rsp {dfphase1} | R Documentation |
Distribution-Free Phase I Analysis of Univariate Data based on Recursive Segmentation and Permutation
Description
rsp
implements the Phase I method described in Capizzi and Masarotto (2013).
Usage
rsp(y, plot = TRUE, L = 1000, seed = 11642257, alpha = 0.05,
maxsteps = min(50, round(NROW(y)/15)), lmin = max(5, min(10, round(NROW(y)/10))))
Arguments
y |
Phase I data; |
plot |
logical; if |
L |
integer; the number of random permutations used to compute the p-values. |
seed |
positive integer; if not |
alpha |
real; the significance level used to compute the level and scale
estimates; if one of the p-values is greater than
|
maxsteps |
integer; the maximum number of step shifts which the procedure tries to detect. |
lmin |
integer; the minimum length of a step. |
Value
A list with elements
p |
the adjusted p-values |
stat |
the summary statistics (a mx2 matrix) |
fitted |
the (possibly time-variant) estimates of the process level and scale (a mx2 matrix). |
Author(s)
Giovanna Capizzi and Guido Masarotto.
References
G. Capizzi, G. Masarotto (2013), “Phase I Distribution-Free Analysis of Univariate Data”. Journal of Quality Technology, 45, pp. 273-284, doi:10.1080/00224065.2013.11917938.
Examples
# Individual observations with a transient level change
set.seed(112233)
level <- c(rep(0,20),rep(3,10),rep(0,20))
x <- level+rt(50,4)
rsp(x)
# Individual observations with a scale step change
scale <- c(rep(1,25),rep(3,25))
x <- scale*rt(50,4)
rsp(x)
data(fe)
rsp(fe)
data(colonscopy)
rsp(colonscopy)