c(1, 2, 5, 6, 8, 9, 10, 11, 3, 4, 12, 7)])
# Start categorical variables at 1 rather than 0
Y[, 9:12] <- Y[, 9:12] + 1
# Standardise continuous variables
Y[, 1:8] <- scale(Y[, 1:8])
# Merge categories of EKG variable for efficiency
Yekg <- rep(NA, nrow(Y))
Yekg[Y[,12]==1] <- 1
Yekg[(Y[,12]==2)|(Y[,12]==3)|(Y[,12]==4)] <- 2
Yekg[(Y[,12]==5)|(Y[,12]==6)|(Y[,12]==7)] <- 3
Y[, 12] <- Yekg
## Not run:
res <- clustMDparallel(X = Y, G = 1:3, CnsIndx = 8, OrdIndx = 11, Nnorms = 20000,
MaxIter = 500, models = c("EVI", "EII", "VII"), store.params = FALSE, scale = TRUE,
startCL = "kmeans", autoStop= TRUE, ma.band=30, stop.tol=0.0001)
res$BICarray
## End(Not run)
```

*clustMD*version 1.2.1 Index]