Observation weights and missing data"
In rwa: Perform a Relative Weights Analysis

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(rwa)

Weighted point estimates

Use weight to name a numeric column of observation weights. Non-missing weights must be finite and strictly positive; zero weights are not supported. The outcome and predictors must be numeric. Weighted RWA decomposes the R-squared from the weighted correlation matrix, matching weighted least-squares regression with an intercept on the same retained rows.

d <- mtcars
d$observation_weight <- rep(c(1, 2, 5, 3), 8)
fit <- rwa(d, "mpg", c("hp", "wt", "disp"), method = "multiple",
           weight = "observation_weight", sort = FALSE)
fit$result
c(rwa = fit$rsquare,
  weighted_lm = summary(lm(mpg ~ hp + wt + disp, data = d,
                           weights = observation_weight))$r.squared)

Multiplying every weight by the same positive constant leaves relative weights, R-squared, and n_effective unchanged. Integer weights also give the same point estimates as repeating each row that many times. These point-estimate equivalences do not imply equivalent bootstrap sampling designs.

d$scaled_weight <- 100 * d$observation_weight
scaled <- rwa_multiregress(d, "mpg", c("hp", "wt", "disp"), weight = "scaled_weight")
all.equal(fit$result, scaled$result)
c(original_effective = fit$n_effective, scaled_effective = scaled$n_effective)

Missing-data contract

Rows with missing outcomes are always removed first, even with use = "all.obs" or "everything". For unweighted analysis, use then applies to correlations among the retained outcome and predictors:

| use | Unweighted behavior after removing missing outcomes | |---|---| | "pairwise.complete.obs" (default) | Use available pairs for each correlation. An incompatible joint matrix causes an error. | | "complete.obs" | Use rows complete on all analysis variables. | | "na.or.complete" | Use complete rows when available. cor() would return NAs when none exist; RWA instead reports insufficient data. | | "all.obs" | Error if remaining predictors contain missing values. | | "everything" | Missing values propagate to correlations; a non-finite matrix causes an informative RWA error. |

With weights, the default is effectively complete-case analysis, not weighted pairwise correlation. After removing missing outcomes:

Missing weights cause an error for "all.obs" and are removed for every other mode.
Rows with missing predictors are removed for every weighted mode, including "all.obs" and "everything".

These exceptions preserve the package's existing preprocessing. all.obs does not mean that every value in the original input must be observed. Weights are validated for type, finiteness, and positivity before row filtering.

d$mpg[1] <- NA
d$observation_weight[2] <- NA
d$hp[3] <- NA
missing_fit <- rwa(d, "mpg", c("hp", "wt", "disp"), method = "multiple",
                   weight = "observation_weight")
retained <- complete.cases(d[c("mpg", "hp", "wt", "disp", "observation_weight")])
c(n = missing_fit$n, retained_rows = sum(retained),
  n_weighted = missing_fit$n_weighted,
  retained_weight_sum = sum(d$observation_weight[retained]))

Counts and effective sample size

n retains its existing meaning: the complete-case count for the selected variables (and weight, if provided). Unweighted pairwise correlations may use more observations than this conservative count.
n_weighted is included only for weighted results and sums original weights after outcome, missing-weight, and predictor-completeness filters. It represents population size only when weights are calibrated to that scale and the retained rows represent the intended population scope. Arbitrarily normalized weights do not supply a population count.
n_effective is included only for weighted results and is Kish's unequal-weighting effective sample size: (sum(w)^2) / sum(w^2). The calculation first scales weights by their maximum to avoid overflow or underflow when squaring them. It ignores clustering, stratification, and relationships between weights and outcomes. It is not model degrees of freedom, exact RWA precision, or a complete survey design effect.

Both exported paths, rwa() and rwa_multiregress(), return these weighted-only diagnostics. Unweighted result names and structure are unchanged.

plot_rwa() reports both diagnostics in the caption for weighted results, so a weighted chart is not mistaken for an unweighted one:

plot_rwa(fit)

Bootstrap scope and failure policy

bootstrap = TRUE performs independent, identically distributed (iid) individual-row resampling with replacement. Each sampled row carries its original weight, and sampling is not proportional to weights. Missing outcomes are removed before resampling, but missing-weight and predictor filters apply within each sample. Consequently, the sampling frame can be larger than the point estimate's complete-case data and individual samples can retain different numbers of rows.

A weight column does not encode clusters, strata, selection dependence, or replicate-weight designs. This interface is observation-weighted RWA with an iid-row bootstrap, not general complex-survey variance estimation. The appropriateness of ordinary row resampling depends on the sampling design; use design-aware methods when the iid-row assumptions are unsuitable.

set.seed(42)
boot_fit <- rwa(d, "mpg", c("hp", "wt", "disp"), method = "multiple",
                weight = "observation_weight", bootstrap = TRUE,
                n_bootstrap = 1000, comprehensive = TRUE)
boot_fit$bootstrap$ci_results$random_comparison

Comprehensive analysis compares each requested predictor to an added random predictor, even without a focal predictor. Supplying, for example, focal = "wt" additionally compares the other predictors to wt. Interval labels follow the requested predictor order, independently of result sorting.

Every sample must estimate the same requested model. A constant column, singular predictor matrix, or invalid joint matrix causes a clear error and stops the bootstrap. Samples are not skipped or retried, predictors are not dropped, and statistics are not recycled. A rare binary predictor that loses its minority category in a sample is one such failure. Inspect predictor variation, sample size, and missingness rather than treating incomplete bootstrap output as valid intervals.

Matrix diagnostics

The full joint outcome/predictor correlation matrix must be finite, have the expected variable identities and dimensions, and be positive semidefinite within tolerance. Its smallest eigenvalue must be at least minus sqrt(.Machine$double.eps) * max(1, max(abs(eigenvalues))). The predictor block must have strictly positive computed eigenvalues and its transformation must be solvable. No additional conditioning cutoff is imposed on previously estimable models. Highly correlated predictors can still produce sensitive estimates, so inspect their stability rather than treating successful numerical execution as evidence of reliable individual weights.

Calculated R-squared is also checked: values above 1 + sqrt(.Machine$double.eps) produce an error, not a clipped result. This separate check is needed because nearly collinear predictors can amplify a small joint-matrix error into a materially impossible fit.

A singular joint matrix is valid for an exact fit when the predictor block remains invertible. A materially indefinite joint matrix is not valid, even if its predictor block is invertible: pairwise correlations computed on different row subsets can otherwise produce an impossible R-squared above one. Consider use = "complete.obs" and inspect missingness if this occurs; changing the retained sample changes the analysis and is not an automatic repair.

No eigenvalue clipping, matrix repair, or automatic change to complete cases is performed. Constant variables, insufficient observations, and zero R-squared (for which rescaled relative weights are undefined) also produce informative errors.