SAS PROC MCMC example 12 in R: Change point model

June 21, 2015
By

(This article was first published on Wiekvoet, and kindly contributed to R-bloggers)

I restarted at working my way through the PROC MCMC examples. The SAS manual describes this example: Consider the data set from Bacon and Watts (1971), where $y_ i$ is the logarithm of the height of the stagnant surface layer and the covariate $x_ i$ is the logarithm of the flow rate of water. 
It is a simple example. It provided no problems at all for STAN and Jags. For LaplacesDemon on the other hand I had some problems. It took me quite some effort to obtain samples which seemed to be behaving. I did not try to do this in MCMCpack, but noted that the function MCMCregressChange() uses a slightly different model. The section below shows first the results, at the bottom the code is given.

Previous post in the series PROC MCMC examples programmed in R were: example 61.1: sampling from a known densityexample 61.2: Box Cox transformationexample 61.5: Poisson regressionexample 61.6: Non-Linear Poisson Regressionexample 61.7: Logistic Regression Random-Effects Model, and example 61.8: Nonlinear Poisson Regression Multilevel Random-Effects Model

Data

Data are read as below.
observed <-
‘1.12  -1.39   1.12  -1.39   0.99  -1.08   1.03  -1.08
0.92  -0.94   0.90  -0.80   0.81  -0.63   0.83  -0.63
0.65  -0.25   0.67  -0.25   0.60  -0.12   0.59  -0.12
0.51   0.01   0.44   0.11   0.43   0.11   0.43   0.11
0.33   0.25   0.30   0.25   0.25   0.34   0.24   0.34
0.13   0.44  -0.01   0.59  -0.13   0.70  -0.14   0.70
-0.30   0.85  -0.33   0.85  -0.46   0.99  -0.43   0.99
-0.65   1.19′
observed <- scan(text=gsub(‘[[:space:]]+’,’ ‘,observed),
    what=list(y=double(),x=double()),
    sep=’ ‘)
stagnant <- as.data.frame(observed)

LaplacesDemon

I have been playing around with LaplacesDemon. There is actually a function
LaplacesDemon.hpc which can use multiple cores. However, on my computer it seems more efficient just to use mclapply() from the parallel package and give the result class LaplacesDemon.hpc . Having said that, I had again quite some trouble to get LaplacesDemon to work well. In the end I used a combination of two calls to LaplacesDemon. The plot below shows selected samples after the first run. Not good enough, but that I do like this way of displaying the results of chains. It should be added that the labels looked correct with all parameters. However, that gave to much output for this blog. In addition, after the second call the results looked acceptable.


Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = apply(cc1$Posterior1,
    2, median), Covar = var(cc1$Posterior1), Iterations = 1e+05,
    Algorithm = “RWM”)

Acceptance Rate: 0.2408
Algorithm: Random-Walk Metropolis
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
       alpha        beta1        beta2           cp           s2
4.920676e-04 2.199525e-04 3.753738e-04 8.680339e-04 6.122862e-08

Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
          All Stationary
Dbar -144.660   -144.660
pD      7.174      7.174
DIC  -137.486   -137.486
Initial Values:
        alpha         beta1         beta2            cp            s2
 0.5467048515 -0.4100040451 -1.0194586232  0.0166405998  0.0004800931

Iterations: 4e+05
Log(Marginal Likelihood): 56.92606
Minutes of run-time: 1.32
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 5
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 0
Recommended Burn-In of Un-thinned Samples: 0
Recommended Thinning: 5
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 40000
Thinning: 1

Summary of All Samples
                  Mean           SD         MCSE      ESS            LB
alpha     5.348239e-01 0.0244216567 2.999100e-04 11442.06  4.895229e-01
beta1    -4.196180e-01 0.0142422533 1.661658e-04 12726.60 -4.469688e-01
beta2    -1.013882e+00 0.0164892337 1.681833e-04 15191.59 -1.046349e+00
cp        2.855852e-02 0.0308177765 3.649332e-04 11945.66 -3.406306e-02
s2        4.472644e-04 0.0001429674 1.383748e-06 16571.94  2.474185e-04
Deviance -1.446602e+02 3.7879060637 4.940488e-02 10134.91 -1.496950e+02
LP        4.636511e+01 1.8939530321 2.470244e-02 10134.91  4.164313e+01
                Median            UB
alpha       0.53339024  5.842152e-01
beta1      -0.41996859 -3.903572e-01
beta2      -1.01387256 -9.815650e-01
cp          0.03110570  8.398674e-02
s2          0.00042101  8.006666e-04
Deviance -145.46896682 -1.352162e+02
LP         46.76949458  4.888251e+01

Summary of Stationary Samples
                  Mean           SD         MCSE      ESS            LB
alpha     5.348239e-01 0.0244216567 2.999100e-04 11442.06  4.895229e-01
beta1    -4.196180e-01 0.0142422533 1.661658e-04 12726.60 -4.469688e-01
beta2    -1.013882e+00 0.0164892337 1.681833e-04 15191.59 -1.046349e+00
cp        2.855852e-02 0.0308177765 3.649332e-04 11945.66 -3.406306e-02
s2        4.472644e-04 0.0001429674 1.383748e-06 16571.94  2.474185e-04
Deviance -1.446602e+02 3.7879060637 4.940488e-02 10134.91 -1.496950e+02
LP        4.636511e+01 1.8939530321 2.470244e-02 10134.91  4.164313e+01
                Median            UB
alpha       0.53339024  5.842152e-01
beta1      -0.41996859 -3.903572e-01
beta2      -1.01387256 -9.815650e-01
cp          0.03110570  8.398674e-02
s2          0.00042101  8.006666e-04
Deviance -145.46896682 -1.352162e+02
LP         46.76949458  4.888251e+01

STAN

Stan did not give much problems for this analysis.
Inference for Stan model: smodel.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.

         mean se_mean   sd  2.5%   25%   50%   75% 97.5% n_eff Rhat
Beta[1] -0.42    0.00 0.01 -0.45 -0.43 -0.42 -0.41 -0.39  1017 1.00
Beta[2] -1.01    0.00 0.02 -1.05 -1.02 -1.01 -1.00 -0.98  1032 1.00
Alpha    0.54    0.00 0.03  0.49  0.52  0.53  0.55  0.59   680 1.00
s2       0.00    0.00 0.00  0.00  0.00  0.00  0.00  0.00  1361 1.00
cp       0.03    0.00 0.03 -0.04  0.00  0.03  0.05  0.09   636 1.00
lp__    90.63    0.06 1.78 86.17 89.71 91.00 91.91 93.03   935 1.01

Samples were drawn using NUTS(diag_e) at Fri Jun 19 21:17:54 2015.
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).

JAGS

Again no problems for Jags.
Inference for Bugs model at “/tmp/Rtmpy4a6C5/modeld4f6e9c9055.txt”, fit using jags,
 4 chains, each with 10000 iterations (first 5000 discarded), n.thin = 5
 n.sims = 4000 iterations saved
          mu.vect sd.vect     2.5%      25%      50%      75%    97.5%  Rhat
alpha       0.534   0.027    0.479    0.518    0.533    0.552    0.586 1.040
beta[1]    -0.420   0.015   -0.450   -0.429   -0.420   -0.410   -0.389 1.013
beta[2]    -1.014   0.017   -1.049   -1.024   -1.014   -1.003   -0.980 1.023
cp          0.029   0.035   -0.038    0.006    0.032    0.051    0.100 1.037
s2          0.000   0.000    0.000    0.000    0.000    0.001    0.001 1.004
deviance -144.501   3.986 -149.634 -147.378 -145.432 -142.584 -134.378 1.021
         n.eff
alpha      160
beta[1]    380
beta[2]    290
cp         160
s2         710
deviance   290

For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).

DIC info (using the rule, pD = var(deviance)/2)
pD = 7.9 and DIC = -136.6
DIC is an estimate of expected predictive error (lower deviance is better).

CODE used

# http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_mcmc_examples18.htm
# Example 61.12 Change Point Models
##############
#Data                                                   
##############
observed <-
‘1.12  -1.39   1.12  -1.39   0.99  -1.08   1.03  -1.08
0.92  -0.94   0.90  -0.80   0.81  -0.63   0.83  -0.63
0.65  -0.25   0.67  -0.25   0.60  -0.12   0.59  -0.12
0.51   0.01   0.44   0.11   0.43   0.11   0.43   0.11
0.33   0.25   0.30   0.25   0.25   0.34   0.24   0.34
0.13   0.44  -0.01   0.59  -0.13   0.70  -0.14   0.70
-0.30   0.85  -0.33   0.85  -0.46   0.99  -0.43   0.99
-0.65   1.19′
observed <- scan(text=gsub(‘[[:space:]]+’,’ ‘,observed),
    what=list(y=double(),x=double()),
    sep=’ ‘)
stagnant <- as.data.frame(observed)
#plot(y~x,data=stagnant)
##############
#LaplacesDemon                                                   
##############
library(‘LaplacesDemon’)
library(parallel)

mon.names <- “LP”
parm.names <- c(‘alpha’,paste(‘beta’,1:2,sep=”),’cp’,’s2′)

PGF <- function(Data) {
  x <-c(rnorm(5,0,1))
  x[4] <- runif(1,-1.3,1.1)
  x[5] <- runif(1,0,2)
  x
}
MyData <- list(mon.names=mon.names,
    parm.names=parm.names,
    PGF=PGF,
    x=stagnant$x,
    y=stagnant$y)
#N<-1
Model <- function(parm, Data)
{
  alpha=parm[1]
  beta=parm[2:3]
  cp=parm[4]
  s2=parm[5]
  yhat <- alpha+(Data$x-cp)*beta[1+(Data$x>=cp)]
  LL <- sum(dnorm(Data$y,yhat,sd=sqrt(s2),log=TRUE))
  prior <- sum(dnorm(parm[1:3],0,1e3,log=TRUE))+
      dunif(cp,-1.3,1.1,log=TRUE)+
      dunif(s2,0,5,log=TRUE)
  LP=LL+prior
  Modelout <- list(LP=LP, Dev=-2*LL, Monitor=LP,
      yhat=yhat,
      parm=parm)
  return(Modelout)
}
Fit1 <- mclapply(1:4,function(i)
      LaplacesDemon(Model,
    Data=MyData,
    Iterations=100000,
    Algorithm=’RWM’,
    Covar=c(rep(.01,4),.00001),
    Initial.Values = c(.5,-.4,-1,.05,.001)) #Initial.Values 
)
class(Fit1) <- ‘demonoid.hpc’
plot(Fit1,Data=MyData,Parms=c(‘alpha’))
cc1 <- Combine(Fit1,MyData)
#
Fit2 <- mclapply(1:4,function(i)
      LaplacesDemon(Model,
          Data=MyData,
          Iterations=100000,
          Algorithm=’RWM’,
          Covar=var(cc1$Posterior1),
          Initial.Values = apply(cc1$Posterior1,2,median)) #Initial.Values 
)
class(Fit2) <- ‘demonoid.hpc’
#plot(Fit2,Data=MyData,Parms=c(‘alpha’))
cc2 <- Combine(Fit2,MyData)
cc2
##############
#STAN                                                   
##############
stanData <- list(
    N=nrow(stagnant),
    x=stagnant$x,
    y=stagnant$y)

library(rstan)
smodel <- ‘
    data {
    int N;
    vector[N] x;
    vector[N] y;
    }
    parameters {
    real Beta[2];
    real Alpha;
    real s2;
    real cp;
    }
    transformed parameters {
    vector[N] yhat;
    for (i in 1:N)
       yhat[i] <- Alpha+(x[i]-cp)*Beta[1+(x[i]>cp)];
    }
    model {
    y ~ normal(yhat,sqrt(s2));
    s2 ~ uniform(0,1e3);
    cp ~ uniform(-1.3,1.1);
    Alpha ~ normal(0,1000);
    Beta ~ normal(0,1000);
    }
    ‘
fstan <- stan(model_code = smodel,
    data = stanData,
    pars=c(‘Beta’,’Alpha’,’s2′,’cp’))
fstan
##############
#Jags                                                  
##############
library(R2jags)jagsdata <- list(
    N=nrow(stagnant),
    x=stagnant$x,
    y=stagnant$y)

jagsm <- function() {
  for(i in 1:N) {
    yhat[i] <- alpha+(x[i]-cp)*beta[1+(x[i]>cp)]
    y[i] ~ dnorm(yhat[i],tau)
  }
  tau <- 1/s2
  s2 ~  dunif(0,1e3)
  cp ~ dunif(-1.3,1.1)
  alpha ~ dnorm(0,0.001)
  beta[1] ~ dnorm(0,0.001)
  beta[2] ~ dnorm(0,0.001)
}
params <- c(‘alpha’,’beta’,’s2′,’cp’)
myj <-jags(model=jagsm,
    data = jagsdata,
    n.chains=4,
    n.iter=10000,
    parameters=params,
)
myj

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