Description Usage Arguments Value Examples
The input graph object should be of class causact_graph
and created using dag_create()
. The specification of a completely consistent joint distribution is left to the user. Helpful error messages are scheduled for future versions of the causact
package.
1  dag_greta(graph, mcmc = TRUE, meaningfulLabels = TRUE, ...)

graph 
a graph object of class 
mcmc 
a logical value indicating whether to sample from the posterior distribution. When 
meaningfulLabels 
a logical value indicating whether to replace the indexed variable names in 
... 
additional arguments to be passed onto 
If mcmc=TRUE
, returns a dataframe of posterior distribution samples corresponding to the input 'causact_graph'. Each column is a parameter and each row a draw from the posterior sample output. If mcmc=FALSE
, running dag_greta
returns a character string of code that would help the user create three objects representing the posterior distribution:
draws
: An mcmc.list object containing raw output from the HMCMC sampler used by greta
.
drawsDF
: A wide data frame with all latent variables as columns and all draws as rows. This data frame is useful for calculations based on the posterior
tidyDrawsDF
: A long data frame with each draw represented on one line. This data frame is useful for plotting posterior distributions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  library(greta)
graph = dag_create() %>%
dag_node("Get Card","y",
rhs = bernoulli(theta),
data = carModelDF$getCard) %>%
dag_node(descr = "Card Probability by Car",label = "theta",
rhs = beta(2,2),
child = "y") %>%
dag_node("Car Model","x",
data = carModelDF$carModel,
child = "y") %>%
dag_plate("Car Model","x",
data = carModelDF$carModel,
nodeLabels = "theta")
graph %>% dag_render()
gretaCode = graph %>% dag_greta(mcmc=FALSE)
## Not run:
## default functionality returns a data frame
# below requires Tensorflow installation
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
## End(Not run)

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