Package 'ipf'

ANES 2024 Time Series Study (subset)

Description

A subset of the 2024 American National Election Study (ANES) Time Series face-to-face sample, containing demographic and vote choice variables for 966 respondents. Useful for demonstrating survey raking workflows.

Usage

anes24

Format

A tibble with 966 rows and 7 columns:

state

Two-letter US state abbreviation. NA for respondents whose state is not identified (106 missing).

sex

Respondent sex: "Male" or "Female" (5 missing).

race

Race/ethnicity: "White", "Black", "Hispanic", "Asian", or "Other" (11 missing).

income

Household income bracket: "Under $50k", "$50k-$100k", or "Over $100k" (47 missing).

education

Education: "Less than HS", "High school", "Some college", "Bachelor's", or "Graduate" (451 missing).

married

Marital status: "Married", "Widowed", "Divorced", "Separated", or "Never married" (277 missing).

presidential

2024 presidential vote choice: "Harris" or "Trump" (335 missing).

Source

https://electionstudies.org/data-center/2024-time-series-study/

References

American National Election Studies. 2025. ANES 2024 Time Series Study Full Release (dataset and documentation). August 8, 2025 version. https://www.electionstudies.org/

---

Augment data with raked weights

Description

Returns the original data frame with a .weight column appended.

Usage

## S3 method for class 'ipf_rake'
augment(x, ...)

Arguments

x	An ipf_rake object.
...	Additional arguments (ignored).

Value

A tibble with all original columns plus .weight.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
augment(result)

---

Compute design effect and effective sample size

Description

The design effect (deff) measures the variance inflation factor due to unequal weighting. The effective sample size is n / deff.

Usage

design_effect(weights)

Arguments

weights

Numeric weight vector.

Value

A list with deff (design effect) and n_eff (effective sample size).

Examples

w <- c(1.2, 0.8, 1.5, 0.5, 1.0)
design_effect(w)

---

Find discrepant variables and their aggregate discrepancy scores

Description

Calculates discrepancy between the current weighted distribution and target distributions for each variable, then aggregates using the chosen method.

Usage

find_discrepant_vars(
  data,
  targets,
  weights,
  choosemethod = "total",
  na_method = c("exclude", "bucket")
)

Arguments

data	Data frame.
targets	Named list of named numeric target vectors (proportions).
weights	Numeric weight vector.
choosemethod	Method for aggregating per-category discrepancies. One of "total", "max", "average", "totalsquared", "maxsquared", "averagesquared".
na_method	How to handle NA values. "exclude" skips NA cases from that margin. "bucket" treats missing values as an implicit extra category.

Value

Named numeric vector of aggregate discrepancy per variable.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4)),
  age = sample(c('young', 'old'), 100, replace = TRUE, prob = c(0.7, 0.3))
)
targets <- list(
  gender = c(M = 0.5, F = 0.5),
  age = c(young = 0.6, old = 0.4)
)
find_discrepant_vars(data, targets, weights = rep(1, 100))

---

Glance at an ipf_rake object

Description

Returns a single-row tibble with summary statistics.

Usage

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

Arguments

x	An ipf_rake object.
...	Additional arguments (ignored).

Value

A single-row tibble with columns: converged, iterations, max_prop_err, deff, n_eff, n_obs, n_vars.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
glance(result)

---

Print an ipf_rake object

Description

Print an ipf_rake object

Usage

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

Arguments

x	An ipf_rake object.
...	Additional arguments (ignored).

Value

Invisibly returns x.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
print(result)

---

Iterative proportional fitting (raking)

Description

Adjusts survey weights so that weighted marginal distributions match known population targets. Supports automatic variable selection, iterative re-raking, and weight bounding.

Usage

rake(
  data,
  targets,
  base_weights = NULL,
  cap = 5,
  bounds = NULL,
  type = c("nolim", "pctlim", "nlim"),
  pctlim = 0.05,
  nlim = 5L,
  choosemethod = c("total", "max", "average", "totalsquared", "maxsquared",
    "averagesquared"),
  na_method = c("exclude", "bucket"),
  iterate = TRUE,
  max_iter = 1000L,
  tol = 1e-06,
  verbose = FALSE,
  diagnostics_every = 0L
)

Arguments

data	A data frame or tibble containing the survey data.
targets	A named list of named numeric vectors specifying target proportions for each raking variable. Names of the list must match column names in data. Each vector's names must match the levels of the corresponding variable. Values should sum to 1 (proportions); if not, they are normalized with a warning.
base_weights	Optional numeric vector of base (design) weights. If NULL (default), uniform weights of 1 are used. Centered to mean 1 before raking.
cap	Maximum weight value (ratio cap). Weights exceeding this value are trimmed and all weights are renormalized. Default 5. Ignored if bounds is specified.
bounds	Optional numeric vector of length 2, c(lo, hi), specifying minimum and maximum weight bounds. Overrides cap.
type	Variable selection method: "nolim" (default): use all variables in targets. "pctlim": use only variables with discrepancy >= pctlim. "nlim": use the nlim most discrepant variables.
pctlim	Discrepancy threshold for type = "pctlim". Default 0.05 (5 percentage points).
nlim	Number of variables for type = "nlim". Default 5.
choosemethod	Method for aggregating per-category discrepancies into a single variable score. One of "total", "max", "average", "totalsquared", "maxsquared", "averagesquared".
na_method	How to handle NA values in raking variables. "exclude" (default): targets are proportions among non-NA cases only; NA cases are invisible to that margin. Matches anesrake. "bucket": NAs become a frozen extra category; their total weight is preserved and the named targets are rescaled to the remaining non-NA mass.
iterate	Logical. If TRUE and type = "pctlim", re-check discrepancies after raking and add newly discrepant variables, repeating up to 10 times. Default TRUE.
max_iter	Maximum number of raking iterations. Default 1000.
tol	Convergence tolerance (max proportional error). Default 1e-6.
verbose	Logical. If TRUE, print iteration progress. Default FALSE.
diagnostics_every	Record per-margin diagnostics every k iterations. 0 means only baseline. Default 0.

Value

An ipf_rake object (S3 class) containing:

• weights: final raked weight vector

• data: the input data frame

• converged: logical

• iterations: number of iterations

• max_prop_err: final max proportional error

• targets: normalized targets used

• vars_used: character vector of variables raked on

• base_weights: original base weights

• type, choosemethod, na_method, cap: settings used

• deff, n_eff: design effect and effective sample size

• diagnostics: tibble of per-iteration diagnostics

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4)),
  age = sample(c('young', 'old'), 100, replace = TRUE, prob = c(0.7, 0.3))
)
targets <- list(
  gender = c(M = 0.5, F = 0.5),
  age = c(young = 0.6, old = 0.4)
)
result <- rake(data, targets)
print(result)

---

Summarize an ipf_rake object

Description

Produces a detailed summary including per-variable diagnostic tables showing target vs. achieved distributions.

Usage

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

Arguments

object	An ipf_rake object.
...	Additional arguments (ignored).

Value

Invisibly returns a list with convergence info, weight summary, design effect, and per-variable assessment tibbles.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
summary(result)

---

Tidy an ipf_rake object

Description

Returns a one-row-per-variable-per-level tibble with target proportions, weighted proportions, and discrepancy.

Usage

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

Arguments

x	An ipf_rake object.
...	Additional arguments (ignored).

Value

A tibble with columns: variable, level, target, weighted_pct, discrepancy.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
tidy(result)

---

Assess weight quality with diagnostic tables

Description

Produces a per-variable diagnostic table comparing target distributions to unweighted and weighted distributions.

Usage

weight_assess(
  data,
  targets,
  weights,
  base_weights = NULL,
  na_method = c("exclude", "bucket")
)

Arguments

data	Data frame.
targets	Named list of named numeric target vectors (proportions).
weights	Final raked weight vector.
base_weights	Original base weights before raking. If NULL, uses uniform weights.
na_method	How to handle NA values. "exclude" skips NA cases from that margin. "bucket" treats missing values as an implicit extra category.

Value

Named list of tibbles, one per variable.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
weight_assess(data, targets, result$weights)

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