causact (version 0.5.7)

dagp_plot: Plot posterior distribution from dataframe of posterior draws.

Description

Plot the posterior distribution of all latent parameters using a dataframe of posterior draws from a causact_graph model.

Usage

dagp_plot(drawsDF, densityPlot = FALSE, abbrevLabels = FALSE)

Value

a credible interval plot of all latent posterior distribution parameters.

Arguments

drawsDF

the dataframe output of dag_numpyro(mcmc=TRUE) where each column is a parameter and each row a single draw from a representative sample.

densityPlot

If TRUE, each parameter gets its own density plot. If FALSE (recommended usage), parameters are grouped into facets based on whether they share the same prior or not. 10 and 90 percent credible intervals are displayed for the posterior distributions.

abbrevLabels

If TRUE, long labels on the plot are abbreviated to 10 characters. If FALSE the entire label is used.

Examples

# A simple example
posteriorDF = data.frame(x = rnorm(100),
y = rexp(100),
z = runif(100))
posteriorDF %>%
dagp_plot(densityPlot = TRUE)

# More complicated example requiring 'numpyro'
if (FALSE) {
# Create a 2 node graph
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")
graph %>% dag_render()

# below requires Tensorflow installation
drawsDF = graph %>% dag_numpyro(mcmc=TRUE)
drawsDF %>% dagp_plot()
}

# A multiple plate example
library(dplyr)
poolTimeGymDF = gymDF %>%
mutate(stretchType = ifelse(yogaStretch == 1,
                            "Yoga Stretch",
                            "Traditional")) %>%
group_by(gymID,stretchType,yogaStretch) %>%
  summarize(nTrialCustomers = sum(nTrialCustomers),
            nSigned = sum(nSigned))
graph = dag_create() %>%
  dag_node("Cust Signed","k",
           rhs = binomial(n,p),
           data = poolTimeGymDF$nSigned) %>%
  dag_node("Probability of Signing","p",
           rhs = beta(2,2),
           child = "k") %>%
  dag_node("Trial Size","n",
           data = poolTimeGymDF$nTrialCustomers,
           child = "k") %>%
  dag_plate("Yoga Stretch","x",
            nodeLabels = c("p"),
            data = poolTimeGymDF$stretchType,
            addDataNode = TRUE) %>%
  dag_plate("Observation","i",
            nodeLabels = c("x","k","n")) %>%
  dag_plate("Gym","j",
            nodeLabels = "p",
            data = poolTimeGymDF$gymID,
            addDataNode = TRUE)
graph %>% dag_render()
if (FALSE) {
# below requires Tensorflow installation
drawsDF = graph %>% dag_numpyro(mcmc=TRUE)
drawsDF %>% dagp_plot()
}