Description Usage Arguments Value See Also Examples
View source: R/vMask.method6.R
The V-mask method in vMask
Package, is considered on the basis of variety of situations/information by different functions.
One of these functions is vMask.method1
which can plot the V-mask based on μ_0, μ_1, σ, h and w.
1 2 |
data |
Type of inputted data must be a matrix with dim=c(m,n), in which m is size of samples and n is size of each subsample. Meanwhile, data can be a numerical vector where its elements are sub-sample means. |
mu0 |
The target value for process mean, with default |
mu1 |
The mean under the alternative hypothesis, i.e., μ_1 = μ_0 + k σ. |
sigma |
The standard deviation of the process, with default |
h |
The length of decision making (h) is equal to the vertical distance between the origin and the upper (or lower) arm of V-mask. Its default is h = 5 \times sd(data), since usually h \in [4.5 σ, 5 σ] is proposed. |
w |
The ration of vertical unit into horizontal unit in CUSUM plot, with default w = 2 \times sd(data) (this default causes θ is approximately equal to 14 degrees). |
sleep |
Sleeping time (in secound) of the program between showing figures. This time needs for see the result of checking i-th point on CUSUM control chart by V-mask. Also, if |
d |
The distance between two points: (1) O, i.e the latest cumulative sum point (which duty of the V-mask is checking this point), and (2) P, i.e. the junction points of V-mask arms. |
k |
Difference/shift in the mean of process after a probably change. Its default is considered 0.5 σ and it depends on the form of the alternative hypothesis H_1: μ = μ_1 \ (μ_1=μ_0+k σ). |
theta |
half of the angle formed by the V-mask arms (in degrees). |
c |
A vector of the cumulative differences between statistic (mean) values and the mean target value; i.e. c=(c_1, ..., c_m) where c_i=∑_{j=1}^{i} (\bar{x}_j - μ_0) . |
OutControl |
The number of Out-of-control points in CUSUM chart. |
InControl |
The number of In-control points in CUSUM chart. |
vMask.method4, vMask.method5
1 2 3 4 5 6 7 | m = 30 #The size of samples
n = 3 #The size of each subsample
set.seed(2345)
Data = matrix(rnorm(m*n, 0,1), nrow=m)
vMask.method6( data=Data, mu0=0, mu1=1, h=1, w=2, sl=0 )
vMask.method6( data=Data, mu0=0, mu1=.7, h=1, w=2, sl=0 ) #Compare with the previous line
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