Rcode tobit BBB-Rblog
Thang, TB
18 March 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/dataR2.dta")
r1 <- subset(r, costincrease <=150000)
attach(r1)
newdata2=r1
attach(newdata2 )## The following objects are masked from r1:
##
## 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, w4define varibles
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$trans_Y <- log10(newdata2$costincrease+1)
attach(newdata2)## 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:
##
## 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, w4qqnorm(trans_Y )
qqline(trans_Y )
summary(trans_Y)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.301 4.301 4.602 4.551 4.778 5.176re-organise our data by sampling .
newdata2=newdata2[sample(1:nrow(newdata2), 440, replace=F),]Create new data frame (90%-10%) (N=440)
Train data
train=newdata2[1:308, ]
View(train)Test data
test=newdata2[309:440, ]
View(test)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: 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())set up for fomulation
library("MCMCpack")## Warning: package 'MCMCpack' was built under R version 3.2.5## Loading required package: MASS## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)## ## Copyright (C) 2003-2017 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park## ##
## ## Support provided by the U.S. National Science Foundation## ## (Grants SES-0350646 and SES-0350613)
## ##set.seed(1234)
bayes.tobit <- MCMCtobit(trans_Y~age_group+h1+ d1a+educ2+ter_in+selfhealth+smostt
+b16a+b18a+label1+anticam+noEnvi2, above = 150000, mcmc = 30000, verbose = 1000, data=train)##
##
## MCMCtobit iteration 1 of 31000
## beta =
## 4.56899
## 0.02488
## -0.00099
## -0.06299
## -0.23030
## -0.09764
## -0.00163
## 0.09593
## 0.24832
## 0.02890
## -0.03906
## 0.04857
## -0.02295
## 0.01341
## -0.11670
## 0.03256
## -0.08559
## 0.00484
## -0.10560
## sigma2 = 0.11237
##
##
## MCMCtobit iteration 1001 of 31000
## beta =
## 4.35739
## -0.06658
## -0.02784
## -0.11317
## -0.15202
## -0.06204
## 0.04460
## 0.05936
## 0.24458
## 0.00982
## 0.01747
## 0.16005
## 0.08980
## 0.04630
## -0.02008
## 0.04063
## -0.17386
## 0.13526
## -0.05343
## sigma2 = 0.12833
##
##
## MCMCtobit iteration 2001 of 31000
## beta =
## 4.44477
## -0.08082
## -0.12908
## -0.25250
## -0.13886
## -0.02180
## 0.00515
## 0.04748
## 0.17592
## -0.03556
## -0.01058
## 0.11186
## 0.03044
## 0.06334
## -0.05316
## 0.21518
## -0.05089
## 0.03241
## -0.02808
## sigma2 = 0.10941
##
##
## MCMCtobit iteration 3001 of 31000
## beta =
## 4.38023
## -0.02083
## -0.07341
## -0.17295
## -0.21280
## -0.09973
## 0.05683
## 0.11896
## 0.26317
## 0.07710
## -0.03324
## 0.13499
## 0.12100
## 0.16430
## -0.07396
## 0.02619
## -0.03483
## 0.03715
## -0.01178
## sigma2 = 0.11803
##
##
## MCMCtobit iteration 4001 of 31000
## beta =
## 4.45327
## 0.05839
## 0.10725
## 0.08394
## 0.03963
## -0.08612
## -0.02346
## 0.11437
## 0.23458
## 0.04112
## 0.02201
## 0.07993
## 0.08478
## 0.04969
## -0.11675
## 0.02035
## -0.13391
## 0.08037
## -0.12981
## sigma2 = 0.10513
##
##
## MCMCtobit iteration 5001 of 31000
## beta =
## 4.43665
## -0.12908
## -0.15933
## -0.20391
## -0.36530
## -0.07668
## 0.19824
## 0.09990
## 0.27036
## 0.00410
## -0.02146
## 0.11792
## 0.10555
## 0.10247
## -0.06726
## 0.05972
## -0.08536
## -0.01280
## -0.12445
## sigma2 = 0.10621
##
##
## MCMCtobit iteration 6001 of 31000
## beta =
## 4.29130
## -0.02499
## -0.04714
## 0.05295
## -0.15410
## 0.05088
## 0.05040
## 0.04340
## 0.25256
## 0.05797
## 0.04001
## 0.06969
## 0.12489
## 0.14364
## -0.05068
## 0.10531
## -0.11130
## 0.02563
## -0.11430
## sigma2 = 0.10767
##
##
## MCMCtobit iteration 7001 of 31000
## beta =
## 4.45718
## -0.07470
## -0.16418
## -0.15771
## -0.17185
## 0.05197
## 0.06989
## 0.08223
## 0.18607
## 0.09751
## 0.01325
## 0.06443
## -0.01025
## 0.05528
## -0.05860
## 0.08870
## -0.21853
## 0.12957
## -0.10820
## sigma2 = 0.12273
##
##
## MCMCtobit iteration 8001 of 31000
## beta =
## 4.45387
## 0.03680
## 0.07987
## -0.01887
## -0.09005
## -0.04031
## 0.02783
## 0.03755
## 0.22131
## -0.06300
## -0.08231
## 0.04669
## 0.11097
## 0.07835
## -0.06534
## 0.11341
## -0.14320
## 0.05511
## -0.15860
## sigma2 = 0.11162
##
##
## MCMCtobit iteration 9001 of 31000
## beta =
## 4.41375
## 0.07167
## 0.07178
## 0.08711
## 0.01327
## -0.00117
## -0.05390
## 0.09232
## 0.21896
## 0.08253
## 0.01568
## 0.07989
## 0.03734
## 0.05774
## -0.04035
## 0.04663
## -0.17310
## 0.03219
## -0.13766
## sigma2 = 0.12335
##
##
## MCMCtobit iteration 10001 of 31000
## beta =
## 4.54360
## -0.00334
## -0.00340
## -0.09216
## -0.12548
## -0.05455
## 0.03760
## 0.02245
## 0.17966
## 0.07685
## -0.02205
## 0.04995
## 0.06200
## -0.00780
## -0.11064
## 0.05017
## -0.05057
## 0.06290
## -0.06340
## sigma2 = 0.10333
##
##
## MCMCtobit iteration 11001 of 31000
## beta =
## 4.46445
## -0.00004
## -0.02352
## 0.00478
## -0.08869
## 0.01028
## 0.08805
## 0.01808
## 0.22796
## -0.00908
## -0.05453
## 0.10802
## 0.01955
## -0.02130
## -0.16783
## 0.14046
## -0.16196
## 0.16886
## -0.01610
## sigma2 = 0.11864
##
##
## MCMCtobit iteration 12001 of 31000
## beta =
## 4.19063
## 0.04754
## -0.00358
## 0.01367
## -0.16134
## -0.01581
## 0.00368
## 0.15284
## 0.32968
## 0.03757
## -0.07855
## 0.11813
## 0.17807
## 0.14891
## 0.03087
## 0.08350
## -0.18677
## 0.05636
## -0.08914
## sigma2 = 0.12443
##
##
## MCMCtobit iteration 13001 of 31000
## beta =
## 4.59390
## -0.07663
## -0.06155
## -0.08804
## -0.18323
## 0.01029
## -0.02361
## 0.08866
## 0.18965
## -0.03478
## 0.11934
## 0.03795
## 0.01478
## 0.09278
## -0.13729
## -0.03621
## -0.15701
## 0.07837
## -0.09073
## sigma2 = 0.12801
##
##
## MCMCtobit iteration 14001 of 31000
## beta =
## 4.48978
## -0.04370
## -0.16385
## -0.15097
## -0.19528
## -0.10905
## 0.04931
## 0.09127
## 0.19389
## 0.01445
## -0.02679
## 0.07554
## 0.05233
## 0.08526
## -0.00453
## 0.03431
## -0.16300
## 0.00915
## -0.11246
## sigma2 = 0.11026
##
##
## MCMCtobit iteration 15001 of 31000
## beta =
## 4.35004
## 0.01051
## 0.03644
## -0.05259
## -0.29856
## -0.03542
## -0.00005
## 0.13597
## 0.30693
## 0.02652
## -0.03214
## 0.14437
## 0.12887
## 0.17799
## -0.09911
## -0.00699
## -0.06190
## 0.06986
## 0.01263
## sigma2 = 0.12208
##
##
## MCMCtobit iteration 16001 of 31000
## beta =
## 4.46624
## 0.09155
## -0.01486
## 0.01169
## -0.11764
## -0.03518
## -0.00228
## 0.05932
## 0.25182
## -0.01011
## -0.10515
## 0.09077
## 0.06690
## 0.05848
## -0.01517
## 0.02921
## -0.23453
## 0.07716
## -0.09279
## sigma2 = 0.10646
##
##
## MCMCtobit iteration 17001 of 31000
## beta =
## 4.52079
## -0.00444
## -0.09994
## -0.17962
## -0.29618
## -0.00597
## 0.07980
## 0.07332
## 0.18753
## 0.00369
## -0.06650
## 0.06450
## 0.05812
## 0.03431
## -0.10315
## 0.05262
## -0.16557
## 0.11646
## 0.00029
## sigma2 = 0.10459
##
##
## MCMCtobit iteration 18001 of 31000
## beta =
## 4.37324
## 0.07416
## -0.13321
## -0.00962
## -0.15680
## 0.02660
## 0.02015
## 0.11334
## 0.16886
## 0.02186
## 0.05470
## 0.10283
## 0.04562
## 0.06281
## -0.01140
## 0.04906
## -0.09272
## 0.14970
## -0.13794
## sigma2 = 0.11200
##
##
## MCMCtobit iteration 19001 of 31000
## beta =
## 4.28658
## -0.07038
## -0.00569
## -0.00305
## -0.25377
## -0.02335
## 0.08695
## 0.09962
## 0.29063
## -0.01559
## -0.03440
## 0.08233
## -0.01091
## 0.00034
## 0.08898
## 0.08365
## -0.03288
## 0.03223
## -0.22537
## sigma2 = 0.11760
##
##
## MCMCtobit iteration 20001 of 31000
## beta =
## 4.56803
## 0.10135
## 0.09110
## -0.11760
## -0.14052
## -0.05940
## -0.00948
## -0.00087
## 0.22473
## -0.08623
## -0.09574
## 0.07891
## 0.14150
## 0.16415
## -0.18164
## 0.04104
## -0.11009
## 0.05317
## 0.04136
## sigma2 = 0.13376
##
##
## MCMCtobit iteration 21001 of 31000
## beta =
## 4.36429
## -0.05203
## 0.01874
## -0.01392
## -0.01060
## 0.03516
## -0.02238
## 0.09238
## 0.32159
## -0.15749
## -0.10057
## 0.10365
## 0.15161
## 0.16622
## -0.06016
## 0.02806
## -0.11605
## 0.05215
## -0.13542
## sigma2 = 0.11841
##
##
## MCMCtobit iteration 22001 of 31000
## beta =
## 4.47952
## 0.04260
## -0.12763
## -0.05165
## -0.24382
## -0.06179
## 0.09511
## 0.07548
## 0.25811
## -0.07684
## -0.07327
## 0.02772
## 0.13279
## 0.08304
## -0.07115
## 0.01340
## -0.10517
## 0.10472
## -0.14247
## sigma2 = 0.11882
##
##
## MCMCtobit iteration 23001 of 31000
## beta =
## 4.47407
## -0.04559
## 0.00766
## -0.09226
## -0.21920
## -0.18275
## 0.04631
## 0.02118
## 0.22101
## 0.02632
## -0.09338
## 0.11497
## 0.11341
## 0.06604
## -0.00081
## 0.01281
## -0.15296
## 0.05286
## -0.10098
## sigma2 = 0.10206
##
##
## MCMCtobit iteration 24001 of 31000
## beta =
## 4.42164
## -0.00026
## -0.04018
## -0.13826
## -0.09918
## -0.02882
## 0.07926
## 0.05344
## 0.23806
## -0.02546
## -0.05729
## 0.16722
## 0.03654
## 0.14477
## -0.04075
## -0.01900
## -0.16809
## 0.09957
## -0.10923
## sigma2 = 0.11344
##
##
## MCMCtobit iteration 25001 of 31000
## beta =
## 4.34352
## -0.03485
## -0.05280
## -0.06305
## -0.11415
## -0.00576
## 0.06118
## 0.11790
## 0.24276
## 0.00854
## -0.02614
## 0.08794
## 0.11133
## 0.07884
## -0.06882
## 0.03402
## -0.10478
## 0.04650
## -0.06701
## sigma2 = 0.11487
##
##
## MCMCtobit iteration 26001 of 31000
## beta =
## 4.56500
## -0.08199
## -0.27667
## -0.23806
## -0.19829
## -0.03547
## 0.22181
## 0.02745
## 0.04517
## 0.03768
## -0.05267
## 0.06923
## 0.02069
## -0.03908
## -0.07001
## 0.05106
## -0.25734
## 0.13173
## -0.10255
## sigma2 = 0.12336
##
##
## MCMCtobit iteration 27001 of 31000
## beta =
## 4.38376
## -0.08126
## -0.04905
## -0.15730
## -0.12093
## -0.01602
## 0.07851
## 0.10537
## 0.20907
## 0.03690
## 0.02857
## 0.17731
## 0.07982
## 0.10422
## -0.10032
## 0.03854
## -0.08621
## -0.00034
## -0.07438
## sigma2 = 0.12415
##
##
## MCMCtobit iteration 28001 of 31000
## beta =
## 4.50493
## -0.05824
## 0.07958
## -0.01641
## -0.02846
## -0.00123
## 0.03734
## 0.17987
## 0.27724
## 0.03181
## -0.00531
## 0.13013
## 0.04465
## -0.03728
## -0.09237
## -0.04098
## -0.15247
## -0.00916
## -0.04247
## sigma2 = 0.13854
##
##
## MCMCtobit iteration 29001 of 31000
## beta =
## 4.29775
## -0.01763
## -0.00768
## -0.11391
## -0.16345
## -0.01786
## 0.06053
## 0.02076
## 0.18418
## 0.03454
## -0.00188
## 0.05253
## 0.08954
## 0.04535
## 0.04520
## 0.10881
## -0.16145
## 0.04876
## -0.01065
## sigma2 = 0.10830
##
##
## MCMCtobit iteration 30001 of 31000
## beta =
## 4.63069
## -0.13462
## -0.12933
## -0.10680
## -0.16683
## -0.08385
## 0.01057
## -0.02132
## 0.09838
## 0.03941
## 0.04226
## 0.11720
## 0.08785
## 0.07890
## -0.16106
## 0.05800
## -0.17272
## 0.08323
## -0.10192
## sigma2 = 0.10930summary(bayes.tobit)##
## Iterations = 1001:31000
## Thinning interval = 1
## Number of chains = 1
## Sample size per chain = 30000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## (Intercept) 4.430606 0.098242 5.672e-04 5.672e-04
## age_groupgr3039 -0.019432 0.070342 4.061e-04 4.107e-04
## age_groupgr4049 -0.038693 0.073946 4.269e-04 4.269e-04
## age_groupgr5059 -0.081334 0.085307 4.925e-04 4.977e-04
## age_group60plus -0.163759 0.104571 6.037e-04 6.037e-04
## h12 -0.036086 0.044088 2.545e-04 2.545e-04
## d1amarried 0.052952 0.061618 3.558e-04 3.558e-04
## educ22 0.067866 0.057073 3.295e-04 3.295e-04
## educ23 0.214069 0.069754 4.027e-04 4.027e-04
## ter_in2 0.007316 0.051779 2.989e-04 2.989e-04
## ter_in3 -0.019013 0.052154 3.011e-04 3.011e-04
## selfhealthgood 0.099041 0.042196 2.436e-04 2.436e-04
## smosttmedium 0.069675 0.052550 3.034e-04 3.034e-04
## smosttheavy 0.060141 0.056076 3.238e-04 3.238e-04
## b16a1 -0.056816 0.062936 3.634e-04 3.634e-04
## b18abad 0.051333 0.044017 2.541e-04 2.541e-04
## label11 -0.133114 0.062535 3.610e-04 3.610e-04
## anticam 0.067007 0.054075 3.122e-04 3.161e-04
## noEnvi2 -0.079353 0.052643 3.039e-04 3.039e-04
## sigma2 0.115144 0.009936 5.736e-05 6.173e-05
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## (Intercept) 4.23717 4.36479 4.430717 4.496180 4.62485
## age_groupgr3039 -0.15647 -0.06666 -0.019485 0.027709 0.11958
## age_groupgr4049 -0.18366 -0.08833 -0.038654 0.011141 0.10628
## age_groupgr5059 -0.24856 -0.13919 -0.080502 -0.024024 0.08541
## age_group60plus -0.36980 -0.23434 -0.163507 -0.093475 0.04001
## h12 -0.12162 -0.06583 -0.036417 -0.006173 0.05076
## d1amarried -0.06679 0.01101 0.052808 0.094557 0.17364
## educ22 -0.04321 0.02930 0.067822 0.106401 0.18003
## educ23 0.07712 0.16708 0.214089 0.261200 0.34985
## ter_in2 -0.09444 -0.02761 0.007389 0.041726 0.11021
## ter_in3 -0.12196 -0.05392 -0.018950 0.015661 0.08307
## selfhealthgood 0.01566 0.07076 0.099003 0.127609 0.18235
## smosttmedium -0.03354 0.03432 0.069574 0.104927 0.17310
## smosttheavy -0.04940 0.02236 0.060313 0.097560 0.17031
## b16a1 -0.18231 -0.09883 -0.056477 -0.014555 0.06665
## b18abad -0.03511 0.02192 0.051223 0.080904 0.13841
## label11 -0.25618 -0.17545 -0.133333 -0.090160 -0.01109
## anticam -0.03801 0.03096 0.066737 0.102775 0.17343
## noEnvi2 -0.18325 -0.11463 -0.079584 -0.043858 0.02323
## sigma2 0.09744 0.10816 0.114578 0.121400 0.13599summary(bayes.tobit) bayes.tobit$fit
prob=as.data.frame(predict(bayes.tobit ,test,type=“response”))
library(e1071) pred=ifelse(prob$Estimate> 0.5, 1, 0)
##Results
confusionMatrix(data=pred,reference=test$costincrease) ```