Rangreihe: Rangreihe

In stp4/stp25stat: Functions for statistical computations

Description

Rangordnungen von Objekten koennen durch eine Transformation der Rangreihen in Intervallskalierte Merkmale ueberfuehrt werden. Die Grundidee dieser Methode geht auf Thurstone (1927) nach dem "Law of Categorical Judgement" zurueck.

Usage

Rangreihe(
  ...,
  caption = "Rangreihe",
  note = "Law of Categorical Judgement",
  output = stp25output::which_output(),
  na.action = na.pass,
  include.percent = TRUE,
  include.freq = TRUE,
  include.mean = TRUE,
  include.z = TRUE,
  include.na = TRUE,
  groups = NULL,
  order = TRUE,
  decreasing = TRUE,
  digits.mean = 2
)

Rangreihe_default(
  items,
  caption = "",
  note = "",
  output = stp25output::which_output(),
  include.percent = TRUE,
  include.freq = TRUE,
  include.mean = TRUE,
  include.z = TRUE,
  include.na = TRUE,
  groups = NULL,
  order = TRUE,
  decreasing = TRUE,
  digits.mean = 2,
  input = NULL,
  pattern = "____"
)

Arguments

...	Weitere Argumente
caption, note, output	an stp25output
include.percent, include.freq, include.mean, include.z, include.na	was soll ausgewertet werden
groups	gruppen
digits.mean, order, decreasing	sortierung
items	data.frame
input	Format der Items c("ranking", "ordering"),
pattern	intern gruppen

Details

Dabei werden die kumulierten Haeufigkeiten in Normalverteilte z-Werte uebergefuehrt und aus diesen die Intervallskalierten Markmalsauspraegungen gebildet.

Literatur: Bortz, J. & Doering, N. (2006). Forschungsmethoden und Evaluation fuer Human-und Sozialwissenschaftler (4. Auflage). Berlin: Springer. Seite 155

Value

Vector

Examples

require(stpvers)
library(PlackettLuce)

nlv <- 5
n <- 2 * 3 * nlv * 1
set.seed(n)

DF <-
  data.frame(
    Geschlecht = gl(2, n / 2, labels = c("Maennlich", "Weiblich")),
    Alter = gl(4, n / 4,   labels = c("20-29", "30-39", "40-49", "50-59")),
    Landwirtschaft = gl(2, n / 2, labels = c("konventionell", "biologisch"))
  )

Attribute <-
  as.data.frame(t(apply(matrix(NA, ncol = n, nrow = 5), 2,
                        function(x)
                          sample.int(5))))

Attribute[1, ] <- c(5, 1, 4, 2, 3)
Attribute[2, ] <- c(5, 1, 4, 2, 3)
Attribute[3, ] <- c(5, 2, 4, 3, 1)
Attribute[4, ] <- c(5, 1, 4, 3, 2)
Attribute[5, ] <- c(5, 1, 4, 3, 2)

Attribute[21, ] <- c(1, 2, 5, 4, 3)
Attribute[22, ] <- c(1, 4, 5, 3, 2)
Attribute[23, ] <- c(2, 5, 1, 4, 3)
Attribute[24, ] <- c(1, 4, 2, 5, 3)
Attribute[25, ] <- c(1, 4, 3, 5, 2)

attribute  <- c("Verfuegbarkeit",
                "Vielfalt",
                "Qualitaet",
                "Geschmack",
                "Preis")

Attribute<- dapply2(Attribute, function(x) factor(x, 1:5, attribute))

DF <- cbind(DF, Attribute)

head(DF)

res <-
  Rangreihe( ~ V1+V2+V3+V4+V5,
             DF, include.percent=FALSE, order=FALSE, include.na=FALSE,
             caption="Produkte aus konventioneller und biologischer  Landwirtschaft")

res$input
names(res)
x<- res$res





R <- as.rankings(res$items, res$input)

mod <- PlackettLuce( R )
coef(mod)


summary(mod)
x$pc <-  round(coef(mod, log = FALSE) ,2)
x$log.pc <- round(coef(mod, log = TRUE) ,2)
x[order(x$pc,decreasing=TRUE),] 



DF1 <-  data.frame(
  A = c(1, 1, 1, 2, 3, 1),
  B = c(2, 2, 2, 3, 2, 3),
  C = c(3, 3, 3, 1, 1, NA),
  D = c(NA, NA, NA, NA, NA, 2)
)
DF2 <-   data.frame(
  R1 = factor(c("A", "A", "A", "C", "C", "A"),   c("A", "B", "C", "D")),
  R2 = factor(c("B", "B", "B", "A", "B", "D"),   c("A", "B", "C", "D")),
  R3 = factor(c("C", "C", "C", "B", "A", "B"),   c("A", "B", "C", "D"))
)


Rangreihe(DF1)$mean
Rangreihe(DF2)$mean

dat_bortz<-
  as.table(matrix(c(
    2,8,10,13,17,
    5,10,15,18,2,
    10,12,20,5,3,
    15,20,10,3,2,
    22,18,7,2,1)
    , nrow = 5, ncol=5, byrow=TRUE,
    dimnames = list(c("A", "B", "C", "D", "E"),1:5)))  

Rangreihe(dat_bortz)



# dat_table <-
#   as.table(matrix(c(
#     50,0,0,0,0,
#     0,50,0,0,0,
#     0,0,50,0,0,
#     0,0,0,50,0,
#     0,0,0,0,50
#      )
#     , nrow = 5, ncol=5, byrow=TRUE,
#     dimnames = list(c("A", "B", "C", "D", "E"),1:5)))
#  # Calc_Rank(dat_table)



n <- 2 * 3 * 4 * 1

set.seed(n)
kaffee <- c("Guatemala", "Vietnam", "Honduras", "Äthiopien")
sex<- c("male", "female")
age<- c("20-29", "30-39", "40-49", "50-59")
kaffe<- c("Espresso", "Filterkaffee", "Milchkaffee")

DF <-
  data.frame(
    sex = factor("male",sex),
    Alter = factor("20-29",age ),
    Kaffeeform = factor("Espresso", kaffe),
    R1 = factor(kaffee[1], kaffee),
    R2 = factor(kaffee[2], kaffee),
    R3 = factor(kaffee[3], kaffee),
    R4 = factor(kaffee[4], kaffee)
  )

DF<- rbind(DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF,DF)

for(i in 1:n){
  DF<- rbind(DF,
             c(sample(sex)[1],
               sample(age)[1],
               sample(kaffe)[1],
               sample(kaffee)
             ))
}

x <- DF[4:7]
Rangreihe(x, include.percent=FALSE, groups=DF$sex)
x<-Rangreihe(R1 + R2 +R3 ~sex, DF, include.percent=FALSE, output=FALSE)


names( x)
x$mean

#' 
#lattice::dotplot( reorder(Items, mean)~ mean|"Kaffee", 
                  x$mean, groups=group , xlab="",
#                  xlim=range(x$mean$mean)*1.10 , auto.key=list(), cex=1)
 

stp4/stp25stat documentation built on Sept. 17, 2021, 2:03 p.m.