make_priors {CausalQueries} | R Documentation |

A flexible function to generate priors for a model.

make_priors( model, alphas = NA, distribution = NA, node = NA, label = NA, statement = NA, confound = NA, nodal_type = NA, param_names = NA, param_set = NA )

`model` |
A model created with |

`alphas` |
Real positive numbers giving hyperparameters of the Dirichlet distribution |

`distribution` |
String (or list of strings) indicating a common prior distribution (uniform, jeffreys or certainty) |

`node` |
A string (or list of strings) indicating nodes for which priors are to be altered |

`label` |
A string. Label for nodal type indicating nodal types for which priors are to be altered |

`statement` |
A causal query (or list of queries) that determines nodal types for which priors are to be altered |

`confound` |
A confound named list that restricts nodal types for which priors are to be altered. Adjustments are limited to nodes in the named list. |

`nodal_type` |
A string. Label for nodal type indicating nodal types for which priors are to be altered |

`param_names` |
A string. The name of specific parameter in the form of, for example, 'X.1', 'Y.01' |

`param_set` |
A string. Indicates the name of the set of parameters to be modified (useful when setting confounds) |

Seven arguments govern *which* parameters should be altered. The default is 'all' but this can be reduced by specifying

* `label`

or `nodal_type`

The label of a particular nodal type, written either in the form Y0000 or Y.Y0000

* `node`

, which restricts for example to parameters associated with node 'X'

* `statement`

, which restricts for example to nodal types that satisfy the statement 'Y[X=1] > Y[X=0]'

* `confound`

, which restricts for example to nodal types that satisfy the statement 'Y[X=1] > Y[X=0]'

* `param_set`

, which us useful when setting confound statements that produces several sets of parameters

* `param_names`

, which restricts in specific parameters by naming them

Two arguments govern what values to apply:

* `alphas`

is one or more non negative numbers and

* `distribution`

indicates one of a common class: uniform, jeffreys, or 'certain'

Any arguments entered as lists or vectors of size > 1 should be of the same length as each other.

A vector indicating the hyperparameters of the prior distribution of the nodal types.

For instance `confound = list(X = Y[X=1]> Y[X=0])`

adjust parameters on X that are conditional on nodal types for Y.

Other priors:
`get_priors()`

,
`make_par_values_multiple()`

,
`make_par_values()`

,
`make_values_task_list()`

,
`set_priors()`

# Pass all nodal types model <- make_model("Y <- X") make_priors(model, alphas = .4) make_priors(model, distribution = "jeffreys") # Passing by names of node, parameter set or label model <- make_model('X -> M -> Y') make_priors(model, param_name = "X.1", alphas = 2) make_priors(model, node = 'X', alphas = 3) make_priors(model, param_set = 'Y', alphas = 5) make_priors(model, node = c('X', 'Y'), alphas = 3) make_priors(model, param_set = c('X', 'Y'), alphas = 5) make_priors(model, node = list('X', 'Y'), alphas = list(3, 6)) make_priors(model, param_set = list('X', 'Y'), alphas = list(4, 6)) make_priors(model, node = c('X', 'Y'), distribution = c('certainty', 'jeffreys')) make_priors(model, param_set = c('X', 'Y'), distribution = c('jeffreys', 'certainty')) make_priors(model, label = '01', alphas = 5) make_priors(model, node = 'Y', label = '00', alphas = 2) make_priors(model, node =c('M', 'Y'), label = '11', alphas = 4) # Passing a causal statement make_priors(model, statement = 'Y[M=1] > Y[M=0]', alphas = 3) make_priors(model, statement = c('Y[M=1] > Y[M=0]', 'M[X=1]== M[X=0]'), alphas = c(3, 2)) # Passing a confound statement model <- make_model('X->Y') %>% set_confound(list(X = 'Y[X=1] > Y[X=0]', X = 'Y[X=1] < Y[X=0]')) make_priors(model, confound = list(X='Y[X=1] > Y[X=0]', X='Y[X=1] < Y[X=0]'), alphas = c(3, 6)) make_priors(model, confound= list(X='Y[X=1] > Y[X=0]'), alphas = 4) make_priors(model, param_set='X_1', alphas = 5) make_priors(model, param_names='X_2.1', alphas = .75) make_model('X -> Y') %>% set_confound(list(X = 'Y[X=1]>Y[X=0]'))%>% make_priors(statement = 'X[]==1', confound = list(X = 'Y[X=1]>Y[X=0]', X = 'Y[X=1]<Y[X=0]'), alphas = c(2, .5))

[Package *CausalQueries* version 0.0.3 Index]