MSALinearity-class: Class '"MSALinearity"'

In qualityTools: Statistical Methods for Quality Science

Description

MSALinearity Class

Objects from the Class

Objects can be created by calls of the form new("MSALinearity", ...).

Slots

X:

Object of class "data.frame" ~~

Y:

Object of class "data.frame" ~~

model:

Object of class "lm" ~~

conf.level:

Object of class "numeric" ~~

Linearity:

Object of class "numeric" ~~

GageName:

Object of class "character" ~~

GageTolerance:

Object of class "numeric" ~~

DateOfStudy:

Object of class "character" ~~

PersonResponsible:

Object of class "character" ~~

Comments:

Object of class "character" ~~

facNames:

Object of class "character" ~~

Methods

as.data.frame

signature(x = "MSALinearity"): ...

plot

signature(x = "MSALinearity"): ...

response

signature(object = "MSALinearity"): ...

response<-

signature(object = "MSALinearity"): ...

show

signature(object = "MSALinearity"): ...

summary

signature(object = "MSALinearity"): ...

Author(s)

Thomas Roth: [e-mail]
Etienne Stockhausen: [e-mail]

Examples

showClass("MSALinearity")

Example output

Loading required package: Rsolnp
Loading required package: MASS

Attaching package: 'qualityTools'

The following object is masked from 'package:stats':

    sigma

Class "MSALinearity" [package "qualityTools"]

Slots:
                                                                              
Name:                  X                 Y             model        conf.level
Class:        data.frame        data.frame                lm           numeric
                                                                              
Name:          Linearity          GageName     GageTolerance       DateOfStudy
Class:           numeric         character           numeric         character
                                                            
Name:  PersonResponsible          Comments          facNames
Class:         character         character         character

qualityTools documentation built on May 2, 2019, 10:21 a.m.