# Data and Examples from Winkelmann and Boes (2009) This manual page collects a list of examples from the book. Some solutions might not be exact and the list is not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. ## References Winkelmann, R., and Boes, S. (2009). **Analysis of Microdata**, 2nd ed. Berlin and Heidelberg: Springer-Verlag. ## See Also `GSS7402`, `GSOEP9402`, `PSID1976` ## Examples ```r ######################################### ## US General Social Survey 1974--2002 ## ######################################### ## data data("GSS7402", package = "AER") ## completed fertility subset gss40 <- subset(GSS7402, age >= 40) ## Chapter 1 ## Table 1.1 gss_kids <- table(gss40$kids) cbind(absolute = gss_kids, relative = round(prop.table(gss_kids) * 100, digits = 2)) ## Table 1.2 sd1 <- function(x) sd(x) / sqrt(length(x)) with(gss40, round(cbind( "obs" = tapply(kids, year, length), "av kids" = tapply(kids, year, mean), " " = tapply(kids, year, sd1), "prop childless" = tapply(kids, year, function(x) mean(x <= 0)), " " = tapply(kids, year, function(x) sd1(x <= 0)), "av schooling" = tapply(education, year, mean), " " = tapply(education, year, sd1) ), digits = 2)) ## Table 1.3 gss40$trend <- gss40$year - 1974 kids_lm1 <- lm(kids ~ factor(year), data = gss40) kids_lm2 <- lm(kids ~ trend, data = gss40) kids_lm3 <- lm(kids ~ trend + education, data = gss40) ## Chapter 2 ## Table 2.1 kids_tab <- prop.table(xtabs(~ kids + year, data = gss40), 2) * 100 round(kids_tab[,c(4, 8)], digits = 2) ## Figure 2.1 barplot(t(kids_tab[, c(4, 8)]), beside = TRUE, legend = TRUE) ## Chapter 3, Example 3.14 ## Table 3.1 gss40$nokids <- factor(gss40$kids <= 0, levels = c(FALSE, TRUE), labels = c("no", "yes")) nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit")) nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit")) nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings, data = gss40, family = binomial(link = "probit")) ## p. 87 lrtest(nokids_p1, nokids_p2, nokids_p3) ## Chapter 4, Example 4.1 gss40$nokids01 <- as.numeric(gss40$nokids) - 1 nokids_lm3 <- lm(nokids01 ~ trend + education + ethnicity + siblings, data = gss40) coeftest(nokids_lm3, vcov = sandwich) ## Example 4.3 ## Table 4.1 nokids_l1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "logit")) nokids_l3 <- glm(nokids ~ trend + education + ethnicity + siblings, data = gss40, family = binomial(link = "logit")) lrtest(nokids_p3) lrtest(nokids_l3) ## Table 4.2 nokids_xbar <- colMeans(model.matrix(nokids_l3)) sum(coef(nokids_p3) * nokids_xbar) sum(coef(nokids_l3) * nokids_xbar) dnorm(sum(coef(nokids_p3) * nokids_xbar)) dlogis(sum(coef(nokids_l3) * nokids_xbar)) dnorm(sum(coef(nokids_p3) * nokids_xbar)) * coef(nokids_p3)[3] dlogis(sum(coef(nokids_l3) * nokids_xbar)) * coef(nokids_l3)[3] exp(coef(nokids_l3)[3]) ## Figure 4.4 ## everything by hand (for ethnicity = "cauc" group) nokids_xbar <- as.vector(nokids_xbar) nokids_nd <- data.frame(education = seq(0, 20, by = 0.5), trend = nokids_xbar[2], ethnicity = "cauc", siblings = nokids_xbar[4]) nokids_p3_fit <- predict(nokids_p3, newdata = nokids_nd, type = "response", se.fit = TRUE) plot(nokids_nd$education, nokids_p3_fit$fit, type = "l", xlab = "education", ylab = "predicted probability", ylim = c(0, 0.3)) polygon(c(nokids_nd$education, rev(nokids_nd$education)), c(nokids_p3_fit$fit + 1.96 * nokids_p3_fit$se.fit, rev(nokids_p3_fit$fit - 1.96 * nokids_p3_fit$se.fit)), col = "lightgray", border = "lightgray") lines(nokids_nd$education, nokids_p3_fit$fit) ## using "effects" package (for average "ethnicity" variable) library("effects") nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20)) plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3)) ## using "effects" plus modification by hand nokids_p3_ef1 <- as.data.frame(nokids_p3_ef) plot(pnorm(fit) ~ education, data = nokids_p3_ef1, type = "n", ylim = c(0, 0.3)) polygon(c(0:20, 20:0), pnorm(c(nokids_p3_ef1$upper, rev(nokids_p3_ef1$lower))), col = "lightgray", border = "lightgray") lines(pnorm(fit) ~ education, data = nokids_p3_ef1) ## Table 4.6 ## McFadden's R^2 1 - as.numeric( logLik(nokids_p3) / logLik(nokids_p1) ) 1 - as.numeric( logLik(nokids_l3) / logLik(nokids_l1) ) ## McKelvey and Zavoina R^2 r2mz <- function(obj) { ystar <- predict(obj) sse <- sum((ystar - mean(ystar))^2) s2 <- switch(obj$family$link, "probit" = 1, "logit" = pi^2/3, NA) n <- length(residuals(obj)) sse / (n * s2 + sse) } r2mz(nokids_p3) r2mz(nokids_l3) ## AUC library("ROCR") nokids_p3_pred <- prediction(fitted(nokids_p3), gss40$nokids) nokids_l3_pred <- prediction(fitted(nokids_l3), gss40$nokids) plot(performance(nokids_p3_pred, "tpr", "fpr")) abline(0, 1, lty = 2) performance(nokids_p3_pred, "auc") plot(performance(nokids_l3_pred, "tpr", "fpr")) abline(0, 1, lty = 2) performance(nokids_l3_pred, "auc")@y.values ## Chapter 7 ## Table 7.3 ## subset selection gss02 <- subset(GSS7402, year == 2002 & (age < 40 | !is.na(agefirstbirth))) #Z# This selection conforms with top of page 229. However, there #Z# are too many observations: 1374. Furthermore, there are six #Z# observations with agefirstbirth <= 14 which will cause problems in #Z# taking logs! ## computing time to first birth gss02$tfb <- with(gss02, ifelse(is.na(agefirstbirth), age - 14, agefirstbirth - 14)) #Z# currently this is still needed before taking logs gss02$tfb <- pmax(gss02$tfb, 1) tfb_tobit <- tobit(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant, data = gss02, left = -Inf, right = log(gss02$age - 14)) tfb_ols <- lm(log(tfb) ~ education + ethnicity + siblings + city16 + immigrant, data = gss02, subset = !is.na(agefirstbirth)) ## Chapter 8 ## Example 8.3 gss2002 <- subset(GSS7402, year == 2002 & (agefirstbirth < 40 | age < 40)) gss2002$afb <- with(gss2002, Surv(ifelse(kids > 0, agefirstbirth, age), kids > 0)) afb_km <- survfit(afb ~ 1, data = gss2002) afb_skm <- summary(afb_km) print(afb_skm) with(afb_skm, plot(n.event/n.risk ~ time, type = "s")) plot(afb_km, xlim = c(10, 40), conf.int = FALSE) ## Example 8.9 library("survival") afb_ex <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "exponential") afb_wb <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "weibull") afb_ln <- survreg( afb ~ education + siblings + ethnicity + immigrant + lowincome16 + city16, data = gss2002, dist = "lognormal") ## Example 8.11 kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40, family = poisson) library("MASS") kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16, data = gss40) lrtest(kids_pois, kids_nb) ############################################ ## German Socio-Economic Panel 1994--2002 ## ############################################ ## data data("GSOEP9402", package = "AER") ## some convenience data transformations gsoep <- GSOEP9402 gsoep$meducation2 <- cut(gsoep$meducation, breaks = c(6, 10.25, 12.25, 18), labels = c("7-10", "10.5-12", "12.5-18")) gsoep$year2 <- factor(gsoep$year) ## Chapter 1 ## Table 1.4 plus visualizations gsoep_tab <- xtabs(~ meducation2 + school, data = gsoep) round(prop.table(gsoep_tab, 1) * 100, digits = 2) spineplot(gsoep_tab) plot(school ~ meducation, data = gsoep, breaks = c(7, 10.25, 12.25, 18)) plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18)) ## Chapter 5 ## Table 5.1 library("nnet") gsoep_mnl <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep) coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),] ## alternatively library("mlogit") gsoep_mnl2 <- mlogit(school ~ 0 | meducation + memployment + log(income) + log(size) + parity + year2, data = gsoep, shape = "wide", reflevel = "Hauptschule") coeftest(gsoep_mnl2)[1:12,] ## Table 5.2 library("effects") gsoep_eff <- effect("meducation", gsoep_mnl, xlevels = list(meducation = sort(unique(gsoep$meducation)))) gsoep_eff$prob plot(gsoep_eff, confint = FALSE) ## Table 5.3, odds exp(coef(gsoep_mnl)[, "meducation"]) ## all effects eff_mnl <- allEffects(gsoep_mnl) plot(eff_mnl, ask = FALSE, confint = FALSE) plot(eff_mnl, ask = FALSE, style = "stacked", colors = gray.colors(3)) ## omit year gsoep_mnl1 <- multinom( school ~ meducation + memployment + log(income) + log(size) + parity, data = gsoep) lrtest(gsoep_mnl, gsoep_mnl1) eff_mnl1 <- allEffects(gsoep_mnl1) plot(eff_mnl1, ask = FALSE, confint = FALSE) plot(eff_mnl1, ask = FALSE, style = "stacked", colors = gray.colors(3)) ## Chapter 6 ## Table 6.1 library("MASS") gsoep$munemp <- factor(gsoep$memployment != "none", levels = c(FALSE, TRUE), labels = c("no", "yes")) gsoep_pop <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2, data = gsoep, method = "probit", Hess = TRUE) gsoep_pol <- polr(school ~ meducation + munemp + log(income) + log(size) + parity + year2, data = gsoep, Hess = TRUE) lrtest(gsoep_pop) lrtest(gsoep_pol) ## Table 6.2 ## todo eff_pol <- allEffects(gsoep_pol) plot(eff_pol, ask = FALSE, confint = FALSE) plot(eff_pol, ask = FALSE, style = "stacked", colors = gray.colors(3)) #################################### ## Labor Force Participation Data ## #################################### ## Mroz data data("PSID1976", package = "AER") PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000) ## visualizations plot(hours ~ nwincome, data = PSID1976, xlab = "Non-wife income (in USD 1000)", ylab = "Hours of work in 1975") plot(jitter(hours, 200) ~ jitter(wage, 50), data = PSID1976, xlab = "Wife's average hourly wage (jittered)", ylab = "Hours of work in 1975 (jittered)") ## Chapter 1, p. 18 hours_lm <- lm(hours ~ wage + nwincome + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Chapter 7 ## Example 7.2, Table 7.1 hours_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) hours_ols1 <- lm(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) hours_ols2 <- lm(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Example 7.10, Table 7.4 wage_ols <- lm(log(wage) ~ education + experience + I(experience^2), data = PSID1976, subset = participation == "yes") library("sampleSelection") wage_ghr <- selection(participation ~ nwincome + age + youngkids + oldkids + education + experience + I(experience^2), log(wage) ~ education + experience + I(experience^2), data = PSID1976) ## Exercise 7.13 hours_cragg1 <- glm(participation ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, family = binomial(link = "probit")) library("truncreg") hours_cragg2 <- truncreg(hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976, subset = participation == "yes") ## Exercise 7.15 wage_olscoef <- sapply(c(-Inf, 0.5, 1, 1.5, 2), function(censpoint) coef(lm(log(wage) ~ education + experience + I(experience^2), data = PSID1976[log(PSID1976$wage) > censpoint,]))) wage_mlcoef <- sapply(c(0.5, 1, 1.5, 2), function(censpoint) coef(tobit(log(wage) ~ education + experience + I(experience^2), data = PSID1976, left = censpoint))) ################################## ## Choice of Brand for Crackers ## ################################## ## data library("mlogit") data("Cracker", package = "mlogit") head(Cracker, 3) crack <- mlogit.data(Cracker, varying = 2:13, shape = "wide", choice = "choice") head(crack, 12) ## Table 5.6 (model 3 probably not fully converged in W&B) crack$price <- crack$price/100 crack_mlogit1 <- mlogit(choice ~ price | 0, data = crack, reflevel = "private") crack_mlogit2 <- mlogit(choice ~ price | 1, data = crack, reflevel = "private") crack_mlogit3 <- mlogit(choice ~ price + feat + disp | 1, data = crack, reflevel = "private") lrtest(crack_mlogit1, crack_mlogit2, crack_mlogit3) ## IIA test crack_mlogit_all <- update(crack_mlogit2, reflevel = "nabisco") crack_mlogit_res <- update(crack_mlogit_all, alt.subset = c("keebler", "nabisco", "sunshine")) hmftest(crack_mlogit_all, crack_mlogit_res) ```

WinkelmannBoes2009 function