Description Usage Arguments Details Value Author(s) Examples

The functions fits an HFM (Hidden Factor Graph Model) to the (tree structured) data.

1 2 |

`data` |
The observations. A list of measurements for every observation. An entry can be a matrix of size KxN, whereas K is the dimension of the data and N the lenght of the sequence or of size 1xK whereas the entries are discrete. |

`nHStates` |
Number of hidden states H |

`dataNodeIndices` |
The data structure. A list of the toplogy of every data entry of the format [parentIndex childIndex]. |

`priorInit` |
The inital state probabilities |

`transmatInit` |
The initial transtition probabilities for sequential transitions (H x H) |

`transInitDiv` |
The initial transtition probabilities for splitting events (H x (H*H) |

`type` |
Type of the data. ('d' discrete or 'c' continous) |

`emissionProb` |
Initial emission probilities for discrete data, a matrix of size E x H, whereas E are the number of discrete observable states. |

`SigmaInit` |
Initial covariance matrix for continous data |

`muInit` |
Initial means for continous data |

The algorithm fits an HFM to sequential data (in this case essentially a Hidden Markov Model is fitted) or to data that is structured as a binary tree. An HFM consists of two transition probability matrices. One for the sequential transitions and one for transitions where a splitting happens.

An HFM:

`$initProb` |
The initial probabilities (prior). |

`$transMatSeq` |
The transition matrix for sequential events |

`$transMatDiv` |
The transition matrix for division events |

`$ll` |
The log likelihood |

`$emission` |
The emission probabilities of the hidden states |

Henrik Failmezger, failmezger@googlemail.com

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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
## Fit a discrete treeHFM ##
nOStates = 3;
nHStates = 2;
#
T = 1;
nex = 10;
obs1=sample(1:nOStates,5,replace=T)
obs2=sample(1:nOStates,5,replace=T)
data=list()
data[[1]]=obs1;
#
nodeIndices1=cbind(c(0,1,2,2,4),c(1,2,3,4,5));
dataNodeIndices=list()
dataNodeIndices[[1]]=nodeIndices1;
prior1=array(1,nHStates);
priorInit = array(1,nHStates)/nHStates;
transmatInit = matrix(1,nHStates,nHStates)*(1/nHStates);
transInitDiv= matrix(1,nHStates,nHStates*nHStates)*(1/(nHStates*nHStates))
obsmatInit = matrix(sample(1:nOStates,nHStates*nOStates,replace=T),nHStates,nOStates);
obsmatInit=obsmatInit/rowSums(obsmatInit,1);
hfm=HFMfit(data,nHStates,dataNodeIndices,priorInit,transmatInit,transInitDiv,
'd',emissionProb=obsmatInit);
## Fit a continous treeHFM ##
nHStates = 2;
########create observation sequences########
obs1 <- rbind(runif(10,0,1),runif(10,0,1))
obs2 <- rbind(runif(8,0,1),runif(8,0,1))
data=list()
data[[1]]=obs1
data[[2]]=obs2
######### create guesses for gaussian covariance matrix and means #########
mc2 <- Mclust(t(cbind(obs1,obs2)), G=2)
muInit=mc2$parameters$mean;
SigmaInit=mc2$parameters$variance$sigma
#########create tree topology####################################
nodeIndices1=cbind(c(0,1,2,3,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8,9,10));
nodeIndices2=cbind(c(0,1,2,3,4,4,5,6),c(1,2,3,4,5,6,7,8));
dataNodeIndices=list()
dataNodeIndices[[1]]=nodeIndices1;
dataNodeIndices[[2]]=nodeIndices2;
######### create guesses for prior and transition matrices#########
prior1=array(1,nHStates);
priorInit = array(1,nHStates)/nHStates;
transmatInit = matrix(1,nHStates,nHStates)*(1/nHStates);
transInitDiv= matrix(1,nHStates,nHStates*nHStates)*(1/(nHStates*nHStates))
#
hfm=HFMfit(data,nHStates,dataNodeIndices,priorInit,transmatInit,transInitDiv,'c',
SigmaInit=SigmaInit, muInit=muInit);
#
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.