describle data
outcome: WTP ()
It results in a likelihood maximized when a parameter is extremely large, and causes trouble with ordinary maximum likelihood approached.) Another option is to use Bayesian methods. Here we focus on Markov chain Monte Carlo (MCMC) approaches to Bayesian analysis.
steps to solve
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/dataR3.dta")
r1 <- subset(r)
attach(r1)
r1$wtp[cost1 == 0] <- 0
r1$wtp[cost1 == 1] <- 0
r1$wtp[is.na(r1$wtp)] <- 1
r1$b16a[b16a == 5] <- 0
attach(r1)## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, branch3, c1, c2, c23a, c3, c4, c5,
## c6, c7, c8, c9, 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, itc1, itc2, l1, l2, l3, l4, l5, l6,
## label1, label2, 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, noEnvi2, occup1, policy,
## policy_a, policy2, reasons, reasons1, s1, Screening,
## selfhealth, SH1, smostt, ter_fa1, ter_in, tertile_fa,
## tertile_indi, test, unitsdiffi1, var242, w1, w2, w3, w4mytable <- table(wtp, c1)
print(mytable)## c1
## wtp 1 2
## 0 398 63
## 1 329 32prop.table(mytable)## c1
## wtp 1 2
## 0 0.48418491 0.07664234
## 1 0.40024331 0.03892944newdata2=r1
attach(newdata2 )## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, branch3, c1, c2, c23a, c3, c4, c5,
## c6, c7, c8, c9, 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, itc1, itc2, l1, l2, l3, l4, l5, l6,
## label1, label2, 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, noEnvi2, occup1, policy,
## policy_a, policy2, reasons, reasons1, s1, Screening,
## selfhealth, SH1, smostt, ter_fa1, ter_in, tertile_fa,
## tertile_indi, test, unitsdiffi1, var242, w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 4):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, branch3, c1, c2, c23a, c3, c4, c5,
## c6, c7, c8, c9, 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, itc1, itc2, l1, l2, l3, l4, l5, l6,
## label1, label2, 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, noEnvi2, occup1, policy,
## policy_a, policy2, reasons, reasons1, s1, Screening,
## selfhealth, SH1, smostt, ter_fa1, ter_in, tertile_fa,
## tertile_indi, test, unitsdiffi1, var242, w1, w2, w3, w4newdata2$cost1=as.factor(newdata2$cost1)
newdata2$c1=as.factor(newdata2$c1)
newdata2$h1=as.factor(newdata2$h1)
newdata2$label1=as.factor(newdata2$label1)
newdata2$policy=as.factor(newdata2$policy)
newdata2$educ2=as.factor(newdata2$educ2)
newdata2$age_group=as.factor(newdata2$age_group)
newdata2$d1a=as.factor(newdata2$d1a)
newdata2$selfhealth=as.factor(newdata2$selfhealth)
newdata2$b18a=as.factor(newdata2$b18a)
newdata2$b16a=as.factor(newdata2$b16a)
newdata2$b6a=as.factor(newdata2$b6a)
newdata2$smostt=as.factor(newdata2$smostt)
newdata2$reasons1=as.factor(newdata2$reasons1)
newdata2$ter_in=as.factor(newdata2$ter_in)
newdata2$group_age1=as.factor(newdata2$group_age1)newdata2=newdata2[sample(1:nrow(newdata2), 822, replace=F),]Train data
train=newdata2[1:740, ]
View(train)Test data
test=newdata2[741:822, ]
View(test)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: latticelibrary("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':
##
## traceplotoptions(mc.cores = parallel::detectCores())prior=get_prior(formula=wtp~age_group+h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
baybin=brm(formula=wtp~age_group+h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start samplingsummary(baybin)## Family: bernoulli (logit)
## Formula: wtp ~ age_group + h1 + d1a + educ2 + ter_in + selfhealth + smostt + b16a + b18a + label1 + anticam + noEnvi2
## Data: train (Number of observations: 708)
## 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 0.28 0.36 -0.44 1.00 5000 1
## age_groupgr3039 0.46 0.27 -0.06 0.99 3805 1
## age_groupgr4049 0.64 0.29 0.07 1.21 3359 1
## age_groupgr5059 0.56 0.31 -0.04 1.17 3181 1
## age_group60plus 0.95 0.40 0.16 1.73 4107 1
## h12 0.06 0.17 -0.28 0.40 5000 1
## d1amarried -0.30 0.24 -0.76 0.17 3913 1
## educ22 -0.37 0.21 -0.78 0.04 5000 1
## educ23 -0.06 0.25 -0.55 0.43 4311 1
## ter_in2 0.32 0.21 -0.09 0.75 5000 1
## ter_in3 0.48 0.21 0.07 0.88 5000 1
## selfhealthgood 0.06 0.17 -0.27 0.40 5000 1
## smosttmedium -0.29 0.22 -0.72 0.14 5000 1
## smosttheavy 0.26 0.22 -0.18 0.71 5000 1
## b16a1 -0.88 0.22 -1.32 -0.47 5000 1
## b18abad -0.38 0.17 -0.72 -0.05 5000 1
## label11 0.42 0.24 -0.07 0.88 5000 1
## anticam 0.00 0.22 -0.45 0.43 5000 1
## noEnvi2 -0.22 0.22 -0.66 0.22 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).WAIC(baybin)## WAIC SE
## 937.89 18.19Notes: If you wanna report OR and 95%CI, you might need the exp funtion (using in R or in excel)
baybin$fit## Inference for Stan model: bernoulli(logit) brms-model.
## 5 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=5000.
##
## mean se_mean sd 2.5% 25% 50% 75%
## b_Intercept 0.28 0.01 0.36 -0.44 0.04 0.28 0.53
## b_age_groupgr3039 0.46 0.00 0.27 -0.06 0.28 0.46 0.65
## b_age_groupgr4049 0.64 0.01 0.29 0.07 0.44 0.63 0.84
## b_age_groupgr5059 0.56 0.01 0.31 -0.04 0.34 0.56 0.77
## b_age_group60plus 0.95 0.01 0.40 0.16 0.68 0.95 1.21
## b_h12 0.06 0.00 0.17 -0.28 -0.05 0.06 0.18
## b_d1amarried -0.30 0.00 0.24 -0.76 -0.46 -0.30 -0.13
## b_educ22 -0.37 0.00 0.21 -0.78 -0.51 -0.36 -0.23
## b_educ23 -0.06 0.00 0.25 -0.55 -0.23 -0.06 0.11
## b_ter_in2 0.32 0.00 0.21 -0.09 0.17 0.32 0.46
## b_ter_in3 0.48 0.00 0.21 0.07 0.34 0.48 0.63
## b_selfhealthgood 0.06 0.00 0.17 -0.27 -0.05 0.06 0.18
## b_smosttmedium -0.29 0.00 0.22 -0.72 -0.45 -0.29 -0.14
## b_smosttheavy 0.26 0.00 0.22 -0.18 0.11 0.25 0.41
## b_b16a1 -0.88 0.00 0.22 -1.32 -1.02 -0.88 -0.74
## b_b18abad -0.38 0.00 0.17 -0.72 -0.50 -0.38 -0.26
## b_label11 0.42 0.00 0.24 -0.07 0.25 0.42 0.58
## b_anticam 0.00 0.00 0.22 -0.45 -0.16 0.00 0.15
## b_noEnvi2 -0.22 0.00 0.22 -0.66 -0.37 -0.22 -0.08
## lp__ -458.73 0.07 3.11 -465.70 -460.63 -458.42 -456.43
## 97.5% n_eff Rhat
## b_Intercept 1.00 5000 1
## b_age_groupgr3039 0.99 3805 1
## b_age_groupgr4049 1.21 3359 1
## b_age_groupgr5059 1.17 3181 1
## b_age_group60plus 1.73 4107 1
## b_h12 0.40 5000 1
## b_d1amarried 0.17 3913 1
## b_educ22 0.04 5000 1
## b_educ23 0.43 4311 1
## b_ter_in2 0.75 5000 1
## b_ter_in3 0.88 5000 1
## b_selfhealthgood 0.40 5000 1
## b_smosttmedium 0.14 5000 1
## b_smosttheavy 0.71 5000 1
## b_b16a1 -0.47 5000 1
## b_b18abad -0.05 5000 1
## b_label11 0.88 5000 1
## b_anticam 0.43 5000 1
## b_noEnvi2 0.22 5000 1
## lp__ -453.68 2277 1
##
## Samples were drawn using NUTS(diag_e) at Wed Mar 22 00:17:12 2017.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).next steps, ww will check the accuracy of model using ROC
prob=as.data.frame(predict(baybin,test,type="response"))
library(e1071)## Warning: package 'e1071' was built under R version 3.2.5pred=ifelse(prob$Estimate> 0.5, 1, 0)confusionMatrix(data=pred,reference=test$wtp)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 31 26
## 1 13 12
##
## Accuracy : 0.5244
## 95% CI : (0.4111, 0.6359)
## No Information Rate : 0.5366
## P-Value [Acc > NIR] : 0.63092
##
## Kappa : 0.0208
## Mcnemar's Test P-Value : 0.05466
##
## Sensitivity : 0.7045
## Specificity : 0.3158
## Pos Pred Value : 0.5439
## Neg Pred Value : 0.4800
## Prevalence : 0.5366
## Detection Rate : 0.3780
## Detection Prevalence : 0.6951
## Balanced Accuracy : 0.5102
##
## 'Positive' Class : 0
## library(foreign)
r=read.dta("C:/Users/BINH THANG/Dropbox/Korea/STudy/Thesis/data management/DataR/dataR2.dta")
r1 <- subset(r, group_age1==1)
attach(r1)## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from newdata2:
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 13):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 14):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4r1$wtp[cost1 == 0] <- 0
r1$wtp[cost1 == 1] <- 0
r1$wtp[is.na(r1$wtp)] <- 1
r1$b16a[b16a == 5] <- 0
attach(r1)## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2:
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 14):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 15):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4mytable <- table(wtp, c1)
print(mytable)## c1
## wtp 1 2
## 0 180 14
## 1 181 5prop.table(mytable)## c1
## wtp 1 2
## 0 0.47368421 0.03684211
## 1 0.47631579 0.01315789newdata2=r1
attach(newdata2 )## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 4):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 14):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 15):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 16):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4newdata2$cost1=as.factor(newdata2$cost1)
newdata2$c1=as.factor(newdata2$c1)
newdata2$h1=as.factor(newdata2$h1)
newdata2$label1=as.factor(newdata2$label1)
newdata2$policy=as.factor(newdata2$policy)
newdata2$educ2=as.factor(newdata2$educ2)
newdata2$age_group=as.factor(newdata2$age_group)
newdata2$d1a=as.factor(newdata2$d1a)
newdata2$selfhealth=as.factor(newdata2$selfhealth)
newdata2$b18a=as.factor(newdata2$b18a)
newdata2$b16a=as.factor(newdata2$b16a)
newdata2$b6a=as.factor(newdata2$b6a)
newdata2$smostt=as.factor(newdata2$smostt)
newdata2$reasons1=as.factor(newdata2$reasons1)
newdata2$ter_in=as.factor(newdata2$ter_in)
newdata2$group_age1=as.factor(newdata2$group_age1)newdata2=newdata2[sample(1:nrow(newdata2), 380, replace=F),]Train data
train=newdata2[1:342, ]
View(train)Test data
test=newdata2[343:380, ]
View(test)library("brms")
library("caret")
library("coda")
library("rstan", lib.loc="~/R/win-library/3.2")
options(mc.cores = parallel::detectCores())prior=get_prior(formula=wtp~h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
baybin=brm(formula=wtp~h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start samplingsummary(baybin)## Family: bernoulli (logit)
## Formula: wtp ~ h1 + d1a + educ2 + ter_in + selfhealth + smostt + b16a + b18a + label1 + anticam + noEnvi2
## Data: train (Number of observations: 329)
## 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 0.66 0.61 -0.55 1.87 5000 1
## h12 0.31 0.26 -0.21 0.81 5000 1
## d1amarried -0.37 0.44 -1.25 0.52 5000 1
## educ22 -0.22 0.27 -0.75 0.31 5000 1
## educ23 0.64 0.42 -0.18 1.46 5000 1
## ter_in2 0.32 0.31 -0.27 0.93 5000 1
## ter_in3 0.35 0.29 -0.25 0.93 5000 1
## selfhealthgood 0.22 0.25 -0.26 0.72 5000 1
## smosttmedium -0.29 0.35 -0.99 0.39 5000 1
## smosttheavy 0.13 0.34 -0.53 0.78 5000 1
## b16a1 -0.92 0.30 -1.49 -0.33 5000 1
## b18abad -0.42 0.27 -0.96 0.09 5000 1
## label11 0.42 0.35 -0.25 1.11 5000 1
## anticam 0.29 0.33 -0.34 0.94 5000 1
## noEnvi2 -0.01 0.33 -0.66 0.65 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).WAIC(baybin)## Warning: 1 (0.3%) p_waic estimates greater than 0.4.
## We recommend trying loo() instead.## WAIC SE
## 449.81 13.62Notes: If you wanna report OR and 95%CI, you might need the exp funtion (using in R or in excel)
baybin$fit## Inference for Stan model: bernoulli(logit) brms-model.
## 5 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=5000.
##
## mean se_mean sd 2.5% 25% 50% 75%
## b_Intercept 0.66 0.01 0.61 -0.55 0.26 0.66 1.06
## b_h12 0.31 0.00 0.26 -0.21 0.14 0.32 0.48
## b_d1amarried -0.37 0.01 0.44 -1.25 -0.65 -0.36 -0.09
## b_educ22 -0.22 0.00 0.27 -0.75 -0.39 -0.22 -0.03
## b_educ23 0.64 0.01 0.42 -0.18 0.36 0.64 0.92
## b_ter_in2 0.32 0.00 0.31 -0.27 0.11 0.32 0.53
## b_ter_in3 0.35 0.00 0.29 -0.25 0.16 0.35 0.54
## b_selfhealthgood 0.22 0.00 0.25 -0.26 0.05 0.22 0.40
## b_smosttmedium -0.29 0.01 0.35 -0.99 -0.53 -0.29 -0.05
## b_smosttheavy 0.13 0.00 0.34 -0.53 -0.10 0.14 0.36
## b_b16a1 -0.92 0.00 0.30 -1.49 -1.11 -0.92 -0.71
## b_b18abad -0.42 0.00 0.27 -0.96 -0.60 -0.42 -0.25
## b_label11 0.42 0.00 0.35 -0.25 0.19 0.42 0.66
## b_anticam 0.29 0.00 0.33 -0.34 0.07 0.29 0.51
## b_noEnvi2 -0.01 0.00 0.33 -0.66 -0.24 -0.02 0.20
## lp__ -216.00 0.06 2.84 -222.12 -217.68 -215.66 -213.93
## 97.5% n_eff Rhat
## b_Intercept 1.87 5000 1
## b_h12 0.81 5000 1
## b_d1amarried 0.52 5000 1
## b_educ22 0.31 5000 1
## b_educ23 1.46 5000 1
## b_ter_in2 0.93 5000 1
## b_ter_in3 0.93 5000 1
## b_selfhealthgood 0.72 5000 1
## b_smosttmedium 0.39 5000 1
## b_smosttheavy 0.78 5000 1
## b_b16a1 -0.33 5000 1
## b_b18abad 0.09 5000 1
## b_label11 1.11 5000 1
## b_anticam 0.94 5000 1
## b_noEnvi2 0.65 5000 1
## lp__ -211.43 2099 1
##
## Samples were drawn using NUTS(diag_e) at Wed Mar 22 00:18:52 2017.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).next steps, ww will check the accuracy of model using ROC
prob=as.data.frame(predict(baybin,test,type="response"))
library(e1071)
pred=ifelse(prob$Estimate> 0.5, 1, 0)confusionMatrix(data=pred,reference=test$wtp)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 12 10
## 1 5 11
##
## Accuracy : 0.6053
## 95% CI : (0.4339, 0.7596)
## No Information Rate : 0.5526
## P-Value [Acc > NIR] : 0.3142
##
## Kappa : 0.2234
## Mcnemar's Test P-Value : 0.3017
##
## Sensitivity : 0.7059
## Specificity : 0.5238
## Pos Pred Value : 0.5455
## Neg Pred Value : 0.6875
## Prevalence : 0.4474
## Detection Rate : 0.3158
## Detection Prevalence : 0.5789
## Balanced Accuracy : 0.6148
##
## 'Positive' Class : 0
## library(foreign)
r=read.dta("C:/Users/BINH THANG/Dropbox/Korea/STudy/Thesis/data management/DataR/dataR2.dta")
r1 <- subset(r, group_age1==0)
attach(r1)## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from newdata2 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 4):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 5):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 15):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 16):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from r1 (pos = 17):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4r1$wtp[cost1 == 0] <- 0
r1$wtp[cost1 == 1] <- 0
r1$wtp[is.na(r1$wtp)] <- 1
r1$b16a[b16a == 5] <- 0
attach(r1)## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 4):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 5):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 6):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 16):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 17):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 18):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4mytable <- table(wtp, c1)
print(mytable)## c1
## wtp 1 2
## 0 218 49
## 1 148 27prop.table(mytable)## c1
## wtp 1 2
## 0 0.49321267 0.11085973
## 1 0.33484163 0.06108597newdata2=r1
attach(newdata2 )## The following object is masked _by_ .GlobalEnv:
##
## test## The following objects are masked from r1 (pos = 3):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 4):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 5):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 6):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 7):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, cost1, cost2, 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, itc1, itc2, l1, l2, l3, l4, l5, l6, label1,
## label2, 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4## The following objects are masked from newdata2 (pos = 17):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 18):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4, wtp## The following objects are masked from r1 (pos = 19):
##
## a1, advice, advice1, ag, age_group, anticam, anticam2, b1,
## b10, b10a1, b10a2, b10a3, b10a4, b10a5, b10a6, b10a7, b11,
## b11a, b11a2, b11a3, b12, b12a, b13, b14, b15, b16, b16a, b17,
## b18, b18a, b1a, b2, b2a1, b3, b4, b5, b5a, b6, b6a, b6a1,
## b6a2, b6a3, b6a4, b6a5, b6a6, b6a7, b6a7a, b7, b8, b9, br,
## branch, branch1, branch2, c1, c2, c23a, c3, c4, c5, c6, c7,
## c8, c9, COST, 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,
## itc1, itc2, l1, l2, l3, l4, l5, l6, label1, label2,
## 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, noEnvi2, occup1, policy, policy_a, policy2,
## reasons, reasons1, s1, Screening, selfhealth, SH1, smostt,
## ter_fa1, ter_in, tertile_fa, tertile_indi, test, unitsdiffi1,
## w1, w2, w3, w4newdata2$cost1=as.factor(newdata2$cost1)
newdata2$c1=as.factor(newdata2$c1)
newdata2$h1=as.factor(newdata2$h1)
newdata2$label1=as.factor(newdata2$label1)
newdata2$policy=as.factor(newdata2$policy)
newdata2$educ2=as.factor(newdata2$educ2)
newdata2$age_group=as.factor(newdata2$age_group)
newdata2$d1a=as.factor(newdata2$d1a)
newdata2$selfhealth=as.factor(newdata2$selfhealth)
newdata2$b18a=as.factor(newdata2$b18a)
newdata2$b16a=as.factor(newdata2$b16a)
newdata2$b6a=as.factor(newdata2$b6a)
newdata2$smostt=as.factor(newdata2$smostt)
newdata2$reasons1=as.factor(newdata2$reasons1)
newdata2$ter_in=as.factor(newdata2$ter_in)
newdata2$group_age1=as.factor(newdata2$group_age1)newdata2=newdata2[sample(1:nrow(newdata2), 442, replace=F),]Train data
train=newdata2[1:396, ]
View(train)Test data
test=newdata2[397:442, ]
View(test)library("brms")
library("caret")
library("coda")
library("rstan", lib.loc="~/R/win-library/3.2")
options(mc.cores = parallel::detectCores())prior=get_prior(formula=wtp~h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train)## Warning: Rows containing NAs were excluded from the modelset.seed(1234)
baybin=brm(formula=wtp~h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, family="bernoulli", data=train, chains=5, iter=2000, warmup=1000, prior=prior)## Warning: Rows containing NAs were excluded from the model## Compiling the C++ model## Start samplingsummary(baybin)## Family: bernoulli (logit)
## Formula: wtp ~ h1 + d1a + educ2 + ter_in + selfhealth + smostt + b16a + b18a + label1 + anticam + noEnvi2
## Data: train (Number of observations: 381)
## 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 0.69 0.55 -0.38 1.79 5000 1
## h12 -0.30 0.25 -0.78 0.18 5000 1
## d1amarried -0.45 0.26 -0.96 0.05 5000 1
## educ22 -0.89 0.38 -1.65 -0.13 4130 1
## educ23 -0.41 0.40 -1.19 0.39 4120 1
## ter_in2 0.48 0.31 -0.10 1.08 5000 1
## ter_in3 0.79 0.30 0.19 1.38 5000 1
## selfhealthgood -0.09 0.23 -0.56 0.36 5000 1
## smosttmedium -0.24 0.30 -0.83 0.33 4174 1
## smosttheavy 0.75 0.31 0.15 1.35 4199 1
## b16a1 -0.69 0.31 -1.29 -0.09 5000 1
## b18abad -0.44 0.25 -0.94 0.08 5000 1
## label11 1.06 0.38 0.33 1.81 4527 1
## anticam -0.47 0.36 -1.19 0.22 4365 1
## noEnvi2 -0.56 0.32 -1.19 0.06 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).WAIC(baybin)## WAIC SE
## 494.09 15.94Notes: If you wanna report OR and 95%CI, you might need the exp funtion (using in R or in excel)
baybin$fit## Inference for Stan model: bernoulli(logit) brms-model.
## 5 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=5000.
##
## mean se_mean sd 2.5% 25% 50% 75%
## b_Intercept 0.69 0.01 0.55 -0.38 0.32 0.70 1.05
## b_h12 -0.30 0.00 0.25 -0.78 -0.47 -0.30 -0.14
## b_d1amarried -0.45 0.00 0.26 -0.96 -0.63 -0.45 -0.27
## b_educ22 -0.89 0.01 0.38 -1.65 -1.15 -0.88 -0.62
## b_educ23 -0.41 0.01 0.40 -1.19 -0.69 -0.41 -0.15
## b_ter_in2 0.48 0.00 0.31 -0.10 0.27 0.47 0.68
## b_ter_in3 0.79 0.00 0.30 0.19 0.59 0.79 1.00
## b_selfhealthgood -0.09 0.00 0.23 -0.56 -0.25 -0.09 0.07
## b_smosttmedium -0.24 0.00 0.30 -0.83 -0.45 -0.24 -0.04
## b_smosttheavy 0.75 0.00 0.31 0.15 0.55 0.75 0.95
## b_b16a1 -0.69 0.00 0.31 -1.29 -0.90 -0.69 -0.49
## b_b18abad -0.44 0.00 0.25 -0.94 -0.61 -0.44 -0.27
## b_label11 1.06 0.01 0.38 0.33 0.81 1.06 1.32
## b_anticam -0.47 0.01 0.36 -1.19 -0.70 -0.46 -0.23
## b_noEnvi2 -0.56 0.00 0.32 -1.19 -0.77 -0.56 -0.35
## lp__ -238.71 0.06 2.74 -244.88 -240.36 -238.33 -236.75
## 97.5% n_eff Rhat
## b_Intercept 1.79 5000 1
## b_h12 0.18 5000 1
## b_d1amarried 0.05 5000 1
## b_educ22 -0.13 4130 1
## b_educ23 0.39 4120 1
## b_ter_in2 1.08 5000 1
## b_ter_in3 1.38 5000 1
## b_selfhealthgood 0.36 5000 1
## b_smosttmedium 0.33 4174 1
## b_smosttheavy 1.35 4199 1
## b_b16a1 -0.09 5000 1
## b_b18abad 0.08 5000 1
## b_label11 1.81 4527 1
## b_anticam 0.22 4365 1
## b_noEnvi2 0.06 5000 1
## lp__ -234.35 2178 1
##
## Samples were drawn using NUTS(diag_e) at Wed Mar 22 00:21:05 2017.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).next steps, ww will check the accuracy of model using ROC
prob=as.data.frame(predict(baybin,test,type="response"))
library(e1071)
pred=ifelse(prob$Estimate> 0.5, 1, 0)confusionMatrix(data=pred,reference=test$wtp)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 22 15
## 1 5 4
##
## Accuracy : 0.5652
## 95% CI : (0.4111, 0.7107)
## No Information Rate : 0.587
## P-Value [Acc > NIR] : 0.67562
##
## Kappa : 0.0275
## Mcnemar's Test P-Value : 0.04417
##
## Sensitivity : 0.8148
## Specificity : 0.2105
## Pos Pred Value : 0.5946
## Neg Pred Value : 0.4444
## Prevalence : 0.5870
## Detection Rate : 0.4783
## Detection Prevalence : 0.8043
## Balanced Accuracy : 0.5127
##
## 'Positive' Class : 0
##