Package {Rfuzzydid}

coef.fuzzydid

Description

Extract fuzzydid point estimates.

Usage

## S3 method for class 'fuzzydid'
coef(object, ...)

Arguments

Value

A named numeric vector of LATE and LQTE point estimates.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE, nose = TRUE)
coef(fit)

confint.fuzzydid

Description

Extract stored percentile bootstrap confidence intervals.

Usage

## S3 method for class 'fuzzydid'
confint(object, parm, level = 0.95, ...)

Arguments

Value

A matrix with lower and upper confidence limits.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE, nose = TRUE)
confint(fit)

formula.fuzzydid

Description

Extract the formula used to fit a fuzzydid object.

Usage

## S3 method for class 'fuzzydid'
formula(x, ...)

Arguments

Value

The original formula.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE, nose = TRUE)
formula(fit)

fuzzydid

Description

Formula-first interface for fuzzy difference-in-differences estimators. Estimation is fully native in R. The object returned by fuzzydid() is an estimand summary rather than a predictive regression model: it stores local average and local quantile treatment-effect estimates, bootstrap uncertainty summaries, design cell counts, and metadata needed by extractor methods.

Usage

fuzzydid(
  data,
  formula,
  group,
  time,
  group_forward = NULL,
  did = FALSE,
  tc = FALSE,
  cic = FALSE,
  lqte = FALSE,
  newcateg = NULL,
  numerator = FALSE,
  partial = FALSE,
  nose = FALSE,
  cluster = NULL,
  breps = 50,
  eqtest = FALSE,
  modelx = NULL,
  sieves = FALSE,
  sieveorder = NULL,
  tagobs = FALSE,
  backend = c("auto", "native"),
  seed = NULL,
  treatment = NULL
)

Arguments

Details

fuzzydid() uses complete cases across the outcome, treatment, group, time, optional forward-group, covariate, and cluster variables. Missing NA and NaN values are dropped; non-finite numeric values such as Inf and -Inf are rejected. The outcome and treatment must be numeric vectors. Group and time identifiers must be numeric vectors; with one group variable, group values must be in {0, 1, NA}. Covariates may be numeric, factor, character, or logical vectors. Numeric covariates enter as continuous predictors; factor, character, and logical covariates enter as qualitative predictors expanded to indicator columns. When sieves = TRUE, continuous covariates are expanded to polynomial sieve terms.

Standard errors and confidence intervals are percentile bootstrap summaries. Use seed to make bootstrap draws reproducible. If tagobs = TRUE, the returned object includes a logical vector identifying the input rows retained after complete-case filtering.

Value

An object of class "fuzzydid". This is a list whose late component is a data frame of requested LATE-type estimators with columns estimator, estimate, std.error, conf.low, and conf.high. eqtest is either NULL or an analogous data frame of pairwise equality contrasts, and lqte is either NULL or a data frame with columns quantile, estimate, std.error, conf.low, and conf.high for local quantile treatment effects. Additional components include matrices, a named list of Stata-style result matrices; tagobs, an optional logical mask of retained observations; sample-size diagnostics n, n11, n10, n01, and n00; bootstrap diagnostics n_reps, n_misreps, and share_failures; and metadata such as backend, call, and options. The estimate tables report point estimates and, unless nose = TRUE, bootstrap standard errors and percentile confidence limits.

Examples

make_example_cell <- function(g, t, ones, n_cell = 20L) {
  data.frame(
    g = rep.int(g, n_cell),
    t = rep.int(t, n_cell),
    d = c(rep.int(1L, ones), rep.int(0L, n_cell - ones))
  )
}

df <- rbind(
  make_example_cell(0L, 0L, 4L),
  make_example_cell(0L, 1L, 8L),
  make_example_cell(1L, 0L, 6L),
  make_example_cell(1L, 1L, 16L)
)
df$id <- seq_len(nrow(df))
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$id / 7)
example_data <- df

fit <- fuzzydid(
  data = example_data,
  formula = y ~ d,
  treatment = NULL,
  group = "g",
  time = "t",
  group_forward = NULL,
  did = TRUE,
  tc = TRUE,
  cic = TRUE,
  lqte = TRUE,
  newcateg = c(0, 1),
  cluster = NULL,
  modelx = NULL,
  sieveorder = NULL,
  seed = NULL,
  nose = TRUE
)

fit$late

glance.fuzzydid

Description

One-row summary for fuzzydid objects.

Usage

## S3 method for class 'fuzzydid'
glance(x, ...)

Arguments

Value

A one-row data frame with class "data.frame" summarizing the fitted object. backend reports the computation path and Num.Obs. reports the estimation sample size. N.11, N.10, N.01, and N.00 give the four design cell counts. N.reps, N.misreps, and Share.failures describe bootstrap replication totals and failure rates, or are NA when nose = TRUE.

Examples

make_example_cell <- function(g, t, ones, n_cell = 20L) {
  data.frame(
    g = rep.int(g, n_cell),
    t = rep.int(t, n_cell),
    d = c(rep.int(1L, ones), rep.int(0L, n_cell - ones))
  )
}

df <- rbind(
  make_example_cell(0L, 0L, 4L),
  make_example_cell(0L, 1L, 8L),
  make_example_cell(1L, 0L, 6L),
  make_example_cell(1L, 1L, 16L)
)
df$id <- seq_len(nrow(df))
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$id / 7)

fit <- fuzzydid(
  data = df[, c("y", "g", "t", "d")],
  formula = y ~ d,
  group = "g",
  time = "t",
  did = TRUE,
  nose = TRUE
)

generics::glance(fit)

nobs.fuzzydid

Description

Extract the estimation sample size.

Usage

## S3 method for class 'fuzzydid'
nobs(object, ...)

Arguments

Value

Integer number of observations used for estimation.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE, nose = TRUE)
nobs(fit)

plot.fuzzydid

Description

Plot fuzzydid point estimates and stored confidence intervals as a base R dot-and-whisker plot.

Usage

## S3 method for class 'fuzzydid'
plot(x, ...)

Arguments

Value

The input x, returned invisibly.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE,
                breps = 5, seed = 1)
plot(fit)

print.fuzzydid

Description

Print a compact fuzzydid object header and estimator table.

Usage

## S3 method for class 'fuzzydid'
print(x, ...)

Arguments

Value

The input x, returned invisibly.

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE, nose = TRUE)
print(fit)

summary.fuzzydid

Description

Print a compact summary table for fuzzydid results.

Usage

## S3 method for class 'fuzzydid'
summary(object, ...)

Arguments

Value

The input object, returned invisibly with class "fuzzydid", after printing the available estimator tables. This method is called for its side effect of displaying the late, eqtest, and lqte components in a compact tabular form.

Examples

make_example_cell <- function(g, t, ones, n_cell = 20L) {
  data.frame(
    g = rep.int(g, n_cell),
    t = rep.int(t, n_cell),
    d = c(rep.int(1L, ones), rep.int(0L, n_cell - ones))
  )
}

df <- rbind(
  make_example_cell(0L, 0L, 4L),
  make_example_cell(0L, 1L, 8L),
  make_example_cell(1L, 0L, 6L),
  make_example_cell(1L, 1L, 16L)
)
df$id <- seq_len(nrow(df))
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$id / 7)

fit <- fuzzydid(
  data = df[, c("y", "g", "t", "d")],
  formula = y ~ d,
  group = "g",
  time = "t",
  did = TRUE,
  nose = TRUE
)

summary(fit)

tidy.fuzzydid

Description

Tidy extractor for fuzzydid objects.

Usage

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

Arguments

Value

A data frame with class "data.frame" and one row per available estimate or contrast. component identifies whether the row comes from the LATE table, equality-test table, or LQTE table. model and term label the estimator or contrast. estimate is the point estimate, while std.error, conf.low, and conf.high contain bootstrap uncertainty summaries when available and NA otherwise. If no estimates are available, an empty data frame with the same columns is returned.

Examples

make_example_cell <- function(g, t, ones, n_cell = 20L) {
  data.frame(
    g = rep.int(g, n_cell),
    t = rep.int(t, n_cell),
    d = c(rep.int(1L, ones), rep.int(0L, n_cell - ones))
  )
}

df <- rbind(
  make_example_cell(0L, 0L, 4L),
  make_example_cell(0L, 1L, 8L),
  make_example_cell(1L, 0L, 6L),
  make_example_cell(1L, 1L, 16L)
)
df$id <- seq_len(nrow(df))
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$id / 7)

fit <- fuzzydid(
  data = df[, c("y", "g", "t", "d")],
  formula = y ~ d,
  group = "g",
  time = "t",
  did = TRUE,
  nose = TRUE
)

generics::tidy(fit)

vcov.fuzzydid

Description

Extract the bootstrap covariance matrix for stored estimates.

Usage

## S3 method for class 'fuzzydid'
vcov(object, ...)

Arguments

Value

A covariance matrix for coef(object).

Examples

df <- expand.grid(i = seq_len(20), g = 0:1, t = 0:1)
df$d <- as.integer(df$i <= c(4, 8, 6, 16)[1 + df$t + 2 * df$g])
df$y <- 1 + 0.5 * df$g + 0.4 * df$t + 2 * df$d + sin(df$i / 7)

fit <- fuzzydid(df, y ~ d, group = "g", time = "t", did = TRUE,
                breps = 5, seed = 1)
vcov(fit)