all models: cigarette price
Binh Thang, Tran
23 July 2017
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/dataR5a.dta")
r1 <- subset(r, cost_inc>1 )
attach(r1)
r1$logC1=log10(r1$cost_inc)
r1$logC2=r1$cost_inc/1000
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$wtp)] <- 1
newdata2=r1
attach(newdata2 )## The following objects are masked from r1:
##
## a, a1, advice, ag, age_group, anticam, b1, b10, b10a1, b10a2,
## b10a3, b10a4, b10a5, b10a6, b10a7, b11, b11a, b11a2, b11a3,
## b12, b12a, b13, b14, b15, b16, b16a, b17, b18, b18a, b1a,
## b1a1, b1a2, b1a3, b2, b2a1, b3, b4, b5, b5a, b6, b6_123, b6a,
## b6a1, b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b6b, b7, b8,
## b9, br, branch, branch1, branch2, branch3, c1, c2, c23a, c3,
## c4, c5, c6, c6a111, c7, c8, c9, clog, clog1, COST, cost_inc,
## cost1, costincrease, ct, d1, d10, d11, d1a, d2, d3, d3a, d4,
## d5, d6, d7, d8, d9, Decision, e1, e2, edu, educ1, educ2, f1,
## ghi1, ghi2, ghiro2, giadinhkoUH, group_age, group_age1, h1,
## h10a, h10a1, h10a10, h10a11, h10a11a, h10a2, h10a3, h10a4,
## h10a5, h10a6, h10a7, h10a8, h10a9, h12, h12a, h12a_1, h12log,
## h13, h2, h3, h4, h5, h6, h7, h8, h9, ha000, ict1a, ict2a,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label_1, label1, label1a,
## logb7, logC, logitCost, moneyspent, msdt, n01, n02, n03, n05,
## n06, n07, n08, n1, n10, n100, n101, n102, n103, n11, n12, n13,
## n14, n15, n16, n1b, n2, n3, n35, n36, n37, n38, n39,
## n3posterb, n4, n40, n41, n42, n43, n44, n45, n46, n47, n48,
## n49, n5, n50, n51, n52, n53, n54, n55, n56, n57, n58, n59, n6,
## n60, n61, n61a, n62, n62a, n63, n63a, n64, n64a, n65, n65a,
## n66, n66a, n67, n67a, n68, n68a, n7, n77, n78, n7tren, n8,
## n88, n89, n8nha, n9, n97, n98, n99, n9khach, noE, noEnvi2,
## occup1, poli4, poli4a, policy, policy_a, policyeffect,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## taxIn, ter_fa1, ter_in, tertile_fa, tertile_indi, test,
## unitsdiffi1, var242, w1, w2, w3, w40.1 define varibles
newdata2$age_group1[newdata2$age_group=="group18-29"] <- 1
newdata2$age_group1[newdata2$age_group=="gr3039"] <- 2
newdata2$age_group1[newdata2$age_group=="gr4049"] <- 3
newdata2$age_group1[newdata2$age_group=="gr5059"] <- 4
newdata2$age_group1[newdata2$age_group=="60plus"] <- 4
newdata2$age_group1=as.factor(newdata2$age_group1)
newdata2$educ2=as.factor(newdata2$educ2)
newdata2$h1=as.factor(newdata2$h1)
newdata2$d1a=as.factor(newdata2$d1a)
newdata2$b6b=as.factor(newdata2$b6b)
newdata2$selfhealth=as.factor(newdata2$selfhealth)
newdata2$b18a=as.factor(newdata2$b18a)
newdata2$b16a=as.factor(newdata2$b16a)
newdata2$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, b6b, 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, logC2,
## 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, b6b, 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, w41 new dataset will work for futher analyst
data=subset(newdata2, select=c(age_group1, d1a,in00, educ2, selfhealth, c1, smostt, b6b, h1, b18a,b6a,label1,freeEn,ant, c7ad, p, logC1, highper))
attach(data)## The following objects are masked from newdata2 (pos = 3):
##
## age_group1, ant, b18a, b6a, b6b, c1, c7ad, d1a, educ2, freeEn,
## h1, highper, in00, label1, logC1, p, selfhealth, smostt## The following objects are masked from newdata2 (pos = 4):
##
## ant, b18a, b6a, b6b, c1, c7ad, d1a, educ2, freeEn, h1,
## highper, label1, logC1, p, selfhealth, smostt## The following objects are masked from r1:
##
## b18a, b6a, b6b, c1, d1a, educ2, h1, label1, selfhealth, smostt2 Tabe 1
require(moonBook)## Loading required package: moonBook## Warning: package 'moonBook' was built under R version 3.2.5mytable(in00~.,data=data)##
## Descriptive Statistics by 'in00'
## __________________________________________
## 0 1 p
## (N=262) (N=191)
## ------------------------------------------
## age_group1 0.000
## - 1 96 (36.6%) 90 (47.1%)
## - 2 60 (22.9%) 16 ( 8.4%)
## - 3 68 (26.0%) 26 (13.6%)
## - 4 38 (14.5%) 59 (30.9%)
## d1a 0.000
## - none 79 (30.2%) 97 (50.8%)
## - married 183 (69.8%) 94 (49.2%)
## educ2 0.035
## - 1 44 (17.2%) 51 (27.0%)
## - 2 134 (52.3%) 82 (43.4%)
## - 3 78 (30.5%) 56 (29.6%)
## selfhealth 0.631
## - notwell 141 (53.8%) 108 (56.5%)
## - good 121 (46.2%) 83 (43.5%)
## c1 0.030
## - 1 235 (89.7%) 157 (82.2%)
## - 2 27 (10.3%) 34 (17.8%)
## smostt 0.151
## - light 63 (24.0%) 59 (30.9%)
## - medium 98 (37.4%) 73 (38.2%)
## - heavy 101 (38.5%) 59 (30.9%)
## b6b 0.071
## - 0 178 (69.5%) 115 (60.8%)
## - 1 78 (30.5%) 74 (39.2%)
## h1 1.000
## - 0 97 (37.0%) 70 (36.6%)
## - 1 165 (63.0%) 121 (63.4%)
## b18a 0.843
## - Good 86 (33.2%) 66 (34.6%)
## - bad 173 (66.8%) 125 (65.4%)
## b6a 0.050
## - no 221 (86.3%) 149 (78.8%)
## - yes 35 (13.7%) 40 (21.2%)
## label1 0.264
## - 0 222 (85.1%) 154 (80.6%)
## - 1 39 (14.9%) 37 (19.4%)
## freeEn 0.839
## - 0 198 (76.2%) 143 (74.9%)
## - 1 62 (23.8%) 48 (25.1%)
## ant 0.433
## - 0 144 (55.0%) 97 (50.8%)
## - 1 118 (45.0%) 94 (49.2%)
## c7ad 0.015
## - 0 185 (70.6%) 113 (59.2%)
## - 1 77 (29.4%) 78 (40.8%)
## p 0.390
## - 0 82 (31.5%) 51 (26.7%)
## - 1 83 (31.9%) 59 (30.9%)
## - 2 95 (36.5%) 81 (42.4%)
## logC1 4.8 ± 0.3 4.8 ± 0.2 0.116
## highper 0.389
## - 0 128 (48.9%) 102 (53.4%)
## - 1 134 (51.1%) 89 (46.6%)
## ------------------------------------------2.1 Install packages and library
library("brms")## Warning: package 'brms' was built under R version 3.2.5## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 3.2.5## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.2.5## Loading 'brms' package (version 1.5.1). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').library("caret")## Warning: package 'caret' was built under R version 3.2.5## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:moonBook':
##
## densityplotlibrary("coda")## Warning: package 'coda' was built under R version 3.2.5library("rstan", lib.loc="~/R/win-library/3.2")## Warning: package 'rstan' was built under R version 3.2.5## Loading required package: StanHeaders## Warning: package 'StanHeaders' was built under R version 3.2.5## rstan (Version 2.14.1, packaged: 2016-12-28 14:55:41 UTC, GitRev: 5fa1e80eb817)## For execution on a local, multicore CPU with excess RAM we recommend calling
## rstan_options(auto_write = TRUE)
## options(mc.cores = parallel::detectCores())##
## Attaching package: 'rstan'## The following object is masked from 'package:coda':
##
## traceplot2.2 Model 1.1: model for cost-single policy
data1=subset(data, in00==0)
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.25), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 2.597 seconds (Warm-up)
## 2.938 seconds (Sampling)
## 5.535 seconds (Total)
##
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## Elapsed Time: 2.568 seconds (Warm-up)
## 2.505 seconds (Sampling)
## 5.073 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.25
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.48 0.06 4.37 4.58 2592 1
## age_group12 0.06 0.03 0.00 0.13 2290 1
## age_group13 0.03 0.04 -0.05 0.12 1572 1
## age_group14 -0.05 0.05 -0.15 0.05 1644 1
## educ22 0.05 0.04 -0.03 0.13 1744 1
## educ23 0.13 0.04 0.04 0.21 1591 1
## d1amarried -0.01 0.04 -0.08 0.06 1716 1
## h11 0.01 0.03 -0.04 0.07 2594 1
## selfhealthgood 0.09 0.03 0.04 0.14 2562 1
## smosttmedium 0.04 0.04 -0.03 0.11 1933 1
## smosttheavy 0.04 0.04 -0.03 0.12 1823 1
## b18abad 0.07 0.03 0.01 0.13 2351 1
## b6b1 -0.02 0.03 -0.08 0.04 2573 1
## label11 -0.07 0.04 -0.14 0.00 2866 1
## freeEn1 -0.04 0.03 -0.11 0.02 2767 1
## ant1 0.02 0.03 -0.03 0.08 2499 1
## c7ad1 0.03 0.03 -0.02 0.08 3149 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.07 0 0.06 0.08 2988 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit1)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.06 0.03 0.01 Inf 31 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 0.03 0.04 -0.03 Inf 3.99
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.05 0.05 -0.13 Inf 0.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 -0.01 0.04 -0.07 Inf 0.69
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.01 0.03 -0.04 Inf 1.91
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.05 0.04 -0.01 Inf 8.73
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.13 0.04 0.05 Inf 499 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.09 0.03 0.04 Inf 1332.33 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.04 0.04 -0.02 Inf 5.68
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.04 0.04 -0.02 Inf 6.65
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.07 0.03 0.02 Inf 141.86 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.02 0.03 -0.07 Inf 0.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.07 0.04 -0.13 Inf 0.02
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.04 0.03 -0.1 Inf 0.12
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.02 0.03 -0.02 Inf 4.17
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 0.03 0.03 -0.02 Inf 5.94
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
2.3 Model 1.1: model for cost-single policy -0.5
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.5), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 2.343 seconds (Warm-up)
## 2.453 seconds (Sampling)
## 4.796 seconds (Total)
##
##
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## Elapsed Time: 2.999 seconds (Warm-up)
## 2.954 seconds (Sampling)
## 5.953 seconds (Total)
##
##
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## Elapsed Time: 2.829 seconds (Warm-up)
## 2.359 seconds (Sampling)
## 5.188 seconds (Total)
##
##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 4).
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## Elapsed Time: 2.631 seconds (Warm-up)
## 2.385 seconds (Sampling)
## 5.016 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.5
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.64 0.08 4.49 4.79 2262 1
## age_group12 0.07 0.05 -0.02 0.16 2000 1
## age_group13 0.01 0.05 -0.09 0.10 1970 1
## age_group14 0.00 0.07 -0.13 0.14 1966 1
## educ22 0.01 0.06 -0.09 0.12 1773 1
## educ23 0.11 0.06 -0.01 0.24 1911 1
## d1amarried 0.01 0.04 -0.07 0.09 2198 1
## h11 0.02 0.03 -0.04 0.09 3133 1
## selfhealthgood 0.08 0.03 0.01 0.15 3028 1
## smosttmedium 0.00 0.04 -0.08 0.08 2545 1
## smosttheavy 0.06 0.04 -0.02 0.15 2232 1
## b18abad 0.06 0.04 -0.01 0.13 3345 1
## b6b1 -0.04 0.04 -0.11 0.04 2767 1
## label11 -0.10 0.04 -0.18 -0.01 2561 1
## freeEn1 -0.05 0.04 -0.13 0.04 2532 1
## ant1 0.02 0.04 -0.05 0.10 2226 1
## c7ad1 0.07 0.04 -0.01 0.15 1985 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.1 0.01 0.09 0.11 3512 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit1)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.07 0.05 0 Inf 18.51
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 0.01 0.05 -0.07 Inf 1.32
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0 0.07 -0.1 Inf 1.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.01 0.04 -0.06 Inf 1.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.02 0.03 -0.03 Inf 3.17
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.01 0.06 -0.08 Inf 1.48
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.11 0.06 0.01 Inf 26.78 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.08 0.03 0.02 Inf 101.56 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0 0.04 -0.07 Inf 0.98
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.06 0.04 -0.01 Inf 11.99
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.06 0.04 0 Inf 17.26
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.04 0.04 -0.1 Inf 0.18
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.1 0.04 -0.17 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.05 0.04 -0.12 Inf 0.12
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.02 0.04 -0.04 Inf 2.65
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 0.07 0.04 0 Inf 18.8
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
2.4 Model 1.1: model for cost-single policy -0.75
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.75), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 3.137 seconds (Warm-up)
## 3.052 seconds (Sampling)
## 6.189 seconds (Total)
##
##
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## Elapsed Time: 3.214 seconds (Warm-up)
## 2.875 seconds (Sampling)
## 6.089 seconds (Total)
##
##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 3).
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## Elapsed Time: 3 seconds (Warm-up)
## 3.081 seconds (Sampling)
## 6.081 seconds (Total)
##
##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 4).
##
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## Elapsed Time: 3.109 seconds (Warm-up)
## 2.703 seconds (Sampling)
## 5.812 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.75
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.93 0.08 4.77 5.09 2618 1
## age_group12 0.03 0.05 -0.07 0.13 2117 1
## age_group13 -0.01 0.06 -0.12 0.11 2208 1
## age_group14 0.01 0.08 -0.14 0.18 2377 1
## educ22 -0.05 0.06 -0.16 0.07 2003 1
## educ23 0.02 0.06 -0.10 0.13 2166 1
## d1amarried 0.00 0.05 -0.10 0.10 2309 1
## h11 0.03 0.04 -0.04 0.10 3063 1
## selfhealthgood 0.05 0.04 -0.02 0.12 2683 1
## smosttmedium -0.03 0.05 -0.12 0.07 2217 1
## smosttheavy 0.05 0.05 -0.05 0.15 2473 1
## b18abad 0.04 0.04 -0.04 0.12 2748 1
## b6b1 -0.07 0.04 -0.14 0.01 2807 1
## label11 -0.13 0.06 -0.24 -0.02 2875 1
## freeEn1 0.05 0.04 -0.03 0.13 2914 1
## ant1 0.03 0.04 -0.03 0.11 2747 1
## c7ad1 0.10 0.04 0.03 0.18 2502 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.08 0.01 0.07 0.09 3548 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit1)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0 0.05 -0.08 Inf 1.02
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.03 0.04 -0.03 Inf 4.68
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.05 0.06 -0.14 Inf 0.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.02 0.06 -0.08 Inf 1.6
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.05 0.04 -0.01 Inf 12.16
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 -0.03 0.05 -0.1 Inf 0.41
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.05 0.05 -0.03 Inf 5.91
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.04 0.04 -0.03 Inf 4.33
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.07 0.04 -0.13 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.13 0.06 -0.22 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 0.05 0.04 -0.01 Inf 10.2
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.03 0.04 -0.02 Inf 4.71
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 0.1 0.04 0.04 Inf 284.71 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
WAIC(fit, fit10, fit100)
2.5 Model 1.2: model for cost-policy combination
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.25), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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##
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##
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##
##
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## Elapsed Time: 3.433 seconds (Warm-up)
## 3.049 seconds (Sampling)
## 6.482 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.25
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.49 0.06 4.38 4.60 2849 1
## age_group12 0.05 0.04 -0.02 0.12 2767 1
## age_group13 0.03 0.04 -0.04 0.12 2213 1
## age_group14 -0.06 0.05 -0.16 0.04 2547 1
## educ22 0.05 0.04 -0.02 0.12 2735 1
## educ23 0.13 0.04 0.05 0.21 2813 1
## d1amarried 0.00 0.04 -0.07 0.07 2245 1
## h11 0.01 0.03 -0.05 0.07 3097 1
## selfhealthgood 0.10 0.03 0.05 0.15 3300 1
## smosttmedium 0.02 0.04 -0.05 0.09 2245 1
## smosttheavy 0.04 0.04 -0.04 0.11 2426 1
## b18abad 0.08 0.03 0.03 0.14 3221 1
## b6b1 -0.06 0.06 -0.17 0.05 1869 1
## p1 -0.03 0.04 -0.11 0.04 2198 1
## p2 -0.03 0.04 -0.12 0.05 2064 1
## b6b1:p1 0.02 0.07 -0.13 0.16 2069 1
## b6b1:p2 0.05 0.07 -0.09 0.19 1886 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.07 0 0.06 0.08 3196 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit2)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0 0.04 -0.06 Inf 1.18
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.01 0.03 -0.04 Inf 1.92
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.05 0.04 -0.01 Inf 9.9
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.13 0.04 0.06 Inf 1332.33 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.1 0.03 0.06 Inf Inf *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.02 0.04 -0.04 Inf 2.51
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.04 0.04 -0.02 Inf 4.99
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.08 0.03 0.04 Inf 443.44 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.06 0.06 -0.15 Inf 0.2
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 -0.03 0.04 -0.1 Inf 0.22
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 -0.03 0.04 -0.1 Inf 0.28
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 0.02 0.07 -0.1 Inf 1.37
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 0.05 0.07 -0.07 Inf 2.79
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
2.6 Model 1.2: model for cost-policy combination p=0.5
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.5), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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## 2.985 seconds (Sampling)
## 5.954 seconds (Total)
##
##
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##
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## 3.531 seconds (Sampling)
## 6.744 seconds (Total)
##
##
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## Elapsed Time: 3.173 seconds (Warm-up)
## 2.531 seconds (Sampling)
## 5.704 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.5
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.66 0.08 4.51 4.82 2324 1
## age_group12 0.05 0.04 -0.04 0.14 2880 1
## age_group13 0.01 0.05 -0.09 0.10 2443 1
## age_group14 -0.03 0.06 -0.16 0.09 2163 1
## educ22 0.02 0.06 -0.08 0.14 1977 1
## educ23 0.11 0.06 -0.01 0.24 1922 1
## d1amarried 0.00 0.04 -0.08 0.08 2429 1
## h11 0.01 0.04 -0.06 0.08 2793 1
## selfhealthgood 0.07 0.03 0.00 0.14 2763 1
## smosttmedium 0.00 0.04 -0.09 0.08 2576 1
## smosttheavy 0.06 0.04 -0.03 0.14 2684 1
## b18abad 0.05 0.04 -0.02 0.13 2718 1
## b6b1 -0.05 0.07 -0.18 0.08 1927 1
## p1 0.05 0.06 -0.06 0.15 2140 1
## p2 0.00 0.05 -0.10 0.09 1965 1
## b6b1:p1 -0.06 0.09 -0.24 0.13 1951 1
## b6b1:p2 0.06 0.09 -0.11 0.25 1833 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.1 0.01 0.09 0.11 3552 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit2)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0 0.04 -0.07 Inf 1.03
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.01 0.04 -0.05 Inf 1.59
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.02 0.06 -0.06 Inf 1.9
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.11 0.06 0 Inf 22.12 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.07 0.03 0.01 Inf 54.56 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0 0.04 -0.07 Inf 0.99
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.06 0.04 -0.02 Inf 8.52
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.05 0.04 -0.01 Inf 13.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.05 0.07 -0.16 Inf 0.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 0.05 0.06 -0.05 Inf 4.58
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 0 0.05 -0.08 Inf 0.88
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 -0.06 0.09 -0.21 Inf 0.33
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 0.06 0.09 -0.09 Inf 2.8
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
2.7 Model 1.2: model for cost-policy combination p=0.75
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.75), data = data1,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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## 3.035 seconds (Sampling)
## 6.871 seconds (Total)
##
##
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## 7.131 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.75
## Data: data1 (Number of observations: 248)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.90 0.08 4.74 5.06 2181 1.00
## age_group12 0.04 0.05 -0.07 0.15 1724 1.00
## age_group13 0.03 0.06 -0.09 0.13 1412 1.00
## age_group14 0.07 0.08 -0.10 0.22 1676 1.00
## educ22 0.01 0.06 -0.11 0.11 1848 1.00
## educ23 0.04 0.05 -0.07 0.15 2022 1.00
## d1amarried 0.00 0.05 -0.10 0.10 1794 1.00
## h11 0.03 0.04 -0.04 0.10 2231 1.00
## selfhealthgood 0.04 0.04 -0.03 0.11 2434 1.00
## smosttmedium -0.08 0.05 -0.17 0.01 1963 1.00
## smosttheavy 0.03 0.05 -0.06 0.12 1831 1.00
## b18abad 0.08 0.04 -0.01 0.15 2139 1.00
## b6b1 -0.16 0.06 -0.28 -0.03 1244 1.01
## p1 0.06 0.05 -0.04 0.17 1800 1.00
## p2 -0.01 0.05 -0.11 0.10 1645 1.00
## b6b1:p1 0.12 0.10 -0.06 0.31 1872 1.00
## b6b1:p2 0.26 0.09 0.08 0.43 1219 1.01
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.08 0.01 0.07 0.09 3553 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0 0.05 -0.09 Inf 0.86
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.03 0.04 -0.03 Inf 3.85
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.01 0.06 -0.09 Inf 1.42
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.04 0.05 -0.05 Inf 3.96
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.04 0.04 -0.02 Inf 7.79
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 -0.08 0.05 -0.16 Inf 0.05
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.03 0.05 -0.05 Inf 2.53
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.08 0.04 0.01 Inf 29.53 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.16 0.06 -0.26 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 0.06 0.05 -0.02 Inf 7.35
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 -0.01 0.05 -0.09 Inf 0.81
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 0.12 0.1 -0.03 Inf 8.83
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 0.26 0.09 0.11 Inf 332.33 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
#Model 2: model for persistence
2.8 Model 2.1: single policy
prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data1)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
fit3=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data1, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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## 1.148 seconds (Sampling)
## 2.285 seconds (Total)summary(fit3)## Family: bernoulli (logit)
## Formula: highper ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## Data: data1 (Number of observations: 248)
## Samples: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 5000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -1.51 0.63 -2.73 -0.25 5000 1
## age_group12 0.55 0.44 -0.30 1.43 3120 1
## age_group13 0.16 0.46 -0.76 1.07 3159 1
## age_group14 0.00 0.53 -1.04 1.05 3524 1
## educ22 -0.25 0.42 -1.06 0.57 3600 1
## educ23 0.81 0.49 -0.14 1.78 3450 1
## d1amarried -0.13 0.41 -0.93 0.66 3463 1
## h11 0.35 0.31 -0.27 0.97 5000 1
## selfhealthgood 0.77 0.29 0.19 1.34 5000 1
## smosttmedium 0.42 0.38 -0.32 1.16 4700 1
## smosttheavy 0.69 0.40 -0.08 1.47 4500 1
## b18abad 0.59 0.31 -0.05 1.20 5000 1
## b6b1 -0.19 0.32 -0.82 0.41 5000 1
## label11 -0.60 0.44 -1.45 0.25 5000 1
## freeEn1 -0.15 0.34 -0.82 0.52 5000 1
## ant1 0.15 0.29 -0.42 0.73 5000 1
## c7ad1 0.48 0.32 -0.13 1.13 5000 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 -0.13 0.41 -0.81 Inf 0.59
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.35 0.31 -0.17 Inf 6.86
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.25 0.42 -0.94 Inf 0.37
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.81 0.49 0.02 Inf 20.65 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.77 0.29 0.29 Inf 216.39 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.42 0.38 -0.22 Inf 6.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.69 0.4 0.03 Inf 23.51 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.59 0.31 0.08 Inf 29.86 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.19 0.32 -0.72 Inf 0.38
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.6 0.44 -1.32 Inf 0.1
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.15 0.34 -0.71 Inf 0.5
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.15 0.29 -0.33 Inf 2.26
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 0.48 0.32 -0.04 Inf 15.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit3)
2.9 Model 2.2: policy combination: persistence
prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, family="bernoulli", data=data1)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
fit4=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, family="bernoulli", data=data1, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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##
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##
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## Elapsed Time: 1.156 seconds (Warm-up)
## 1.676 seconds (Sampling)
## 2.832 seconds (Total)summary(fit4)## Family: bernoulli (logit)
## Formula: highper ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## Data: data1 (Number of observations: 248)
## Samples: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 5000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -1.60 0.67 -2.93 -0.32 5000 1
## age_group12 0.47 0.44 -0.40 1.35 4826 1
## age_group13 0.10 0.47 -0.81 1.01 4182 1
## age_group14 -0.13 0.53 -1.16 0.93 4232 1
## educ22 -0.10 0.42 -0.92 0.71 4434 1
## educ23 0.90 0.49 -0.05 1.88 4773 1
## d1amarried -0.07 0.41 -0.88 0.74 4429 1
## h11 0.24 0.31 -0.35 0.86 5000 1
## selfhealthgood 0.73 0.29 0.20 1.29 5000 1
## smosttmedium 0.33 0.38 -0.41 1.06 5000 1
## smosttheavy 0.68 0.41 -0.12 1.47 5000 1
## b18abad 0.59 0.31 -0.01 1.21 5000 1
## b6b1 -0.03 0.61 -1.20 1.17 3827 1
## p1 0.44 0.43 -0.40 1.28 5000 1
## p2 0.12 0.43 -0.71 0.97 4661 1
## b6b1:p1 -0.79 0.81 -2.36 0.81 3897 1
## b6b1:p2 0.28 0.78 -1.22 1.82 3935 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 0.03 0.05 -0.06 Inf 2.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.01 0.06 -0.1 Inf 0.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 0.01 0.08 -0.12 Inf 1.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 -0.07 0.41 -0.75 Inf 0.77
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.24 0.31 -0.26 Inf 3.47
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.1 0.42 -0.8 Inf 0.69
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.9 0.49 0.09 Inf 30.65 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.73 0.29 0.27 Inf 191.31 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.33 0.38 -0.3 Inf 4.09
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.68 0.41 0.01 Inf 20.19 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.59 0.31 0.09 Inf 35.23 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.03 0.61 -1.02 Inf 0.93
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 0.44 0.43 -0.28 Inf 5.47
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 0.12 0.43 -0.58 Inf 1.51
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 -0.79 0.81 -2.11 Inf 0.2
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 0.28 0.78 -1 Inf 1.76
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit4)
3 model for income=1
3.1 Model 1.1: model for cost-single policy
data2=subset(data, in00==1)
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.25), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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##
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##
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## Elapsed Time: 3.145 seconds (Warm-up)
## 3.084 seconds (Sampling)
## 6.229 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.25
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.65 0.07 4.50 4.79 2532 1
## age_group12 -0.07 0.09 -0.27 0.08 2043 1
## age_group13 -0.14 0.07 -0.28 0.00 1630 1
## age_group14 -0.10 0.07 -0.23 0.04 1584 1
## educ22 -0.01 0.04 -0.09 0.08 2265 1
## educ23 0.09 0.06 -0.03 0.20 2021 1
## d1amarried 0.01 0.06 -0.12 0.13 1604 1
## h11 0.05 0.04 -0.02 0.12 2699 1
## selfhealthgood 0.06 0.03 -0.01 0.13 3123 1
## smosttmedium 0.07 0.05 -0.04 0.17 2069 1
## smosttheavy -0.01 0.05 -0.12 0.10 2433 1
## b18abad 0.00 0.04 -0.07 0.07 2348 1
## b6b1 -0.02 0.04 -0.09 0.05 3174 1
## label11 -0.01 0.04 -0.09 0.07 2690 1
## freeEn1 -0.11 0.04 -0.19 -0.02 2442 1
## ant1 0.05 0.04 -0.02 0.11 2940 1
## c7ad1 -0.06 0.04 -0.13 0.01 2561 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.07 0.01 0.06 0.08 3497 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.07 0.09 -0.23 Inf 0.25
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.14 0.07 -0.26 Inf 0.02
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.21 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.01 0.06 -0.1 Inf 1.16
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.05 0.04 -0.01 Inf 12.33
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.01 0.04 -0.08 Inf 0.68
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.09 0.06 -0.01 Inf 13.87
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.06 0.03 0 Inf 24.48 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.07 0.05 -0.02 Inf 8.13
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 -0.01 0.05 -0.1 Inf 0.66
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0 0.04 -0.06 Inf 0.96
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.02 0.04 -0.07 Inf 0.48
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.01 0.04 -0.08 Inf 0.69
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.11 0.04 -0.18 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.05 0.04 -0.01 Inf 10.24
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 -0.06 0.04 -0.12 Inf 0.05
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
3.2 Model 1.1: model for cost-single policy -0.5
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.5), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## 5.01 seconds (Total)
##
##
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##
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##
##
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## Elapsed Time: 2.618 seconds (Warm-up)
## 2.477 seconds (Sampling)
## 5.095 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.5
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.73 0.07 4.59 4.87 2367 1
## age_group12 -0.03 0.07 -0.18 0.11 2133 1
## age_group13 -0.14 0.07 -0.28 0.00 1751 1
## age_group14 -0.08 0.06 -0.21 0.05 1672 1
## educ22 0.00 0.05 -0.09 0.09 1981 1
## educ23 0.10 0.06 -0.02 0.22 1842 1
## d1amarried 0.01 0.05 -0.10 0.12 1800 1
## h11 0.08 0.04 0.00 0.15 2921 1
## selfhealthgood 0.05 0.03 -0.02 0.11 3325 1
## smosttmedium 0.07 0.05 -0.02 0.16 2024 1
## smosttheavy 0.03 0.05 -0.07 0.14 1805 1
## b18abad 0.03 0.04 -0.04 0.10 3221 1
## b6b1 -0.03 0.03 -0.10 0.04 2979 1
## label11 -0.03 0.05 -0.13 0.06 2854 1
## freeEn1 -0.10 0.04 -0.19 -0.02 3001 1
## ant1 0.05 0.04 -0.03 0.11 2694 1
## c7ad1 -0.07 0.03 -0.14 -0.01 2887 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.09 0.01 0.08 0.1 3350 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.03 0.07 -0.16 Inf 0.5
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.14 0.07 -0.26 Inf 0.03
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.08 0.06 -0.18 Inf 0.13
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.01 0.05 -0.08 Inf 1.6
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.08 0.04 0.02 Inf 46.62 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0 0.05 -0.08 Inf 0.89
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.1 0.06 -0.01 Inf 16.09
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.05 0.03 0 Inf 14.75
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.07 0.05 -0.01 Inf 13.55
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.03 0.05 -0.05 Inf 2.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.03 0.04 -0.03 Inf 4.55
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.03 0.03 -0.09 Inf 0.21
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.03 0.05 -0.11 Inf 0.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.1 0.04 -0.17 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.05 0.04 -0.01 Inf 8.69
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 -0.07 0.03 -0.13 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
3.3 Model 1.1: model for cost-single policy -0.75
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.75), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 3.822 seconds (Warm-up)
## 3.693 seconds (Sampling)
## 7.515 seconds (Total)
##
##
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## 3.013 seconds (Sampling)
## 7.351 seconds (Total)
##
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## Elapsed Time: 3.604 seconds (Warm-up)
## 3.704 seconds (Sampling)
## 7.308 seconds (Total)
##
##
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## Elapsed Time: 3.304 seconds (Warm-up)
## 3.63 seconds (Sampling)
## 6.934 seconds (Total)summary(fit1)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## quantile = 0.75
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.90 0.08 4.74 5.06 2180 1
## age_group12 -0.02 0.09 -0.19 0.17 2118 1
## age_group13 -0.15 0.08 -0.31 0.02 1735 1
## age_group14 -0.10 0.07 -0.24 0.04 1687 1
## educ22 0.00 0.05 -0.09 0.11 2108 1
## educ23 0.09 0.07 -0.05 0.22 1957 1
## d1amarried 0.00 0.06 -0.12 0.11 1667 1
## h11 0.08 0.04 0.00 0.16 3112 1
## selfhealthgood 0.04 0.04 -0.03 0.11 3603 1
## smosttmedium 0.03 0.05 -0.06 0.13 2548 1
## smosttheavy 0.12 0.05 0.02 0.22 2807 1
## b18abad 0.03 0.04 -0.05 0.11 2882 1
## b6b1 -0.03 0.04 -0.11 0.05 2203 1
## label11 0.04 0.06 -0.07 0.15 2654 1
## freeEn1 -0.05 0.05 -0.15 0.04 3201 1
## ant1 0.01 0.04 -0.08 0.09 3054 1
## c7ad1 -0.08 0.05 -0.17 0.01 2327 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.08 0.01 0.07 0.09 4000 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0 0.06 -0.1 Inf 1.11
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.08 0.04 0.02 Inf 40.24 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0 0.05 -0.08 Inf 1.09
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.09 0.07 -0.03 Inf 7.71
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.04 0.04 -0.02 Inf 6.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.03 0.05 -0.05 Inf 2.8
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.12 0.05 0.03 Inf 75.92 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.03 0.04 -0.04 Inf 3.15
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.03 0.04 -0.09 Inf 0.35
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 0.04 0.06 -0.05 Inf 3.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -0.05 0.05 -0.13 Inf 0.15
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.01 0.04 -0.06 Inf 1.27
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 -0.08 0.05 -0.15 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit1)
WAIC(fit, fit10, fit100)
3.4 Model 1.2: model for cost-policy combination
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.25), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 4.614 seconds (Warm-up)
## 5.024 seconds (Sampling)
## 9.638 seconds (Total)
##
##
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## 10.268 seconds (Total)
##
##
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## 4.853 seconds (Sampling)
## 11.271 seconds (Total)
##
##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 4).
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## Elapsed Time: 5.598 seconds (Warm-up)
## 4.832 seconds (Sampling)
## 10.43 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.25
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.65 0.09 4.47 4.82 1504 1.00
## age_group12 -0.14 0.08 -0.30 0.03 1997 1.00
## age_group13 -0.16 0.07 -0.30 -0.03 1481 1.00
## age_group14 -0.11 0.07 -0.23 0.02 1338 1.00
## educ22 -0.03 0.05 -0.11 0.06 1771 1.00
## educ23 0.03 0.06 -0.09 0.16 1436 1.01
## d1amarried 0.05 0.06 -0.07 0.17 1362 1.00
## h11 0.06 0.04 -0.02 0.13 2737 1.00
## selfhealthgood 0.05 0.04 -0.02 0.13 2348 1.00
## smosttmedium 0.07 0.05 -0.03 0.17 1631 1.01
## smosttheavy 0.00 0.06 -0.11 0.11 1417 1.01
## b18abad -0.04 0.03 -0.10 0.03 2659 1.00
## b6b1 0.02 0.09 -0.17 0.19 1026 1.00
## p1 -0.02 0.06 -0.14 0.09 1418 1.00
## p2 0.00 0.06 -0.13 0.12 1349 1.00
## b6b1:p1 0.02 0.11 -0.20 0.23 1084 1.00
## b6b1:p2 -0.09 0.10 -0.29 0.11 966 1.01
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.07 0.01 0.06 0.08 2928 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.05 0.06 -0.05 Inf 4.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.06 0.04 -0.01 Inf 13.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.03 0.05 -0.1 Inf 0.39
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.03 0.06 -0.07 Inf 2.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.05 0.04 -0.01 Inf 11.74
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.07 0.05 -0.02 Inf 9.75
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0 0.06 -0.09 Inf 1.03
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 -0.04 0.03 -0.09 Inf 0.16
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 0.02 0.09 -0.14 Inf 1.37
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 -0.02 0.06 -0.13 Inf 0.53
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 0 0.06 -0.11 Inf 1
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 0.02 0.11 -0.16 Inf 1.26
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 -0.09 0.1 -0.26 Inf 0.24
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
3.5 Model 1.2: model for cost-policy combination p=0.5
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.5), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 3.609 seconds (Warm-up)
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##
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##
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## Elapsed Time: 4.728 seconds (Warm-up)
## 2.795 seconds (Sampling)
## 7.523 seconds (Total)
##
##
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## Elapsed Time: 3.421 seconds (Warm-up)
## 3.011 seconds (Sampling)
## 6.432 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.5
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.77 0.08 4.61 4.92 1896 1
## age_group12 -0.02 0.08 -0.18 0.13 2128 1
## age_group13 -0.14 0.07 -0.28 0.00 1632 1
## age_group14 -0.08 0.07 -0.20 0.05 1489 1
## educ22 -0.01 0.05 -0.10 0.08 1968 1
## educ23 0.08 0.07 -0.04 0.21 1677 1
## d1amarried 0.04 0.05 -0.07 0.14 1921 1
## h11 0.06 0.04 -0.01 0.13 3219 1
## selfhealthgood 0.05 0.04 -0.03 0.12 2981 1
## smosttmedium 0.07 0.04 -0.02 0.16 2423 1
## smosttheavy 0.05 0.05 -0.05 0.15 2518 1
## b18abad -0.01 0.04 -0.08 0.06 3110 1
## b6b1 0.01 0.07 -0.13 0.16 1826 1
## p1 -0.05 0.06 -0.16 0.06 2296 1
## p2 -0.07 0.05 -0.18 0.03 2124 1
## b6b1:p1 -0.04 0.09 -0.23 0.14 2053 1
## b6b1:p2 -0.06 0.09 -0.24 0.11 1749 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.09 0.01 0.08 0.11 3399 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit2)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.04 0.05 -0.05 Inf 3
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.06 0.04 0 Inf 19.94 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.01 0.05 -0.08 Inf 0.81
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.08 0.07 -0.03 Inf 8.64
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.05 0.04 -0.01 Inf 8.98
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.07 0.04 0 Inf 18.14
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.05 0.05 -0.03 Inf 4.66
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 -0.01 0.04 -0.07 Inf 0.67
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 0.01 0.07 -0.11 Inf 1.28
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 -0.05 0.06 -0.14 Inf 0.2
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 -0.07 0.05 -0.16 Inf 0.09
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 -0.04 0.09 -0.2 Inf 0.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 -0.06 0.09 -0.21 Inf 0.31
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
3.6 Model 1.2: model for cost-policy combination p=0.75
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, quantile = 0.75), data = data2,
family = asym_laplace())## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 1).
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## Elapsed Time: 3.854 seconds (Warm-up)
## 3.688 seconds (Sampling)
## 7.542 seconds (Total)
##
##
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## Elapsed Time: 3.404 seconds (Warm-up)
## 4.643 seconds (Sampling)
## 8.047 seconds (Total)
##
##
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## Elapsed Time: 3.429 seconds (Warm-up)
## 3.08 seconds (Sampling)
## 6.509 seconds (Total)
##
##
## SAMPLING FOR MODEL 'asym_laplace(identity) brms-model' NOW (CHAIN 4).
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## Elapsed Time: 3.441 seconds (Warm-up)
## 3.114 seconds (Sampling)
## 6.555 seconds (Total)summary(fit2)## Family: asym_laplace (identity)
## Formula: logC1 ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## quantile = 0.75
## Data: data2 (Number of observations: 187)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 4.92 0.09 4.75 5.11 1980 1
## age_group12 -0.03 0.09 -0.20 0.17 1609 1
## age_group13 -0.18 0.08 -0.33 -0.01 1410 1
## age_group14 -0.10 0.07 -0.24 0.04 1328 1
## educ22 -0.02 0.05 -0.11 0.07 2342 1
## educ23 0.08 0.07 -0.06 0.22 1850 1
## d1amarried 0.02 0.06 -0.11 0.12 1364 1
## h11 0.08 0.04 0.01 0.16 3562 1
## selfhealthgood 0.04 0.04 -0.04 0.11 3189 1
## smosttmedium 0.01 0.05 -0.08 0.10 2159 1
## smosttheavy 0.09 0.05 -0.01 0.20 2348 1
## b18abad 0.01 0.04 -0.07 0.09 2901 1
## b6b1 0.07 0.08 -0.09 0.23 1562 1
## p1 0.00 0.06 -0.13 0.12 1809 1
## p2 -0.03 0.06 -0.16 0.08 1957 1
## b6b1:p1 -0.14 0.11 -0.36 0.08 1738 1
## b6b1:p2 -0.12 0.10 -0.30 0.07 1698 1
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.08 0.01 0.06 0.09 3333 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fit2)
#evidence ratio
hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.02 0.06 -0.09 Inf 1.67
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.08 0.04 0.02 Inf 58.7 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 -0.02 0.05 -0.1 Inf 0.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.08 0.07 -0.03 Inf 7.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.04 0.04 -0.02 Inf 6.03
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.01 0.05 -0.07 Inf 1.28
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.09 0.05 0.01 Inf 24.32 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.01 0.04 -0.06 Inf 1.32
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 0.07 0.08 -0.06 Inf 4.42
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 0 0.06 -0.1 Inf 1.12
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 -0.03 0.06 -0.14 Inf 0.4
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 -0.14 0.11 -0.32 Inf 0.11
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit2,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 -0.12 0.1 -0.27 Inf 0.13
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit2)
#Model 2: model for persistence
3.7 Model 2.1: single policy
prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data2)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
fit3=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data2, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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##
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## 0.705 seconds (Sampling)
## 1.386 seconds (Total)summary(fit3)## Family: bernoulli (logit)
## Formula: highper ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b + label1 + freeEn + ant + c7ad
## Data: data2 (Number of observations: 187)
## Samples: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 5000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -1.18 0.76 -2.69 0.28 4373 1
## age_group12 -0.14 0.80 -1.72 1.43 4178 1
## age_group13 -1.34 0.76 -2.91 0.09 3639 1
## age_group14 -0.85 0.66 -2.18 0.38 3376 1
## educ22 0.16 0.46 -0.74 1.07 4649 1
## educ23 1.21 0.63 0.00 2.47 4080 1
## d1amarried 0.20 0.61 -0.92 1.42 3541 1
## h11 0.89 0.38 0.18 1.65 5000 1
## selfhealthgood 0.55 0.36 -0.12 1.27 5000 1
## smosttmedium 0.41 0.50 -0.54 1.40 4236 1
## smosttheavy 0.51 0.53 -0.54 1.53 4059 1
## b18abad 0.36 0.39 -0.39 1.11 5000 1
## b6b1 -0.16 0.37 -0.88 0.55 5000 1
## label11 -0.19 0.48 -1.12 0.75 5000 1
## freeEn1 -1.03 0.43 -1.89 -0.20 5000 1
## ant1 0.29 0.39 -0.48 1.07 5000 1
## c7ad1 -0.33 0.38 -1.08 0.41 5000 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.2 0.61 -0.78 Inf 1.61
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.89 0.38 0.29 Inf 137.89 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.16 0.46 -0.59 Inf 1.8
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 1.21 0.63 0.18 Inf 38.37 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.55 0.36 -0.02 Inf 16.61
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.41 0.5 -0.41 Inf 3.96
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.51 0.53 -0.35 Inf 5.07
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.36 0.39 -0.27 Inf 4.53
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.16 0.37 -0.78 Inf 0.49
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"label11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (label11) > 0 -0.19 0.48 -0.97 Inf 0.54
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"freeEn1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (freeEn1) > 0 -1.03 0.43 -1.75 Inf 0.01
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"ant1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (ant1) > 0 0.29 0.39 -0.35 Inf 3.39
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit3,"c7ad1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (c7ad1) > 0 -0.33 0.38 -0.95 Inf 0.24
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit3)
3.8 Model 2.2: policy combination: persistence
prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, family="bernoulli", data=data2)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
fit4=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
+b18a+b6b*p, family="bernoulli", data=data2, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start sampling##
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## 1.003 seconds (Sampling)
## 1.925 seconds (Total)summary(fit4)## Family: bernoulli (logit)
## Formula: highper ~ age_group1 + educ2 + d1a + h1 + selfhealth + smostt + b18a + b6b * p
## Data: data2 (Number of observations: 187)
## Samples: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 5000
## WAIC: Not computed
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -0.70 0.81 -2.30 0.89 5000 1
## age_group12 -0.01 0.79 -1.55 1.59 5000 1
## age_group13 -1.40 0.76 -2.92 0.04 4113 1
## age_group14 -0.82 0.67 -2.15 0.47 3680 1
## educ22 0.09 0.45 -0.80 0.98 5000 1
## educ23 0.93 0.64 -0.30 2.19 5000 1
## d1amarried 0.31 0.59 -0.81 1.48 4123 1
## h11 0.91 0.38 0.19 1.66 5000 1
## selfhealthgood 0.54 0.36 -0.17 1.22 5000 1
## smosttmedium 0.49 0.49 -0.43 1.50 5000 1
## smosttheavy 0.54 0.52 -0.45 1.55 5000 1
## b18abad 0.15 0.36 -0.56 0.85 5000 1
## b6b1 -0.12 0.76 -1.58 1.38 3078 1
## p1 -1.04 0.58 -2.19 0.11 3962 1
## p2 -0.70 0.58 -1.88 0.43 4334 1
## b6b1:p1 0.35 0.99 -1.60 2.27 3527 1
## b6b1:p2 -0.39 0.92 -2.17 1.40 3177 1
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).hypothesis(fit1,"age_group12>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group12) > 0 -0.02 0.09 -0.17 Inf 0.62
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group13 >0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group13) > 0 -0.15 0.08 -0.28 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit1,"age_group14>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (age_group14) > 0 -0.1 0.07 -0.22 Inf 0.08
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"d1amarried>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (d1amarried) > 0 0.31 0.59 -0.64 Inf 2.34
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"h11>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (h11) > 0 0.91 0.38 0.31 Inf 146.06 *
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"educ22>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ22) > 0 0.09 0.45 -0.66 Inf 1.37
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"educ23>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (educ23) > 0 0.93 0.64 -0.11 Inf 12.89
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"selfhealthgood>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (selfhealthgood) > 0 0.54 0.36 -0.05 Inf 13.84
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"smosttmedium>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttmedium) > 0 0.49 0.49 -0.3 Inf 5.3
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"smosttheavy>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (smosttheavy) > 0 0.54 0.52 -0.31 Inf 5.5
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b18abad>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b18abad) > 0 0.15 0.36 -0.45 Inf 1.9
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1) > 0 -0.12 0.76 -1.34 Inf 0.73
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p1) > 0 -1.04 0.58 -2 Inf 0.04
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (p2) > 0 -0.7 0.58 -1.66 Inf 0.13
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1:p1>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p1) > 0 0.35 0.99 -1.29 Inf 1.77
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.hypothesis(fit4,"b6b1:p2>0",alpha=0.05)## Hypothesis Tests for class b:
## Estimate Est.Error l-95% CI u-95% CI Evid.Ratio
## (b6b1:p2) > 0 -0.39 0.92 -1.89 Inf 0.51
## ---
## '*': The expected value under the hypothesis lies outside the 95% CI.plot(fit4)
---
title: 'all models: cigarette price'
author: "Binh Thang, Tran"
date: "23 July 2017"
output: 
  html_document: 
    code_download: true
    code_folding: hide
    number_sections: yes
    theme: journal
    toc: TRUE
    toc_float: TRUE
---
```{r }
library(foreign)
r=read.dta("C:/Users/BINH THANG/Dropbox/Korea/STudy/Thesis/data management/DataR/dataR5a.dta")

r1 <- subset(r, cost_inc>1 )

attach(r1)

r1$logC1=log10(r1$cost_inc)

r1$logC2=r1$cost_inc/1000

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 )
```

## define varibles



```{r}


newdata2$age_group1[newdata2$age_group=="group18-29"] <- 1
newdata2$age_group1[newdata2$age_group=="gr3039"] <- 2
newdata2$age_group1[newdata2$age_group=="gr4049"] <- 3
newdata2$age_group1[newdata2$age_group=="gr5059"] <- 4
newdata2$age_group1[newdata2$age_group=="60plus"] <- 4

newdata2$age_group1=as.factor(newdata2$age_group1)


newdata2$educ2=as.factor(newdata2$educ2)

newdata2$h1=as.factor(newdata2$h1)

newdata2$d1a=as.factor(newdata2$d1a)

newdata2$b6b=as.factor(newdata2$b6b)
newdata2$selfhealth=as.factor(newdata2$selfhealth)

newdata2$b18a=as.factor(newdata2$b18a)

newdata2$b16a=as.factor(newdata2$b16a)



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)

```


#new dataset will work for futher analyst

```{r}
data=subset(newdata2, select=c(age_group1, d1a,in00, educ2, selfhealth, c1, smostt, b6b, h1, b18a,b6a,label1,freeEn,ant, c7ad, p, logC1, highper))

attach(data)
```


#Tabe 1
```{r}
require(moonBook)
mytable(in00~.,data=data)


```


##Install  packages and library

```{r}
library("brms")
library("caret")
library("coda")

library("rstan", lib.loc="~/R/win-library/3.2")


```



##Model 1.1: model for cost-single policy 

```{r}

data1=subset(data, in00==0)

fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.25), data = data1, 
            family = asym_laplace())
summary(fit1)

marginal_effects(fit1)

#evidence ratio


hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)


hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```



##Model 1.1: model for cost-single policy -0.5

```{r}
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.5), data = data1, 
            family = asym_laplace())
summary(fit1)

marginal_effects(fit1)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```



##Model 1.1: model for cost-single policy -0.75

```{r}
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.75), data = data1, 
            family = asym_laplace())
summary(fit1)

marginal_effects(fit1)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```

WAIC(fit, fit10, fit100)

##Model 1.2: model for cost-policy combination
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.25), data = data1, 
            family = asym_laplace())
summary(fit2)
marginal_effects(fit2)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```



##Model 1.2: model for cost-policy combination p=0.5
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.5), data = data1, 
            family = asym_laplace())
summary(fit2)
marginal_effects(fit2)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```


##Model 1.2: model for cost-policy combination p=0.75
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.75), data = data1, 
            family = asym_laplace())
summary(fit2)


#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```
#Model 2: model for persistence 

##Model 2.1:  single policy

```{r}



prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data1)
 
set.seed(1234) 
 
fit3=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data1, chains=5, iter=2000, warmup=1000, prior=prior)

summary(fit3)

hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit3,"d1amarried>0",alpha=0.05)
hypothesis(fit3,"h11>0",alpha=0.05)

hypothesis(fit3,"educ22>0",alpha=0.05)
hypothesis(fit3,"educ23>0",alpha=0.05)


hypothesis(fit3,"selfhealthgood>0",alpha=0.05)

hypothesis(fit3,"smosttmedium>0",alpha=0.05)
hypothesis(fit3,"smosttheavy>0",alpha=0.05)

hypothesis(fit3,"b18abad>0",alpha=0.05)

hypothesis(fit3,"b6b1>0",alpha=0.05)

hypothesis(fit3,"label11>0",alpha=0.05)
hypothesis(fit3,"freeEn1>0",alpha=0.05)
hypothesis(fit3,"ant1>0",alpha=0.05)
hypothesis(fit3,"c7ad1>0",alpha=0.05)


plot(fit3)

```


##Model 2.2:  policy combination: persistence


```{r}

prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, family="bernoulli", data=data1)
 
set.seed(1234) 
 
fit4=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, family="bernoulli", data=data1, chains=5, iter=2000, warmup=1000, prior=prior)

summary(fit4)

hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit4,"d1amarried>0",alpha=0.05)
hypothesis(fit4,"h11>0",alpha=0.05)

hypothesis(fit4,"educ22>0",alpha=0.05)
hypothesis(fit4,"educ23>0",alpha=0.05)


hypothesis(fit4,"selfhealthgood>0",alpha=0.05)

hypothesis(fit4,"smosttmedium>0",alpha=0.05)
hypothesis(fit4,"smosttheavy>0",alpha=0.05)

hypothesis(fit4,"b18abad>0",alpha=0.05)

hypothesis(fit4,"b6b1>0",alpha=0.05)

hypothesis(fit4,"p1>0",alpha=0.05)
hypothesis(fit4,"p2>0",alpha=0.05)

hypothesis(fit4,"b6b1:p1>0",alpha=0.05)
hypothesis(fit4,"b6b1:p2>0",alpha=0.05)

plot(fit4)

```





#model for income=1



##Model 1.1: model for cost-single policy 

```{r}
data2=subset(data, in00==1)
            
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.25), data = data2, 
            family = asym_laplace())
summary(fit1)


#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```



##Model 1.1: model for cost-single policy -0.5

```{r}
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.5), data = data2, 
            family = asym_laplace())
summary(fit1)



#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```



##Model 1.1: model for cost-single policy -0.75

```{r}
fit1 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, quantile = 0.75), data = data2, 
            family = asym_laplace())
summary(fit1)



#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit1,"d1amarried>0",alpha=0.05)
hypothesis(fit1,"h11>0",alpha=0.05)

hypothesis(fit1,"educ22>0",alpha=0.05)
hypothesis(fit1,"educ23>0",alpha=0.05)


hypothesis(fit1,"selfhealthgood>0",alpha=0.05)

hypothesis(fit1,"smosttmedium>0",alpha=0.05)
hypothesis(fit1,"smosttheavy>0",alpha=0.05)

hypothesis(fit1,"b18abad>0",alpha=0.05)

hypothesis(fit1,"b6b1>0",alpha=0.05)

hypothesis(fit1,"label11>0",alpha=0.05)
hypothesis(fit1,"freeEn1>0",alpha=0.05)
hypothesis(fit1,"ant1>0",alpha=0.05)
hypothesis(fit1,"c7ad1>0",alpha=0.05)

plot(fit1)

```

WAIC(fit, fit10, fit100)

##Model 1.2: model for cost-policy combination
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.25), data = data2, 
            family = asym_laplace())
summary(fit2)


#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```



##Model 1.2: model for cost-policy combination p=0.5
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.5), data = data2, 
            family = asym_laplace())
summary(fit2)
marginal_effects(fit2)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```


##Model 1.2: model for cost-policy combination p=0.75
```{r}
fit2 <- brm(bf(logC1~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, quantile = 0.75), data = data2, 
            family = asym_laplace())
summary(fit2)
marginal_effects(fit2)

#evidence ratio



hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit2,"d1amarried>0",alpha=0.05)
hypothesis(fit2,"h11>0",alpha=0.05)

hypothesis(fit2,"educ22>0",alpha=0.05)
hypothesis(fit2,"educ23>0",alpha=0.05)


hypothesis(fit2,"selfhealthgood>0",alpha=0.05)

hypothesis(fit2,"smosttmedium>0",alpha=0.05)
hypothesis(fit2,"smosttheavy>0",alpha=0.05)

hypothesis(fit2,"b18abad>0",alpha=0.05)

hypothesis(fit2,"b6b1>0",alpha=0.05)

hypothesis(fit2,"p1>0",alpha=0.05)
hypothesis(fit2,"p2>0",alpha=0.05)

hypothesis(fit2,"b6b1:p1>0",alpha=0.05)
hypothesis(fit2,"b6b1:p2>0",alpha=0.05)

plot(fit2)



```
#Model 2: model for persistence 

##Model 2.1:  single policy

```{r}



prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data2)
 
set.seed(1234) 
 
fit3=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b+label1+freeEn+ant+c7ad, family="bernoulli", data=data2, chains=5, iter=2000, warmup=1000, prior=prior)

summary(fit3)

hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit3,"d1amarried>0",alpha=0.05)
hypothesis(fit3,"h11>0",alpha=0.05)

hypothesis(fit3,"educ22>0",alpha=0.05)
hypothesis(fit3,"educ23>0",alpha=0.05)


hypothesis(fit3,"selfhealthgood>0",alpha=0.05)

hypothesis(fit3,"smosttmedium>0",alpha=0.05)
hypothesis(fit3,"smosttheavy>0",alpha=0.05)

hypothesis(fit3,"b18abad>0",alpha=0.05)

hypothesis(fit3,"b6b1>0",alpha=0.05)

hypothesis(fit3,"label11>0",alpha=0.05)
hypothesis(fit3,"freeEn1>0",alpha=0.05)
hypothesis(fit3,"ant1>0",alpha=0.05)
hypothesis(fit3,"c7ad1>0",alpha=0.05)


plot(fit3)

```


##Model 2.2:  policy combination: persistence


```{r}

prior=get_prior(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, family="bernoulli", data=data2)
 
set.seed(1234) 
 
fit4=brm(formula=highper~age_group1+educ2+d1a+h1+selfhealth+smostt
               +b18a+b6b*p, family="bernoulli", data=data2, chains=5, iter=2000, warmup=1000, prior=prior)

summary(fit4)

hypothesis(fit1,"age_group12>0",alpha=0.05)
hypothesis(fit1,"age_group13 >0",alpha=0.05)
hypothesis(fit1,"age_group14>0",alpha=0.05)

hypothesis(fit4,"d1amarried>0",alpha=0.05)
hypothesis(fit4,"h11>0",alpha=0.05)

hypothesis(fit4,"educ22>0",alpha=0.05)
hypothesis(fit4,"educ23>0",alpha=0.05)


hypothesis(fit4,"selfhealthgood>0",alpha=0.05)

hypothesis(fit4,"smosttmedium>0",alpha=0.05)
hypothesis(fit4,"smosttheavy>0",alpha=0.05)

hypothesis(fit4,"b18abad>0",alpha=0.05)

hypothesis(fit4,"b6b1>0",alpha=0.05)

hypothesis(fit4,"p1>0",alpha=0.05)
hypothesis(fit4,"p2>0",alpha=0.05)

hypothesis(fit4,"b6b1:p1>0",alpha=0.05)
hypothesis(fit4,"b6b1:p2>0",alpha=0.05)

plot(fit4)

```





