causact: Fast, Easy, and Visual Bayesian Inference

Description

Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.

Author(s)

Maintainer: Adam Fleischhacker ajf@udel.edu [copyright holder]

Other contributors:

• Daniela Dapena [contributor]

• Rose Nguyen [contributor]

• Jared Sharpe [contributor]

See Also

Useful links:

• https://github.com/flyaflya/causact

• https://www.causact.com/

• Report bugs at https://github.com/flyaflya/causact/issues

The magrittr pipe

Description

causact uses the pipe function, ⁠\%>\%⁠ to turn function composition into a series of imperative statements.

Value

Pipe a value forward into a function- or call expression and return the function on the rhs with the lhs used as the first argument.

Group together latent parameters by prior distribution.

Description

Add a column to a tidy dataframe of draws that groups parameters by their prior distribution. All parameters with the same prior distribution receive the same index.

Usage

addPriorGroups(drawsDF)

Arguments

Value

a tidy dataframe of posterior draws. Useful for passing to dagp_plot() or for creating plots using ggplot().

Dataframe of 12,145 observations of baseball games in 2010 - 2014

Description

Dataframe of 12,145 observations of baseball games in 2010 - 2014

Usage

baseballData

Format

A data frame with 12145 rows and 5 variables:

Date

date game was played

Home

abbreviation for home team (i.e. stadium where game played)

Visitor

abbreviation for visiting team

HomeScore

Runs scored by the home team

VisitorScore

Runs scored by the visiting team

Dataframe where each row represents data about one of the 26 mile markers (fake) from mile 0 to mile 2.5 along the Ocean City, MD beach/boardwalk.

Description

Dataframe where each row represents data about one of the 26 mile markers (fake) from mile 0 to mile 2.5 along the Ocean City, MD beach/boardwalk.

Usage

beachLocDF

Format

A data frame with 26 rows and 3 variables:

mileMarker

a number representing a location on the Ocean City beach/boardwalk.

beachgoerProb

The probability of any Ocean City, MD beachgoer (during the hot swimming days) exiting the beach at that mile marker.

expenseEst

The estimated annual expenses of running a business at that location on the beach. It is assumed a large portion of the expense is based on commercial rental rates at that location. More populated locations tend to have higher expenses.

Dataframe of 1000 (fake) observations of whether certain car buyers were willing to get information on a credit card speciailizing in rewards for adventure travellers.

Description

Dataframe of 1000 (fake) observations of whether certain car buyers were willing to get information on a credit card speciailizing in rewards for adventure travellers.

Usage

carModelDF

Format

A data frame with 1000 rows and 3 variables:

customerID

a unique id of a potential credit card customer. They just bought a car and are asked if they want information on the credit card.

carModel

The model of car purchased.

getCard

Whether the customer expressed interest in hearing more about the card.

Check if 'r-causact' Conda environment exists

Description

Check if 'r-causact' Conda environment exists

Usage

check_r_causact_env()

Data from behavior trials in a captive group of chimpanzees, housed in Lousiana. From Silk et al. 2005. Nature 437:1357-1359 and further popularized in McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press, 2020. Experiment

Description

Data from behavior trials in a captive group of chimpanzees, housed in Lousiana. From Silk et al. 2005. Nature 437:1357-1359 and further popularized in McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press, 2020. Experiment

Usage

chimpanzeesDF

Format

A data frame with 504 rows and 9 variables:

actor

name of actor

recipient

name of recipient (NA for partner absent condition)

condition

partner absent (0), partner present (1)

block

block of trials (each actor x each recipient 1 time)

trial

trial number (by chimp = ordinal sequence of trials for each chimp, ranges from 1-72; partner present trials were interspersed with partner absent trials)

prosoc_left

prosocial_left : 1 if prosocial (1/1) option was on left

chose_prosoc

choice chimp made (0 = 1/0 option, 1 = 1/1 option)

pulled_left

which side did chimp pull (1 = left, 0 = right)

treatment

narrative description combining condition and prosoc_left that describes the side the prosical food option was on and whether a partner was present

Source

Silk et al. 2005. Nature 437:1357-1359..

Dataframe of 174 observations where information on the human developmet index (HDI) and the corruption perceptions index (CPI) both exist. Each observation is a country.

Description

Dataframe of 174 observations where information on the human developmet index (HDI) and the corruption perceptions index (CPI) both exist. Each observation is a country.

Usage

corruptDF

Format

A data frame with 174 rows and 7 variables:

country

country name

region

region name as given with CPI rating

countryCode

three letter abbreviation for country

regionCode

four letter or less abbreviation for country

population

2017 country population

CPI2017

The Corruption Perceptions Index score for 2017: A country/territory’s score indicates the perceived level of public sector corruption on a scale of 0-100, where 0 means that a country is perceived as highly corrupt and a 100 means that a country is perceived as very clean.

HDI2017

The human development index score for 2017: the Human Development Index (HDI) is a measure of achievement in the basic dimensions of human development across countries. It is an index made from a simple unweighted average of a nation’s longevity, education and income and is widely accepted in development discourse.

Source

https://www.transparency.org/en/cpi/2017 CPI data available from https://www.transparency.org/en/cpi/2017. Accessed Feb 24, 2024. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.

https://hdr.undp.org/data-center/human-development-index#/indicies/HDI HDA data accessed on Oct 1, 2018.

https://data.worldbank.org/ Population data accessed on Oct 1, 2018.

Create a graph object for drawing a DAG.

Description

Generates a causact_graph graph object that is set-up for drawing DAG graphs.

Usage

dag_create()

Value

a list object of class causact_graph consisting of 6 dataframes. Each data frame is responsible for storing information about nodes, edges, plates, and the relationships among them.

Examples

# With `dag_create()` we can create an empty graph and
# add in nodes (`dag_node()`), add edges (`dag_edge`), and
# view the graph with `dag_render()`.
dag_create()

Convert graph to Diagrammer object for visualization

Description

Convert a causact_graph to a DiagrammeR object for visualization.

Usage

dag_diagrammer(
  graph,
  wrapWidth = 24,
  shortLabel = FALSE,
  fillColor = "aliceblue",
  fillColorObs = "cadetblue"
)

Arguments

Value

a graph object of class dgr_graph. Useful for further customizing graph displays using the DiagrammeR package.

Examples

library("DiagrammeR")
dag_create() %>%
dag_node("Get Card","y",
         rhs = bernoulli(theta),
         data = carModelDF$getCard) %>%
  dag_diagrammer() %>%
  render_graph(title = "DiagrammeR Version of causact_graph")

Add dimension information to causact_graph

Description

Internal function that is used as part of rendering graph or running greta.

Usage

dag_dim(graph)

Arguments

Value

a graph object of class causact_graph with populated dimension information.

Add edge (or edges) between nodes

Description

With a graph object of class causact_graph created from dag_create, add an edge between nodes in the graph. Vector recycling is used for all arguments.

Usage

dag_edge(graph, from, to, type = as.character(NA))

Arguments

Value

a graph object of class dgr_graph with additional edges created by this function.

Examples

# Create a graph with 2 connected nodes
dag_create() %>%
  dag_node("X") %>%
  dag_node("Y") %>%
  dag_edge(from = "X", to = "Y") %>%
  dag_render(shortLabel = TRUE)

Generate a representative sample of the posterior distribution

Description

This function is currently defunct. It has been superseded by dag_numpyro() because of tricky and sometimes unresolvable installation issues related to the greta package's use of tensorflow. If the greta package resolves those issues, this function may return, but please use dag_numpyro() as a direct replacement.

Generate a representative sample of the posterior distribution. 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.

Usage

dag_greta(graph, mcmc = TRUE, meaningfulLabels = TRUE, ...)

Arguments

Value

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:

1. draws: An mcmc.list object containing raw output from the HMCMC sampler used by greta.

2. 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

3. tidyDrawsDF: A long data frame with each draw represented on one line. This data frame is useful for plotting posterior distributions.

Examples

## Not run: 
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)
## default functionality returns a data frame
# below requires Tensorflow installation
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()

## End(Not run)

Merge two non-intersecting causact_graph objects

Description

Generates a single causact_graph graph object that combines multiple graphs.

Usage

dag_merge(graph1, ...)

Arguments

Value

a merged graph object of class causact_graph. Useful for creating simple graphs and then merging them into a more complex structure.

Examples

# With `dag_merge()` we
# reset the node ID's and all other item ID's,
# bind together the rows of all given graphs, and
# add in nodes and edges later
# with other functions
# to connect the graph.
#
# THE GRAPHS TO BE MERGED MUST BE DISJOINT
# THERE CAN BE NO IDENTICAL NODES OR PLATES
# IN EACH GRAPH TO BE MERGED, AT THIS TIME


g1 = dag_create() %>%
 dag_node("Demand for A","dA",
           rhs = normal(15,4)) %>%
  dag_node("Supply for A","sA",
           rhs = uniform(0,100)) %>%
  dag_node("Profit for A","pA",
           rhs = min(sA,dA)) %>%
  dag_edge(from = c("dA","sA"),to = c("pA"))


g2 <- dag_create() %>%
  dag_node("Demand for B","dB",
           rhs = normal(20,8)) %>%
  dag_node("Supply for B","sB",
           rhs = uniform(0,100)) %>%
  dag_node("Profit for B","pB",
           rhs = min(sB,dB)) %>%
  dag_edge(from = c("dB","sB"),to = c("pB"))

g1 %>% dag_merge(g2) %>%
  dag_node("Total Profit", "TP",
           rhs = sum(pA,pB)) %>%
  dag_edge(from=c("pA","pB"), to=c("TP")) %>%
  dag_render()

Add a node to an existing causact_graph object

Description

Add a node to an existing causact_graph object. The graph object should be of class causact_graph and created using dag_create().

Usage

dag_node(
  graph,
  descr = as.character(NA),
  label = as.character(NA),
  rhs = NA,
  child = as.character(NA),
  data = NULL,
  obs = FALSE,
  keepAsDF = FALSE,
  extract = as.logical(NA),
  dec = FALSE,
  det = FALSE
)

Arguments

Value

a graph object of class causact_graph with an additional node(s).

Examples

# Create an empty graph and add 2 nodes by using
# the `dag_node()` function twice
graph2 = 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")
graph2 %>% dag_render()


# The Eight Schools Example from Gelman et al.:

schools_dat <- data.frame(y = c(28,  8, -3,  7, -1,  1, 18, 12),
sigma = c(15, 10, 16, 11,  9, 11, 10, 18), schoolName = paste0("School",1:8))

graph = dag_create() %>%
  dag_node("Treatment Effect","y",
           rhs = normal(theta, sigma),
           data = schools_dat$y) %>%
  dag_node("Std Error of Effect Estimates","sigma",
           data = schools_dat$sigma,
           child = "y") %>%
  dag_node("Exp. Treatment Effect","theta",
           child = "y",
           rhs = avgEffect + schoolEffect) %>%
  dag_node("Pop Treatment Effect","avgEffect",
           child = "theta",
           rhs = normal(0,30)) %>%
  dag_node("School Level Effects","schoolEffect",
           rhs = normal(0,30),
           child = "theta") %>%
  dag_plate("Observation","i",nodeLabels = c("sigma","y","theta")) %>%
  dag_plate("School Name","school",
            nodeLabels = "schoolEffect",
            data = schools_dat$schoolName,
            addDataNode = TRUE)

graph %>% dag_render()
## Not run: 
# below requires Tensorflow installation
drawsDF = graph %>% dag_numpyro(mcmc=TRUE)
tidyDrawsDF %>% dagp_plot()

## End(Not run)

Generate a representative sample of the posterior distribution

Description

Generate a representative sample of the posterior distribution. 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.

Usage

dag_numpyro(
  graph,
  mcmc = TRUE,
  num_warmup = 1000,
  num_samples = 4000,
  seed = 1234567
)

Arguments

Value

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_numpyro returns a character string of code that would help the user generate the posterior distribution; useful for debugging.

Examples

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()
numpyroCode = graph %>% dag_numpyro(mcmc=FALSE)
## Not run: 
## default functionality returns a data frame
# below requires numpyro installation
drawsDF = graph %>% dag_numpyro()
drawsDF %>% dagp_plot()

## End(Not run)

Create a plate representation for repeated nodes.

Description

Given a graph object of class causact_graph, create collections of nodes that should be repeated i.e. represent multiple instances of a random variable, random vector, or random matrix. When nodes are on more than one plate, graph rendering will treat each unique combination of plates as separate plates.

Usage

dag_plate(
  graph,
  descr,
  label,
  nodeLabels,
  data = as.character(NA),
  addDataNode = FALSE,
  rhs = NA
)

Arguments

Value

an expansion of the input causact_graph object with an added plate representing the repetition of nodeLabels for each unique value of data.

Examples

# single plate example
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()

# 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()

Render the graph as an htmlwidget

Description

Using a causact_graph object, render the graph in the RStudio Viewer.

Usage

dag_render(
  graph,
  shortLabel = FALSE,
  wrapWidth = 24,
  width = NULL,
  height = NULL,
  fillColor = "aliceblue",
  fillColorObs = "cadetblue"
)

Arguments

Value

Returns an object of class grViz and htmlwidget that is also rendered in the RStudio viewer for interactive buidling of graphical models.

Examples

# Render a simple graph
dag_create() %>%
  dag_node("Demand","X") %>%
  dag_node("Price","Y", child = "X") %>%
  dag_render()

# Hide the mathematical details of a graph
dag_create() %>%
  dag_node("Demand","X") %>%
  dag_node("Price","Y", child = "X") %>%
  dag_render(shortLabel = TRUE)

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)

Arguments

Value

a credible interval plot of all latent posterior distribution parameters.

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'
## Not run: 
# 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()

## End(Not run)

# 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()
## Not run: 
# below requires Tensorflow installation
drawsDF = graph %>% dag_numpyro(mcmc=TRUE)
drawsDF %>% dagp_plot()

## End(Not run)

117,790 line items associated with 23,339 shipments.

Description

A dataset containing the line items, mostly parts, asssociated with 23,339 shipments from a US-based warehouse.

Usage

delivDF

Format

A data frame (tibble) with 117,790 rows and 5 variables:

shipID

unique ID for each shipment

plannedShipDate

shipment date promised to customer

actualShipDate

date the shipment was actually shipped

partID

unique part identifier

quantity

quantity of partID in shipment

Source

Adam Fleischhacker

probability distributions

Description

These functions can be used to define random variables in a causact model.

Usage

uniform(min, max, dim = NULL)

normal(mean, sd, dim = NULL, truncation = c(-Inf, Inf))

lognormal(meanlog, sdlog, dim = NULL)

bernoulli(prob, dim = NULL)

binomial(size, prob, dim = NULL)

negative_binomial(size, prob, dim = NULL)

poisson(lambda, dim = NULL)

gamma(shape, rate, dim = NULL)

inverse_gamma(alpha, beta, dim = NULL, truncation = c(0, Inf))

weibull(shape, scale, dim = NULL)

exponential(rate, dim = NULL)

pareto(a, b, dim = NULL)

student(df, mu, sigma, dim = NULL, truncation = c(-Inf, Inf))

laplace(mu, sigma, dim = NULL, truncation = c(-Inf, Inf))

beta(shape1, shape2, dim = NULL)

cauchy(location, scale, dim = NULL, truncation = c(-Inf, Inf))

chi_squared(df, dim = NULL)

logistic(location, scale, dim = NULL, truncation = c(-Inf, Inf))

multivariate_normal(mean, Sigma, dimension = NULL)

lkj_correlation(eta, dimension = 2)

multinomial(size, prob, dimension = NULL)

categorical(prob, dimension = NULL)

dirichlet(alpha, dimension = NULL)

Arguments

Details

The discrete probability distributions (bernoulli, binomial, negative_binomial, poisson, multinomial, categorical) can be used when they have fixed values, but not as unknown variables.

For univariate distributions dim gives the dimensions of the array to create. Each element will be (independently) distributed according to the distribution. dim can also be left at its default of NULL, in which case the dimension will be detected from the dimensions of the parameters (provided they are compatible with one another).

For multivariate distributions (multivariate_normal(), multinomial(), categorical(), and dirichlet() each row of the output and parameters corresponds to an independent realisation. If a single realisation or parameter value is specified, it must therefore be a row vector (see example). n_realisations gives the number of rows/realisations, and dimension gives the dimension of the distribution. I.e. a bivariate normal distribution would be produced with multivariate_normal(..., dimension = 2). The dimension can usually be detected from the parameters.

multinomial() does not check that observed values sum to size, and categorical() does not check that only one of the observed entries is 1. It's the user's responsibility to check their data matches the distribution!

Wherever possible, the parameterizations and argument names of causact distributions match commonly used R functions for distributions, such as those in the stats or extraDistr packages. The following table states the distribution function to which causact's implementation corresponds (this code largely borrowed from the greta package):

Examples

## Not run: 

# a uniform parameter constrained to be between 0 and 1
phi <- uniform(min = 0, max = 1)

# a length-three variable, with each element following a standard normal
# distribution
alpha <- normal(0, 1, dim = 3)

# a length-three variable of lognormals
sigma <- lognormal(0, 3, dim = 3)

# a hierarchical uniform, constrained between alpha and alpha + sigma,
eta <- alpha + uniform(0, 1, dim = 3) * sigma

# a hierarchical distribution
mu <- normal(0, 1)
sigma <- lognormal(0, 1)
theta <- normal(mu, sigma)

# a vector of 3 variables drawn from the same hierarchical distribution
thetas <- normal(mu, sigma, dim = 3)

# a matrix of 12 variables drawn from the same hierarchical distribution
thetas <- normal(mu, sigma, dim = c(3, 4))

# a multivariate normal variable, with correlation between two elements
# note that the parameter must be a row vector
Sig <- diag(4)
Sig[3, 4] <- Sig[4, 3] <- 0.6
theta <- multivariate_normal(t(rep(mu, 4)), Sig)

# 10 independent replicates of that
theta <- multivariate_normal(t(rep(mu, 4)), Sig, n_realisations = 10)

# 10 multivariate normal replicates, each with a different mean vector,
# but the same covariance matrix
means <- matrix(rnorm(40), 10, 4)
theta <- multivariate_normal(means, Sig, n_realisations = 10)
dim(theta)

# a Wishart variable with the same covariance parameter
theta <- wishart(df = 5, Sigma = Sig)

## End(Not run)

Dataframe of 44 observations of free crossfit classes data Each observation indicates how many students that participated in the free month of crossfit signed up for the monthly membership afterwards

Description

Dataframe of 44 observations of free crossfit classes data Each observation indicates how many students that participated in the free month of crossfit signed up for the monthly membership afterwards

Usage

gymDF

Format

A data frame with 44 rows and 5 variables:

gymID

unique gym identifier

nTrialCustomers

number of unique customers taking free trial classes

nSigned

number of customers from trial that sign up for membership

yogaStretch

whether trial classes included a yoga type stretch

timePeriod

month number, since inception of company, for which trial period was offered

Dataframe of 1,460 observations of home sales in Ames, Iowa. Known as The Ames Housing dataset, it was compiled by Dean De Cock for use in data science education. Each observation is a home sale. See houseDFDescr for more info.

Description

Dataframe of 1,460 observations of home sales in Ames, Iowa. Known as The Ames Housing dataset, it was compiled by Dean De Cock for use in data science education. Each observation is a home sale. See houseDFDescr for more info.

Usage

houseDF

Format

A data frame with 1,460 rows and 37 variables:

SalePrice

the property's sale price in dollars. This is the target variable

MSSubClass

The building class

MSZoning

The general zoning classification

LotFrontage

Linear feet of street connected to property

LotArea

Lot size in square feet

Street

Type of road access

LotShape

General shape of property

Utilities

Type of utilities available

LotConfig

Lot configuration

Neighborhood

Physical locations within Ames city limits

BldgType

Type of dwelling

HouseStyle

Style of dwelling

OverallQual

Overall material and finish quality

OverallCond

Overall condition rating

YearBuilt

Original construction date

YearRemodAdd

Remodel date

ExterQual

Exterior material quality

ExterCond

Present condition of the material on the exterior

BsmtQual

Height of the basement

BsmtCond

General condition of the basement

BsmtExposure

Walkout or garden level basement walls

BsmtUnfSF

Unfinished square feet of basement area

TotalBsmtSF

Total square feet of basement area

1stFlrSF

First Floor square feet

2ndFlrSF

Second floor square feet

LowQualFinSF

Low quality finished square feet (all floors)

GrLivArea

Above grade (ground) living area square feet

FullBath

Full bathrooms above grade

HalfBath

Half baths above grade

BedroomAbvGr

Number of bedrooms above basement level

TotRmsAbvGrd

Total rooms above grade (does not include bathrooms)

Functional

Home functionality rating

GarageCars

Size of garage in car capacity

MoSold

Month Sold

YrSold

Year Sold

SaleType

Type of sale

SaleCondition

Condition of sale

Source

Accessed Jan 22, 2019. Kaggle dataset on "House Prices: Advanced Regression Techniques".

Dataframe of 523 descriptions of data values from "The Ames Housing dataset", compiled by Dean De Cock for use in data science education. Each observation is a possible value from a variable in the houseDF dataset.

Description

Dataframe of 523 descriptions of data values from "The Ames Housing dataset", compiled by Dean De Cock for use in data science education. Each observation is a possible value from a variable in the houseDF dataset.

Usage

houseDFDescr

Format

A data frame with 260 rows and 2 variables:

varName

the name and description of a variable stored in the houseDF dataset

varValueDescr

The value and accompanying interpretation for values in the houseDF dataset

Source

Accessed Jan 22, 2019. Kaggle dataset on "House Prices: Advanced Regression Techniques".

Install causact's python dependencies like numpyro, arviz, and xarray.

Description

install_causact_deps() installs python, the numpyro and arviz packages, and their direct dependencies.

Usage

install_causact_deps()

Details

You may be prompted to download and install miniconda if reticulate did not find a non-system installation of python. Miniconda is the only supported installation method for users, as it ensures that the R python installation is isolated from other python installations. All python packages will by default be installed into a self-contained conda or venv environment named "r-causact". Note that "conda" is the only supported method for install.

If you initially declined the miniconda installation prompt, you can later manually install miniconda by running reticulate::install_miniconda().

If you manually configure a python environment with the required dependencies, you can tell R to use it by pointing reticulate at it, commonly by setting an environment variable:

Sys.setenv("RETICULATE_PYTHON" = "~/path/to/python-env/bin/python")

Store meaningful parameter labels

Description

Store meaningful parameter labels as as part of running dag_numpyro(). When numpyro creates posterior distributions for multi-dimensional parameters, it creates an often meaningless number system for the parameter (e.g. beta[1,1], beta[2,1], etc.). Since parameter dimensionality is often determined by a factor, this function creates labels from the factors unqiue values. replaceLabels() applies the text labels stored using this function to the numpyro output. The meaningful parameter names are stored in an environment, cacheEnv.

Usage

meaningfulLabels(graph)

Arguments

Value

a data frame meaningfulLabels stored in an environment named cacheEnv that contains a lookup table between greta labels and meaningful labels.

Product line and product category assignments for 12,026 partID's.

Description

A dataset containing partID attributes.

Usage

prodLineDF

Format

A data frame (tibble) with 117,790 rows and 5 variables:

partID

unique part identifier

productLine

a product line associated with the partID

prodCategory

a product category associated with the partID

Source

Adam Fleischhacker

The Bernoulli Distribution

Description

#'

Density, distribution function, quantile function and random generation for the benoulli distribution with parameter prob.

Usage

rbern(n, prob)

Arguments

Value

A vector of 0's and 1's representing failure and success.

Examples

#Return a random result of a Bernoulli trial given `prob`.
rbern(n =1, prob = 0.5)

This example, often referred to as 8-schools, was popularized by its inclusion in Bayesian Data Analysis (Gelman, Carlin, & Rubin 1997).

Description

This example, often referred to as 8-schools, was popularized by its inclusion in Bayesian Data Analysis (Gelman, Carlin, & Rubin 1997).

Usage

schoolsDF

Format

A data frame with 8 rows and 3 variables:

y

estimated treatment effect at a particular school

sigma

standard error of the treamtment effect estimate

schoolName

an identifier for the school represented by this row

Set DiagrammeR defaults for graphical models

Description

setDirectedGraph returns a graph with good defaults.

Usage

setDirectedGraphTheme(
  dgrGraph,
  fillColor = "aliceblue",
  fillColorObs = "cadetblue"
)

Arguments

Value

An updated version of dgrGraph with good defaults for graphical models.

return a dgrGraph object with the color and shape defaults used by the causact package.

Examples

library(DiagrammeR)
create_graph() %>% add_node() %>% render_graph()  # default DiagrammeR aesthetics
create_graph() %>% add_node() %>% setDirectedGraphTheme() %>% render_graph() ## causact aesthetics

Dataframe of 55,167 observations of the number of tickets written by NYC precincts each day Data modified from https://github.com/stan-dev/stancon_talks/tree/master/2018/Contributed-Talks/01_auerbach which originally sourced data from https://opendata.cityofnewyork.us/

Description

Dataframe of 55,167 observations of the number of tickets written by NYC precincts each day Data modified from https://github.com/stan-dev/stancon_talks/tree/master/2018/Contributed-Talks/01_auerbach which originally sourced data from https://opendata.cityofnewyork.us/

Usage

ticketsDF

Format

A data frame with 55167 rows and 4 variables:

precinct

unique precinct identifier representing precinct of issuing officer

date

the date on which ticket violations occurred

month_year

the month_year extracted from date column

daily_tickets

Number of tickets issued out of precinct on this day

A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days. Think of this representative sample as coming from either a prior or posterior distribution. An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at https://www.causact.com/.

Description

A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days. Think of this representative sample as coming from either a prior or posterior distribution. An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at https://www.causact.com/.

Usage

totalBeachgoersRepSample

Format

A 4,000 element vector.

totalBeachgoersRepSample

a draw from a representative sample of total beachgoers to Ocean City, MD.