目前，我正在R：
中试验这个功能rbprobitGibbs(Data, Prior, Mcmc)
当我在rtprobitGibbs的RStudio帮助中查看示例时，它有一个带有此代码的n * 1 y向量：
##
## rbprobitGibbs example
##
if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10}
set.seed(66)
simbprobit=
function(X,beta) {
## function to simulate from binary probit including x variable
y=ifelse((X%*%beta+rnorm(nrow(X)))<0,0,1)
list(X=X,y=y,beta=beta)
}
nobs=200
X=cbind(rep(1,nobs),runif(nobs),runif(nobs))
beta=c(0,1,-1)
nvar=ncol(X)
simout=simbprobit(X,beta)
Data1=list(X=simout$X,y=simout$y)
Mcmc1=list(R=R,keep=1)
out=rbprobitGibbs(Data=Data1,Mcmc=Mcmc1)
summary(out$betadraw,tvalues=beta)
if(0){
## plotting example
plot(out$betadraw,tvalues=beta)
}
有人可以告诉我，我如何更新上面的代码来计算rbprobitGibbs而不是2或者任意数量的y向量？这里的前四列是样本中的数据，y2列是我添加的列：
X.1 X.2 X.3 y1 y2
1 0.9899365748 0.2189661944 1 1
1 0.8296926252 0.9985278335 0 0
1 0.5858869043 0.4508356177 1 1
1 0.4170461195 0.5318313679 1 0
1 0.6620968801 0.5661155705 0 0
1 0.3793020903 0.4270007173 0 0
1 0.4242656054 0.7121669375 0 0
1 0.8425126909 0.0662683414 1 1
1 0.3764421751 0.5297549635 1 1
1 0.9115300649 0.6154659826 0 1
1 0.2585383623 0.861666956 0 0
1 0.2032627692 0.6300459378 0 0
1 0.2246137869 0.8552307657 0 0
1 0.9146362843 0.7148247214 0 0
1 0.8902309975 0.4851149949 1 1
1 0.2795407828 0.750800706 1 1
1 0.4293785084 0.2013569023 1 1
1 0.2740470199 0.1700141362 0 0
1 0.5124748724 0.3394885478 1 1
1 0.3552741136 0.4214046651 0 0
1 0.1278469148 0.7162302185 0 0
1 0.2943667781 0.5867321899 0 0
1 0.1614354805 0.4957168296 1 1
1 0.5066605383 0.5413606348 1 1
1 0.3777150062 0.4739717268 1 1
1 0.1853615218 0.3201939461 0 0
1 0.0973946755 0.8336658452 0 0
1 0.8443719402 0.5456923875 1 1
1 0.1721531036 0.1616139291 0 0
1 0.0249645228 0.8461988585 1 1
1 0.5729625325 0.0658818937 1 1
1 0.2316438637 0.3755099219 1 1
1 0.3851218582 0.0061001917 0 0
1 0.1498707412 0.1630114091 0 0
1 0.5519695727 0.5340546053 1 1
1 0.278166075 0.4493952126 1 1
1 0.5418767433 0.4024042937 0 0
1 0.9727253027 0.3172571538 1 1
1 0.6974664936 0.9042428946 1 1
1 0.4901860307 0.8092677956 1 1
1 0.4725901203 0.6991247302 1 1
1 0.24298591 0.702485577 1 1
1 0.5262798222 0.3325769177 1 1
1 0.998526135 0.2580267722 1 1
1 0.9664521548 0.1097839777 1 1
1 0.4240157611 0.6493905277 1 1
1 0.2575837581 0.6835418411 1 1
1 0.9943112358 0.0192416003 1 1
1 0.1853664671 0.9032788952 1 1
1 0.758035332 0.5458013031 0 0
1 0.0594366395 0.1449510658 0 0
1 0.1858018977 0.3166872617 1 0
1 0.6369692264 0.3176534963 1 1
1 0.6418922141 0.6724918736 0 0
1 0.6624171205 0.6146978349 0 0
1 0.4099654155 0.4726984168 1 1
1 0.3839501769 0.6608771463 0 0
1 0.4312540782 0.7527925344 0 0
1 0.063719698 0.1763143158 1 1
1 0.3058558635 0.217456545 0 0
1 0.9693015772 0.1797125733 1 1
1 0.1807862343 0.2376139732 0 1
1 0.8151498642 0.0461780084 1 1
1 0.6328742586 0.35979084 1 1
1 0.3616710461 0.969523683 0 0
1 0.281503205 0.7784934922 1 1
1 0.7212781536 0.7413479725 0 0
1 0.092384431 0.5874917486 0 0
1 0.155009425 0.8415817369 0 0
1 0.8922143609 0.6630925562 1 1
1 0.991042807 0.0252894 1 1
1 0.4425945459 0.8692532447 0 0
1 0.3441495837 0.9671099493 1 1
1 0.1620598785 0.3095594603 1 1
1 0.8981178766 0.8030338185 1 1
1 0.2345796963 0.4723579886 0 0
1 0.5316903049 0.510752735 1 1
1 0.7979545509 0.330074837 1 1
1 0.2654725285 0.205431531 1 1
1 0.3717567755 0.6099570901 1 1
1 0.2746311817 0.5846611073 1 1
1 0.6063400249 0.5772228637 0 0
1 0.0473926074 0.2661940577 1 1
1 0.6541800608 0.9317179173 1 1
1 0.9319066966 0.6864310182 0 0
1 0.6435363619 0.6030408067 1 1
1 0.2995573319 0.9094474597 1 1
1 0.171410341 0.437170333 0 0
1 0.6401165337 0.3053631645 1 1
1 0.3017032004 0.0369891548 0 0
1 0.3860893343 0.6809475985 0 0
1 0.4636157434 0.2839248523 0 0
1 0.1536822913 0.8689155467 0 0
1 0.6962356898 0.4797217257 1 1
1 0.4657603616 0.4761086726 1 1
1 0.2734947701 0.3373264791 0 1
1 0.0445950362 0.1672765177 0 0
1 0.5687612037 0.4581195179 0 0
1 0.4707629299 0.4836922633 0 0
1 0.751863339 0.2289583082 1 1
1 0.259972115 0.4522923154 0 0
1 0.710883829 0.531755195 0 0
1 0.328196065 0.3619224844 0 0
1 0.3967730084 0.0183546853 0 0
1 0.215346405 0.7562066903 0 0
1 0.1864551497 0.5278709074 0 0
1 0.9422974272 0.6206218491 1 1
1 0.6341338102 0.3694500523 1 1
1 0.6171285694 0.4848789659 1 0
1 0.5353122416 0.1012676626 1 1
1 0.9107316111 0.4414798988 0 0
1 0.364191507 0.5692639218 0 0
1 0.7192769244 0.871085976 0 0
1 0.5625851532 0.6071606656 0 0
1 0.6643338243 0.3915698901 1 1
1 0.1365202225 0.8386956837 1 1
1 0.1951833158 0.6553574409 0 0
1 0.3314071957 0.5843182474 0 0
1 0.94224557 0.3040650734 1 1
1 0.0419700646 0.7691562055 0 0
1 0.43872105 0.0486131848 0 0
1 0.5100067658 0.6341659653 1 1
1 0.1222995215 0.5880768083 0 0
1 0.1578365152 0.6505905646 1 1
1 0.3995162598 0.8664736878 1 1
1 0.947365944 0.2596085703 0 0
1 0.5945339077 0.4999730913 1 1
1 0.6945818942 0.5181555478 1 1
1 0.1378006986 0.7515407458 0 0
1 0.2357496801 0.6892036018 1 1
1 0.333463666 0.3506809592 1 1
1 0.7776793852 0.9664791177 1 1
1 0.254368332 0.3952343608 1 0
1 0.3824195687 0.4121196822 1 1
1 0.1427211207 0.0237048781 1 1
1 0.7924600174 0.8477656154 1 1
1 0.3051217012 0.5423933757 0 0
1 0.4072035847 0.612145344 1 1
1 0.0192040021 0.3364684819 1 1
1 0.7076993815 0.3081624294 1 1
1 0.3519693122 0.4135619369 1 1
1 0.3202238996 0.4520605011 1 1
1 0.2489268424 0.2608512486 0 0
1 0.1009442504 0.7335172631 0 1
1 0.6263556832 0.6289500771 0 0
1 0.7245263092 0.6750859108 1 1
1 0.8317463186 0.8096570096 0 0
1 0.2434659142 0.9338806737 0 0
1 0.4874795836 0.3839077372 0 0
1 0.6801484544 0.1946305826 1 1
1 0.643040627 0.7182156285 1 1
1 0.5217843335 0.7021823346 1 1
1 0.9625178338 0.9834434157 1 1
1 0.6535910789 0.5019235369 0 0
1 0.94050515 0.7863713149 1 1
1 0.8751446512 0.1334866604 1 1
1 0.8634884465 0.3494574034 0 0
1 0.91536376 0.2091044907 1 1
1 0.1773750747 0.652113735 0 0
1 0.225825988 0.125594883 1 1
1 0.9055066747 0.4528328346 0 0
1 0.7427957433 0.924980263 0 0
1 0.4384290115 0.3252121131 1 1
1 0.255896548 0.3704734396 1 1
1 0.0225097234 0.5561372044 0 0
1 0.6844344195 0.4750160228 1 1
1 0.1892332132 0.9139610652 0 0
1 0.2052155717 0.7026022857 0 0
1 0.7506253854 0.700289489 1 1
1 0.1649265396 0.1530481223 1 1
1 0.5506814695 0.7213967661 0 1
1 0.2912465036 0.7983302297 1 1
1 0.7702643026 0.527433767 1 1
1 0.9710474953 0.8381176256 0 0
1 0.8290705713 0.971519822 1 1
1 0.632064248 0.6417001493 1 1
1 0.4600081004 0.1881597352 1 1
1 0.7718191738 0.8489826284 0 0
1 0.8830631888 0.638571579 0 0
1 0.2392087474 0.1479197666 1 1
1 0.6529120326 0.3340752197 1 1
1 0.4075479258 0.746168833 0 0
1 0.1362040308 0.127894334 0 0
1 0.5595724836 0.9276634019 1 1
1 0.9293997863 0.3116247791 1 1
1 0.0010047755 0.151142987 1 1
1 0.8327829326 0.3474256487 1 1
1 0.4180099745 0.8976569436 0 0
1 0.6827635004 0.8410718208 0 0
1 0.2065993831 0.5254033669 1 1
1 0.6488550527 0.3361348431 0 0
1 0.960010844 0.4958175228 0 0
1 0.8394188522 0.7944452723 1 1
1 0.5054272702 0.4813854268 0 0
1 0.9041459351 0.1566028744 1 1
1 0.6693580367 0.0554579678 1 1
1 0.3972949465 0.6975388743 0 0
1 0.7972005522 0.7535337424 0 1
1 0.8931960308 0.5674132451 0 0
1 0.1921383152 0.055920549 1 1
更新
仅供参考，这是我正在处理的书：
http://ca.wiley.com/WileyCDA/WileyTitle/productCd-0470863676.html
答案 0 :(得分：0)
Actually, I found out I was working with the wrong bayesm function. Instead, I should be using this for a multivariate probit model which handles multiple y vectors:
rmvpGibbs - implements the Edwards/Allenby Gibbs Sampler for the multivariate probit model