library(foreign)## Warning: package 'foreign' was built under R version 3.2.5r=read.dta("C:/Users/BINH THANG/Dropbox/Korea/STudy/Thesis/data management/DataR/dataR5.dta")
r1 <- subset(r )
attach(r1)
r1$logC1=log10(r1$cost_inc)
r1$highper[cost_inc<=62000] <- 0
r1$highper[cost_inc>62000] <- 1
r1$b16a[b16a == 5] <- 0
r1$freeEn[c9==1] <- 1
r1$freeEn[c9>1] <- 0
r1$h1[h1== 1] <- 1
r1$h1[h1== 2] <- 0
r1$c7ad[c7== 2] <- 0
r1$c7ad[c7== 1] <- 1
r1$c7ad[is.na(r1$c7)] <- 0
r1$ant[c5==1& c5==2 & c5==7 & c5==8 & c5==9] <- 0
r1$ant[c5==3|c5==4|c5==5] <- 1
r1$ant[is.na(r1$c5)] <- 0
r1$p1=r1$label1+r1$freeEn+r1$ant+r1$c7ad
r1$p[r1$p1==0] <- 0
r1$p[r1$p1==1] <- 1
r1$p[r1$p1>=2] <- 2
r1$wtp[cost_inc >=1] <- 0
r1$wtp[is.na(r1$wtp)] <- 1
newdata2=r1
attach(newdata2 )## The following objects are masked from r1:
##
## a, a1, advice, ag, age_group, anticam, b1, b10, b10a1, b10a2,
## b10a3, b10a4, b10a5, b10a6, b10a7, b11, b11a, b11a2, b11a3,
## b12, b12a, b13, b14, b15, b16, b16a, b17, b18, b18a, b1a,
## b1a1, b1a2, b1a3, b2, b2a1, b3, b4, b5, b5a, b6, b6_123, b6a,
## b6a1, b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9,
## br, branch, branch1, branch2, branch3, c1, c2, c23a, c3, c4,
## c5, c6, c6a111, c7, c8, c9, clog, clog1, COST, cost_inc,
## cost1, costincrease, ct, d1, d10, d11, d1a, d2, d3, d3a, d4,
## d5, d6, d7, d8, d9, Decision, e1, e2, edu, educ1, educ2, f1,
## ghi1, ghi2, ghiro2, giadinhkoUH, group_age, group_age1, h1,
## h10a, h10a1, h10a10, h10a11, h10a11a, h10a2, h10a3, h10a4,
## h10a5, h10a6, h10a7, h10a8, h10a9, h12, h12a, h12a_1, h12log,
## h13, h2, h3, h4, h5, h6, h7, h8, h9, ha000, ict1a, ict2a,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label_1, label1, label1a,
## logb7, logC, logitCost, moneyspent, msdt, n01, n02, n03, n05,
## n06, n07, n08, n1, n10, n100, n101, n102, n103, n11, n12, n13,
## n14, n15, n16, n1b, n2, n3, n35, n36, n37, n38, n39,
## n3posterb, n4, n40, n41, n42, n43, n44, n45, n46, n47, n48,
## n49, n5, n50, n51, n52, n53, n54, n55, n56, n57, n58, n59, n6,
## n60, n61, n61a, n62, n62a, n63, n63a, n64, n64a, n65, n65a,
## n66, n66a, n67, n67a, n68, n68a, n7, n77, n78, n7tren, n8,
## n88, n89, n8nha, n9, n97, n98, n99, n9khach, noE, noEnvi2,
## occup1, poli4, poli4a, policy, policy_a, policyeffect,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## taxIn, ter_fa1, ter_in, tertile_fa, tertile_indi, test,
## unitsdiffi1, var242, w1, w2, w3, w4newdata2$age_group=as.factor(newdata2$age_group)
newdata2$educ2=as.factor(newdata2$educ2)
newdata2$h1=as.factor(newdata2$h1)
newdata2$d1a=as.factor(newdata2$d1a)
newdata2$selfhealth=as.factor(newdata2$selfhealth)
newdata2$b18a=as.factor(newdata2$b18a)
newdata2$b16a=as.factor(newdata2$b16a)
newdata2$b6a=as.factor(newdata2$b6a)
newdata2$smostt=as.factor(newdata2$smostt)
newdata2$ter_in=as.factor(newdata2$ter_in)
newdata2$group_age1=as.factor(newdata2$group_age1)
newdata2$label1=as.factor(newdata2$label1)
newdata2$freeEn=as.factor(newdata2$freeEn)
newdata2$c7ad=as.factor(newdata2$c7ad)
newdata2$ant=as.factor(newdata2$ant)
newdata2$p=as.factor(newdata2$p)
newdata2$in00[newdata2$ter_in==1] <- 1
newdata2$in00[newdata2$ter_in==2 | ter_in==3] <- 0
newdata2$in00=as.factor(newdata2$in00)
attach(newdata2)## The following objects are masked from newdata2 (pos = 3):
##
## a, a1, advice, ag, age_group, ant, anticam, b1, b10, b10a1,
## b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11, b11a, b11a2,
## b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17, b18, b18a,
## b1a, b1a1, b1a2, b1a3, b2, b2a1, b3, b4, b5, b5a, b6, b6_123,
## b6a, b6a1, b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8,
## b9, br, branch, branch1, branch2, branch3, c1, c2, c23a, c3,
## c4, c5, c6, c6a111, c7, c7ad, c8, c9, clog, clog1, COST,
## cost_inc, cost1, costincrease, ct, d1, d10, d11, d1a, d2, d3,
## d3a, d4, d5, d6, d7, d8, d9, Decision, e1, e2, edu, educ1,
## educ2, f1, freeEn, ghi1, ghi2, ghiro2, giadinhkoUH, group_age,
## group_age1, h1, h10a, h10a1, h10a10, h10a11, h10a11a, h10a2,
## h10a3, h10a4, h10a5, h10a6, h10a7, h10a8, h10a9, h12, h12a,
## h12a_1, h12log, h13, h2, h3, h4, h5, h6, h7, h8, h9, ha000,
## highper, ict1a, ict2a, itc1, itc2, l1, l2, l3, l4, l5, l6,
## label_1, label1, label1a, logb7, logC, logC1, logitCost,
## moneyspent, msdt, n01, n02, n03, n05, n06, n07, n08, n1, n10,
## n100, n101, n102, n103, n11, n12, n13, n14, n15, n16, n1b, n2,
## n3, n35, n36, n37, n38, n39, n3posterb, n4, n40, n41, n42,
## n43, n44, n45, n46, n47, n48, n49, n5, n50, n51, n52, n53,
## n54, n55, n56, n57, n58, n59, n6, n60, n61, n61a, n62, n62a,
## n63, n63a, n64, n64a, n65, n65a, n66, n66a, n67, n67a, n68,
## n68a, n7, n77, n78, n7tren, n8, n88, n89, n8nha, n9, n97, n98,
## n99, n9khach, noE, noEnvi2, occup1, p, p1, poli4, poli4a,
## policy, policy_a, policyeffect, reasons, reasons1, s1,
## Screening, selfhealth, SH1, smostt, taxIn, ter_fa1, ter_in,
## tertile_fa, tertile_indi, test, unitsdiffi1, var242, w1, w2,
## w3, w4, wtp## The following objects are masked from r1:
##
## a, a1, advice, ag, age_group, anticam, b1, b10, b10a1, b10a2,
## b10a3, b10a4, b10a5, b10a6, b10a7, b11, b11a, b11a2, b11a3,
## b12, b12a, b13, b14, b15, b16, b16a, b17, b18, b18a, b1a,
## b1a1, b1a2, b1a3, b2, b2a1, b3, b4, b5, b5a, b6, b6_123, b6a,
## b6a1, b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9,
## br, branch, branch1, branch2, branch3, c1, c2, c23a, c3, c4,
## c5, c6, c6a111, c7, c8, c9, clog, clog1, COST, cost_inc,
## cost1, costincrease, ct, d1, d10, d11, d1a, d2, d3, d3a, d4,
## d5, d6, d7, d8, d9, Decision, e1, e2, edu, educ1, educ2, f1,
## ghi1, ghi2, ghiro2, giadinhkoUH, group_age, group_age1, h1,
## h10a, h10a1, h10a10, h10a11, h10a11a, h10a2, h10a3, h10a4,
## h10a5, h10a6, h10a7, h10a8, h10a9, h12, h12a, h12a_1, h12log,
## h13, h2, h3, h4, h5, h6, h7, h8, h9, ha000, ict1a, ict2a,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label_1, label1, label1a,
## logb7, logC, logitCost, moneyspent, msdt, n01, n02, n03, n05,
## n06, n07, n08, n1, n10, n100, n101, n102, n103, n11, n12, n13,
## n14, n15, n16, n1b, n2, n3, n35, n36, n37, n38, n39,
## n3posterb, n4, n40, n41, n42, n43, n44, n45, n46, n47, n48,
## n49, n5, n50, n51, n52, n53, n54, n55, n56, n57, n58, n59, n6,
## n60, n61, n61a, n62, n62a, n63, n63a, n64, n64a, n65, n65a,
## n66, n66a, n67, n67a, n68, n68a, n7, n77, n78, n7tren, n8,
## n88, n89, n8nha, n9, n97, n98, n99, n9khach, noE, noEnvi2,
## occup1, poli4, poli4a, policy, policy_a, policyeffect,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## taxIn, ter_fa1, ter_in, tertile_fa, tertile_indi, test,
## unitsdiffi1, var242, w1, w2, w3, w4mydata=newdata2library(quantreg)## Warning: package 'quantreg' was built under R version 3.2.5## Loading required package: SparseM## Warning: package 'SparseM' was built under R version 3.2.5##
## Attaching package: 'SparseM'## The following object is masked from 'package:base':
##
## backsolve# OLS regression
olsreg <- lm(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad)
summary(olsreg)##
## Call:
## lm(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58597 -0.14941 -0.02762 0.15352 0.83642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.750190 0.044780 106.078 <2e-16 ***
## age_groupgr3039 0.041115 0.034263 1.200 0.2308
## age_groupgr4049 -0.035890 0.033814 -1.061 0.2891
## age_groupgr5059 -0.024212 0.038185 -0.634 0.5264
## age_group60plus -0.017801 0.053678 -0.332 0.7403
## educ22 0.014834 0.032073 0.462 0.6440
## educ23 0.097123 0.039283 2.472 0.0138 *
## selfhealthgood 0.051691 0.024217 2.134 0.0334 *
## smosttmedium 0.009067 0.030829 0.294 0.7688
## smosttheavy 0.062158 0.032810 1.894 0.0588 .
## b6ayes 0.047223 0.032351 1.460 0.1451
## label11 -0.045376 0.033578 -1.351 0.1773
## freeEn1 -0.055279 0.027909 -1.981 0.0483 *
## ant1 0.019953 0.024810 0.804 0.4217
## c7ad1 0.008815 0.025045 0.352 0.7250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2434 on 429 degrees of freedom
## (379 observations deleted due to missingness)
## Multiple R-squared: 0.07387, Adjusted R-squared: 0.04365
## F-statistic: 2.444 on 14 and 429 DF, p-value: 0.00254# Quantile regression
quantreg25 <- rq(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad, data=mydata, tau=0.25)
summary(quantreg25)## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = 0.25, data = mydata)
##
## tau: [1] 0.25
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.54111 4.46221 4.64306
## age_groupgr3039 0.02797 -0.03756 0.08699
## age_groupgr4049 -0.00872 -0.04341 0.04580
## age_groupgr5059 -0.06894 -0.13996 0.06175
## age_group60plus 0.03386 -0.11555 0.08795
## educ22 0.04444 -0.03729 0.09083
## educ23 0.13698 0.05965 0.18640
## selfhealthgood 0.07858 0.03582 0.12927
## smosttmedium 0.05802 -0.00478 0.11153
## smosttheavy 0.03381 -0.04271 0.09230
## b6ayes 0.03729 -0.03184 0.10838
## label11 -0.06727 -0.12296 -0.01920
## freeEn1 -0.10229 -0.13542 -0.04101
## ant1 0.05114 0.01089 0.10197
## c7ad1 0.01165 -0.04995 0.03956quantreg50 <- rq(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad, data=mydata, tau=0.5)## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonuniquesummary(quantreg50)## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = 0.5, data = mydata)
##
## tau: [1] 0.5
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.69412 4.62913 4.76234
## age_groupgr3039 0.07017 -0.00762 0.11965
## age_groupgr4049 -0.02611 -0.08464 0.02735
## age_groupgr5059 -0.01223 -0.10514 0.06287
## age_group60plus -0.00361 -0.09816 0.07310
## educ22 0.01193 -0.04384 0.05624
## educ23 0.10597 0.02678 0.18937
## selfhealthgood 0.07110 0.02635 0.11524
## smosttmedium 0.03412 -0.01380 0.09696
## smosttheavy 0.08018 0.02539 0.11943
## b6ayes 0.07304 -0.00478 0.12704
## label11 -0.07665 -0.12112 0.01765
## freeEn1 -0.09498 -0.14810 -0.01532
## ant1 0.02996 -0.02534 0.07243
## c7ad1 -0.02312 -0.06710 0.05301quantreg75 <- rq(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad, data=mydata, tau=0.75)
summary(quantreg75)## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = 0.75, data = mydata)
##
## tau: [1] 0.75
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.92917 4.78560 5.03721
## age_groupgr3039 -0.02281 -0.12216 0.04341
## age_groupgr4049 -0.12399 -0.18326 -0.01014
## age_groupgr5059 -0.05394 -0.14683 0.04638
## age_group60plus -0.02148 -0.14957 0.10207
## educ22 0.01410 -0.08124 0.12073
## educ23 0.07281 -0.01582 0.17435
## selfhealthgood 0.07720 -0.00562 0.12324
## smosttmedium -0.02142 -0.12731 0.07491
## smosttheavy 0.07281 0.00062 0.19915
## b6ayes 0.03675 -0.02471 0.11453
## label11 -0.00871 -0.09766 0.05914
## freeEn1 0.00698 -0.06362 0.09088
## ant1 0.00891 -0.05427 0.06194
## c7ad1 0.01052 -0.04921 0.09845# Simultaneous quantile regression
quantreg2575 <- rq(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad, data=mydata, tau=c(0.25, 0.5, 0.75))## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonuniquesummary(quantreg2575)## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = c(0.25, 0.5, 0.75),
## data = mydata)
##
## tau: [1] 0.25
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.54111 4.46221 4.64306
## age_groupgr3039 0.02797 -0.03756 0.08699
## age_groupgr4049 -0.00872 -0.04341 0.04580
## age_groupgr5059 -0.06894 -0.13996 0.06175
## age_group60plus 0.03386 -0.11555 0.08795
## educ22 0.04444 -0.03729 0.09083
## educ23 0.13698 0.05965 0.18640
## selfhealthgood 0.07858 0.03582 0.12927
## smosttmedium 0.05802 -0.00478 0.11153
## smosttheavy 0.03381 -0.04271 0.09230
## b6ayes 0.03729 -0.03184 0.10838
## label11 -0.06727 -0.12296 -0.01920
## freeEn1 -0.10229 -0.13542 -0.04101
## ant1 0.05114 0.01089 0.10197
## c7ad1 0.01165 -0.04995 0.03956
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = c(0.25, 0.5, 0.75),
## data = mydata)
##
## tau: [1] 0.5
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.69412 4.62913 4.76234
## age_groupgr3039 0.07017 -0.00762 0.11965
## age_groupgr4049 -0.02611 -0.08464 0.02735
## age_groupgr5059 -0.01223 -0.10514 0.06287
## age_group60plus -0.00361 -0.09816 0.07310
## educ22 0.01193 -0.04384 0.05624
## educ23 0.10597 0.02678 0.18937
## selfhealthgood 0.07110 0.02635 0.11524
## smosttmedium 0.03412 -0.01380 0.09696
## smosttheavy 0.08018 0.02539 0.11943
## b6ayes 0.07304 -0.00478 0.12704
## label11 -0.07665 -0.12112 0.01765
## freeEn1 -0.09498 -0.14810 -0.01532
## ant1 0.02996 -0.02534 0.07243
## c7ad1 -0.02312 -0.06710 0.05301
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = c(0.25, 0.5, 0.75),
## data = mydata)
##
## tau: [1] 0.75
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.92917 4.78560 5.03721
## age_groupgr3039 -0.02281 -0.12216 0.04341
## age_groupgr4049 -0.12399 -0.18326 -0.01014
## age_groupgr5059 -0.05394 -0.14683 0.04638
## age_group60plus -0.02148 -0.14957 0.10207
## educ22 0.01410 -0.08124 0.12073
## educ23 0.07281 -0.01582 0.17435
## selfhealthgood 0.07720 -0.00562 0.12324
## smosttmedium -0.02142 -0.12731 0.07491
## smosttheavy 0.07281 0.00062 0.19915
## b6ayes 0.03675 -0.02471 0.11453
## label11 -0.00871 -0.09766 0.05914
## freeEn1 0.00698 -0.06362 0.09088
## ant1 0.00891 -0.05427 0.06194
## c7ad1 0.01052 -0.04921 0.09845# ANOVA test for coefficient differences
anova(quantreg25, quantreg50, quantreg75)## Warning in summary.rq(x, se = se, covariance = TRUE): 1 non-positive fis## Quantile Regression Analysis of Deviance Table
##
## Model: logC1 ~ age_group + educ2 + selfhealth + smostt + b6a + label1 + freeEn + ant + c7ad
## Joint Test of Equality of Slopes: tau in { 0.25 0.5 0.75 }
##
## Df Resid Df F value Pr(>F)
## 1 28 1304 1.7252 0.01098 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Plotting data
quantreg.all <- rq(logC1~age_group+educ2+selfhealth+smostt
+b6a+label1+freeEn+ant+c7ad, tau = seq(0.05, 0.95, by = 0.05), data=mydata)## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonuniquesummary(quantreg.all)## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique
## Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be
## nonunique##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.05
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.362190e+00 4.179280e+00 4.505370e+00
## age_groupgr3039 -3.176000e-02 -1.370500e-01 9.552000e-02
## age_groupgr4049 -2.383000e-02 -1.501800e-01 1.020400e-01
## age_groupgr5059 -4.585000e-02 -1.913500e-01 1.869000e-02
## age_group60plus 6.140000e-03 -1.797693e+308 1.449200e-01
## educ22 8.266000e-02 -3.270000e-03 1.472200e-01
## educ23 1.285500e-01 -5.324000e-02 2.519800e-01
## selfhealthgood 5.610000e-02 -3.934000e-02 1.390000e-01
## smosttmedium 3.407000e-02 -5.822000e-02 2.009700e-01
## smosttheavy 2.173000e-02 -5.596000e-02 9.502000e-02
## b6ayes -1.331000e-02 -2.489400e-01 7.276000e-02
## label11 4.753000e-02 -8.289000e-02 8.809000e-02
## freeEn1 -1.048500e-01 -2.427400e-01 -2.168000e-02
## ant1 -2.770000e-03 -6.002000e-02 7.743000e-02
## c7ad1 4.231000e-02 -2.713000e-02 1.597200e-01
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.1
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.40284 4.27843 4.50078
## age_groupgr3039 0.05229 -0.07672 0.11307
## age_groupgr4049 0.02927 -0.08061 0.10081
## age_groupgr5059 -0.06038 -0.14656 -0.01703
## age_group60plus 0.00657 -0.27256 0.09151
## educ22 0.05158 -0.00828 0.15138
## educ23 0.16536 0.04357 0.26260
## selfhealthgood 0.06038 0.00543 0.12584
## smosttmedium 0.06499 -0.01506 0.15403
## smosttheavy 0.01812 -0.07130 0.11687
## b6ayes -0.00461 -0.10681 0.11975
## label11 -0.00952 -0.09242 0.03793
## freeEn1 -0.05301 -0.14817 -0.02339
## ant1 0.05759 -0.01168 0.11159
## c7ad1 0.01207 -0.03861 0.08679
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.15
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.44716 4.37282 4.53055
## age_groupgr3039 0.05224 -0.04511 0.09742
## age_groupgr4049 0.03509 -0.07487 0.08760
## age_groupgr5059 -0.08573 -0.14346 -0.02412
## age_group60plus 0.02125 -0.07258 0.09064
## educ22 0.05310 0.00255 0.11280
## educ23 0.15381 0.07549 0.21307
## selfhealthgood 0.07762 0.02593 0.12644
## smosttmedium 0.05224 0.00061 0.11822
## smosttheavy 0.00872 -0.05172 0.10260
## b6ayes 0.04076 -0.05238 0.11818
## label11 -0.04048 -0.09142 -0.00667
## freeEn1 -0.08174 -0.12933 -0.04038
## ant1 0.05799 0.02700 0.09904
## c7ad1 0.01945 -0.04614 0.06581
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.2
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.47840 4.44346 4.58408
## age_groupgr3039 0.03549 -0.03266 0.10698
## age_groupgr4049 0.01642 -0.03261 0.05849
## age_groupgr5059 -0.06569 -0.14418 0.00410
## age_group60plus -0.00331 -0.07310 0.11698
## educ22 0.05029 -0.01537 0.10982
## educ23 0.14540 0.08185 0.18804
## selfhealthgood 0.08817 0.03950 0.13196
## smosttmedium 0.05846 0.00625 0.11817
## smosttheavy 0.04102 -0.01823 0.08252
## b6ayes 0.04887 -0.04529 0.09785
## label11 -0.05256 -0.10846 -0.00978
## freeEn1 -0.07869 -0.11983 -0.05141
## ant1 0.05810 0.00772 0.08163
## c7ad1 0.00808 -0.03928 0.05121
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.25
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.54111 4.46221 4.64306
## age_groupgr3039 0.02797 -0.03756 0.08699
## age_groupgr4049 -0.00872 -0.04341 0.04580
## age_groupgr5059 -0.06894 -0.13996 0.06175
## age_group60plus 0.03386 -0.11555 0.08795
## educ22 0.04444 -0.03729 0.09083
## educ23 0.13698 0.05965 0.18640
## selfhealthgood 0.07858 0.03582 0.12927
## smosttmedium 0.05802 -0.00478 0.11153
## smosttheavy 0.03381 -0.04271 0.09230
## b6ayes 0.03729 -0.03184 0.10838
## label11 -0.06727 -0.12296 -0.01920
## freeEn1 -0.10229 -0.13542 -0.04101
## ant1 0.05114 0.01089 0.10197
## c7ad1 0.01165 -0.04995 0.03956
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.3
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.62839 4.51907 4.69888
## age_groupgr3039 0.01685 -0.04011 0.08383
## age_groupgr4049 -0.02397 -0.06517 0.01069
## age_groupgr5059 -0.02338 -0.14086 0.02430
## age_group60plus -0.01081 -0.13452 0.06941
## educ22 0.02482 -0.04118 0.09631
## educ23 0.11355 0.06286 0.19617
## selfhealthgood 0.06695 0.02032 0.11974
## smosttmedium 0.03621 -0.02492 0.10817
## smosttheavy 0.02579 -0.01913 0.10819
## b6ayes 0.04845 -0.00933 0.10348
## label11 -0.04903 -0.11332 -0.01453
## freeEn1 -0.11046 -0.15132 -0.03748
## ant1 0.03351 0.00261 0.09032
## c7ad1 -0.02115 -0.05091 0.02090
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.35
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.64179 4.59763 4.72188
## age_groupgr3039 0.03031 -0.05357 0.07431
## age_groupgr4049 -0.02384 -0.07943 0.00495
## age_groupgr5059 -0.02384 -0.12303 0.03652
## age_group60plus -0.00632 -0.12391 0.03695
## educ22 0.02080 -0.03858 0.06057
## educ23 0.10911 0.04592 0.16310
## selfhealthgood 0.06556 0.02314 0.10592
## smosttmedium 0.04173 -0.01199 0.10740
## smosttheavy 0.03945 -0.02145 0.10743
## b6ayes 0.04838 -0.01498 0.10138
## label11 -0.05982 -0.10848 -0.01835
## freeEn1 -0.10338 -0.15708 -0.04275
## ant1 0.04156 0.00118 0.07433
## c7ad1 -0.01448 -0.05334 0.01456
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.4
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.69665 4.60345 4.74102
## age_groupgr3039 0.02717 -0.03831 0.10119
## age_groupgr4049 -0.04382 -0.08790 0.00955
## age_groupgr5059 -0.04382 -0.09918 0.04291
## age_group60plus -0.03071 -0.10266 0.03283
## educ22 0.00722 -0.03665 0.06820
## educ23 0.09453 0.03314 0.15093
## selfhealthgood 0.05638 0.02479 0.10822
## smosttmedium 0.03175 -0.00396 0.09052
## smosttheavy 0.03865 -0.00791 0.09553
## b6ayes 0.05340 0.00563 0.09965
## label11 -0.05882 -0.12105 -0.01148
## freeEn1 -0.09937 -0.15600 -0.04520
## ant1 0.04136 -0.00123 0.08155
## c7ad1 -0.02559 -0.06681 0.01549
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.45
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.70407 4.61650 4.75136
## age_groupgr3039 0.02328 -0.02085 0.10731
## age_groupgr4049 -0.05251 -0.09313 0.01575
## age_groupgr5059 -0.02140 -0.11858 0.00882
## age_group60plus -0.03722 -0.07659 0.06267
## educ22 0.00350 -0.03772 0.07137
## educ23 0.08618 0.01816 0.15231
## selfhealthgood 0.06848 0.02569 0.10764
## smosttmedium 0.03333 0.00307 0.09761
## smosttheavy 0.05670 0.00405 0.10543
## b6ayes 0.06543 0.02039 0.11926
## label11 -0.07198 -0.11175 -0.00757
## freeEn1 -0.10201 -0.14538 -0.03195
## ant1 0.04741 -0.00091 0.07873
## c7ad1 -0.02808 -0.05611 0.02180
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.5
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.69412 4.62913 4.76234
## age_groupgr3039 0.07017 -0.00762 0.11965
## age_groupgr4049 -0.02611 -0.08464 0.02735
## age_groupgr5059 -0.01223 -0.10514 0.06287
## age_group60plus -0.00361 -0.09816 0.07310
## educ22 0.01193 -0.04384 0.05624
## educ23 0.10597 0.02678 0.18937
## selfhealthgood 0.07110 0.02635 0.11524
## smosttmedium 0.03412 -0.01380 0.09696
## smosttheavy 0.08018 0.02539 0.11943
## b6ayes 0.07304 -0.00478 0.12704
## label11 -0.07665 -0.12112 0.01765
## freeEn1 -0.09498 -0.14810 -0.01532
## ant1 0.02996 -0.02534 0.07243
## c7ad1 -0.02312 -0.06710 0.05301
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.55
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.68238 4.64525 4.78327
## age_groupgr3039 0.07299 -0.00859 0.10645
## age_groupgr4049 -0.01508 -0.10234 0.02243
## age_groupgr5059 0.02347 -0.10836 0.05260
## age_group60plus 0.00023 -0.10365 0.14939
## educ22 0.02912 -0.02585 0.06173
## educ23 0.14831 0.04161 0.20912
## selfhealthgood 0.06779 0.02842 0.09957
## smosttmedium 0.05080 -0.00151 0.08093
## smosttheavy 0.08089 0.03350 0.14809
## b6ayes 0.06190 -0.00739 0.10287
## label11 -0.07302 -0.11077 0.04160
## freeEn1 -0.08281 -0.14566 -0.00142
## ant1 0.02066 -0.02154 0.06808
## c7ad1 0.01465 -0.05023 0.06040
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.6
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.69942 4.64635 4.82672
## age_groupgr3039 0.05075 -0.01653 0.12946
## age_groupgr4049 -0.02190 -0.09561 0.03565
## age_groupgr5059 0.00319 -0.11474 0.13089
## age_group60plus 0.02509 -0.09696 0.16828
## educ22 0.02892 -0.04301 0.08117
## educ23 0.16224 0.01256 0.21244
## selfhealthgood 0.06405 0.02376 0.13080
## smosttmedium 0.05699 -0.02300 0.09488
## smosttheavy 0.13242 0.01469 0.16793
## b6ayes 0.06022 -0.00295 0.12851
## label11 -0.05677 -0.13893 0.05315
## freeEn1 -0.09321 -0.13587 0.02842
## ant1 0.02145 -0.03963 0.07624
## c7ad1 0.02573 -0.07209 0.08930
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.65
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.74441 4.68050 4.89209
## age_groupgr3039 0.01691 -0.05267 0.10686
## age_groupgr4049 -0.02897 -0.12869 0.00690
## age_groupgr5059 0.00048 -0.10182 0.10567
## age_group60plus 0.02172 -0.09181 0.12317
## educ22 0.02173 -0.08852 0.06915
## educ23 0.13116 0.00522 0.20437
## selfhealthgood 0.07044 0.02588 0.12200
## smosttmedium 0.02626 -0.04559 0.06434
## smosttheavy 0.13317 0.05073 0.17826
## b6ayes 0.05922 0.01355 0.10710
## label11 -0.02848 -0.15143 0.06033
## freeEn1 -0.03164 -0.13785 0.06392
## ant1 0.01875 -0.03840 0.06835
## c7ad1 0.02547 -0.06401 0.08896
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.7
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.84415 4.72392 5.03597
## age_groupgr3039 -0.01733 -0.07560 0.06377
## age_groupgr4049 -0.10246 -0.14573 -0.00057
## age_groupgr5059 -0.02120 -0.13075 0.09384
## age_group60plus -0.00021 -0.10649 0.11548
## educ22 0.02833 -0.08993 0.09084
## educ23 0.12943 0.01494 0.18537
## selfhealthgood 0.09067 0.02376 0.13576
## smosttmedium 0.00452 -0.10142 0.06439
## smosttheavy 0.11079 0.04363 0.19912
## b6ayes 0.04269 0.00267 0.09999
## label11 -0.01219 -0.11919 0.06466
## freeEn1 -0.01253 -0.06753 0.07692
## ant1 -0.02738 -0.05907 0.07696
## c7ad1 0.02986 -0.05416 0.08291
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.75
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.92917 4.78370 5.03762
## age_groupgr3039 -0.02281 -0.12220 0.04341
## age_groupgr4049 -0.12399 -0.18326 -0.01014
## age_groupgr5059 -0.05394 -0.14683 0.04638
## age_group60plus -0.02148 -0.14957 0.10207
## educ22 0.01410 -0.08124 0.12073
## educ23 0.07281 -0.01582 0.17435
## selfhealthgood 0.07720 -0.00562 0.12324
## smosttmedium -0.02142 -0.12731 0.07491
## smosttheavy 0.07281 0.00062 0.19915
## b6ayes 0.03675 -0.02471 0.11453
## label11 -0.00871 -0.09766 0.05703
## freeEn1 0.00698 -0.06362 0.09088
## ant1 0.00891 -0.05427 0.05700
## c7ad1 0.01052 -0.04921 0.09845
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.8
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 4.99658 4.88549 5.10263
## age_groupgr3039 -0.00327 -0.08757 0.06544
## age_groupgr4049 -0.05255 -0.16680 -0.00103
## age_groupgr5059 -0.04041 -0.13253 0.01804
## age_group60plus -0.06053 -0.11755 0.06841
## educ22 0.01504 -0.06191 0.06097
## educ23 0.06150 -0.00981 0.14048
## selfhealthgood 0.02505 -0.00473 0.07195
## smosttmedium -0.02661 -0.11363 0.01367
## smosttheavy 0.06025 -0.01043 0.17260
## b6ayes 0.04041 -0.03732 0.09429
## label11 -0.02208 -0.12054 0.02247
## freeEn1 0.01827 -0.05064 0.07114
## ant1 -0.00728 -0.04636 0.06717
## c7ad1 0.02429 -0.04995 0.07120
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.85
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 5.07320 4.96908 5.17210
## age_groupgr3039 0.01142 -0.05003 0.20968
## age_groupgr4049 -0.02681 -0.10646 0.03340
## age_groupgr5059 -0.02681 -0.08428 0.04362
## age_group60plus -0.02409 -0.10005 0.02783
## educ22 0.01022 -0.05807 0.07665
## educ23 0.03781 -0.00493 0.11052
## selfhealthgood 0.01119 -0.03776 0.07074
## smosttmedium -0.03671 -0.11158 0.00121
## smosttheavy 0.01316 -0.05641 0.12208
## b6ayes 0.01594 -0.01875 0.11936
## label11 -0.04123 -0.11608 0.01404
## freeEn1 0.00318 -0.03888 0.08373
## ant1 0.00222 -0.05207 0.05881
## c7ad1 0.01029 -0.01952 0.08037
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.9
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 5.20574 5.03681 5.33833
## age_groupgr3039 0.11432 -0.04343 0.24918
## age_groupgr4049 0.00518 -0.13447 0.10786
## age_groupgr5059 0.05268 -0.06554 0.26193
## age_group60plus -0.00005 -0.13212 0.32766
## educ22 -0.04267 -0.18594 0.10362
## educ23 0.01933 -0.09073 0.16028
## selfhealthgood 0.00537 -0.05663 0.10870
## smosttmedium -0.13517 -0.20815 -0.03905
## smosttheavy -0.03295 -0.18626 0.15386
## b6ayes 0.07949 -0.01367 0.17476
## label11 -0.12122 -0.19229 0.06932
## freeEn1 -0.01410 -0.09392 0.09928
## ant1 -0.00877 -0.08065 0.07386
## c7ad1 -0.01142 -0.08619 0.08804
##
## Call: rq(formula = logC1 ~ age_group + educ2 + selfhealth + smostt +
## b6a + label1 + freeEn + ant + c7ad, tau = seq(0.05, 0.95,
## by = 0.05), data = mydata)
##
## tau: [1] 0.95
##
## Coefficients:
## coefficients lower bd upper bd
## (Intercept) 5.320450e+00 5.170560e+00 5.705200e+00
## age_groupgr3039 1.245000e-01 2.492000e-02 3.686400e-01
## age_groupgr4049 1.260000e-03 -1.533000e-01 1.384700e-01
## age_groupgr5059 6.718000e-02 -1.098300e-01 3.403900e-01
## age_group60plus -1.040500e-01 -2.032300e-01 1.797693e+308
## educ22 -4.446000e-02 -3.327500e-01 1.329100e-01
## educ23 -3.728000e-02 -3.326100e-01 4.066000e-02
## selfhealthgood 2.803000e-02 -5.029000e-02 1.221000e-01
## smosttmedium -1.968100e-01 -3.297000e-01 1.485000e-02
## smosttheavy 5.100000e-04 -2.041200e-01 1.763600e-01
## b6ayes 7.027000e-02 2.713000e-02 3.870700e-01
## label11 -4.405000e-02 -1.964400e-01 2.152300e-01
## freeEn1 -2.929000e-02 -1.223400e-01 5.802000e-02
## ant1 -3.596000e-02 -6.639000e-02 5.656000e-02
## c7ad1 -1.260000e-03 -1.154300e-01 1.305100e-01`` #plot
quantreg.plot <- summary(quantreg.all)
plot(quantreg.plot)
```