tests/testthat/_snaps/evalPbc.md

In sachseka/eatPrep: Prepare Data for IRT Analyses

evalPbc identifies non-problematic frequency and correlation pattern [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies non-problematic frequency and correlation pattern [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies non-problematic frequency and correlation pattern [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies non-problematic frequency and correlation pattern [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies zero-frequencies for attractors and throws message [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  x The attractors (score 1) of the following 3 items were chosen with a
  frequency of zero: I1, I2, and I3. This should not happen. Please check.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$zeroFreqAtt

evalPbc identifies zero-frequencies for attractors and throws message [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [31mx[39m The attractors (score 1) of the following 3 items were chosen with a
  frequency of zero: [32mI1[39m, [32mI2[39m, and [32mI3[39m. This should not happen. Please check.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$zeroFreqAtt

evalPbc identifies zero-frequencies for attractors and throws message [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✖ The attractors (score 1) of the following 3 items were chosen with a
  frequency of zero: I1, I2, and I3. This should not happen. Please check.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$zeroFreqAtt

evalPbc identifies zero-frequencies for attractors and throws message [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [31m✖[39m The attractors (score 1) of the following 3 items were chosen with a
  frequency of zero: [32mI1[39m, [32mI2[39m, and [32mI3[39m. This should not happen. Please check.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$zeroFreqAtt

evalPbc identifies (unproblematic) zero-frequencies for distractors and throws message [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ! The distractors (score 0) of the following 3 items were chosen with a
  frequency of zero: I1_1, I2_2, I3_1, I1_2, I2_1, and I3_2. This may happen, but
  is probably not intended.
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$zeroFreqDis

evalPbc identifies (unproblematic) zero-frequencies for distractors and throws message [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [33m![39m The distractors (score 0) of the following 3 items were chosen with a
  frequency of zero: [32mI1_1[39m, [32mI2_2[39m, [32mI3_1[39m, [32mI1_2[39m, [32mI2_1[39m, and [32mI3_2[39m. This may happen, but
  is probably not intended.
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$zeroFreqDis

evalPbc identifies (unproblematic) zero-frequencies for distractors and throws message [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ! The distractors (score 0) of the following 3 items were chosen with a
  frequency of zero: I1_1, I2_2, I3_1, I1_2, I2_1, and I3_2. This may happen, but
  is probably not intended.
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$zeroFreqDis

evalPbc identifies (unproblematic) zero-frequencies for distractors and throws message [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [33m![39m The distractors (score 0) of the following 3 items were chosen with a
  frequency of zero: [32mI1_1[39m, [32mI2_2[39m, [32mI3_1[39m, [32mI1_2[39m, [32mI2_1[39m, and [32mI3_2[39m. This may happen, but
  is probably not intended.
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$zeroFreqDis

evalPbc identifies low correlations (< .05) for attractors per default [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for attractors (score 1) of the following 2 items are worrisome low (< 0.05) or missing: I1:_0.05 and I2:_0.05
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies low correlations (< .05) for attractors per default [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for attractors (score 1) of the following 2 items are worrisome low (< 0.05) or missing: [32m[32mI1:_0.05[32m[39m and [32m[32mI2:_0.05[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies low correlations (< .05) for attractors per default [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for attractors (score 1) of the following 2 items are worrisome low (< 0.05) or missing: I1:_0.05 and I2:_0.05
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies low correlations (< .05) for attractors per default [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for attractors (score 1) of the following 2 items are worrisome low (< 0.05) or missing: [32m[32mI1:_0.05[32m[39m and [32m[32mI2:_0.05[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc accepts user-defined correlation cutoffs for attractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: I1:_0.02
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc accepts user-defined correlation cutoffs for attractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: [32m[32mI1:_0.02[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc accepts user-defined correlation cutoffs for attractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: I1:_0.02
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc accepts user-defined correlation cutoffs for attractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: [32m[32mI1:_0.02[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies low user-defined correlations for attractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), minPbcAtt = 0.1)
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.1) or missing: I1:_0.09
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies low user-defined correlations for attractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), minPbcAtt = 0.1)
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.1) or missing: [32m[32mI1:_0.09[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies low user-defined correlations for attractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), minPbcAtt = 0.1)
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.1) or missing: I1:_0.09
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies low user-defined correlations for attractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), minPbcAtt = 0.1)
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.1) or missing: [32m[32mI1:_0.09[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies missing correlations for attractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: I1:_NA
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies missing correlations for attractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: [32m[32mI1:_NA[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$lowMisPbcAtt

evalPbc identifies missing correlations for attractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: I1:_NA
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies missing correlations for attractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for attractors (score 1) of the following 1 item are worrisome low (< 0.05) or missing: [32m[32mI1:_NA[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$lowMisPbcAtt

evalPbc identifies too high correlations (> .005) for distractors per default [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for distractors (score 0) of the following 2 items are unexpectedly high (> 0.005): I1_1_0.01, I1_2_0.01, and I2_1_0.01
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$highPbcDis

evalPbc identifies too high correlations (> .005) for distractors per default [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for distractors (score 0) of the following 2 items are unexpectedly high (> 0.005): [32m[32mI1_1_0.01[32m[39m, [32m[32mI1_2_0.01[32m[39m, and [32m[32mI2_1_0.01[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$highPbcDis

evalPbc identifies too high correlations (> .005) for distractors per default [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for distractors (score 0) of the following 2 items are unexpectedly high (> 0.005): I1_1_0.01, I1_2_0.01, and I2_1_0.01
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$highPbcDis

evalPbc identifies too high correlations (> .005) for distractors per default [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for distractors (score 0) of the following 2 items are unexpectedly high (> 0.005): [32m[32mI1_1_0.01[32m[39m, [32m[32mI1_2_0.01[32m[39m, and [32m[32mI2_1_0.01[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$highPbcDis

evalPbc accepts user-defined correlation cutoffs for distractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 0.1)
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for distractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 0.1)
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for distractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 0.1)
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for distractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 0.1)
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies too high user defined correlations for distractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 1e-04)
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  x catPbcs for distractors (score 0) of the following 1 item are unexpectedly high (> 0.0001): I1_1_0
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$highPbcDis

evalPbc identifies too high user defined correlations for distractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 1e-04)
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31mx[39m catPbcs for distractors (score 0) of the following 1 item are unexpectedly high (> 0.0001): [32m[32mI1_1_0[32m[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * output$highPbcDis

evalPbc identifies too high user defined correlations for distractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 1e-04)
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ✖ catPbcs for distractors (score 0) of the following 1 item are unexpectedly high (> 0.0001): I1_1_0
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$highPbcDis

evalPbc identifies too high user defined correlations for distractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcDis = 1e-04)
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [31m✖[39m catPbcs for distractors (score 0) of the following 1 item are unexpectedly high (> 0.0001): [32m[32mI1_1_0[32m[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • output$highPbcDis

evalPbc ignores missing correlations for distractors [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for distractors [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for distractors [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for distractors [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies too high correlations (> .07) for missings per default [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ! catPbcs for mistype 'mir' of the following 2 items are relatively high (>
  0.07): I1_8_0.08 and I2_8_0.08
  ! catPbcs for mistype 'mbi' of the following 1 item are relatively high (>
  0.07): I1_9_0.08
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * `output$highPbcMis$mir`
  * `output$highPbcMis$mbi`

evalPbc identifies too high correlations (> .07) for missings per default [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [33m![39m catPbcs for mistype 'mir' of the following 2 items are relatively high (>
  0.07): [32mI1_8_0.08[39m and [32mI2_8_0.08[39m
  [33m![39m catPbcs for mistype 'mbi' of the following 1 item are relatively high (>
  0.07): [32mI1_9_0.08[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * `output$highPbcMis$mir`
  * `output$highPbcMis$mbi`

evalPbc identifies too high correlations (> .07) for missings per default [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ! catPbcs for mistype 'mir' of the following 2 items are relatively high (>
  0.07): I1_8_0.08 and I2_8_0.08
  ! catPbcs for mistype 'mbi' of the following 1 item are relatively high (>
  0.07): I1_9_0.08
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • `output$highPbcMis$mir`
  • `output$highPbcMis$mbi`

evalPbc identifies too high correlations (> .07) for missings per default [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [33m![39m catPbcs for mistype 'mir' of the following 2 items are relatively high (>
  0.07): [32mI1_8_0.08[39m and [32mI2_8_0.08[39m
  [33m![39m catPbcs for mistype 'mbi' of the following 1 item are relatively high (>
  0.07): [32mI1_9_0.08[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • `output$highPbcMis$mir`
  • `output$highPbcMis$mbi`

evalPbc accepts user-defined correlation cutoffs for missings [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.11)
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for missings [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.11)
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for missings [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.11)
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc accepts user-defined correlation cutoffs for missings [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.11)
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc identifies too high user-defined correlations for missings [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.01)
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ! catPbcs for mistype 'mir' of the following 1 item are relatively high (>
  0.01): I1_8_0.05
  i For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * `output$highPbcMis$mir`

evalPbc identifies too high user-defined correlations for missings [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.01)
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [33m![39m catPbcs for mistype 'mir' of the following 1 item are relatively high (>
  0.01): [32mI1_8_0.05[39m
  [36mi[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  * `output$highPbcMis$mir`

evalPbc identifies too high user-defined correlations for missings [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.01)
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.
  ! catPbcs for mistype 'mir' of the following 1 item are relatively high (>
  0.01): I1_8_0.05
  ℹ For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • `output$highPbcMis$mir`

evalPbc identifies too high user-defined correlations for missings [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"), maxPbcMis = 0.01)
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.
  [33m![39m catPbcs for mistype 'mir' of the following 1 item are relatively high (>
  0.01): [32mI1_8_0.05[39m
  [36mℹ[39m For a list of problematic items, save the `output` of this function and
  return the item names as a vector:
  • `output$highPbcMis$mir`

evalPbc ignores missing correlations for missings [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for missings [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for missings [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc ignores missing correlations for missings [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc allows for user-defined missing codes [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc allows for user-defined missing codes [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc allows for user-defined missing codes [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc allows for user-defined missing codes [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws an error if the data frame does not contain freq, recodevalue, and catPbc (with the exact spelling [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Condition
  Error in `evalPbc()`:
  ! 'pbcs' should be a data.frame as generated by eatPrep::catPbc()

evalPbc throws an error if the data frame does not contain freq, recodevalue, and catPbc (with the exact spelling [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Condition
  [1m[33mError[39m in `evalPbc()`:[22m
  [1m[22m[33m![39m 'pbcs' should be a [34mdata.frame[39m as generated by [34meatPrep::catPbc()[39m

evalPbc throws an error if the data frame does not contain freq, recodevalue, and catPbc (with the exact spelling [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Condition
  Error in `evalPbc()`:
  ! 'pbcs' should be a data.frame as generated by eatPrep::catPbc()

evalPbc throws an error if the data frame does not contain freq, recodevalue, and catPbc (with the exact spelling [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Condition
  [1m[33mError[39m in `evalPbc()`:[22m
  [1m[22m[33m![39m 'pbcs' should be a [34mdata.frame[39m as generated by [34meatPrep::catPbc()[39m

evalPbc throws a message if the data frame contains missing types that are not specified in the mistypes argument [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  i catPbc contains other values than 0, 1 and the specified mistypes: Other
  value(s): mycode
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws a message if the data frame contains missing types that are not specified in the mistypes argument [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [36mi[39m [34mcatPbc[39m contains other values than 0, 1 and the specified mistypes: Other
  value(s): [32mmycode[39m
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws a message if the data frame contains missing types that are not specified in the mistypes argument [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  ℹ catPbc contains other values than 0, 1 and the specified mistypes: Other
  value(s): mycode
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws a message if the data frame contains missing types that are not specified in the mistypes argument [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mir", "mbi"))
Message
  [36mℹ[39m [34mcatPbc[39m contains other values than 0, 1 and the specified mistypes: Other
  value(s): [32mmycode[39m
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws an error if the mistypes specification contains missing types that are not specified in the data frame [plain]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  i catPbc contains other values than 0, 1 and the specified mistypes: Other
  value(s): mir
  v Excellent, no attractors (score 1) were chosen with a frequency of zero.
  v Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws an error if the mistypes specification contains missing types that are not specified in the data frame [ansi]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  [36mi[39m [34mcatPbc[39m contains other values than 0, 1 and the specified mistypes: Other
  value(s): [32mmir[39m
  [32mv[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32mv[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws an error if the mistypes specification contains missing types that are not specified in the data frame [unicode]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  ℹ catPbc contains other values than 0, 1 and the specified mistypes: Other
  value(s): mir
  ✔ Excellent, no attractors (score 1) were chosen with a frequency of zero.
  ✔ Excellent, no distractors (score 0) were chosen with a frequency of zero.

evalPbc throws an error if the mistypes specification contains missing types that are not specified in the data frame [fancy]

Code
  evalPbc(test_pbc, mistypes = c("mnr", "mbd", "mycode", "mbi"))
Message
  [36mℹ[39m [34mcatPbc[39m contains other values than 0, 1 and the specified mistypes: Other
  value(s): [32mmir[39m
  [32m✔[39m Excellent, no attractors (score 1) were chosen with a frequency of zero.
  [32m✔[39m Excellent, no distractors (score 0) were chosen with a frequency of zero.

sachseka/eatPrep documentation built on June 9, 2025, 9:36 a.m.