MultCapability: Principal Component Based Multivariate Process Capability...

In mvdalab: Multivariate Data Analysis Laboratory

Principal Component Based Multivariate Process Capability Indices

Description

Provides three multivariate capability indices for correlated multivariate processes based on Principal Component Analysis.

Usage

MultCapability(data, lsls, usls, targets, ncomps = NULL, Target = FALSE)

Arguments

data	a multivariable dataset
lsls	is the vector of the lower specification limits
usls	is the vector of the upper specification limits
targets	is the vector of the target of the process
ncomps	is the number of principal component to use
Target	Use targets for calculation of univariate PpKs; otherwise the average is used

Details

ncomps has to be set prior to running the analysis. The user is strongly encouraged to use pcaFit in order to determine the optimal number of principal components using cross-validation.

When the parameter targets is not specified, then is estimated of centered way as targets = lsls + (usls - lsls)/2.

Ppk values are provided to allow the user to compare the multivariate results to the univariate results.

Value

A list with the following elements:

For mpca_wang, the following is returned:

ncomps	number of components used
mcp_wang	index greater than 1, the process is capable
mcpk_wang	index greater than 1, the process is capable
mcpm_wang	index greater than 1, the process is capable
mcpmk_wang	index greater than 1, the process is capable

For mcp_xe, the following is returned:

ncomps	number of components used
mcp_wang_2	index greater than 1, the process is capable
mcpk_wang_2	index greater than 1, the process is capable
mcpm_wang_2	index greater than 1, the process is capable
mcpmk_wang_2	index greater than 1, the process is capable

For mpca_wang_2, the following is returned:

ncomps	number of components used
mcp_xe	index greater than 1, the process is capable
mcpk_xe	index greater than 1, the process is capable
mcpm_xe	index greater than 1, the process is capable
mcpmk_xe	index greater than 1, the process is capable

For Ppk, the following is returned:

Individual.Ppks

univariate Ppks; index greater than 1, the process is capable

Author(s)

Nelson Lee Afanador ([e-mail])

References

Wang F, Chen J (1998). Capability index using principal components analysis. Quality Engineering, 11, 21-27.

Xekalaki E, Perakis M (2002). The Use of principal component analysis in the assessment of process capability indices. Proceedings of the Joint Statistical Meetings of the American Statistical Association, The Institute of Mathematical Statistics, The Canadian Statistical Society. New York.

Wang, C (2005). Constructing multivariate process capability indices for short-run production. The International Journal of Advanced Manufacturing Technology, 26, 1306-1311.

Scagliarini, M (2011). Multivariate process capability using principal component analysis in the presence of measurement errors. AStA Adv Stat Anal, 95, 113-128.

Santos-Fernandez E, Scagliarini M (2012). "MPCI: An R Package for Computing Multivariate Process Capability Indices". Journal of Statistical Software, 47(7), 1-15, URL http://www.jstatsoft.org/v47/i07/.

Examples

data(Wang_Chen_Sim)
lsls1 <- c(2.1, 304.5, 304.5)
usLs1 <- c(2.3, 305.1, 305.1)
targets1 <- c(2.2, 304.8, 304.8)

MultCapability(Wang_Chen_Sim, lsls = lsls1, usls = usLs1, targets = targets1, ncomps = 2)

data(Wang_Chen)
targets2 <- c(177, 53)
lsls2 <- c(112.7, 32.7)
usLs2 <- c(241.3, 73.3)

MultCapability(Wang_Chen, lsls = lsls2, usls = usLs2, targets = targets2, ncomps = 1)

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.