mvdalab (version 1.4)

MultCapability: 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

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

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.

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

# NOT RUN {
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)

# }