get_started.R
In greta: Simple and Scalable Statistical Modelling in R

## ----setup, include = FALSE---------------------------------------------------

knitr::opts_chunk$set(echo = TRUE,
                      eval = greta:::check_tf_version("message"),
                      cache = TRUE,
                      comment = NA,
                      progress = FALSE)

set.seed(2018-07-02)

library(greta)

if (!file.exists("figures"))
  dir.create("figures")

file.copy("../man/figures/plotlegend.png",
          "figures/plotlegend.png")

## ----install_greta, eval = FALSE----------------------------------------------
#  install.packages("greta")

## ----install_greta_github, eval = FALSE---------------------------------------
#  devtools::install_github("greta-dev/greta")

## ----load---------------------------------------------------------------------
library(greta)

## ----install_tensorflow, eval = FALSE-----------------------------------------
#  reticulate::install_miniconda()
#  reticulate::conda_create(
#          envname = "greta-env",
#          python_version = "3.7"
#        )
#  reticulate::conda_install(
#          envname = "greta-env",
#          packages = c(
#            "numpy==1.16.4",
#            "tensorflow-probability==0.7.0",
#            "tensorflow==1.14.0"
#          )
#        )

## ----install-deps-plain, eval = FALSE-----------------------------------------
#  reticulate::install_miniconda()
#  reticulate::conda_install(
#          packages = c(
#            "numpy==1.16.4",
#            "tensorflow-probability==0.7.0",
#            "tensorflow==1.14.0"
#          )
#        )

## ----install_diagrammer, eval = FALSE-----------------------------------------
#  install.packages("igraph")
#  install.packages("DiagrammeR")

## ----ones---------------------------------------------------------------------
(z <- ones(3, 3))

## ----ones_op------------------------------------------------------------------
(z2 <- z + z ^ 2)

## ----variable-----------------------------------------------------------------
(a <- variable(dim = c(3, 3)))
(a2 <- a + a ^ 2)

## ----first_model, eval = FALSE------------------------------------------------
#  library(greta)
#  
#  # data
#  x <- as_data(iris$Petal.Length)
#  y <- as_data(iris$Sepal.Length)
#  
#  # variables and priors
#  int <- normal(0, 1)
#  coef <- normal(0, 3)
#  sd <- student(3, 0, 1, truncation = c(0, Inf))
#  
#  # operations
#  mean <- int + coef * x
#  
#  # likelihood
#  distribution(y) <- normal(mean, sd)
#  
#  # defining the model
#  m <- model(int, coef, sd)
#  
#  # plotting
#  plot(m)
#  
#  # sampling
#  draws <- mcmc(m, n_samples = 1000)

## ----data---------------------------------------------------------------------
x <- as_data(iris$Petal.Length)
y <- as_data(iris$Sepal.Length)

## ----print_greta_array--------------------------------------------------------
as_data(iris[1:5, 1:4])

## ----logical_data-------------------------------------------------------------
(is_setosa <- iris$Species[c(1, 41, 81, 121)] == "setosa")
as_data(is_setosa)

## ----dim----------------------------------------------------------------------
dim(as_data(is_setosa))

## ----structures---------------------------------------------------------------
ones(1, 3)
zeros(2, 2)

## ----greta_array--------------------------------------------------------------
greta_array(pi, dim = c(2, 2))
greta_array(0:1, dim = c(3, 3))

## ----variables----------------------------------------------------------------
int <- normal(0, 1)
coef <- normal(0, 3)
sd <- student(3, 0, 1, truncation = c(0, Inf))

## ----int_variable-------------------------------------------------------------
(int <- variable())

## ----positive_variable--------------------------------------------------------
(sd <- variable(lower = 0))

## ----matrix_variable----------------------------------------------------------
variable(lower = 0, dim = c(2, 3))

## ----truncated1---------------------------------------------------------------
(z <- normal(0, 1, truncation = c(-1, 1)))

## ----linear_predictor---------------------------------------------------------
mean <- int + coef * x

## ----mean---------------------------------------------------------------------
dim(mean)
head(mean)

## ----extract------------------------------------------------------------------
mean[1:3]

## ----replace------------------------------------------------------------------
z <- zeros(4, 3)
z[, 1] <- normal(0, 1, dim = 4)
z

## ----drop---------------------------------------------------------------------
z <- matrix(1, nrow = 2, ncol = 2)
dim(z[, 1])
dim(z[, 1, drop = FALSE])

## ----drop_greta---------------------------------------------------------------
z_greta <- as_data(z)
dim(z_greta[, 1])

## ----function1----------------------------------------------------------------
atanh <- function (z)
  (log(1 + z) - log(1 - z)) / 2

atanh(z_greta)

## ----likelihood---------------------------------------------------------------
distribution(y) <- normal(mean, sd)

## ----hidden_model, echo = FALSE-----------------------------------------------
x <- as_data(iris$Petal.Length)
y <- as_data(iris$Sepal.Length)
int <- normal(0, 1)
coef <- normal(0, 3)
sd <- student(3, 0, 1, truncation = c(0, Inf))
mean <- int + coef * x
distribution(y) <- normal(mean, sd)

## ----define_model-------------------------------------------------------------
m <- model(int, coef, sd)

## ----plot, eval = FALSE-------------------------------------------------------
#  plot(m)

## ----plot_hidden, echo = FALSE------------------------------------------------
gr <- plot(m)
DiagrammeR::export_graph(attr(gr, "dgr_graph"),
                         file_name = "figures/full_graph.png",
                         file_type = "png",
                         width = 958 * 2,
                         height = 450 * 2)

## ----plot_coef, echo = FALSE--------------------------------------------------
coef <- normal(0, 3)
m_coef <- model(coef)
gr <- plot(m_coef)
DiagrammeR::export_graph(attr(gr, "dgr_graph"),
                         file_name = "figures/coef_graph.png",
                         file_type = "png",
                         width = 325 * 2,
                         height = 123 * 2)

## ----plot_likelihood, echo = FALSE--------------------------------------------
sd <- variable()
y <- as_data(iris$Sepal.Length)
mean <- ones(150)
distribution(y) <- normal(mean, sd)
m_likelihood <- model(sd)
gr <- plot(m_likelihood)
dgr <- attr(gr, "dgr_graph")
# make the mean look like an operation node
idx <- which(dgr$nodes_df$label == 'mean\n')
dgr$nodes_df$shape[idx] <- 'circle'
dgr$nodes_df$fillcolor[idx] <- 'lightgray'
dgr$nodes_df$color[idx] <- 'lightgray'
dgr$nodes_df$width[idx] <- 0.2
dgr$nodes_df$height[idx] <- 0.2
dgr$nodes_df <- dgr$nodes_df[c(3, 1, 2, 4), ]
DiagrammeR::export_graph(dgr,
                         file_name = "figures/likelihood_graph.png",
                         file_type = "png",
                         width = 325 * 2,
                         height = 105 * 2)


## ----mcmc, message = FALSE, results = "hide", progress = FALSE----------------
draws <- mcmc(m, n_samples = 1000)

## ----coda_summary-------------------------------------------------------------
summary(draws)

## ----mcmcvis, echo = TRUE, message = FALSE, out.width = c('400px', '400px'), fig.height=4, fig.width=5, fig.show='hold'----
library (bayesplot)
# set theme to avoid issues with fonts
ggplot2::theme_set(ggplot2::theme_bw())
mcmc_trace(draws, facet_args = list(nrow = 3, ncol = 1))
mcmc_intervals(draws)