BMRMM {BMRMM} | R Documentation |

Provides inference results of both transition probabilities and duration times using BMRMMs.

```
BMRMM(
data,
num_cov,
switch_off_random = FALSE,
trans_cov_index = 1:num_cov,
duration_type = "Continuous",
duration_cov_index = 1:num_cov,
duration_excl_prev_state = FALSE,
duration_unit = NULL,
duration_num_comp = 4,
duration_init_shape = rep(1, duration_num_comp),
duration_init_rate = rep(1, duration_num_comp),
simsize = 10000,
burnin = simsize/2
)
```

`data` |
a data frame containing – individual ID, covariate values, previous state, current state, duration times (if applicable), in that order |

`num_cov` |
total number of covariates provided in 'data' |

`switch_off_random` |
TRUE if only population-level effects are considered, default is FALSE |

`trans_cov_index` |
indices of covariates that are used for transition probabilities, default is all of the covariates |

`duration_type` |
one of 'None', 'Discrete', 'Continuous', default is 'Continuous' |

`duration_cov_index` |
indices of covariates that are used for duration times, default is all of the covariates plus the previous state |

`duration_excl_prev_state` |
TRUE if the previous state is excluded from duration times inference, default is FALSE |

`duration_unit` |
the discretization of duration times, only used when 'duration_type' is 'Discrete' |

`duration_num_comp` |
number of gamma mixture components for duration times, default is 4 |

`duration_init_shape` |
initialization of mixture gamma shape parameters, default is a vector of 1 of size 'duration_num_comp' |

`duration_init_rate` |
initialization of mixture gamma rate parameters, default is a vector of 1 of size 'duration_num_comp' |

`simsize` |
total number of MCMC iterations, default is 10000 |

`burnin` |
number of burn-ins for the MCMC iterations, default is simsize/2 |

Returns list(trans_results, duration_results)

(1) Users have the option to ignore duration times or model duration times as a discrete or continuous variable via defining 'duration_type':

"None": ignores duration times

"Continuous": treat duration times as a continuous variable (DEFAULT)

"Discrete": treat duration times as a new state with discretization 'duration_unit'.
If 'Discrete' is used, duration times becomes a new state type. 'duration_unit' must be specified if 'duration_type' is 'Discrete'.
For example, if an duration time entry is 20 and 'duration_unit' is 5, then the model will add 4 consecutive new states.
If an duration time entry is 23.33 and 'duration_unit' is 5, then the model will still add 4 consecutive new states
as the blocks are calculated with the floor operation

(2) 'trans_results' contains the results of the transition probabilities:

- "Num_States": total number of states

- "Xexgns": covariates related to transition probabilities

- "dpreds": maximum level for each related covariate

- "MCMCparams": MCMC parameters: simsize, burnin and thinning factor

- "TP_Exgns_Post_Mean": posterior mean of transition probabilities for different combinations of exogenous predictors

- "TP_Exgns_Post_Std": posterior standard deviation of transition probabilities for different combinations of exogenous predictors

- "TP_Anmls_Post_Mean": posterior mean of transition distribution components for different individuals

- "TP_All_Post_Mean": posterior mean of transition distribution components for different combinations of exogenous predictors AND different individuals

- "TP_Anmls_Post_Std": standard deviation of transition probabilities among different mice

- "TP_Exgns_Diffs_Store": difference in posterior mean of transition probabilities for every pair of covariate levels given levels of the other covariates

- "TP_Exgns_All_Itns": exogenous transition probabilities for every MCMC iteration

- "Clusters": number of clusters for each covariate for each MCMC iteration

- "Type": an identifier for results, which is "Transition Probabilities".

(3) 'duration_results' contains the results of the duration times ('duration_results' is NULL if 'duration_type' is 'None'):

"K" <- number of gamma mixture components

"Xexgns" <- covariates related to duration times

"dpreds" <- maximum level for each related covariate

"MCMCparams" <- MCMC parameters: simsize, burnin and thinning factor

"Duration_Times" <- input duration times

"Comp_Assign" <- mixture component assignment for each data point in the last MCMC iteration

"Duration_Exgns_Store" <- posterior mean of mixture probabilities for different combinations of exogenous predictors of each MCMC iteration

"Marginal_Prob" <- estimated marginal mixture probabilities for each iteration

"Shape_Samples" <- estimated shape parameters for gamma mixtures for each iteration

"Rate_Samples" <- estimated rate parameters for gamma mixtures for each iteration

"Clusters" <- number of clusters for each covariate for each MCMC iteration

"Type" <- an identifier for results, which is "Inter-Syllable Intervals".

List of results for transition probabilities and durations times, list(trans_results, duration_results). See details.

Yutong Wu, yutong.wu@utexas.edu

```
# In the examples, we use a shorted version of the foxp2 dataset, foxp2_sm
# ignores duration times and only models transition probabilities using all three covariates
results <- BMRMM(foxp2_sm,num_cov=2,duration_type='None',simsize=50)
# models duration times as a continuous variable with 5 gamma mixture components,
# using covariate 1 and the previous state
results <- BMRMM(foxp2_sm,num_cov=2,trans_cov_index=c(1),'duration_type'='Continuous',
duration_cov_index=c(1),duration_num_comp=5,simsize=50)
# models duration times as a discrete state with discretization 0.25 and
# do not include the previous state as a covariate
results <- BMRMM(foxp2_sm,num_cov=2,duration_type='Discrete',duration_excl_prev_state=TRUE,
duration_unit=0.025,simsize=50)
```

[Package *BMRMM* version 0.0.1 Index]