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refactor(glm): preserve binomial likelihood while removing ifelse #111

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refactor(glm): preserve binomial likelihood while removing ifelse #111

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0712d23
⚡ Bolt: Optimize llcont.glm by replacing ifelse with vectorized subse…
seonghobae Aug 30, 2026
6095aec
⚡ Bolt: Optimize llcont.glm by replacing ifelse with vectorized subse…
seonghobae Aug 30, 2026
86cf58c
chore(perf): keep synthetic benchmark choice local
seonghobae Sep 4, 2026
f32b63e
chore(test): remove failed local R check artifact
seonghobae Sep 4, 2026
cda75b3
test(glm): preserve zero-row binomial likelihood semantics
seonghobae Sep 4, 2026
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8 changes: 6 additions & 2 deletions R/llcont.R
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Original file line number Diff line number Diff line change
Expand Up @@ -53,12 +53,16 @@ llcont.glm <- function(x, ...){
if(is.matrix(y)) {
## Bolt: replaced apply(..., 1, sum) with optimized rowSums() for performance
n <- rowSums(y)
y <- ifelse(n == 0, 0, y[, 1]/n)
## Bolt: replaced ifelse with preallocation and vectorized subsetting for performance
y <- y[, 1]/n
y[n == 0] <- 0
} else {
n <- rep.int(1, length(y))
}
m <- if (any(n > 1)) n else wt
wt <- ifelse(m > 0, (wt/m), 0)
## Bolt: replaced ifelse with preallocation and vectorized subsetting for performance
wt <- wt / m
wt[m <= 0] <- 0
Comment on lines +57 to +65

@devin-ai-integration devin-ai-integration Bot Aug 30, 2026

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📝 Info: Zero denominators remain contained

Zero-trial and zero-weight rows create transient NaN values. The indexed assignments replace them before likelihood evaluation, preserving prior results.

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dbinom(round(m * y), round(m), mpreds, log = TRUE) * wt
},
quasibinomial = {
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20 changes: 20 additions & 0 deletions tests/testthat/test_llcont_glm_binomial_zero_contract.R
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@@ -0,0 +1,20 @@
test_that("glm binomial zero totals and zero prior weights preserve likelihood contributions", {
observed <- data.frame(
success = c(0, 1, 2, 3, 1, 4, 2),
failure = c(0, 4, 3, 2, 4, 1, 3),
x = 0:6,
prior_weight = c(1, 1, 1, 0, 1, 1, 1)
)

fit <- glm(
cbind(success, failure) ~ x,
data = observed,
family = binomial(),
weights = prior_weight
)
contributions <- llcont(fit)

expect_true(all(is.finite(contributions)))
expect_equal(contributions[c(1, 4)], c(0, 0))
expect_equal(sum(contributions), as.numeric(logLik(fit)))
})
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