Description Usage Arguments Details Value Note Author(s) References Examples

Carries out model-based clustering or classification using parsimonious Gaussian mixture models. AECM algorithms are used for parameter estimation. The BIC or the ICL is used for model selection.

1 2 |

`x` |
A matrix or data frame such that rows correspond to observations and columns correspond to variables. |

`rG` |
The range of values for the number of components. |

`rq` |
The range of values for the number of factors. |

`class` |
If |

`icl` |
If |

`zstart` |
A number that controls what starting values are used: ( |

`cccStart` |
If |

`loop` |
A number specifying how many different random starts should be used. Only relevant for |

`zlist` |
A list comprising vectors of initial classifications such that |

`modelSubset` |
A vector of strings giving the models to be used. |

`seed` |
A number giving the pseudo-random number seed to be used. |

`tol` |
A number specifying the epsilon value for the convergence criteria used in the AECM algorithms. For each algorithm, the criterion is based on the difference between the log-likelihood at an iteration and an asymptotic estimate of the log-likelihood at that iteration. This asymptotic estimate is based on the Aitken acceleration and details are given in the References. Values of |

`relax` |
By default, the number of factors cannot exceed half the number of variables. Setting |

The data `x`

are either clustered using the PGMM approach of McNicholas & Murphy (2005, 2008, 2010) or classified using the method described by McNicholas (2010). In either case, all 12 covariance structures given by McNicholas & Murphy (2010) are available. Parameter estimation is carried out using AECM algorithms, as described in McNicholas et al. (2010). Either the BIC or the ICL is used for model-selection. The number of AECM algorithms to be run depends on the range of values for the number of components `rG`

, the range of values for the number of factors `rq`

, and the number of models in `modelSubset`

. Starting values are very important to the successful operation of these algorithms and so care must be taken in the interpretation of results.

An object of class `pgmm`

is a list with components:

`map` |
A vector of integers, taking values in the range |

`model` |
A string giving the name of the best model. |

`g` |
The number of components for the best model. |

`q` |
The number of factors for the best model. |

`zhat` |
A matrix giving the raw values upon which |

`load` |
The factor loadings matrix (Lambda) for the best model. |

`noisev` |
The Psi matrix for the best model. |

`plot_info` |
A list that stores information to enable |

`summ_info` |
A list that stores information to enable |

In addition, the object will contain one of the following, depending on the value of `icl`

.

`bic` |
A number giving the BIC for each model. |

`icl` |
A number giving the ICL for each model. |

Dedicated `print`

, `plot`

, and `summary`

functions are available for objects of class `pgmm`

.

Paul D. McNicholas [aut, cre], Aisha ElSherbiny [aut], K. Raju Jampani [ctb], Aaron McDaid [aut], Brendan Murphy [aut], Larry Banks [ctb]

Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>

Paul D. McNicholas and T. Brendan Murphy (2010). Model-based clustering of microarray expression data via latent Gaussian mixture models. *Bioinformatics* **26**(21), 2705-2712.

Paul D. McNicholas (2010). Model-based classification using latent Gaussian mixture models. *Journal of Statistical Planning and Inference* **140**(5), 1175-1181.

Paul D. McNicholas, T. Brendan Murphy, Aaron F. McDaid and Dermot Frost (2010). Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. *Computational Statistics and Data Analysis* **54**(3), 711-723.

Paul D. McNicholas and T. Brendan Murphy (2008). Parsimonious Gaussian mixture models. *Statistics and Computing* **18**(3), 285-296.

Paul D. McNicholas and T. Brendan Murphy (2005). Parsimonious Gaussian mixture models. Technical Report 05/11, Department of Statistics, Trinity College Dublin.

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## Not run:
# Wine clustering example with three random starts and the CUU model.
data("wine")
x<-wine[,-1]
x<-scale(x)
wine_clust<-pgmmEM(x,rG=1:4,rq=1:4,zstart=1,loop=3,modelSubset=c("CUU"))
table(wine[,1],wine_clust$map)
# Wine clustering example with custom starts and the CUU model.
data("wine")
x<-wine[,-1]
x<-scale(x)
hcl<-hclust(dist(x))
z<-list()
for(g in 1:4){
z[[g]]<-cutree(hcl,k=g)
}
wine_clust2<-pgmmEM(x,1:4,1:4,zstart=3,modelSubset=c("CUU"),zlist=z)
table(wine[,1],wine_clust2$map)
print(wine_clust2)
summary(wine_clust2)
# Olive oil classification by region (there are three regions), with two-thirds of
# the observations taken as having known group memberships, using the CUC, CUU and
# UCU models.
data("olive")
x<-olive[,-c(1,2)]
x<-scale(x)
cls<-olive[,1]
for(i in 1:dim(olive)[1]){
if(i%%3==0){cls[i]<-0}
}
olive_class<-pgmmEM(x,rG=3:3,rq=4:6,cls,modelSubset=c("CUC","CUU",
"CUCU"),relax=TRUE)
cls_ind<-(cls==0)
table(olive[cls_ind,1],olive_class$map[cls_ind])
# Another olive oil classification by region, but this time suppose we only know
# two-thirds of the labels for the first two areas but we suspect that there might
# be a third or even a fourth area.
data("olive")
x<-olive[,-c(1,2)]
x<-scale(x)
cls2<-olive[,1]
for(i in 1:dim(olive)[1]){
if(i%%3==0||i>420){cls2[i]<-0}
}
olive_class2<-pgmmEM(x,2:4,4:6,cls2,modelSubset=c("CUU"),relax=TRUE)
cls_ind2<-(cls2==0)
table(olive[cls_ind2,1],olive_class2$map[cls_ind2])
# Coffee clustering example using k-means starting values for all 12
# models with the ICL being used for model selection instead of the BIC.
data("coffee")
x<-coffee[,-c(1,2)]
x<-scale(x)
coffee_clust<-pgmmEM(x,rG=2:3,rq=1:3,zstart=2,icl=TRUE)
table(coffee[,1],coffee_clust$map)
plot(coffee_clust)
plot(coffee_clust,onlyAll=TRUE)
## End(Not run)
# Coffee clustering example using k-means starting values for the UUU model, i.e., the
# mixture of factor analyzers model, for G=2 and q=1.
data("coffee")
x<-coffee[,-c(1,2)]
x<-scale(x)
coffee_clust_mfa<-pgmmEM(x,2:2,1:1,zstart=2,modelSubset=c("UUU"))
table(coffee[,1],coffee_clust_mfa$map)
``` |

pgmm documentation built on May 30, 2017, 6:42 a.m.

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