CalculateLimits

Description

Evaluates whether to use custom limits or calculate them from the data.

Usage

CalculateLimits(x, lcl = NA, cl = NA, ucl = NA, type = "i",
  controlLimitDistance = 3)

Arguments

Details

If at least two limits were provided, the missing ones are calculated from the them. If one or zero limits were provided the rest is computed from data.

Value

Named list with limits

Examples

CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')

CalculateZoneBorders

Description

Some Nelson rules uses so-called zones. This function calculates the borders of the zones for given limits.

Usage

CalculateZoneBorders(limits, controlLimitDistance = 3)

Arguments

Value

Vector of zones

Examples

limits = CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')
CalculateZoneBorders(limits)
#limits is object created by CalculateLimits() function

EvaluateRules

Description

Evaluates the selected Nelson rules for a given numerical vector.

Usage

EvaluateRules(x, type = "i", whichRules = 1:8, lcl = NA, cl = NA,
  ucl = NA, controlLimitDistance = 3, returnAllSelectedRules = F,
  parRules = NULL)

Arguments

Details

# Only Rules 1-4 relevant for c-chart.
# Check for non negative data for c-chart.
# For controlLimitDistance less than or equal to 2 disable rule 5.
# For controlLimitDistance less than or equal to 1 disable rule 5,6,8.
# For returnAllSelectedRules=TRUE columns of invalid rules for given evaluation are filled with NAs.

Value

Dataframe containing original vector and rules evaluation

Examples

# Evaluate data, use all 8 Nelson rules, limits are specified by user
EvaluateRules(x = rnorm(10), whichRules = 1:8, lcl = 0, cl = 50, ucl = 100)
#Evaluate only rule 1, 3, 5, calculate limits from data using c-chart formula,
#use 2 sigma instead of 3, modify default behaviour of rule by pars variable
#created by function SetParameters()
pars = SetParameters()
EvaluateRules(x = rpois(10, lambda = 15), type = 'c', whichRules = c(1,3,5), lcl = NA, cl = NA,
ucl = NA, controlLimitDistance = 2, parRules = pars)
# pars is object of optional parameters created by SetParameters() function

NelsonRules

Description

Auxiliary function to calling individual Rule functions.

Usage

NelsonRules(ruleN, data, zoneB, limits, parRules = NULL, ...)

Arguments

Details

Handling the missing values:

Missing values are represented by the symbol NA - not available.

Rule 1: NAs do not violate this rule.

Rule 2-8: NAs are ignored, they do not break Rule evaluation. NA values are removed from the vector, the rule function is calculated and then the NAs are returned back to it's original position in the vector.

Value

Result of individual Rule function with predefined parameters

Rule 1

Description

One point beyond the control limits

Usage

Rule1(x, lcl, ucl, sides, ...)

Arguments

Details

0 means: ok
1 means: violation

inequality used during evaluation

parametr sides is internally encoded as: 1 for "two-sided", 2 for "upper", 3 for "lower"

Value

Vector of the same length as x

Examples

Rule1(x = rnorm(10), lcl = 10, ucl = 100, sides = "two-sided")

Rule 2

Description

Nine points in a row are on one side of the central line.

Usage

Rule2(x, cl, nPoints = 9, ...)

Arguments

Details

0 means: ok
1 means: violation

inequality used during evaluation

Value

Vector of the same length as x

Examples

Rule2(x = rnorm(20), cl=0, nPoints = 9)

Rule 3

Description

Six points in a row steadily increasing or decreasing.

Usage

Rule3(x, nPoints = 6, convention = 1, equalBreaksSeries = 1, ...)

Arguments

Details

0 means: ok
1 means: violation

parameter equalBreakSeries is internally encoded as: 1 for TRUE and 2 for FALSE

parameter convention is internally encoded as: 1 for 'minitab' and 2 for 'jmp'

Difference in convention parameter is as follows:
'minitab' - seven points in a row steadily increasing or decreasing
'jmp' - six points in a row steadily increasing or decreasing

Value

Vector of the same length as x

Examples

Rule3(x = rnorm(20), nPoints = 6, convention = 1, equalBreaksSeries = 1)

Rule 4

Description

Fourteen or more points in a row alternate in direction, increasing then decreasing.

Usage

Rule4(x, nPoints = 14, convention = 1, ...)

Arguments

Details

0 means: ok
1 means: violation

parameter convention is internally encoded as: 1 for 'minitab' and 2 for 'jmp'

Difference in convention parameter is as follows:
'minitab' - 15 or more points (14 changes of direction) in a row alternate in direction, increasing then decreasing
'jmp' - 14 or more points (13 changes of direction) in a row alternate in direction, increasing then decreasing

Value

Vector of the same length as x

Examples

Rule4(x = rnorm(20), nPoints = 14,convention = 1)

Rule 5

Description

Two out of three consecutive points beyond the 2*sigma limits on same side of center line.

Usage

Rule5(x, zoneB, minNPoints = 2, nPoints = 3, ...)

Arguments

Details

0 means: ok
1 means: violation

inequality used during evaluation
Rule is violated also if the first two points are beyond the 2*sigma limits During calculation of EvaluateRules function wiht controlLimitDistance <= 2, the evaluation of this rule is suppressed

Value

Vector of the same length as x

Examples

limits = CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')
zones = CalculateZoneBorders(limits)
Rule5(x = rnorm(20), zoneB = zones, minNPoints = 2, nPoints = 3)
#zones is object created by function CalculateZoneBorders()

Rule 6

Description

Four or five out of five points in a row are more than 1 standard deviation from the mean in the same direction.

Usage

Rule6(x, zoneB, minNPoints = 4, nPoints = 5, ...)

Arguments

Details

0 means: ok
1 means: violation

inequality used during evaluation Rule is violated also if the first four points are beyond the 1 standard deviation from the mean During calculation of EvaluateRules function wiht controlLimitDistance <= 1, the evaluation of this rule is suppressed

Value

Vector of the same length as x

Examples

limits = CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')
zones = CalculateZoneBorders(limits)
Rule6(x = rnorm(20), zoneB = zones, minNPoints = 4, nPoints = 5)
#zones is object created by function CalculateZoneBorders()

Rule 7

Description

Fifteen points in a row are all within 1 standard deviation of the mean on either side of the mean.

Usage

Rule7(x, nPoints = 15, zoneB, ...)

Arguments

Details

0 means: ok
1 means: violation

equality used during evaluation

Value

Vector of the same length as x

Examples

limits = CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')
zones = CalculateZoneBorders(limits)
Rule7(x = rnorm(20), zoneB = zones, nPoints = 15)
#zones is object created by function CalculateZoneBorders()

Rule 8

Description

Eight points in a row outside 1 standard deviation of the mean in both directions.

Usage

Rule8(x, nPoints = 8, zoneB, ...)

Arguments

Details

0 means: ok
1 means: violation

inequality used during evaluation During calculation of EvaluateRules function wiht controlLimitDistance <= 1, the evaluation of this rule is suppressed

Value

Vector of the same length as x

Examples

limits = CalculateLimits(x = rnorm(10), lcl = NA, cl = 100, ucl = NA, type = 'i')
zones = CalculateZoneBorders(limits)
Rule8(x = rnorm(20), zoneB = zones, nPoints = 8)
#zones is object created by function CalculateZoneBorders()

SetParameters

Description

Creates optional parameters with default settings.

Usage

SetParameters()

Details

The function is called without any parameter. If you want to modify any or the rules' setting, modify the result of this function and plug it to EvaluateRules's parRules parameter.

Value

List of optional parameters

Examples

pars <- SetParameters()
pars$Rule1$sides <- "upper"
#function doos not need any input parameters