CausalQueries (version 1.0.1)

update_model: Fit causal model using 'stan'

Description

Takes a model and data and returns a model object with data attached and a posterior model

Usage

update_model(
  model,
  data = NULL,
  data_type = NULL,
  keep_fit = FALSE,
  keep_transformed = TRUE,
  keep_event_probabilities = FALSE,
  censored_types = NULL,
  ...
)

Value

An object of class causal_model. The returned model is a list containing the elements comprising a model (e.g. 'statement', 'nodal_types' and 'DAG') with the `posterior_distribution` returned by stan attached to it.

Arguments

model

A causal_model. A model object generated by make_model.

data

A data.frame. Data of nodes that can take three values: 0, 1, and NA. In long form as generated by make_events

data_type

Either 'long' (as made by make_data) or 'compact' (as made by collapse_data). Compact data must have entries for each member of each strategy family to produce a valid simplex.

keep_fit

Logical. Whether to append the stanfit object to the model. Defaults to `FALSE`

keep_transformed

Logical. Whether to keep transformed parameters, prob_of_types, P_lambdas, w, w_full

keep_event_probabilities

Logical. Whether to keep the distribution of event probabilities. Defaults to `FALSE`

censored_types

vector of data types that are selected out of the data, e.g. c("X0Y0")

...

Options passed onto stan call.

Examples

model <- make_model('X->Y')
data_long   <- simulate_data(model, n = 4)
data_short  <- collapse_data(data_long, model)
# \donttest{
model_1 <- update_model(model, data_long)
# }
# \donttest{
model_2 <- update_model(model, data_long, keep_transformed = FALSE)
# }
if (FALSE) {
# Throws error unless compact data indicated:

model_3 <- update_model(model, data_short)
model_4 <- update_model(model, data_short, data_type = 'compact')

# It is possible to implement updating without data, in which
# case the posterior is a stan object that reflects the prior
model_5 <- update_model(model)


# Censored data types
# We update less than we might because we are aware of filtered data
uncensored <-  make_model("X->Y") %>%
  update_model(data.frame(X=rep(0:1, 10), Y=rep(0:1,10))) |>
  query_model(te("X", "Y"), using = "posteriors")

censored <- make_model("X->Y") %>%
  update_model(data.frame(X=rep(0:1, 10), Y=rep(0:1,10)),
  censored_types = c("X1Y0")) %>%
  query_model(te("X", "Y"), using = "posteriors")

# Censored data: We learning nothing because the data
# we see is the only data we could ever see
make_model("X->Y") %>%
  update_model(data.frame(X=rep(1,5), Y=rep(1,5)),
  censored_types = c("X1Y0", "X0Y0", "X0Y1")) %>%
  query_model(te("X", "Y"), using = "posteriors")
}