Package {rdss}

Title: Companion Datasets and Functions for Research Design in the Social Sciences
Version: 1.0.16
Description: Helper functions to accompany the Blair, Coppock, and Humphreys (2023) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" https://book.declaredesign.org. 'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book.
Imports: dplyr, rlang (≥ 1.0.0), generics, ggplot2, tibble, tidyr, dataverse, readr, broom, purrr, estimatr, randomizr
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: testthat (≥ 3.0.0), rdrobust, DIDmultiplegt, broom.mixed, marginaleffects, grf, CausalQueries, metafor, cjoint, lme4, rstanarm, spdep, DeclareDesign, curl, polars
Depends: R (≥ 4.1.0)
Config/testthat/edition: 3
URL: https://declaredesign.org/r/rdss/, https://github.com/DeclareDesign/rdss
BugReports: https://github.com/DeclareDesign/rdss/issues
Additional_repositories: https://rpolars.r-universe.dev
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-10-26 18:34:05 UTC; alexandercoppock
Author: Graeme Blair ORCID iD [aut], Alexander Coppock ORCID iD [aut, cre], Macartan Humphreys ORCID iD [aut]
Maintainer: Alexander Coppock <acoppock@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-26 19:50:02 UTC

rdss package

Description

Companion datasets and functions for the book Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign by Graeme Blair, Alexander Coppock, and Macartan Humphreys (Princeton University Press, 2023), free to read at https://book.declaredesign.org.

Details

Most functions fill one step of a design declaration in the book, and each help page links to the section that uses it. The Get started article on the package website, https://declaredesign.org/r/rdss/articles/rdss.html, lists them chapter by chapter.

Author(s)

Maintainer: Alexander Coppock acoppock@gmail.com (ORCID)

Authors:

See Also

Useful links:

Add parentheses around standard error estimates

Description

Add parentheses around standard error estimates

Usage

add_parens(x, digits = 3)

Arguments

x

A numeric vector.

digits

The number of decimal places. Defaults to 3.

Value

A character vector, e.g. "(0.120)".

Examples


std.error <- c(0.12, 0.001, 1.2)
add_parens(std.error)

Find the covariate that best predicts treatment effect heterogeneity

Description

An inquiry handler. For each covariate, regresses the true unit-level effect tau on the covariate cut into intervals, and reports which covariate gives the highest R-squared. Pair it with causal_forest_handler(), whose var_imp column is the estimate of the same quantity.

Usage

best_predictor(data, covariate_names, cuts = 20)

Arguments

data

A data frame with a column tau holding each unit's treatment effect, and the covariates named in covariate_names.

covariate_names

A character vector of covariates to assess.

cuts

Either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which each covariate is to be cut. Defaults to 20.

Details

See https://book.declaredesign.org/complex-designs.html#discovery-using-causal-forests

Value

A data frame with one row: inquiry ("best_predictor") and estimand, the position in covariate_names of the best predictor.

Examples


set.seed(343)
dat <- data.frame(A = rnorm(500), B = rnorm(500))
dat$tau <- 1 + dat$A

# The effect varies with A, so A (position 1) is the best predictor
best_predictor(dat, covariate_names = c("A", "B"), cuts = 4)

Replication data for Bonilla and Tillery (2020), American Political Science Review (obtained from Dataverse 10.7910/DVN/IUZDQI)

Description

One row per survey respondent. Z is the randomly assigned message (a factor with four levels) and blm_support the outcome; the remaining columns are respondent characteristics.

Usage

bonilla_tillery

Format

A data frame with 849 rows and 10 columns: female, lgbtq, age, religiosity, income, college, blm_familiarity, linked_fate, Z, and blm_support.

Source

Bonilla, Tabitha, and Alvin B. Tillery. 2020. "Which Identity Frames Boost Support for and Mobilization in the #BlackLivesMatter Movement? An Experimental Test." American Political Science Review 114(4): 947-962. doi:10.1017/S0003055420000544. Data from Dataverse, doi:10.7910/DVN/IUZDQI.

Examples


estimatr::lm_robust(blm_support ~ Z, data = bonilla_tillery)

Tidy helper function for causal_forest function

Description

Splits the data at random into a training and a test set, fits grf::causal_forest() on the training set, and predicts each unit's treatment effect. The data must contain the outcome as Y and the treatment as Z.

Usage

causal_forest_handler(data, covariate_names, share_train = 0.5, ...)

Arguments

data

A data frame with columns Y, Z, and the covariates named in covariate_names.

covariate_names

A character vector naming the covariate columns the forest may split on.

share_train

The share of units assigned to the training set. Defaults to 0.5.

...

Further arguments passed to grf::causal_forest().

Details

See https://book.declaredesign.org/complex-designs.html#discovery-using-causal-forests

Value

data with four columns added: pred (the predicted treatment effect), var_imp (the position in covariate_names of the most important covariate), and the logical indicators train and test.

Examples



library(DeclareDesign)
library(ggplot2)

dat <- fabricate(
   N = 1000,
   A = rnorm(N),
   B = rnorm(N),
   Z = complete_rs(N),
   Y = A*Z + rnorm(N))

# note: remove num.threads = 1 to use more processors
estimates <- causal_forest_handler(data = dat, covariate_names = c("A", "B"), num.threads = 1)

ggplot(data = estimates, aes(A, pred)) + geom_point()

Replication data for Clingingsmith, Khwaja, and Kremer (2009), Quarterly Journal of Economics

Description

From "Estimating the Impact of the Hajj: Religion and Tolerance in Islam's Global Gathering," The Quarterly Journal of Economics 124(3): 1133-1170. One row per applicant to the Hajj visa lottery. success is 1 for lottery winners; the views_ columns are attitudes toward each group, and views is their sum.

Usage

clingingsmith_etal

Format

A data frame with 958 rows and 8 columns: success, views_saudi, views_indonesian, views_turkish, views_african, views_chinese, views_european, and views.

Source

Clingingsmith, David, Asim Ijaz Khwaja, and Michael Kremer. 2009. "Estimating the Impact of the Hajj: Religion and Tolerance in Islam's Global Gathering." The Quarterly Journal of Economics 124(3): 1133-1170. doi:10.1162/qjec.2009.124.3.1133.

Examples


estimatr::difference_in_means(views ~ success, data = clingingsmith_etal)

Conjoint experiment assignment handler: conducts complete random assignment of all attribute levels

Description

For each attribute, assigns every row one of that attribute's levels by complete random assignment, so the levels appear in as equal numbers as possible.

Usage

conjoint_assignment(data, levels_list)

Arguments

data

A data frame with one row per profile.

levels_list

A named list with one element per attribute, each a vector of that attribute's levels, e.g. list(gender = c("woman", "man"), age = c("young", "old")).

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Value

data with one column per attribute, named as in levels_list.

Examples


set.seed(343)
levels_list <- list(gender = c("woman", "man"), age = c("young", "old"))

# 25 subjects, each choosing between two profiles in each of four tasks
profiles <- expand.grid(profile = 1:2, task = 1:4, subject = 1:25)

head(conjoint_assignment(profiles, levels_list))

Conjoint experiment inquiries handler

Description

Computes the true average marginal component effect (AMCE) of every attribute level relative to that attribute's first level. Each AMCE is the average change in the probability that the second profile in a task is chosen when the attribute moves from its first level to the given level, with the other attributes drawn at random by conjoint_assignment().

Usage

conjoint_inquiries(data, levels_list, utility_fn)

Arguments

data

A data frame with one row per profile and columns subject, task, and profile (1 or 2).

levels_list

A named list of attribute levels, as in conjoint_assignment(). The first level of each attribute is the reference.

utility_fn

A function that adds the utility column U, as in conjoint_measurement().

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Value

A data frame with one row per non-reference level: attribute, reference, level, inquiry, and estimand.

Examples


set.seed(343)
levels_list <- list(gender = c("woman", "man"), age = c("young", "old"))

# 25 subjects, each choosing between two profiles in each of four tasks
profiles <- expand.grid(profile = 1:2, task = 1:4, subject = 1:25)

utility_fn <- function(data) {
  data$U <- 0.5 * (data$gender == "woman") - 0.2 * (data$age == "old") +
    rnorm(nrow(data))
  data
}

conjoint_inquiries(profiles, levels_list, utility_fn)

Conjoint experiment measurement handler: records which profile in each task is chosen

Description

Applies utility_fn and, within each subject and task, compares the two profiles and sets choice to 1 for the one with the higher utility U (the second profile on a tie).

Usage

conjoint_measurement(data, utility_fn)

Arguments

data

A data frame with one row per profile and columns subject, task, and profile. Each task must hold exactly two profiles.

utility_fn

A function that takes data and returns it with a column U added, the utility the subject gets from each profile.

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Value

data with U and choice added.

Examples


set.seed(343)
levels_list <- list(gender = c("woman", "man"), age = c("young", "old"))

# 25 subjects, each choosing between two profiles in each of four tasks
profiles <- expand.grid(profile = 1:2, task = 1:4, subject = 1:25)
profiles <- conjoint_assignment(profiles, levels_list)

utility_fn <- function(data) {
  data$U <- 0.5 * (data$gender == "woman") - 0.2 * (data$age == "old") +
    rnorm(nrow(data))
  data
}

head(conjoint_measurement(profiles, utility_fn))

Color palettes used in the book Research Design in the Social Sciences

Description

Based on Karthik Ram's wesanderson package (https://github.com/karthik/wesanderson)

Usage

dd_palette(name, n)

Arguments

name

The palette name, as a string. One of the names listed below.

n

The number of colors to return, from the start of the palette. Defaults to all of them.

Details

Palettes:

Single colors: dd_dark_blue ("#3564ED"), dd_light_blue ("#72B4F3"), dd_orange ("#F38672"), dd_purple ("#7E43B6"), dd_gray (gray(0.2)), dd_pink ("#C6227F"), dd_light_gray (gray(0.8)), and the translucent dd_dark_blue_alpha and dd_light_blue_alpha.

Value

A character vector of hex color codes.

Examples

dd_palette("three_color_palette")
dd_palette("quilt_palette", n = 3)

Tidy helper function for did_multiplegt

Description

Runs DIDmultiplegt::did_multiplegt(), the de Chaisemartin and D'Haultfoeuille estimator, and returns its estimates as a data frame.

Usage

did_multiplegt_tidy(data, ...)

Arguments

data

A data frame, passed to did_multiplegt() as df.

...

Further arguments to did_multiplegt(). These must include mode and, for mode = "dyn", the column names of the outcome (outcome), group (group), time period (time), and treatment (treatment), as strings, and the number of effects to estimate (effects). graph_off defaults to TRUE, so that no plot is drawn.

Details

Use mode = "dyn". It estimates the effect l periods after a group first switches treatment, for l from 1 to effects; effects = 1 is the effect in the period of the switch. This mode runs did_multiplegt_dyn() from the 'DIDmultiplegtDYN' package, which also needs the 'polars' package. polars is not on CRAN; install it with install.packages("polars", repos = "https://rpolars.r-universe.dev") and attach it with library(polars).

mode = "old" is still passed through, but in 'DIDmultiplegt' 2.1.0 it returns NaN: it takes first differences with stats::lag(), which does not shift the data, so no group is ever seen to switch. The function warns when that happens.

See https://book.declaredesign.org/observational-causal.html#difference-in-differences

Value

For mode = "dyn", a data frame with one row per effect and columns term ("Effect_1", "Effect_2", ...), estimate, std.error, conf.low, conf.high, n (the number of group-period cells used), and n_switchers (the number of switching groups). For mode = "old", a data frame with one column, estimate.

Examples



library(polars)

set.seed(343)
# 20 units in 10 periods; units switch into treatment at different times
panel <- data.frame(unit = rep(1:20, times = 10), period = rep(1:10, each = 20))
panel$D <- as.numeric(panel$period > (panel$unit %% 5) + 4)
panel$Y <- 0.5 * panel$D + rnorm(nrow(panel))

did_multiplegt_tidy(
  panel, mode = "dyn",
  outcome = "Y", group = "unit", time = "period", treatment = "D",
  effects = 2
)

Tidy helper function for estimator_AS function

Description

Runs the Aronow and Samii estimator from the 'interference' package and returns Horvitz-Thompson and Hajek estimates of the total, direct, and indirect effects.

Usage

estimator_AS_tidy(
  data,
  permutatation_matrix = NULL,
  adj_matrix,
  obs_prob_exposure = NULL
)

Arguments

data

A data frame with columns Z and Y.

permutatation_matrix

(The spelling is the published one and is kept so existing code runs.) A matrix of random treatment assignments with one row per permutation of the treatment vector and one column per unit.

adj_matrix

An adjacency matrix defining the network structure.

obs_prob_exposure

Optional precomputed exposure probabilities.

Details

The 'interference' package is not available on CRAN, so in the CRAN version of rdss this function only explains how to install the GitHub versions of both packages, where it works: remotes::install_github('szonszein/interference') remotes::install_github('DeclareDesign/rdss')

It returns nothing, so a design that includes it still declares and diagnoses.

See https://book.declaredesign.org/experimental-causal.html#experiments-over-networks

Value

Nothing, invisibly, in the CRAN version. The GitHub version returns a data frame with columns term, inquiry, estimator, and estimate.

Shapefile of Fairfax County, Virginia, voting precincts

Description

Boundaries of the 238 voting precincts in Fairfax County, Virginia, with each precinct's ID, name, district, and polling place. One row per precinct. The book uses it to build the adjacency matrix for the experiment over networks; see estimator_AS_tidy().

Usage

fairfax

Format

An sf object with 238 rows and 10 columns:

Source

Fairfax County Government, voting precincts layer (REGMGR.VOTING_PRECINCTS).

Examples


names(fairfax)

Replication data for Foos, John, Muller, and Cunningham (2021), Journal of Politics (derived from Dataverse 10.7910/DVN/NDPXND)

Description

One row per voter. treat is the randomly assigned treatment, marked_register_2014 the turnout outcome, and ward and street (a hashed identifier) the levels at which voters are clustered.

Usage

foos_etal

Format

A data frame with 8,375 rows and 5 columns: marked_register_2014, ward, street, treat, and weights.

Source

Foos, Florian, Peter John, Christian Müller, and Kevin Cunningham. 2021. "Social Mobilization in Partisan Spaces." The Journal of Politics 83(3): 1190-1197. doi:10.1086/710970. Data from Dataverse, doi:10.7910/DVN/NDPXND.

Examples


estimatr::lm_robust(marked_register_2014 ~ treat, clusters = street, data = foos_etal)

Round and pad a number to a specific decimal place

Description

Rounds to digits decimal places and keeps trailing zeros, so a column of numbers lines up in a table.

Usage

format_num(x, digits = 3)

Arguments

x

A numeric vector.

digits

The number of decimal places. Defaults to 3.

Value

A character vector.

Examples


std.error <- c(0.12, 0.001, 1.2)
format_num(std.error)

Helper function to obtain the observed exposure for the Aronow and Samii estimator

Description

Converts the exposure map from interference::make_exposure_map_AS(), a 0/1 matrix with one row per unit and one column per exposure condition, into a vector naming each unit's condition (e.g. "dir_ind1" or "no").

Usage

get_exposure_AS(obs_exposure)

Arguments

obs_exposure

An exposure map, as returned by interference::make_exposure_map_AS().

Details

See https://book.declaredesign.org/experimental-causal.html#experiments-over-networks

Value

A character vector with one element per unit.

Examples

# An exposure map for three units, one column per exposure condition
exposure <- cbind(dir_ind1 = c(1, 0, 0), isol_dir = c(0, 1, 0), no = c(0, 0, 1))
get_exposure_AS(exposure)

Download a replication file from the dataverse archive for Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign

Description

Downloads one of the book's design declarations or diagnosis objects from its Dataverse archive and returns it as an R object. Requires an internet connection.

Usage

get_rdss_file(name, verbose = TRUE)

Arguments

name

The name of the file, as a string, e.g. "declaration_2.1". The names are listed above.

verbose

If TRUE, print the code that creates a declaration when one is requested.

Details

See https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HYVPO5 for further details and the code used to create these files.

The available names include:

Design declaration objects:

declaration_9.5
declaration_2.1
declaration_2.2
declaration_4.1
declaration_5.1
declaration_7.1
declaration_9.1
declaration_9.2
declaration_9.3
declaration_9.4
declaration_9.6
declaration_9.7
declaration_10.1
declaration_10.2
declaration_10.3
declaration_10.4
declaration_10a
declaration_11.1
declaration_11.2
declaration_11.3
declaration_11.4
declaration_11.5
declaration_12.1a
declaration_12.1b
declaration_12.1c
declaration_12.1d
declaration_13.1
declaration_13.2
declaration_15.1
declaration_15.2
declaration_15.3a
declaration_15.3b
declaration_15.3c
declaration_15.4
declaration_15.5
declaration_15.6
declaration_16.1a
declaration_16.1b
declaration_16.2
declaration_16.3
declaration_16.4
declaration_16.5
declaration_16.6
declaration_17.1
declaration_17.2
declaration_17.3
declaration_17.4
declaration_17.5
declaration_17.6_a
declaration_17.6_b
declaration_18.1
declaration_18.2
declaration_18.3
declaration_18.4
declaration_18.5
declaration_18.6
declaration_18.7
declaration_18.8
declaration_18.9a
declaration_18.9b
declaration_18.9c
declaration_18.10
declaration_18.11
declaration_18.12
declaration_18.13
declaration_19.1
declaration_19.2
declaration_19.3
declaration_19.4
declaration_23.1a
declaration_23.1b
declaration_23.1c
declaration_23.1d

Diagnosis objects:

diagnosis_2.1
diagnosis_4.1
diagnosis_9.1
diagnosis_9.2
diagnosis_9.3
diagnosis_9.4
diagnosis_9.5
diagnosis_9.6
diagnosis_9.7
simulation_10.1
diagnosis_10.1
diagnosis_10.2
diagnosis_10.3
diagnosis_10.4
diagnosis_10.5
diagnosis_10a
diagnosis_11.1
diagnosis_11.2
diagnosis_11.3
diagnosis_11.4
diagnosis_11.5
diagnosis_12.1
diagnosis_12.2
diagnosis_13.1
diagnosis_15.1
diagnosis_15.2
diagnosis_15.3
diagnosis_15.4
diagnosis_15.5
diagnosis_16.1
diagnosis_16.2
diagnosis_16.3
diagnosis_16.4
diagnosis_16.5
diagnosis_17.1
diagnosis_17.2
diagnosis_17.3
diagnosis_17.4
diagnosis_17.5
diagnosis_18.1
diagnosis_18.10_encouragment
diagnosis_18.10_placebo
diagnosis_18.11
diagnosis_18.12
diagnosis_18.13
diagnosis_18.2
diagnosis_18.3
diagnosis_18.4
diagnosis_18.5
diagnosis_18.6
diagnosis_18.7
diagnosis_18.8
diagnosis_18.9
diagnosis_19.1
diagnosis_19.2
diagnosis_19.3
diagnosis_19.4
diagnosis_19a
diagnosis_21a
diagnosis_21b
diagnosis_23.1
diagnosis_23a

Value

The requested object: a design declaration, or a diagnosis or simulation data frame.

Examples


## Not run: 
# Requires internet access
if(curl::has_internet()) {
  diagnosis_2.1 <- get_rdss_file("diagnosis_2.1")
  diagnosis_2.1
}

## End(Not run)

Add alpha transparency to a color defined in hexadecimal

Description

Add alpha transparency to a color defined in hexadecimal

Usage

hex_add_alpha(col, alpha)

Arguments

col

A color as a six-digit hex code, e.g. "#72B4F3".

alpha

Opacity, from 0 (transparent) to 1 (opaque).

Value

The color as an eight-digit hex code.

Examples

hex_add_alpha("#72B4F3", 0.5)

Voter file sample for Los Angeles County

Description

Party registration, age, census tract, and 2012 turnout for 1,000 registered voters sampled at random from the Los Angeles County voter file. One row per voter.

Usage

la_voter_file

Format

A data frame with 1000 rows and 4 variables:

party

political party registration

age

age of voter in years

census_tract

US Census tract number

voted_2012

1 if the voter voted in the 2012 general election, 0 otherwise

Source

California Secretary of State.

Examples


table(la_voter_file$party)

Generate lags in grouped data

Description

Lags x within each group, ordering by order_by rather than by row order. Write the variables as bare column names inside fabricatr::fabricate() or dplyr::mutate(); see the README for an example.

Usage

lag_by_group(x, groups, n = 1, order_by, default = NA)

Arguments

x

The variable to lag.

groups

The grouping variable, e.g. a unit identifier.

n

A positive integer: the number of periods to lag by. Defaults to 1.

order_by

Ordering variable within group (e.g., time)

default

The value for rows with no earlier period in their group. Defaults to NA.

Details

See https://book.declaredesign.org/observational-causal.html#difference-in-differences

Value

A vector the length of x, in the original row order.

Examples


# Three units observed in four periods; unit u is treated after period u
panel <- expand.grid(unit = 1:3, period = 1:4)
panel$D <- as.numeric(panel$period > panel$unit)

dplyr::mutate(panel, D_lag = lag_by_group(D, groups = unit, order_by = period))

Teaching data based on the 2018/19 LAPOP AmericasBarometer survey of Brazil

Description

Used in the book's student exercises. These data were resampled with replacement from the LAPOP data, to 10,000 rows, for a subset of variables. These data cannot be used for scientific inferences, and are only useful for teaching purposes. ID numbers were scrambled so that individuals and municipalities cannot easily be identified.

Usage

lapop_brazil

Format

A data frame with 10,000 rows and 10 columns: ID, municipality, and eight survey items (trust_police, govt_responsive, ideology, govt_pride, self_efficacy_political, trust_military, trust_supreme_court, and support_political_system).

Details

Download the original data from https://www.vanderbilt.edu/cgd/data-access/

See https://www.vanderbilt.edu/lapop/core-surveys.php for the survey questionnaire.

Source

LAPOP AmericasBarometer 2018/19 survey of Brazil (fieldwork January to March 2019), Vanderbilt University, https://www.vanderbilt.edu/cgd/data-access/.

Examples


summary(lapop_brazil$trust_police)

Format confidence intervals for nice printing

Description

Format confidence intervals for nice printing

Usage

make_interval_entry(conf.low, conf.high, digits = 2)

Arguments

conf.low

A numeric vector of lower bounds.

conf.high

A numeric vector of upper bounds.

digits

The number of decimal places. Defaults to 2.

Value

A character vector, e.g. "[-0.17, 0.31]".

Examples


conf.low <- c(-0.1652, 0.00304, -6.352)
conf.high <- c(0.3052, 0.00696, -1.648)

make_interval_entry(conf.low, conf.high)

Format estimates and standard errors for nice printing

Description

Format estimates and standard errors for nice printing

Usage

make_se_entry(estimate, std.error, digits = 2)

Arguments

estimate

A numeric vector of estimates.

std.error

A numeric vector of standard errors, the same length as estimate.

digits

The number of decimal places. Defaults to 2.

Value

A character vector, e.g. "0.07 (0.12)".

Examples


estimate <- c(0.07, 0.005, -4)
std.error <- c(0.12, 0.001, 1.2)

make_se_entry(estimate, std.error)

Post stratification estimator helper

Description

Predicts each row's outcome from a fitted model, then averages the predictions within each group using the post-stratification weights. In the book, the rows are demographic cells and the groups are states.

Usage

post_stratification_helper(model_fit, data, group, weights)

Arguments

model_fit

A fitted model that marginaleffects::predictions() accepts, e.g. from lme4::glmer() or estimatr::lm_robust(). For a mixed model, random effects are left out of the predictions (re.form = NA); for any other model no such argument is passed.

data

A data frame of post-stratification cells to predict for.

group

The bare column name of the grouping variable.

weights

The bare column name of the post-stratification weights, e.g. each cell's population share.

Details

See https://book.declaredesign.org/observational-descriptive.html#multi-level-regression-and-poststratification

Value

A data frame with one row per group and columns for the group and estimate.

Examples



set.seed(343)
# Post-stratification cells: high school graduates and non-graduates in ten
# states, weighted by each state's share of graduates
cells <- expand.grid(state = state.name[1:10], HS = 0:1)
prob_HS <- rep(seq(0.5, 0.9, length.out = 10), 2)
cells$PS_weight <- ifelse(cells$HS == 1, prob_HS, 1 - prob_HS)

survey <- cells[sample(nrow(cells), 1000, replace = TRUE), ]
survey$Y <- rbinom(1000, 1, prob = plogis(0.5 * survey$HS))

model_fit <- lme4::glmer(Y ~ HS + (1 | state), data = survey, family = binomial)
post_stratification_helper(model_fit, data = cells, group = state, weights = PS_weight)

Process tracing estimator

Description

Answers a causal query about one case using a causal model from 'CausalQueries', once for each process tracing strategy. A strategy names the nodes whose values are observed, so the estimate shows how the answer changes as more of the case's data is examined.

Usage

process_tracing_estimator(causal_model, query, data, strategies)

Arguments

causal_model

A model from CausalQueries::make_model().

query

The causal query, in 'CausalQueries' syntax, e.g. "Y[X=1] > Y[X=0]".

data

A one-row data frame giving the case's value on each observed node. Nodes that are NA are treated as unobserved.

strategies

A character vector of strategies, each listing the nodes observed, separated by hyphens, e.g. c("X-Y", "X-M-Y").

Details

See https://book.declaredesign.org/observational-causal.html#process-tracing

Value

A data frame with one row per strategy: term (the strategy), XY (the case's values of X and Y, e.g. "X1Y1"), and estimate (the posterior mean of the query).

Examples


# Simple example showing ambiguity in attribution
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> Y"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1),
  strategies = "X-Y")

# Example where M=1 acts as a hoop test
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> M -> Y") |>
   CausalQueries::set_restrictions("Y[M=1] < Y[M=0]") |>
   CausalQueries::set_restrictions("M[X=1] < M[X=0]"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1, M = 0),
  strategies = c("Y", "X-Y", "X-M-Y"))

Helper function for using rdrobust as a model in declare_estimator

Description

Lets rdrobust::rdrobust(), which takes vectors, be called with a data frame and column names. Pass it as .method in declare_estimator(); tidy.rdrobust() then tidies the fit.

Usage

rdrobust_helper(data, y, x, subset = NULL, ...)

Arguments

data

A data frame.

y

The bare column name of the outcome.

x

The bare column name of the running variable.

subset

An optional logical expression in terms of the columns of data, selecting the rows to use.

...

Further arguments to rdrobust::rdrobust(), e.g. c for the cutoff.

Details

See https://book.declaredesign.org/observational-causal.html#regression-discontinuity-designs

Value

rdrobust model fit object

Examples



set.seed(343)
rd <- data.frame(X = runif(1000, min = -1, max = 1))
rd$Y <- 0.5 * (rd$X > 0) + rd$X + rnorm(1000, sd = 0.5)

fit <- rdrobust_helper(rd, y = Y, x = X, c = 0)
tidy(fit)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

tidy()

Helper function for rma function in metafor package

Description

See https://book.declaredesign.org/complex-designs.html#meta-analysis

Usage

rma_helper(data, yi, sei, method = "REML", ...)

Arguments

data

A data frame with one row per study.

yi

The bare column name of the study estimates.

sei

The bare column name of their standard errors.

method

character string to specify whether a fixed- or a random/mixed-effects model should be fitted. A fixed-effects model (with or without moderators) is fitted when using method = "FE". Random/mixed-effects models are fitted by setting method equal to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", "HSk", or "GENQ". Default is "REML".

...

Further arguments to metafor::rma().

Details

Fits metafor::rma() to study-level estimates and standard errors. If the fit fails, it returns the error as an object rma_mu_tau() recognizes, so one failed simulation does not stop a diagnosis.

Value

An rma.uni fit; pass it to rma_mu_tau() to tidy it.

Examples



set.seed(343)
studies <- data.frame(
  est = rnorm(10, mean = 0.2, sd = 0.1),
  se = runif(10, min = 0.05, max = 0.15)
)
fit <- rma_helper(studies, yi = est, sei = se)
fit

Extract mu and tau-squared from a random-effects meta-analysis

Description

A tidier for fits from rma_helper(): mu is the average effect across studies and tau_sq the variance of the true effects across studies.

Usage

rma_mu_tau(fit)

Arguments

fit

A fit from rma_helper() or metafor::rma().

Details

See https://book.declaredesign.org/complex-designs.html#meta-analysis

Value

A data frame with two rows, mu and tau_sq, with estimates, standard errors, and confidence intervals (for tau_sq, intervals only when method = "REML"). If the fit failed, one row with estimate = NA and error = TRUE.

Examples



set.seed(343)
studies <- data.frame(
  est = rnorm(10, mean = 0.2, sd = 0.1),
  se = runif(10, min = 0.05, max = 0.15)
)
fit <- rma_helper(studies, yi = est, sei = se)
rma_mu_tau(fit)

ggplot theme used in the book Research Design in the Social Sciences

Description

ggplot2::theme_minimal() with light gray major gridlines only, no axis ticks, and no legend. The book labels series directly, so it hides the legend; add theme(legend.position = "right") after it to get one back.

Usage

theme_dd()

Value

A ggplot2 theme.

Examples

library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) + geom_point() + theme_dd()

Tidy estimates from the amce estimator

Description

A generics::tidy() method for fits from cjoint::amce(): one row per attribute level, with normal-approximation confidence intervals.

Usage

## S3 method for class 'amce'
tidy(x, alpha = 0.05, ...)

Arguments

x

A fit from cjoint::amce().

alpha

The significance level for the confidence intervals. Defaults to 0.05, which gives 95 percent intervals.

...

Not used.

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Value

A data frame with columns attribute, level, estimate, std.error, statistic, p.value, conf.low, and conf.high.

Examples




library(cjoint)

data(immigrationconjoint)
data(immigrationdesign)

# Run AMCE estimator using all attributes in the design
results <- amce(Chosen_Immigrant ~  Gender + Education + `Language Skills` +
                  `Country of Origin` + Job + `Job Experience` + `Job Plans` +
                  `Reason for Application` + `Prior Entry`, data = immigrationconjoint,
                cluster = TRUE, respondent.id = "CaseID", design = immigrationdesign)

tidy(results)

Tidy helper function for rdrobust function

Description

A generics::tidy() method for fits from rdrobust::rdrobust(), so they can be used in declare_estimator(). Use rdrobust_helper() to fit the model.

Usage

## S3 method for class 'rdrobust'
tidy(x, ...)

Arguments

x

A fit from rdrobust::rdrobust().

...

Not used.

Details

See https://book.declaredesign.org/observational-causal.html#regression-discontinuity-designs

Value

A data frame with one row per estimate rdrobust reports (conventional, bias-corrected, and robust) and columns term, estimate, std.error, statistic, p.value, conf.low, conf.high, and cutoff.

Examples



set.seed(343)
rd <- data.frame(X = runif(1000, min = -1, max = 1))
rd$Y <- 0.5 * (rd$X > 0) + rd$X + rnorm(1000, sd = 0.5)

fit <- rdrobust_helper(rd, y = Y, x = X, c = 0)
tidy(fit)

Tidy results from a stanreg regression (deprecated)

Description

Deprecated: use broom.mixed::tidy(), which this function now calls with the same arguments.

Usage

tidy_stan(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x

A fit from rstanarm::stan_glm().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

exponentiate

Logical indicating whether or not to exponentiate the coefficient estimates. Defaults to FALSE.

...

Further arguments to broom.mixed::tidy().

Details

See https://book.declaredesign.org/choosing-an-answer-strategy.html#bayesian-formalizations

Value

A data frame with one row per coefficient.

Examples




fit <- rstanarm::stan_glm(mpg ~ wt, data = mtcars, chains = 1, iter = 1000,
                          refresh = 0, seed = 343)

# Prefer broom.mixed::tidy(fit, conf.int = TRUE)
tidy_stan(fit, conf.int = TRUE)