BankWages | R Documentation |
Wages of employees of a US bank.
data("BankWages")
A data frame containing 474 observations on 4 variables.
Ordered factor indicating job category, with levels "custodial"
,
"admin"
and "manage"
.
Education in years.
Factor indicating gender.
Factor. Is the employee member of a minority?
Online complements to Heij, de Boer, Franses, Kloek, and van Dijk (2004).
https://global.oup.com/booksites/content/0199268010/datasets/ch6/xr614bwa.asc
Heij, C., de Boer, P.M.C., Franses, P.H., Kloek, T. and van Dijk, H.K. (2004). Econometric Methods with Applications in Business and Economics. Oxford: Oxford University Press.
data("BankWages") ## exploratory analysis of job ~ education ## (tables and spine plots, some education levels merged) xtabs(~ education + job, data = BankWages) edcat <- factor(BankWages$education) levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), c(2, 3, 3)) tab <- xtabs(~ edcat + job, data = BankWages) prop.table(tab, 1) spineplot(tab, off = 0) plot(job ~ edcat, data = BankWages, off = 0) ## fit multinomial model for male employees library("nnet") fm_mnl <- multinom(job ~ education + minority, data = BankWages, subset = gender == "male", trace = FALSE) summary(fm_mnl) confint(fm_mnl) ## same with mlogit package library("mlogit") fm_mlogit <- mlogit(job ~ 1 | education + minority, data = BankWages, subset = gender == "male", shape = "wide", reflevel = "custodial") summary(fm_mlogit)
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