mvPlot: Multi-Vari-Charts

Description Usage Arguments Value Author(s) See Also Examples

View source: R/mvPlot.R View source: R/mul_t.r

Description

Draws a Multi-Vari Chart for 2, 3 or 4 factors.

Usage

1
2
mvPlot(response, fac1, fac2, fac3 = NA, fac4 = NA, sort = TRUE, col, pch, 
       cex.txt = 1, las = 1, labels = FALSE, quantile = TRUE, FUN = NA)

Arguments

response

the values of the response in a vector.response must be declared.

fac1

vector providing factor 1 as shown in the example.fac1 must be declared.

fac2

vector providing factor 2 as shown in the example.fac2 must be declared.

fac3

(optional) vector providing factor 3 as shown in the example.
By default fac3 is set to ‘NA’.

fac4

(optional) vector providing factor 4 as shown in the example.
By default fac3 is set to ‘NA’.

sort

logical value indicating whether the sequence of the factors given by fac1 - fac4 should be
reordered to minimize the space needed to visualize the Multi-Vari-Chart.
By default sort is set to ‘TRUE’.

col

graphical parameter. Vector containing numerical values or character strings giving the colors for the different factors.
By default col starts with the value ‘3’ and is continued as needed.

pch

graphical parameter. Vector containing numerical values or single characters giving plotting points for the different factors.
See points for possible values and their interpretation. Note that only integers and single-character strings can be set as a graphics parameter
(and not NA nor NULL). By default pch starts with the value ‘1’ and is continued as needed.

cex.txt

a numerical value giving the amount by which plotting labels at the single points should be magnified relative to the default.
By default cex.txt is set to ‘1’.

las

graphical parameter for the style of x-axis labels. See par for further information.

labels

logical value indicating whether the single points should be labeld with the row-number of the data.frame
invisibly returned by the function mvPlot. By default labels is set to ‘FALSE’.

quantile

logical value indicating whether the quanitiles (0.00135, 0.5 & 0.99865) should be visualized for the single groups.
By default quantile is set to ‘TRUE’.

FUN

function to be used for calculation of response for unique settings of the factors e.g. the mean.
By default FUN is set to ‘NA’ and therfore omitted.

Value

mvPlot returns invisibly a data.framein which all plotted points are listed.
The option labels can be used to plot the row-numbers at the single points and to ease the identification.

Author(s)

Thomas Roth [email protected]
Etienne Stockhausen [email protected]

See Also

http://www.r-qualitytools.org

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
#Example I
examp1 = expand.grid(c("Engine1","Engine2","Engine3"),c(10,20,30,40))                  
examp1 = as.data.frame(rbind(examp1, examp1, examp1))
examp1 = cbind(examp1, rnorm(36, 1, 0.02))
names(examp1) = c("factor1", "factor2", "response")
test1=mvPlot(response = examp1[,3], fac1 = examp1[,2],
             fac2 = examp1[,1],sort=FALSE,las=2,FUN=mean) 

#Example II
examp2=expand.grid(c("Op I","Op II","Op III"),c(1,2,3,4),
                   c("20.11.1987","21.11.1987"))
examp2=as.data.frame(rbind(examp2, examp2, examp2))
examp2=cbind(examp2, rnorm(72, 22, 2))
names(examp2) = c("factor1", "factor2", "factor3", "response")
test2=mvPlot(response = examp2[,4], fac1 = examp2[,1],
            fac2 = examp2[,2], fac3 = examp2[,3], sort=TRUE, FUN=mean, 
            labels=TRUE)

#Example III
examp3 = expand.grid(c("A","B","C"),c("I","II","III","IV"),c("H","I"),
                     c(1,2,3,4,5))
examp3 = as.data.frame(rbind(examp3, examp3, examp3))
examp3 = cbind(examp3, rnorm(360, 0, 2))
names(examp3) = c("factor1", "factor2", "factor3", "factor4", "response")
test3=mvPlot(response = examp3[,5], fac1 = examp3[,1],
             fac2 = examp3[,2], fac3 = examp3[,3], fac4 = examp3[,4], sort=TRUE, 
             quantile=TRUE, FUN=mean)

 
                              

                

qualityTools documentation built on May 30, 2017, 1:43 a.m.