ggQC (version 0.0.31)

QC_Violations: Calculate QC Violations

Description

function that calculates QC violations on sequentially ordered data based on the following 4 rules:

• Violation Same Side: 8 or more consecutive, same-side points

• Violation 1 Sigma: 4 or more consecutive, same-side points exceeding 1 sigma

• Violation 2 Sigma: 2 or more consecutive, same-side points exceeding 2 sigma

• Violation 3 Sigma: any points exceeding 3 sigma

Usage

QC_Violations(data, value = NULL, grouping = NULL, formula = NULL,
  method = NULL, ...)

Arguments

data

vector or dataframe, as indicated below for each chart type

• Individuals: vector of values;

• Studentized Charts: dataframe

value

Studentized Charts: numeric vector in dataframe with values of interest

grouping

Studentized Charts: single factor/variable to split the dataframe "values" by

formula

Studentized Charts: a formula, such as y ~ x1 + x2, where the y variable is numeric data to be split into groups according to the grouping x factors/variables

method

string, calling the following methods:

• Individuals Charts: XmR,

• Studentized Charts: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar, xMedian.rMedian

...

further arguments passed to or from other methods.

Value

a dataframe, with the following columns

• data: The input data if XmR, mean or median by group for Studentized methods

• z_score: z-score for the data point

• Index: number, indicating the order of the input data

• Violation_Result: description of the type of test being run.

  • Violation Same Side: 8 or more consecutive, same-side points

  • Violation 1 Sigma: 4 or more consecutive, same-side points exceeding 1 sigma

  • Violation 2 Sigma: 2 or more consecutive, same-side points exceeding 2 sigma

  • Violation 3 Sigma: any points exceeding 3 sigma

• Index: boolean, does the data point violate the rule?

References

Wheeler, DJ, and DS Chambers. Understanding Statistical Process Control, 2nd Ed. Knoxville, TN: SPC, 1992. Print.

Examples

# NOT RUN {
#####################################
#  Example 1: XmR Check Violations  #
#####################################
# Load Libraries ----------------------------------------------------------
 require(ggQC)

# Setup Data --------------------------------------------------------------

    set.seed(5555)
    QC_XmR <- data.frame(
    data = c(c(-1, 2.3, 2.4, 2.5),                        #Outlier Data
          sample(c(rnorm(60),5,-5), 62, replace = FALSE), #Normal Data
          c(1,-.3, -2.4,-2.6,-2.5,-2.7, .3)),             #Outlier Data
    Run_Order = 1:73                                      #Run Order
    )

   QC_Vs <- QC_Violations(data  = QC_XmR$data, method = "XmR")

#######################################
#  Example 2: Xbar Check Violations   #
#######################################

# Setup Some Data ------------------------------------------------------------
     QC_xBar.rBar <- do.call(rbind, lapply(1:3, function(X){
       set.seed(5555+X)                                   #Loop over 3 seeds
       data.frame(
         sub_group = rep(1:42),                           #Define Subgroups
         sub_class = letters[X],
         c(
          c(runif(n = 5, min = 2.0,3.2)),                 #Outlier Data
          sample(c(rnorm(30),5,-4), 32, replace = FALSE), #Normal Data
          c(runif(n = 5, min = -3.2, max = -2.0))         #Outlier Data
         )
      )
     }
   )
)

colnames(QC_xBar.rBar) <- c("sub_group","sub_class", "value")
QC_Vs <- QC_Violations(data  = QC_xBar.rBar,
                       formula = value~sub_group,
                       method = "xBar.rBar")
# }