bayeswatch | R Documentation |

Main method of package. MCMC sampling for change-point probabilities with fault detection according to the model by Murph et al. 2023. Creates a bayesWatch object for analysis of change-points.

```
bayeswatch(
data_woTimeValues,
time_of_observations,
time_points,
variable_names = 1:ncol(data_woTimeValues),
not.cont = NULL,
iterations = 100,
burnin = floor(iterations/2),
lower_bounds = NULL,
upper_bounds = NULL,
ordinal_indicators = NULL,
list_of_ordinal_levels = NULL,
categorical_indicators = NULL,
previous_states = NULL,
previous_model_fits = NULL,
linger_parameter = 500,
move_parameter = 100,
g.prior = 0.2,
set_G = NULL,
wishart_df_initial = 1500,
lambda = 1500,
g_sampling_distribution = NULL,
n.cores = 1,
scaleMatrix = NULL,
allow_for_mixture_models = FALSE,
dirichlet_prior = 0.001,
component_truncation = 7,
regime_truncation = 15,
hyperprior_b = 20,
model_params_save_every = 5,
simulation_iter = NULL,
T2_window_size = 3,
determining_p_cutoff = FALSE,
prob_cutoff = 0.5,
model_log_type = "NoModelSpecified",
regime_selection_multiplicative_prior = 2,
split_selection_multiplicative_prior = 2,
is_initial_fit = TRUE,
verbose = FALSE
)
```

`data_woTimeValues` |
matrix. Raw data matrix without datetime stamps. |

`time_of_observations` |
vector. Datetime stamps for every data instance in data_woTimeValues. |

`time_points` |
vector. Time points that mark each 'day' of time. Range should include every datetime in time_of_observations. |

`variable_names` |
vector. Vector of names of columnsof data_woTimeValues. |

`not.cont` |
vector. Indicator variable as to which columns are discrete. |

`iterations` |
integer. Number of MCMC samples to take (including burn-in). |

`burnin` |
integer. Number of burn-in samples. iterations > burnin necessarily. |

`lower_bounds` |
vector. Lower bounds for each data column. |

`upper_bounds` |
vector. Upper bounds for each data column. |

`ordinal_indicators` |
vector. Discrete values, one for each column, indicating which variables are ordinal. |

`list_of_ordinal_levels` |
vector. Discrete values, one for each column, indicating which variables are part of the same ordinal group. |

`categorical_indicators` |
vector. Each nominal d categorical variable must be broken down into d different indicator variables. This vector marks which variables are such indicators. |

`previous_states` |
vector. Starting regime vector, if known, of the same length as the number of 'days' in time_points. |

`previous_model_fits` |
rlist. Starting parameter fits corresponding to regime vector previous_states. |

`linger_parameter` |
float. Prior parameter for Markov chain probability matrix. Larger = less likely to change states. |

`move_parameter` |
float. Prior parameter for Markov chain probability matrix. Larger = more likely to change states. |

`g.prior` |
float in (0,1). Prior probability on edge inclusion for graph structure G. |

`set_G` |
matrix. Starting graph structure, if known. |

`wishart_df_initial` |
integer (>= 3). Starting DF for G-Wishart prior. |

`lambda` |
float. Parameter for NI-G-W prior, controls affect of precision sample on the center sample. |

`g_sampling_distribution` |
matrix. Prior probability on edge inclusion if not uniform across G. |

`n.cores` |
integer. Number of cores available for parallelization. |

`scaleMatrix` |
matrix. Parameter for NI-G-W prior. |

`allow_for_mixture_models` |
logical. Whether or not method should fix mixture distributions to regimes. |

`dirichlet_prior` |
float. Parameter for the dirichlet process for fitting components in the mixture model. |

`component_truncation` |
integer. Maximum component allowed. Should be sufficiently large. |

`regime_truncation` |
integer. Maximum regime allowed. Should be sufficiently large. |

`hyperprior_b` |
integer. Hyperprior on Wishart distribution fit to the scaleMatrix. |

`model_params_save_every` |
integer. How frequently to save model fits for the fault detection method. |

`simulation_iter` |
integer. Used for simulation studies. Deprecated value at package launch. |

`T2_window_size` |
integer. Length of sliding window for Hotelling T2 pre-step. Used when an initial value for previous_states is not provided. |

`determining_p_cutoff` |
logical. Method for estimating the probability cutoff on the posterior distribution for determining change-points. Deprecated at package launch date. |

`prob_cutoff` |
float. Changepoints are determined (for fault detection process) if posterior probability exceeds this value. |

`model_log_type` |
character vector. The type of log (used to distinguish logfiles). |

`regime_selection_multiplicative_prior` |
float. Must be >=1. Gives additional probability to the most recent day for the selection of a new split point. |

`split_selection_multiplicative_prior` |
float. |

`is_initial_fit` |
logical. True when there is no previously fit bayesWatch object fed through the algorithm.. |

`verbose` |
logical. Prints verbose model output for debugging when TRUE. It is highly recommended that you pipe this to a text file. |

bayesWatch object. A model fit for the analysis of posterior change-points and fault detection.

```
library(bayesWatch)
data("full_data")
data("day_of_observations")
data("day_dts")
x = bayeswatch(full_data, day_of_observations, day_dts,
iterations = 500, g.prior = 1, linger_parameter = 20, n.cores=3,
wishart_df_initial = 3, hyperprior_b = 3, lambda = 5)
print(x)
plot(x)
detect_faults(x)
```

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