R: Sequential Normal Scores

SNS {SNSchart}

R Documentation

Sequential Normal Scores

Description

Transform a vector X into SNS using initial observations Y if available SNS follow the order of X.

Usage

SNS(
  X,
  X.id,
  Y = NULL,
  theta = NULL,
  Ftheta = NULL,
  scoring = "Z",
  Chi2corrector = "None",
  alignment = "unadjusted",
  constant = NULL,
  absolute = FALSE,
  chart = "Shewhart",
  chart.par = c(3),
  snsRaw = FALSE,
  isFixed = FALSE,
  omit.id = NULL,
  auto.omit.alarm = TRUE
)

Arguments

`X`	vector. New observations to obtain the N¡normal scores.
`X.id`	vector. The id of the vector `X`.
`Y`	vector. If `Y` is not defined (no previous observation available, `NULL`), NS is relative to `X`. Default `NULL`.
`theta`	scalar. Value corresponig with the `Ftheta` quantile.
`Ftheta`	scalar. Quantile of the data distribution. The values that take are between (0,1).
`scoring`	character string. If "Z" (normal scores) (default). If "Z-SQ" (normal scores squared).
`Chi2corrector`	character string. Only when scoring is Z-SQ. Select from "approx: Z^2*(m + 1 + 1.3)/(m+1). "exact": Z^2/mean(Z). "none": Z^2. If "approx" () (default). If "exact" (normal scores squared).
`alignment`	character string. Aligment of the data `X` and `Y`. Select from "unadjusted": nothing is sustracte from `X` and `Y` (default). "overallmean": overall mean is sustracted from `X` and `Y`. "overallmedian": overall median is sustracted from `X` and `Y`. "samplemean": mean from corresponding group (`X` and `Y`) is sustracted from its corresponing vector. "samplemedian": median from corresponding group (`X` and `Y`) is sustracted from its corresponing vector. "referencemean": mean from `Y` is subtracted from `X` and `Y`. "referencemedian": median from `Y` is subtracted from `X` and `Y`. "constantvalue": a constant value is subtracted from `X` and `Y`.
`constant`	scalar. Only used when the `alignment` is selected "constantvalue". Default `NULL`.
`absolute`	logical. If `TRUE`, the absolute aligned values are obtained. (Default `FALSE`)
`chart`	character string. Selected type of chart. Three options are available: Shewhart, CUSUM, EWMA
`chart.par`	vector. The size depends on the selected chart: Shewhart scheme: is `c(k)`, where `k` comes from `UCL = mu + k\sigma, LCL = mu - k\sigma.` CUSUM scheme: is `c(k, h, t)` where `k` is the reference value and `h` is the control limit, and `t` is the type of the chart (1:positive, 2:negative, 3:two sides) EWMA scheme: is `c(lambda, L)`, where `lambda` is the smoothing constant and `L` multiplies standard deviation to get the control limit
`snsRaw`	logical. If `TRUE` return also the sns for each observation in vector `X`.
`isFixed`	logical. If `TRUE` the reference sample does not update, otherwise the reference sample is updated whenever the batch is in control.
`omit.id`	vector. Elements of the vector are the id which are omitted in the analysis.
`auto.omit.alarm`	logical. Determine if OC signals are added (or not) to reference sample. By default is set to TRUE.

Value

Multiple output. Select by output$

coefficients: list. Three elements: n the number of observation per group in X, chart the selected chart to perform the analysis, and chart.par the parameters of the selected chart.
R: vector. Ranks for the new observations (Monitoring sample).
X: vector. New observations (Monitoring sample) to obtain the SNS.
Z: vector. SNS of the X monitoring sample.
X.id: vector. The id of each column (variable) of the matrix X.
UCL: vector. Upper control limit for each group in X.
LCL: vector. Lower control limit for each group in X.
scoring: string. Selected score to evaluate SNS.

Comments

If ties occur, average ranks are used.

Examples

# EXAMPLE CONDITIONAL WITH REFERENCE SAMPLE
Y <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
X <- c(30, 35, 45)
theta <- 40
Ftheta <- 0.5
sample.id <- c("a", "b", "c")
SNS(X = X, X.id = sample.id, Y = Y, theta = theta, Ftheta = Ftheta)

# EXAMPLE CONDITIONAL WITH REFERENCE SAMPLE
Y <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
X <- c(30, 35, 45)
theta <- 40
Ftheta <- 0.5
sample.id <- c("a", "b", "c")
SNS(X = X, X.id = sample.id, Y = Y, theta = theta, Ftheta = Ftheta)

# EXAMPLE UNCONDITIONAL WITH REFERENCE SAMPLE
Y <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
X <- c(30, 35, 45)
theta <- NULL
Ftheta <- NULL
sample.id <- c("a", "b", "c")
SNS(X = X, X.id = sample.id, Y = Y, theta = theta, Ftheta = Ftheta)

# EXAMPLE CONDITIONAL WITHOUT REFERENCE SAMPLE
Y <- NULL # c(10,20,30,40,50,60,70,80,90,100)
X <- c(30, 35, 45)
theta <- 40
Ftheta <- 0.5
sample.id <- c("a", "b", "c")
SNS(X = X, X.id = sample.id, Y = Y, theta = theta, Ftheta = Ftheta)

# EXAMPLE UNCONDITIONAL WITHOUT REFERENCE SAMPLE
Y <- NULL
X <- c(30, 35, 45)
theta <- NULL
Ftheta <- NULL
sample.id <- c("a", "b", "c")
SNS(X = X, X.id = sample.id, Y = Y, theta = theta, Ftheta = Ftheta)

[Package SNSchart version 1.4.0 Index]