Package {DyadRatios}

dyadratios: Dyad Ratios Algorithm for Latent Public Opinion Estimation

Description

Implements the Dyad Ratios algorithm (Stimson 1991) for estimating latent public mood from a collection of time-series survey marginals. The computationally intensive estimation loop is written in C++ (via Rcpp) and is a faithful translation of James Stimson's original 'MCalc' program. The R layer handles data ingestion, temporal aggregation, result formatting, and visualisation.

Main functions

extract

Run the algorithm on a data frame.

Input data format

The primary input is a data frame where each row is one survey marginal:

• A column identifying the opinion variable (issue series).

• A date column (any format coercible by as.Date).

• An index column: the survey proportion or percentage.

• An optional n column: number of respondents (used as weight; defaults to 1000 if omitted).

Author(s)

Maintainer: Dave Armstrong davearmstrong.ps@gmail.com

Authors:

• James Stimson (Original C++ implementation)

References

Stimson, J. A. (1991). Public Opinion in America: Moods, Cycles, and Swings. Boulder, CO: Westview Press.

Stimson, J. A. (1999). Public Opinion in America, 2nd ed. Westview.

Bootstrap the Dyad Ratios estimate

Description

Generates a sampling distribution around the latent mood trajectory by repeatedly drawing synthetic survey marginals from a binomial model and re-running extract. The original (unperturbed) estimate is used as the point estimate; the bootstrap draws characterise uncertainty around it.

Usage

boot_dr(
  obj,
  data,
  R = 200L,
  level = 0.95,
  pw = FALSE,
  seed = NULL,
  parallel = FALSE
)

Arguments

Details

All model parameters (aggregation interval, column names, smoothing, etc.) are taken directly from the stored call inside obj, so there is no risk of the bootstrap replications being run with different settings than the original.

Each replication draws y_i \sim \text{Binomial}(n_i, p_i) where p_i is the observed proportion and n_i is the sample size, then replaces the index column with y_i / n_i (rescaled to the same 0–100 vs 0–1 convention as the original) and re-runs extract using the exact call stored in obj. Replications that error (e.g. due to a degenerate draw) are silently discarded; attr(result, "R") reports how many succeeded.

Value

A list of class "boot_dr" with two or four components depending on whether the original obj used one or two dimensions:

estimates

A data.frame with one row per time period and columns period, year, month, quarter, mood (original point estimate), lower, and upper (confidence bounds at (1-level)/2 and 1-(1-level)/2). When n_dim = 2, three additional columns are appended: mood_dim2, lower_dim2, and upper_dim2.

samples

An n_periods × R matrix of raw bootstrap dimension-1 trajectories.

samples_dim2

(n_dim = 2 only) An n_periods × R matrix of raw bootstrap dimension-2 trajectories.

The list also carries attributes R (number of successful replications), level, agg_interval, and n_dim.

See Also

extract, plot.boot_dr

Examples

# Build a small synthetic dataset: 4 items measured annually over 20 years
set.seed(42)
n_years <- 20
years   <- seq(1980, length.out = n_years)
items   <- c("item_a", "item_b", "item_c", "item_d")

dat <- do.call(rbind, lapply(items, function(item) {
  data.frame(
    varname = item,
    date    = as.Date(paste0(years, "-07-01")),
    index   = 50 + cumsum(rnorm(n_years, 0, 1.5)) + rnorm(n_years, 0, 2),
    n       = sample(800:1200, n_years, replace = TRUE)
  )
}))

# Run the original estimate first
res <- extract(dat, n_col = "n", smoothing = FALSE)

# Bootstrap with 100 replications (use more in practice)
boot <- boot_dr(res, dat, R = 100, seed = 1)

# estimates is the summary data frame
head(boot$estimates)

# mood is the original point estimate; lower/upper are the 95% CI
boot$estimates[, c("year", "mood", "lower", "upper")]

# samples is the raw n_periods x R matrix of bootstrap trajectories
dim(boot$samples)

# Plot the trajectory with uncertainty ribbon
plot(boot)

Run the Dyad Ratios Algorithm

Description

Estimates one or two latent opinion dimensions from a collection of time-series issue variables using the Dyad Ratios algorithm developed by James Stimson (Stimson 1991, 1999). This function accepts already-loaded data as a data.frame, handles aggregation to the requested interval, standardises the issue matrix, passes it to the compiled C++ core, and returns a richly-annotated result object.

Usage

extract(
  data,
  varname_col = "varname",
  date_col = "date",
  index_col = "index",
  n_col = NULL,
  agg_interval = c("annual", "quarterly", "monthly", "daily", "multi_year"),
  multiple = 1L,
  start_date = NULL,
  end_date = NULL,
  n_dim = 1L,
  smoothing = TRUE,
  tol = 0.001,
  fiscal_year_end = 12L
)

Arguments

Details

The algorithm iterates between a forward pass and a backward pass. In each pass every period's latent score is estimated as the weighted average of ratios of issue values relative to all other periods for which both values are non-missing. The weights are the squared correlations of each issue with the current mood estimate. Convergence is declared when the maximum weighted change in these correlations falls below tol.

Issues are standardised to a mean of 100 and a standard deviation of 10 before estimation, then the final mood series is rescaled to have the weighted mean and standard deviation of the raw issue series.

Value

An object of class "extract", which is a named list containing:

mood

Numeric vector of estimated public mood, one value per period. When n_dim = 2 this is the first dimension.

mood_dim2

Numeric vector of the second dimension, or NA when n_dim = 1.

periods

A data.frame describing each period (year, month/quarter where applicable) matching the length of mood.

loadings

A data.frame of variable names with loading on each extracted dimension.

iterations

A data.frame of the iteration history (convergence, reliability, smoothing alphas per iteration).

n_series

Integer. Number of opinion series retained after dropping constant series.

n_periods

Integer. Number of time periods.

n_obs

Integer. Total number of input observations used.

alpha_F

Final forward-pass smoothing parameter.

alpha_B

Final backward-pass smoothing parameter.

eigenvalue

Eigenvalue estimate for the first dimension.

variance_explained

Proportion of variance accounted for by the extracted dimension(s).

call

The matched call.

settings

List of all parameter values used.

References

Stimson, J. A. (1991). Public Opinion in America: Moods, Cycles, and Swings. Westview Press.

Stimson, J. A. (1999). Public Opinion in America, 2nd ed. Westview Press.

Examples

# Minimal synthetic example
set.seed(42)
n <- 60
dates <- seq(as.Date("1980-01-01"), by = "year", length.out = n / 3)
dat <- data.frame(
  varname = rep(c("item_a", "item_b", "item_c"), each = length(dates)),
  date    = rep(dates, 3),
  index   = c(seq(40, 65, length.out = length(dates)) + rnorm(length(dates), 0, 2),
              seq(55, 35, length.out = length(dates)) + rnorm(length(dates), 0, 2),
              seq(45, 70, length.out = length(dates)) + rnorm(length(dates), 0, 3)),
  n       = 1000
)
result <- extract(dat, n_col = "n", smoothing = FALSE)
print(result)

Extract mood estimates as a data frame

Description

Combines the period descriptor table from an extract result with the estimated mood trajectory, returning a plain data.frame that is convenient for further analysis or export.

Usage

get_mood(obj, ...)

Arguments

Value

A data.frame with one row per time period. Columns period, year, month, and quarter are inherited from the period table inside obj. When a single dimension was estimated an additional column mood is appended. When two dimensions were estimated, columns mood_dim1 and mood_dim2 are appended instead.

Examples

set.seed(1)
dat <- data.frame(
  varname = rep(c("a", "b", "c"), each = 20),
  date    = rep(seq(as.Date("1980-01-01"), by = "year", length.out = 20), 3),
  index   = 50 + rnorm(60, 0, 5),
  n       = 1000L
)
res <- extract(dat, n_col = "n", smoothing = FALSE)
mood_df <- get_mood(res)
head(mood_df)

Jennings Government Trust Data

Description

A dataset of survey marginals from the British Social Attitudes (BSA) survey, measuring public trust in government. These marginals are commonly used as input to the Dyad Ratios Algorithm for constructing latent time series. We replaced missing sample sizes with a value of 850, which is roughly the minimum sample size observed in the data.

Format

A data frame with 4 variables and 'r nrow(jennings)' rows:

variable

Character string identifying the survey question or series.

date

Date the survey was fielded.

value

percentage of people indicating distrust in the government.

n

Sample size for the survey wave.

Source

Jennings, W. N. Clarke, J. Moss and G. Stoker (2017). "The Decline in Diffuse Support for National Politics: The Long View on Political Discontent in Britain" In *Public Opinion Quarterly*, 81(3), 748-758. doi:10.1093/poq/nfx020

Examples

data(jennings)
head(jennings)

Plot a boot_dr object

Description

Draws a ribbon plot of the bootstrap confidence interval around the original mood estimate. Requires ggplot2; falls back to base R if unavailable.

Usage

## S3 method for class 'boot_dr'
plot(x, dim = 1L, title = "Estimated Public Mood", ylab = "Mood", ...)

Arguments

Value

Invisibly, the ggplot2 object or NULL (base R).

Plot estimated public mood

Description

Produces a time-series plot of the estimated latent mood. Requires ggplot2. If ggplot2 is not installed, falls back to base R graphics.

Usage

## S3 method for class 'extract'
plot(x, dim = 1L, title = "Estimated Public Mood", ylab = "Mood", ...)

Arguments

Value

Invisibly, the plot object (ggplot2) or NULL (base R).

Print method for extract objects

Description

Print method for extract objects

Usage

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

Arguments

Summary method for extract objects

Description

Prints a detailed report similar to the log file produced by the original 'MCalc' program, including the iteration history, variable loadings, and variance accounting.

Usage

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

Arguments

Value

Invisibly returns object.