parameter_setting: Setting parameters

In CausalQueries: Make, Update, and Query Binary Causal Models

Setting parameters

Description

Functionality for altering parameters:

A vector of 'true' parameters; possibly drawn from prior or posterior.

Add a true parameter vector to a model. Parameters can be created using arguments passed to make_parameters and make_priors.

Extracts parameters as a named vector

Usage

make_parameters(
  model,
  parameters = NULL,
  param_type = NULL,
  warning = TRUE,
  normalize = TRUE,
  ...
)

set_parameters(
  model,
  parameters = NULL,
  param_type = NULL,
  warning = FALSE,
  ...
)

get_parameters(model, param_type = NULL)

Arguments

model	A causal_model. A model object generated by make_model.
parameters	A vector of real numbers in [0,1]. Values of parameters to specify (optional). By default, parameters is drawn from model$parameters_df.
param_type	A character. String specifying type of parameters to make "flat", "prior_mean", "posterior_mean", "prior_draw", "posterior_draw", "define". With param_type set to define use arguments to be passed to make_priors; otherwise flat sets equal probabilities on each nodal type in each parameter set; prior_mean, prior_draw, posterior_mean, posterior_draw take parameters as the means or as draws from the prior or posterior.
warning	Logical. Whether to warn about parameter renormalization.
normalize	Logical. If parameter given for a subset of a family the residual elements are normalized so that parameters in param_set sum to 1 and provided params are unaltered.
...	Options passed onto make_priors.

Value

A vector of draws from the prior or distribution of parameters

An object of class causal_model. It essentially returns a list containing the elements comprising a model (e.g. 'statement', 'nodal_types' and 'DAG') with true vector of parameters attached to it.

A vector of draws from the prior or distribution of parameters

Examples

# make_parameters examples:

# Simple examples
model <- make_model('X -> Y')
data  <- make_data(model, n = 2)
model <- update_model(model, data)
make_parameters(model, parameters = c(.25, .75, 1.25,.25, .25, .25))
make_parameters(model, param_type = 'flat')
make_parameters(model, param_type = 'prior_draw')
make_parameters(model, param_type = 'prior_mean')
make_parameters(model, param_type = 'posterior_draw')
make_parameters(model, param_type = 'posterior_mean')




#altering values using \code{alter_at}
make_model("X -> Y") %>% make_parameters(parameters = c(0.5,0.25),
alter_at = "node == 'Y' & nodal_type %in% c('00','01')")

#altering values using \code{param_names}
make_model("X -> Y") %>% make_parameters(parameters = c(0.5,0.25),
param_names = c("Y.10","Y.01"))

#altering values using \code{statement}
make_model("X -> Y") %>% make_parameters(parameters = c(0.5),
statement = "Y[X=1] > Y[X=0]")

#altering values using a combination of other arguments
make_model("X -> Y") %>% make_parameters(parameters = c(0.5,0.25),
node = "Y", nodal_type = c("00","01"))

# Normalize renormalizes values not set so that value set is not renomalized
make_parameters(make_model('X -> Y'),
               statement = 'Y[X=1]>Y[X=0]', parameters = .5)
make_parameters(make_model('X -> Y'),
               statement = 'Y[X=1]>Y[X=0]', parameters = .5,
               normalize = FALSE)

  

# set_parameters examples:

make_model('X->Y') %>% set_parameters(1:6) %>% grab("parameters")

# Simple examples
model <- make_model('X -> Y')
data  <- make_data(model, n = 2)
model <- update_model(model, data)
set_parameters(model, parameters = c(.25, .75, 1.25,.25, .25, .25))
set_parameters(model, param_type = 'flat')
set_parameters(model, param_type = 'prior_draw')
set_parameters(model, param_type = 'prior_mean')
set_parameters(model, param_type = 'posterior_draw')
set_parameters(model, param_type = 'posterior_mean')




#altering values using \code{alter_at}
make_model("X -> Y") %>% set_parameters(parameters = c(0.5,0.25),
alter_at = "node == 'Y' & nodal_type %in% c('00','01')")

#altering values using \code{param_names}
make_model("X -> Y") %>% set_parameters(parameters = c(0.5,0.25),
param_names = c("Y.10","Y.01"))

#altering values using \code{statement}
make_model("X -> Y") %>% set_parameters(parameters = c(0.5),
statement = "Y[X=1] > Y[X=0]")

#altering values using a combination of other arguments
make_model("X -> Y") %>% set_parameters(parameters = c(0.5,0.25),
node = "Y", nodal_type = c("00","01"))


  

CausalQueries documentation built on June 22, 2024, 6:50 p.m.