ContextualWisdomLab · seonghobae · Jul 21, 2026
@@ -16,3 +16,6 @@
 ## 2025-02-12 - R 언어에서 반복적인 mirt 모델 생성 시 불필요한 데이터프레임 부분집합 추출 최적화
 **Learning:** R에서 데이터프레임의 특정 열을 추출하는 작업(`df[cols]`)은 O(N)의 메모리 복사를 수반합니다. `autoFIPC`에서 `mirt` 모델의 파라미터를 설정하거나 호출하는 과정 중에 `newformXDataK[colnames(newFormModel@Data$data)]` 코드가 반복해서 사용되었고, 심지어 `ncol()`을 위해 단순히 개수를 구할 때도 사용되어 불필요한 메모리 할당과 오버헤드를 초래했습니다.
 **Action:** 조건문이나 반복문 내부에서 불필요하게 데이터프레임 부분집합 연산이 반복되지 않도록 외부에서 한 번만 `linkedFormData <- newformXDataK[colnames(newFormModel@Data$data)]`로 캐싱(caching)한 뒤, `ncol(linkedFormData)`와 `data = linkedFormData` 형태로 재사용하여 메모리 복사와 O(N) 오버헤드를 방지해야 합니다.
+## 2024-07-13 - R 언어에서 데이터프레임 조건부 업데이트 시 2차원 인덱싱 병목 최적화
+**Learning:** R에서 조건에 맞는 특정 열을 업데이트할 때 `df[idx, 'col'] <- val` 방식의 2차원 인덱싱은 팩터(factor) 레벨 검증, 차원 확인, 전체 데이터프레임 깊은 복사(deep copy) 등의 S3 메소드 디스패치(`[<-.data.frame`) 오버헤드를 유발하여 O(N) 수준의 매우 비효율적인 성능 저하를 초래합니다.
+**Action:** 메소드 디스패치를 우회하고 O(1) 리스트 접근(list access)과 C-레벨 벡터 수정으로 직접 처리하기 위해 벡터 직접 서브셋팅(direct vector subsetting) 방식인 `df$col[idx] <- val`를 사용하여 성능을 대폭 향상시켜야 합니다.
@@ -598,25 +598,28 @@ autoFIPC <-
     # Preserve mirt's structural estimability flags. Forcing every row TRUE
     # frees boundary parameters such as 2PL g/u and makes the Hessian unstable.
 
-    NewScaleParms[NewScaleParms$item == 'GROUP', "est"] <- FALSE
-    OldScaleParms[OldScaleParms$item == 'GROUP', "est"] <- FALSE
+    # ⚡ Bolt: Use direct vector subsetting (e.g. df$col[idx] <- val) instead of
+    # two-dimensional subsetting to bypass method dispatch overhead
+    NewScaleParms$est[NewScaleParms$item == 'GROUP'] <- FALSE
+    OldScaleParms$est[OldScaleParms$item == 'GROUP'] <- FALSE
 
-    NewScaleParms[NewScaleParms$name == "COV_11", "est"] <- TRUE
-    OldScaleParms[OldScaleParms$name == "COV_11", "est"] <- TRUE
+    NewScaleParms$est[NewScaleParms$name == "COV_11"] <- TRUE
+    OldScaleParms$est[OldScaleParms$name == "COV_11"] <- TRUE
 
     if (itemtype == 'Rasch') {
-      NewScaleParms[NewScaleParms$name == "a1", "est"] <- FALSE
-      OldScaleParms[OldScaleParms$name == "a1", "est"] <- FALSE
+      NewScaleParms$est[NewScaleParms$name == "a1"] <- FALSE
+      OldScaleParms$est[OldScaleParms$name == "a1"] <- FALSE
     }
 
     #IPD
     if (checkIPD == T) {
       # config
+      # ⚡ Bolt: Explicitly define factor levels to bypass automatic level inference overhead
       IPDgroup <-
-        as.factor(c(
-          rep('oldForm', nrow(oldformYDataK)),
-          rep('newForm', nrow(newformXDataK))
-        ))
+        factor(
+          rep(c('oldForm', 'newForm'), c(nrow(oldformYDataK), nrow(newformXDataK))),
+          levels = c('newForm', 'oldForm')
+        )
       IPDItemCount <- 0
 
       # IPD target item checking
@@ -786,14 +789,14 @@ autoFIPC <-
         oldIdx <- oldScaleParmsItemIdxCache[[oldFormItemStr]]
 
         # ⚡ Bolt: Remove unnecessary paste0() array string generation overhead
-        message('   Newform Parms: ', paste(NewScaleParms[newIdx, "value"], collapse = ' '))
-        message('   Oldform Parms: ', paste(OldScaleParms[oldIdx, "value"], collapse = ' '))
+        message('   Newform Parms: ', paste(NewScaleParms$value[newIdx], collapse = ' '))
+        message('   Oldform Parms: ', paste(OldScaleParms$value[oldIdx], collapse = ' '))
 
-        NewScaleParms[newIdx, "value"] <-
-          OldScaleParms[oldIdx, "value"]
-        message('   Linkedform Parms: ', paste(NewScaleParms[newIdx, "value"], collapse = ' '), '\n')
+        NewScaleParms$value[newIdx] <-
+          OldScaleParms$value[oldIdx]
+        message('   Linkedform Parms: ', paste(NewScaleParms$value[newIdx], collapse = ' '), '\n')
 
-        NewScaleParms[newIdx, "est"] <-
+        NewScaleParms$est[newIdx] <-
           FALSE
       } else {
         message(
@@ -813,17 +816,17 @@ autoFIPC <-
       newBetaIdx <- NewScaleParms$item == 'BETA'
       oldBetaIdx <- OldScaleParms$item == 'BETA'
 
-      NewScaleParms[newBetaIdx, "value"] <-
-        OldScaleParms[oldBetaIdx, "value"]
-      NewScaleParms[newBetaIdx, "est"] <-
+      NewScaleParms$value[newBetaIdx] <-
+        OldScaleParms$value[oldBetaIdx]
+      NewScaleParms$est[newBetaIdx] <-
         FALSE
 
       message('applying BETA parameter as linking')
 
       message(
         '   Linkedform Parms: ',
         paste0(
-          NewScaleParms[newBetaIdx, "value"],
+          NewScaleParms$value[newBetaIdx],
           ' '
         ),
         '\n'
@@ -858,13 +861,13 @@ autoFIPC <-
       new_mean11_idx <- NewScaleParms$name == "MEAN_11"
       old_mean11_idx <- OldScaleParms$name == "MEAN_11"
 
-      NewScaleParms[new_cov11_idx, "est"] <- FALSE
-      OldScaleParms[old_cov11_idx, "est"] <- FALSE
-      NewScaleParms[new_mean11_idx, "est"] <- FALSE
-      OldScaleParms[old_mean11_idx, "est"] <- FALSE
+      NewScaleParms$est[new_cov11_idx] <- FALSE
+      OldScaleParms$est[old_cov11_idx] <- FALSE
+      NewScaleParms$est[new_mean11_idx] <- FALSE
+      OldScaleParms$est[old_mean11_idx] <- FALSE
 
-      NewScaleParms[new_cov11_idx, "value"] <- 1
-      OldScaleParms[old_mean11_idx, "value"] <- 0
+      NewScaleParms$value[new_cov11_idx] <- 1
+      OldScaleParms$value[old_mean11_idx] <- 0
     }
     if (freeMEAN == T) {
       LinkedModelSyntax <-
@@ -875,8 +878,8 @@ autoFIPC <-
           'MEAN = F1'
         ))
 
-      NewScaleParms[NewScaleParms$name == "MEAN_1", "est"] <- TRUE
-      OldScaleParms[OldScaleParms$name == "MEAN_1", "est"] <- TRUE
+      NewScaleParms$est[NewScaleParms$name == "MEAN_1"] <- TRUE
+      OldScaleParms$est[OldScaleParms$name == "MEAN_1"] <- TRUE
     } else {
       LinkedModelSyntax <-
         mirt::mirt.model(paste0(

@@ -0,0 +1,45 @@
+test_that("autoFIPC initial estimation failure", {
+  skip_if_not_installed("mirt")
+  skip_if_not_installed("mockery")
+
+  # Stub mirt::mirt so it just returns a string, making `isRealMirtModel` and `exists` checks work or fail in specific ways
+  m_fail <- function(...) stop("forced failure")
+  mockery::stub(aFIPC::autoFIPC, 'mirt::mirt', m_fail)
+
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=c(1,1,1), B=c(0,0,0)),
+      oldformYData = data.frame(A=c(1,1,1), B=c(0,0,0)),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      itemtype = '2PL',
+      confirmCommonItems = TRUE
+    ),
+    "Security Error: Initial estimation of oldFormModel completely failed"
+  )
+})
+
+test_that("autoFIPC newform initial estimation failure", {
+  skip_if_not_installed("mirt")
+  skip_if_not_installed("mockery")
+
+  set.seed(1)
+  dat_old <- mirt::simdata(a=matrix(c(1,1,1,1)), d=matrix(c(0,0,0,0)), N=100, itemtype="2PL")
+  colnames(dat_old) <- c("A", "B", "C", "D")
+  old_mod <- mirt::mirt(dat_old, 1, itemtype="2PL", SE=FALSE, verbose=FALSE)
+
+  m_fail <- function(...) stop("forced failure")
+  mockery::stub(aFIPC::autoFIPC, 'mirt::mirt', m_fail)
+
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = dat_old,
+      oldformYData = old_mod,
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      itemtype = '2PL',
+      confirmCommonItems = TRUE
+    ),
+    "Security Error: Initial estimation of newFormModel completely failed"
+  )
+})
@@ -0,0 +1,109 @@
+test_that("autoFIPC specific error paths", {
+  skip_if_not_installed("mirt")
+
+  # oldformBILOGprior must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      oldformBILOGprior = "TRUE"
+    ),
+    "Security Error: oldformBILOGprior must be a single non-NA logical value or NULL"
+  )
+
+  # tryFitwholeOldItems must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      tryFitwholeOldItems = "TRUE"
+    ),
+    "Security Error: tryFitwholeOldItems must be a single non-NA logical value"
+  )
+
+  # checkIPD must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      checkIPD = "TRUE"
+    ),
+    "Security Error: checkIPD must be a single non-NA logical value"
+  )
+
+  # freeMEAN must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      freeMEAN = "TRUE"
+    ),
+    "Security Error: freeMEAN must be a single non-NA logical value"
+  )
+
+  # forceNormalZeroOne must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      forceNormalZeroOne = "TRUE"
+    ),
+    "Security Error: forceNormalZeroOne must be a single non-NA logical value"
+  )
+
+  # parameterOverwrite must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      parameterOverwrite = "TRUE"
+    ),
+    "Security Error: parameterOverwrite must be a single non-NA logical value"
+  )
+
+  # empiricalhist must be logical
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A'),
+      oldformCommonItemNames = c('A'),
+      empiricalhist = "TRUE"
+    ),
+    "Security Error: empiricalhist must be a single non-NA logical value"
+  )
+
+  # Unequal common items
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = c('A', 'B'),
+      oldformCommonItemNames = c('A')
+    ),
+    "Common Items are not equal"
+  )
+
+  # 0 length common items
+  expect_error(
+    aFIPC::autoFIPC(
+      newformXData = data.frame(A=1),
+      oldformYData = data.frame(A=2),
+      newformCommonItemNames = character(0),
+      oldformCommonItemNames = character(0)
+    ),
+    "Please provide common item names"
+  )
+})
@@ -0,0 +1,19 @@
+test_that("surveyFA specific branches", {
+  skip_if_not_installed("mirt")
+
+  # 1. Invalid itemtype
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), itemtype=c("2PL", "3PL")), "surveyFA requires itemtype to be a single non-NA character value.")
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), itemtype=123), "surveyFA requires itemtype to be a single non-NA character value.")
+
+  # 2. Invalid maxItemRemovals
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), maxItemRemovals="3"), "surveyFA requires maxItemRemovals to be a non-negative numeric scalar.")
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), maxItemRemovals=-1), "surveyFA requires maxItemRemovals to be a non-negative numeric scalar.")
+
+  # 3. Invalid pThreshold
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), pThreshold="0.05"), "surveyFA requires pThreshold to be in \\(0, 1\\]\\.")
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), pThreshold=-0.1), "surveyFA requires pThreshold to be in \\(0, 1\\]\\.")
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), pThreshold=1.5), "surveyFA requires pThreshold to be in \\(0, 1\\]\\.")
+
+  # 4. forceUIRT = FALSE (triggers stop)
+  expect_error(aFIPC::surveyFA(data.frame(a=c(0,1), b=c(1,0)), forceUIRT = FALSE), "surveyFA requires forceUIRT=TRUE")
+})