# Compare Model coefficients Scenario A mxl_wtp_c <- as.data.frame(mxl_wtp_case_a$estimate) mxl_wtp_c$se <- mxl_wtp_case_a$robse mxl_wtp_c <- rownames_to_column(mxl_wtp_c, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c$ME <- qnorm(1-alpha/2)*mxl_wtp_c$se mxl_wtp_c$Model <- "without NR" mxl_wtp_c <- mxl_wtp_c %>% rename("Estimate" = `mxl_wtp_case_a$estimate`) mxl_wtp_c_NR <- as.data.frame(mxl_wtp_case_a_NR$estimate) mxl_wtp_c_NR$se <- mxl_wtp_case_a_NR$robse mxl_wtp_c_NR <- rownames_to_column(mxl_wtp_c_NR, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c_NR$ME <- qnorm(1-alpha/2)*mxl_wtp_c_NR$se mxl_wtp_c_NR$Model <- "with NR" mxl_wtp_c_NR <- mxl_wtp_c_NR %>% rename("Estimate" = `mxl_wtp_case_a_NR$estimate`) mxl_wtp_c <- rbind(mxl_wtp_c, mxl_wtp_c_NR) wtp_nat_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_nat_T")) wtp_wd_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_wd_T")) wtp_nat_a<-ggplot(data=wtp_nat_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45)) wtp_wd_a<-ggplot(data=wtp_wd_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45)) # Compare Model coefficients Scenario B mxl_wtp_c <- as.data.frame(mxl_wtp_case_b$estimate) mxl_wtp_c$se <- mxl_wtp_case_b$robse mxl_wtp_c <- rownames_to_column(mxl_wtp_c, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c$ME <- qnorm(1-alpha/2)*mxl_wtp_c$se mxl_wtp_c$Model <- "without NR" mxl_wtp_c <- mxl_wtp_c %>% rename("Estimate" = `mxl_wtp_case_b$estimate`) mxl_wtp_c_NR <- as.data.frame(mxl_wtp_case_b_NR$estimate) mxl_wtp_c_NR$se <- mxl_wtp_case_b_NR$robse mxl_wtp_c_NR <- rownames_to_column(mxl_wtp_c_NR, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c_NR$ME <- qnorm(1-alpha/2)*mxl_wtp_c_NR$se mxl_wtp_c_NR$Model <- "with NR" mxl_wtp_c_NR <- mxl_wtp_c_NR %>% rename("Estimate" = `mxl_wtp_case_b_NR$estimate`) mxl_wtp_c <- rbind(mxl_wtp_c, mxl_wtp_c_NR) wtp_nat_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_nat_T")) wtp_wd_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_wd_T")) wtp_nat_b<-ggplot(data=wtp_nat_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45)) wtp_wd_b<-ggplot(data=wtp_wd_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45)) # Compare Model coefficients Scenario C mxl_wtp_c <- as.data.frame(mxl_wtp_case_c$estimate) mxl_wtp_c$se <- mxl_wtp_case_c$robse mxl_wtp_c <- rownames_to_column(mxl_wtp_c, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c$ME <- qnorm(1-alpha/2)*mxl_wtp_c$se mxl_wtp_c$Model <- "without NR" mxl_wtp_c <- mxl_wtp_c %>% rename("Estimate" = `mxl_wtp_case_c$estimate`) mxl_wtp_c_NR <- as.data.frame(mxl_wtp_case_c_NR$estimate) mxl_wtp_c_NR$se <- mxl_wtp_case_c_NR$robse mxl_wtp_c_NR <- rownames_to_column(mxl_wtp_c_NR, "Coefficent") alpha = 0.1 # Set confidence level mxl_wtp_c_NR$ME <- qnorm(1-alpha/2)*mxl_wtp_c_NR$se mxl_wtp_c_NR$Model <- "with NR" mxl_wtp_c_NR <- mxl_wtp_c_NR %>% rename("Estimate" = `mxl_wtp_case_c_NR$estimate`) mxl_wtp_c <- rbind(mxl_wtp_c, mxl_wtp_c_NR) wtp_nat_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_nat_vid1", "mu_nat_vid2" ,"mu_nat_info_nv1", "mu_nat_info_nv2","mu_nat_no_info")) wtp_wd_c <- mxl_wtp_c %>% filter(Coefficent %in% c("mu_walking_vid1", "mu_walking_vid2" ,"mu_walking_info_nv1", "mu_walking_info_nv2","mu_walking_no_info" )) wtp_nat_c<-ggplot(data=wtp_nat_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45)) wtp_wd_c<-ggplot(data=wtp_wd_c, aes(x=Coefficent, y=Estimate, fill=Model)) + geom_bar(stat="identity", position='dodge', width = 0.9) + geom_errorbar(aes(x=Coefficent, ymin=Estimate-ME, ymax=Estimate+ME), width=0.3, position=position_dodge(0.8)) + ylab("WTP in Euro per month") + xlab("Coefficient") + scale_x_discrete(guide = guide_axis(angle = 45))