Diagnostics and Quality Checks

Outlier Flag

Check Distance From Center

Outlier checks identify observations that sit far away from the usual values.

Program

Play the script to change the distance limit and see how many values are flagged.

limit
outlier_flag.R
Replay: real traced execution (multi-file project)
limit <- 10
value <- c(42, 44, 43, 58)
center <- median(value)
distance <- abs(value - center)
flagged <- distance > limit
count <- sum(flagged)
label <- paste("outliers", count, sep = ":")
cat(label, "\n", sep = "")

limit <- 5
value <- c(42, 44, 43, 58)
center <- median(value)
distance <- abs(value - center)
flagged <- distance > limit
count <- sum(flagged)
label <- paste("outliers", count, sep = ":")
cat(label, "\n", sep = "")

limit <- 20
value <- c(42, 44, 43, 58)
center <- median(value)
distance <- abs(value - center)
flagged <- distance > limit
count <- sum(flagged)
label <- paste("outliers", count, sep = ":")
cat(label, "\n", sep = "")

  1. limit ← 10

    1limit <- 102value <- c(42, 44, 43, 58)
    values this step10limit

  2. value ← 42, 44, 43, 58

    1limit <- 102value <- c(42, 44, 43, 58)3center <- median(value)
    values this step42, 44, 43, 58value

  3. center ← 43.5

    2value <- c(42, 44, 43, 58)3center <- median(value)4distance <- abs(value - center)
    values this step43.5center42, 44, 43, 58value

  4. distance ← 1.5, 0.5, 0.5, 14.5

    3center <- median(value)4distance <- abs(value - center)5flagged <- distance > limit
    values this step1.5, 0.5, 0.5, 14.5distance43.5center

  5. flagged ← FALSE, FALSE, FALSE, TRUE

    4distance <- abs(value - center)5flagged <- distance > limit6count <- sum(flagged)
    values this stepFALSE, FALSE, FALSE, TRUEflagged1.5, 0.5, 0.5, 14.5distance10limit

  6. count ← 1

    5flagged <- distance > limit6count <- sum(flagged)7label <- paste("outliers", count, sep = ":")
    values this step1count1 flagged valueflagged

  7. label ← outliers:1

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

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

    7label <- paste("outliers", count, sep = ":")8cat(label, "\n", sep = "")
    outputoutliers:1
    values this stepoutliers:1label

  1. limit ← 5

    1limit <- 52value <- c(42, 44, 43, 58)
    values this step5limit

  2. value ← 42, 44, 43, 58

    1limit <- 52value <- c(42, 44, 43, 58)3center <- median(value)
    values this step42, 44, 43, 58value

  3. center ← 43.5

    2value <- c(42, 44, 43, 58)3center <- median(value)4distance <- abs(value - center)
    values this step43.5center42, 44, 43, 58value

  4. distance ← 1.5, 0.5, 0.5, 14.5

    3center <- median(value)4distance <- abs(value - center)5flagged <- distance > limit
    values this step1.5, 0.5, 0.5, 14.5distance43.5center

  5. flagged ← FALSE, FALSE, FALSE, TRUE

    4distance <- abs(value - center)5flagged <- distance > limit6count <- sum(flagged)
    values this stepFALSE, FALSE, FALSE, TRUEflagged1.5, 0.5, 0.5, 14.5distance5limit

  6. count ← 1

    5flagged <- distance > limit6count <- sum(flagged)7label <- paste("outliers", count, sep = ":")
    values this step1count1 flagged valueflagged

  7. label ← outliers:1

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

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

    7label <- paste("outliers", count, sep = ":")8cat(label, "\n", sep = "")
    outputoutliers:1
    values this stepoutliers:1label

  1. limit ← 20

    1limit <- 202value <- c(42, 44, 43, 58)
    values this step20limit

  2. value ← 42, 44, 43, 58

    1limit <- 202value <- c(42, 44, 43, 58)3center <- median(value)
    values this step42, 44, 43, 58value

  3. center ← 43.5

    2value <- c(42, 44, 43, 58)3center <- median(value)4distance <- abs(value - center)
    values this step43.5center42, 44, 43, 58value

  4. distance ← 1.5, 0.5, 0.5, 14.5

    3center <- median(value)4distance <- abs(value - center)5flagged <- distance > limit
    values this step1.5, 0.5, 0.5, 14.5distance43.5center

  5. flagged ← FALSE, FALSE, FALSE, FALSE

    4distance <- abs(value - center)5flagged <- distance > limit6count <- sum(flagged)
    values this stepFALSE, FALSE, FALSE, FALSEflagged1.5, 0.5, 0.5, 14.5distance20limit

  6. count ← 0

    5flagged <- distance > limit6count <- sum(flagged)7label <- paste("outliers", count, sep = ":")
    values this step0countno flagged valuesflagged

  7. label ← outliers:0

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

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

    7label <- paste("outliers", count, sep = ":")8cat(label, "\n", sep = "")
    outputoutliers:0
    values this stepoutliers:0label
center `median(value)` gives a robust center for the values.
distance `abs(value - center)` measures how far each value is from the center.
flag `distance > limit` marks unusually distant observations.