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gretaExamples.R
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gretaExamples.R
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### Script contains five examples from
### https://greta-stats.org/articles/example_models.html
library(greta)
library(tidyverse)
library(causact)
### greta example 0
graph = dag_create() %>%
dag_node("Sepal Length","y",
data = iris$Petal.Length,
rhs = normal) %>%
dag_node("Exp Sepal Length (Obs.)","mean",
rhs = int + coef * x,
child = "y") %>%
dag_node("Exp Sepal Length (Pop.)","int",
rhs = normal(0,5),
child = "mean") %>%
dag_node("Sepal / Petal Slope","coef",
rhs = normal(0,3),
child = "mean") %>%
dag_node("Std Dev of Obs. Sepal Length","sd",
rhs = lognormal(0,3),
child = "y") %>%
dag_node("Petal Length","x",
child = "mean",
data = iris$Petal.Length) %>%
dag_plate("Observation","i",
nodeLabels = c("y","mean","x"))
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
graph %>% dag_greta(mcmc = FALSE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### greta example 1
graph = dag_create() %>%
dag_node("Rating","y",
data = attitude$rating,
rhs = normal(mu,sd)) %>%
dag_node("Exp. Rating","mu",
rhs = int + coef * x,
child = "y") %>%
dag_node("Average Population Rating", "int",
rhs = normal(0,10),
child = "mu") %>%
dag_node("Rating Change Per Complaint","coef",
rhs = normal(0,10),
child = "mu") %>%
dag_node("Complaints","x",
data = attitude$complaints,
child = "mu") %>%
dag_node("Std Dev of Ratings","sd",
rhs = cauchy(0, 3, truncation = c(0,Inf)),
child = "y") %>%
dag_plate("Observation", "i",
nodeLabels = c("y","mu","x"))
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### greta example #2
graph = dag_create() %>%
dag_node("Rating","y",
data = attitude$rating,
rhs = normal(mu,sd)) %>%
dag_node("Exp. Rating","mu",
rhs = int + design %*% coefs,
child = "y") %>%
dag_node("Std Dev of Ratings","sd",
rhs = cauchy(0,3, truncation = c(0,Inf)),
child = "y") %>%
dag_node("Predictor Coefficients","coefs",
rhs = normal(0,10),
child = "mu",
extract = FALSE) %>%
dag_node("Predictors","design",
data = attitude[, 2:7],
keepAsDF = TRUE,
child = "mu") %>%
dag_plate(descr = "Predictors",label = "j",
nodeLabels = c("coefs"),
data = names(attitude[, 2:7])) %>%
dag_plate(descr = "Observation", label = "i",
nodeLabels = c("mu","y"))
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### greta example #3
graph = dag_create() %>%
dag_node("Rating","y",
data = attitude$rating,
rhs = normal(mu,sd)) %>%
dag_node("Exp. Rating","mu",
rhs = int + design %*% coefs,
child = "y") %>%
dag_node("Intercept","int",
rhs = normal(0,10),
child = "mu") %>%
dag_node("Std Dev of Ratings","sd",
rhs = cauchy(0,3, truncation = c(0,Inf)),
child = "y") %>%
dag_node("Predictor Coefficients","coefs",
rhs = normal(0,10),
child = "mu",
extract = FALSE) %>%
dag_node("Predictors","design",
data = attitude[, 2:7],
keepAsDF = TRUE,
child = "mu") %>%
dag_plate(descr = "Predictors",label = "j",
nodeLabels = c("coefs"),
data = names(attitude[, 2:7])) %>%
dag_plate(descr = "Observation", label = "i",
nodeLabels = c("mu","y"))
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### greta example #4
df = as.data.frame(model.matrix(breaks ~ wool + tension, warpbreaks))
graph = dag_create() %>%
dag_node("# of Breaks Per Loom","y",
rhs = poisson(exp(eta)),
data = warpbreaks$breaks) %>%
dag_node("Linear Predictor","eta",
rhs = X %*% beta,
child = "y") %>%
dag_node(descr = "Wool + Tension Data", label = "X",
data = df,
keepAsDF = TRUE,
child = "eta") %>%
dag_node("Model Coef. Vector","beta",
rhs = c(int, coefs),
child = "eta") %>%
dag_node("Intercept","int",
rhs = variable(-Inf,Inf),
child = "beta") %>%
dag_node("Slope Coefficients","coefs",
rhs = normal(0,5),
child = "beta",
extract = FALSE) %>%
dag_plate("Predictors","j",
data = names(df[ ,-1]),
nodeLabels = "coefs") %>%
dag_plate("Observations","i",
nodeLabels = c("y","eta"))
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### greta example #5: Multiple Categorical Regression
designDF = as.data.frame(model.matrix(~ Species -1, iris))
speciesCoding = colnames(designDF) %>% head(-1)
graph = dag_create() %>%
dag_node("Species","y",
data = designDF,
keepAsDF = TRUE,
rhs = categorical(prob)) %>%
dag_node("Category Probabilities","prob",
child = "y",
rhs = imultilogit(eta)) %>%
dag_node("Linear Predictor","eta",
rhs = X %*% beta,
child = "prob") %>%
dag_node("Predictors","X",
data = cbind(1,iris[, 1:4]),
keepAsDF = TRUE,
child = "eta") %>%
dag_node("Predictor Coefficients","beta",
rhs = normal(0,5),
child = "eta",
extract = FALSE) %>%
dag_plate(descr = "Predictors",label = "J",
nodeLabels = "beta",
data = c("Intercept",colnames(iris[,1:4]))) %>%
dag_plate("Category Codes","K",
nodeLabels = "beta",
data = speciesCoding)
graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()
### using mixture example
# simulate a mixture of poisson random variables and try to recover the
# parameters with a Bayesian model
x <- c(rpois(800, 3),
rpois(200, 10))
# weights <- uniform(0, 1, dim = 2)
# rates <- normal(0, 10, truncation = c(0, Inf), dim = 2)
# distribution(x) <- mixture(poisson(rates[1]),
# poisson(rates[2]),
# weights = weights)
graph2 = dag_create() %>%
dag_node("Mixed Var","x",
rhs = mixture(alpha, beta, weights = weights),
data = x) %>%
dag_node("Alpha Var","alpha",
rhs = poisson(lambda_a),
child = "x") %>%
dag_node("Beta Var","beta",
rhs = poisson(lambda_b),
child = "x") %>%
dag_node("Weight Vars","weights",
rhs = uniform(0,1, dim =2),
child = "x") %>%
dag_node("Lambda A Rate","lambda_a",
rhs = uniform(1,5),
child = "alpha") %>%
dag_node("Lambda B Rate","lambda_b",
rhs = uniform(6,20),
child = "beta")
graph2 %>% dag_render()
graph2 %>% dag_greta(mcmc=FALSE)
drawsDF = graph2 %>% dag_greta()
drawsDF %>% dagp_plot()
#### use dirichlet instead
library(greta)
library(tidyverse)
library(causact)
## sample data - try to recover params
x <- c(rpois(800, 3),rpois(200, 10))
graph2 = dag_create() %>% ## create generative DAG
dag_node("Mixed Var","x",
rhs = mixture(alpha,beta,
weights = t(weights)),
data = x) %>%
dag_node("Count Var 1","alpha",
rhs = poisson(lambda1),
child = "x") %>%
dag_node("Count Var 2","beta",
rhs = poisson(lambda2),
child = "x") %>%
dag_node("Weight Vars","weights",
rhs = dirichlet(t(c(1,1))),
child = "x") %>%
dag_node("Exp Rate 1","lambda1",
rhs = uniform(1,5),
child = "alpha") %>%
dag_node("Exp Rate 2","lambda2",
rhs = uniform(6,20),
child = "beta")
graph2 %>% dag_render() ## visualize DAG
drawsDF = graph2 %>% dag_greta() ## get posterior
drawsDF %>% dagp_plot() ## visualize posterior