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Fairtrade consumption and social milieu.R
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Fairtrade consumption and social milieu.R
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## Convert R Markdown to R script
## knitr::purl("Fairtrade consumption and social milieu.Rmd")
## ------------------------------------------------------------------------------
rm(list = ls())
## ----setup, include=F----------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
Packages <- c("tidyverse", ## 1.3.0
"naniar", ## 0.5.0
"haven", ## 2.2.0
"survey", ## 3.37
"car", ## 3.0-6
"gmodels", ## 2.18.1
"psych", ## 1.9.12.31
"mfx", ## 1.2-2
"epiDisplay", ## 3.5.0.1
"effects", ## 4.1-4
"DescTools", ## 0.99.37
"margins" ## 0.3.23
)
lapply(Packages, require, character.only = T)
## ----eval=F, message=FALSE-----------------------------------------------------
## Packages <- c("tidyverse", ## 1.3.0
## "naniar", ## 0.5.0
## "haven", ## 2.2.0
## "survey", ## 3.37
## "car", ## 3.0-6
## "gmodels", ## 2.18.1
## "psych", ## 1.9.12.31
## "mfx", ## 1.2-2
## "epiDisplay", ## 3.5.0.1
## "effects", ## 4.1-4
## "DescTools", ## 0.99.37
## "margins" ## 0.3.23
## )
##
## lapply(Packages, require, character.only = T)
## ------------------------------------------------------------------------------
Data <- read_sav("Umweltbewusstsein_DE_2016_Welle1_v1-0-0.sav")
## ------------------------------------------------------------------------------
Data <- Data %>%
dplyr::select(f624_3, ## If possible, only buy products that have been produced under fair working conditions.
f821, ## gender (m = 1 / w = 2)
f8210, ## Total monthly net income of a household (in 11 income groups).
nf822, ## Age of respondents in years.
f823, ## Educational attainment (highest school degree or university degree).
QCL_1, ## Social milieus (six social segments, 17 items for identification).
weight ## Weighting variable (weighting by region, gender, age and education).
)
## ------------------------------------------------------------------------------
Data <- Data %>%
mutate(f624_3 = na_if(f624_3, 5), ## "don't know"
f8210 = na_if(f8210, 12), ## "not specified"
f823 = na_if(f823, 1), ## "still student"
f823 = na_if(f823, 7), ## "Other school diploma"
f823 = na_if(f823, 8) ## "not specified"
)
## ------------------------------------------------------------------------------
Data <- Data %>%
mutate(buy = car::recode(Data$f624_3,
"1:2 = 1; 3:4 = 0; else = NA", as.factor = T))
## ------------------------------------------------------------------------------
Data$female <- dummy.code(Data$f821, group = 2)
## ------------------------------------------------------------------------------
Data <- Data %>%
mutate(milieu = car::recode(Data$QCL_1,
"1 = 1;
2 = 2;
3 = 3;
4 = 4;
5 = 5;
6 = 6",
as.factor = T))
## ------------------------------------------------------------------------------
round(prop.table(table(Data$milieu))*100, digits = 1)
## ------------------------------------------------------------------------------
contrasts(Data$milieu)
## ------------------------------------------------------------------------------
Data$milieu <- relevel (Data$milieu, ref = "4")
## ------------------------------------------------------------------------------
contrasts(Data$milieu)
## ------------------------------------------------------------------------------
Data$education <- factor(Data$f823)
## ------------------------------------------------------------------------------
Data$age <- Data$nf822
## ------------------------------------------------------------------------------
Data$income <- Data$f8210
## ------------------------------------------------------------------------------
summary(Data)
## ---- fig.width=10,fig.height=6------------------------------------------------
variables <-data.frame(Data$female, Data$education, Data$income, Data$age, Data$milieu, Data$buy)
gg_miss_var(variables)
## ------------------------------------------------------------------------------
prop.table(table(Data$buy))*100
## ----include=FALSE-------------------------------------------------------------
## Regression vor Berechnung der vif-Werte, damit Berechnung möglich wird
m6.w <- glm(buy ~ female + education + income + age + milieu,
family = binomial,
data = Data,
weights = weight)
summary(m6.w)
## ------------------------------------------------------------------------------
CrossTable(Data$milieu, Data$buy, prop.r=T, prop.c=F, prop.t=F, prop.chisq=F, digits = 2)
## ------------------------------------------------------------------------------
vif(m6.w)
## ------------------------------------------------------------------------------
m.null <- glm(buy ~ 1,
family = binomial,
data = Data)
summary(m.null)
## ----warning=FALSE-------------------------------------------------------------
m1.w <- glm(buy ~ female,
family = binomial,
data = Data,
weights = weight)
summary(m1.w)
## ----warning=FALSE-------------------------------------------------------------
m2.w <- glm(buy ~ education,
family = binomial,
data = Data,
weights = weight)
summary(m2.w)
## ----warning=FALSE-------------------------------------------------------------
m3.w <- glm(buy ~ income,
family = binomial,
data = Data,
weights = weight)
summary(m3.w)
## ----warning=FALSE-------------------------------------------------------------
m4.w <- glm(buy ~ age,
family = binomial,
data = Data,
weights = weight)
summary(m4.w)
## ----warning=FALSE-------------------------------------------------------------
m5.w <- glm(buy ~ milieu,
family = binomial,
data = Data,
weights = weight)
summary(m5.w)
## ----warning=FALSE-------------------------------------------------------------
m6.w <- glm(buy ~ female + education + income + age + milieu,
family = binomial,
data = Data,
weights = weight)
summary(m6.w)
## ------------------------------------------------------------------------------
logLik(m.null)
logLik(m5.w)
logLik(m6.w)
## ------------------------------------------------------------------------------
lrtest(m.null, m5.w)
## ----warning=FALSE-------------------------------------------------------------
AIC(m.null, m5.w, m6.w)
## ----warning=FALSE-------------------------------------------------------------
BIC(m.null, m5.w, m6.w)
## ----echo=TRUE, warning=FALSE--------------------------------------------------
round((PseudoR2(m1.w, "McKelveyZavoina")*100), digits = 1) ## gender
round((PseudoR2(m2.w, "McKelveyZavoina")*100), digits = 1) ## education
round((PseudoR2(m3.w, "McKelveyZavoina")*100), digits = 2) ## income
round((PseudoR2(m4.w, "McKelveyZavoina")*100), digits = 1) ## age
round((PseudoR2(m5.w, "McKelveyZavoina")*100), digits = 1) ## milieu
## ----warning=FALSE-------------------------------------------------------------
round((PseudoR2(m6.w, "McKelveyZavoina")*100), digits = 1) ## Overall model
## ------------------------------------------------------------------------------
round(11.9/21.2*100, digits = 1)
## ----warning=FALSE-------------------------------------------------------------
logistic.display(m5.w)
logistic.display(m6.w)
## ------------------------------------------------------------------------------
m5.w.marginal <- margins(m5.w)
summary(m5.w.marginal)
m6.w.marginal <- margins(m6.w)
summary(m6.w.marginal)
## ---- fig.width=10,fig.height=6------------------------------------------------
m5.w.marginal.sum <- summary(m5.w.marginal)
ggplot(data = m5.w.marginal.sum) +
geom_point(aes(factor, AME)) +
geom_errorbar(aes(x = factor, ymin = lower, ymax = upper)) +
geom_hline(yintercept = 0) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45))
## ---- fig.width=10,fig.height=6------------------------------------------------
m6.w.marginal.sum <- summary(m6.w.marginal)
ggplot(data = m6.w.marginal.sum) +
geom_point(aes(factor, AME)) +
geom_errorbar(aes(x = factor, ymin = lower, ymax = upper)) +
geom_hline(yintercept = 0) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45))