Regression Workflows

Residual Flags

Check Fit Errors

Residual checks identify observations where the model missed by more than the allowed tolerance.

Program

Play the script to change the tolerance and count flagged residuals.

tolerance
residual_flags.R
Replay: real traced execution (multi-file project)
tolerance <- 3
actual <- c(52, 64, 70, 81)
predicted <- c(52, 61, 70, 79)
residual <- actual - predicted
flagged <- abs(residual) > tolerance
count <- sum(flagged)
label <- paste("flags", count, sep = ":")
cat(label, "\n", sep = "")

tolerance <- 2
actual <- c(52, 64, 70, 81)
predicted <- c(52, 61, 70, 79)
residual <- actual - predicted
flagged <- abs(residual) > tolerance
count <- sum(flagged)
label <- paste("flags", count, sep = ":")
cat(label, "\n", sep = "")

tolerance <- 5
actual <- c(52, 64, 70, 81)
predicted <- c(52, 61, 70, 79)
residual <- actual - predicted
flagged <- abs(residual) > tolerance
count <- sum(flagged)
label <- paste("flags", count, sep = ":")
cat(label, "\n", sep = "")

  1. tolerance ← 3

    1tolerance <- 32actual <- c(52, 64, 70, 81)
    values this step3tolerance

  2. actual ← 52, 64, 70, 81

    1tolerance <- 32actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)
    values this step52, 64, 70, 81actual

  3. predicted ← 52, 61, 70, 79

    2actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted
    values this step52, 61, 70, 79predicted

  4. residual ← 0, 3, 0, 2

    3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted5flagged <- abs(residual) > tolerance
    values this step0, 3, 0, 2residual52, 64, 70, 81actual52, 61, 70, 79predicted

  5. flagged ← FALSE, FALSE, FALSE, FALSE

    4residual <- actual - predicted5flagged <- abs(residual) > tolerance6count <- sum(flagged)
    values this stepFALSE, FALSE, FALSE, FALSEflagged0, 3, 0, 2residual3tolerance

  6. count ← 0

    5flagged <- abs(residual) > tolerance6count <- sum(flagged)7label <- paste("flags", count, sep = ":")
    values this step0countno flagged rowsflagged

  7. label ← flags:0

    6count <- sum(flagged)7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    values this stepflags:0label0count

  8. cat(label, " ", sep = "")

    7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    outputflags:0
    values this stepflags:0label

  1. tolerance ← 2

    1tolerance <- 22actual <- c(52, 64, 70, 81)
    values this step2tolerance

  2. actual ← 52, 64, 70, 81

    1tolerance <- 22actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)
    values this step52, 64, 70, 81actual

  3. predicted ← 52, 61, 70, 79

    2actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted
    values this step52, 61, 70, 79predicted

  4. residual ← 0, 3, 0, 2

    3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted5flagged <- abs(residual) > tolerance
    values this step0, 3, 0, 2residual52, 64, 70, 81actual52, 61, 70, 79predicted

  5. flagged ← FALSE, TRUE, FALSE, FALSE

    4residual <- actual - predicted5flagged <- abs(residual) > tolerance6count <- sum(flagged)
    values this stepFALSE, TRUE, FALSE, FALSEflagged0, 3, 0, 2residual2tolerance

  6. count ← 1

    5flagged <- abs(residual) > tolerance6count <- sum(flagged)7label <- paste("flags", count, sep = ":")
    values this step1count1 flagged rowflagged

  7. label ← flags:1

    6count <- sum(flagged)7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    values this stepflags:1label1count

  8. cat(label, " ", sep = "")

    7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    outputflags:1
    values this stepflags:1label

  1. tolerance ← 5

    1tolerance <- 52actual <- c(52, 64, 70, 81)
    values this step5tolerance

  2. actual ← 52, 64, 70, 81

    1tolerance <- 52actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)
    values this step52, 64, 70, 81actual

  3. predicted ← 52, 61, 70, 79

    2actual <- c(52, 64, 70, 81)3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted
    values this step52, 61, 70, 79predicted

  4. residual ← 0, 3, 0, 2

    3predicted <- c(52, 61, 70, 79)4residual <- actual - predicted5flagged <- abs(residual) > tolerance
    values this step0, 3, 0, 2residual52, 64, 70, 81actual52, 61, 70, 79predicted

  5. flagged ← FALSE, FALSE, FALSE, FALSE

    4residual <- actual - predicted5flagged <- abs(residual) > tolerance6count <- sum(flagged)
    values this stepFALSE, FALSE, FALSE, FALSEflagged0, 3, 0, 2residual5tolerance

  6. count ← 0

    5flagged <- abs(residual) > tolerance6count <- sum(flagged)7label <- paste("flags", count, sep = ":")
    values this step0countno flagged rowsflagged

  7. label ← flags:0

    6count <- sum(flagged)7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    values this stepflags:0label0count

  8. cat(label, " ", sep = "")

    7label <- paste("flags", count, sep = ":")8cat(label, "\n", sep = "")
    outputflags:0
    values this stepflags:0label
residual A residual is the actual value minus the predicted value.
absolute error `abs(residual)` measures miss size without direction.
flag Rows beyond the tolerance deserve another look.