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* checking for file 'scalablebayesm/DESCRIPTION' ... OK
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* this is package 'scalablebayesm' version '0.2'
* package encoding: UTF-8
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Maintainer: 'Federico Bumbaca <federico.bumbaca@colorado.edu>'

New submission

Possibly misspelled words in DESCRIPTION:
  Bumbaca (19:104)
  Logits (18:28, 19:28)
  Misra (19:124)
  Multinomial (18:16, 19:16)
  Rossi (19:137)
  Scalable (19:188)
  covariates (17:66, 18:71, 19:70)
  normals (17:48, 18:53)
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* checking examples ... ERROR
Running examples in 'scalablebayesm-Ex.R' failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: drawMixture
> ### Title: Gibbs Sampler Inference for a Mixture of Multivariate Normals
> ### Aliases: drawMixture
> 
> ### ** Examples
> 
> 
> # Linear DP
> ## Generate single component linear data with Z
> R = 1000
> nreg = 1000
> nobs = 5 #number of observations
> nvar = 3 #columns
> nz = 2
> 
> Z=matrix(runif(nreg*nz),ncol=nz) 
> Z=t(t(Z)-apply(Z,2,mean))
> Delta=matrix(c(1,0,1,0,1,2),ncol=nz) 
> tau0=1
> iota=c(rep(1,nobs)) 
> 
> ## create arguments for rmixture
> tcomps=NULL
> a = diag(1, nrow=3)
> tcomps[[1]] = list(mu=c(1,-2,0),rooti=a) 
> tpvec = 1                            
> ncomp=length(tcomps)
> 
> regdata=NULL
> betas=matrix(double(nreg*nvar),ncol=nvar) 
> tind=double(nreg) 
> 
> for (reg in 1:nreg) { 
+  tempout=bayesm::rmixture(1,tpvec,tcomps)
+  if (is.null(Z)){
+    betas[reg,]= as.vector(tempout$x)  
+  }else{
+    betas[reg,]=Delta%*%Z[reg,]+as.vector(tempout$x)} 
+  tind[reg]=tempout$z
+  X=cbind(iota,matrix(runif(nobs*(nvar-1)),ncol=(nvar-1))) 
+  tau=tau0*runif(1,min=0.5,max=1) 
+  y=X%*%betas[reg,]+sqrt(tau)*rnorm(nobs)
+  regdata[[reg]]=list(y=y,X=X,beta=betas[reg,],tau=tau) 
+ }
> 
> Prior1=list(ncomp=ncomp) 
> keep=1
> Mcmc1=list(R=R,keep=keep)
> Data1=list(list(regdata=regdata,Z=Z))
> 
> #subsample data
> N = length(Data1[[1]]$regdata)
> 
> s=1
> 
> #Partition data into s shards
> Data2 = partition_data(Data = Data1, s = s)
Data already partitioned into 1 shards> 
> #Run distributed first stage
> timing_result1 = system.time({
+  out_distributed = parallel::mclapply(Data2, FUN = rhierLinearDPParallel, 
+  Prior = Prior1, Mcmc = Mcmc1, mc.cores = s, mc.set.seed = FALSE)
+ })
 
Starting MCMC Inference for Hierarchical Logit:
   Dirichlet Process Prior
   for  1000  cross-sectional units
 
 Prior Parms: 
  G0 ~ N(mubar,Sigma (x) Amu^-1)
   mubar =  0
   Sigma ~ IW(nu,nu*v*I)
   Amu ~ uniform[ 0.01 , 10 ]
   nu ~ uniform on log grid  [ 3.01005 , 22.08554 ]
   v ~ uniform[ 0.1 , 4 ]
 
  alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power
   Istarmin =  1
   Istarmax =  50
   alphamin =  0.07501158
   alphamax =  6.613365
   power =  0.8
 
 MCMC Iteration (est time to end - min) 
 MCMC Iteration (est time to end - min) 
 100 (0.0)
 200 (0.0)
 300 (0.0)
 400 (0.0)
 500 (0.0)
 600 (0.0)
 700 (0.0)
 800 (0.0)
 900 (0.0)
 1000 (0.0)
 Total Time Elapsed: 0.03 
> 
> Z = matrix(unlist(Z), ncol = nz, byrow = TRUE)
> 
> # Conduct inference on first-stage draws
> draws = parallel::mclapply(out_distributed, FUN = drawMixture, 
+ Prior=NULL, Mcmc=Mcmc1, N=N, Z = Z,
+                           mc.cores = s, mc.set.seed = FALSE) 
requires number of mix comps -- Prior$ncomp
Starting Gibbs Sampler for Mixture of Normals
1000 observations on 3 dimensional data
Using 1 mixture components
 
Prior Parameters:
mu_j ~ N(mubar, Sigma (x) A^-1)
mubar = 
     [,1] [,2] [,3]
[1,]    0    0    0
Precision parameter for prior variance of mu vectors (A) = 0.01
Sigma_j ~ IW(nu, V) nu = 6
V = 
     [,1] [,2] [,3]
[1,]    6    0    0
[2,]    0    6    0
[3,]    0    0    6
Dirichlet parameters
[1] 5
deltabar
[1] 0 0 0 0 0 0
Ad
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.01 0.00 0.00 0.00 0.00 0.00
[2,] 0.00 0.01 0.00 0.00 0.00 0.00
[3,] 0.00 0.00 0.01 0.00 0.00 0.00
[4,] 0.00 0.00 0.00 0.01 0.00 0.00
[5,] 0.00 0.00 0.00 0.00 0.01 0.00
[6,] 0.00 0.00 0.00 0.00 0.00 0.01
 
MCMC Parameters: R = 1000 keep = 1 nprint = 100 LogLike = FALSE
 TRUE
starting value for indTRUE
1000
 TRUE
 MCMC Iteration (est time to end - min) 
 100 (0.0)
 200 (0.0)
 300 (0.0)
 400 (0.0)
 500 (0.0)
 600 (0.0)
 700 (0.0)
 800 (0.0)
 900 (0.0)
 1000 (0.0)
 Total Time Elapsed: 0.00 
> 
> #Generate single component multinomial data with Z
> ##parameters
> R = 1000
> p = 3 # number of choice alternatives                            
> ncoef = 3
> nlgt=1000
> nz = 2
> 
> # Define Z matrix
> Z = matrix(runif(nz*nlgt),ncol=nz)
> Z = t(t(Z)-apply(Z,2,mean))          # demean Z
> Delta=matrix(c(1,0,1,0,1,2),ncol=2)
> 
> tcomps=NULL
> a = diag(1, nrow=3)
> tcomps[[1]] = list(mu=c(-1,2,4),rooti=a) 
> tpvec = 1                             
> ncomp=length(tcomps)
> 
> simmnlwX= function(n,X,beta){
+  k=length(beta)
+  Xbeta=X %*% beta
+  j=nrow(Xbeta)/n
+  Xbeta=matrix(Xbeta,byrow=TRUE,ncol=j)
+  Prob=exp(Xbeta)
+  iota=c(rep(1,j))
+  denom=Prob %*% iota
+  Prob=Prob/as.vector(denom)
+  y=vector("double",n)
+  ind=1:j
+  for (i in 1:n) { 
+    yvec = rmultinom(1, 1, Prob[i,])
+    y[i] = ind%*%yvec
+  }
+  return(list(y=y,X=X,beta=beta,prob=Prob))
+ }
> 
> ## simulate data
> simlgtdata=NULL
> ni=rep(5,nlgt) 
> for (i in 1:nlgt) 
+ {
+  if (is.null(Z))
+  {
+    betai=as.vector(bayesm::rmixture(1,tpvec,tcomps)$x)
+  } else {
+    betai=Delta %*% Z[i,]+as.vector(bayesm::rmixture(1,tpvec,tcomps)$x)
+  }
+  Xa=matrix(runif(ni[i]*p,min=-1.5,max=0),ncol=p)
+  X=bayesm::createX(p,na=1,nd=NULL,Xa=Xa,Xd=NULL,base=1)
+  outa=simmnlwX(ni[i],X,betai)
+  simlgtdata[[i]]=list(y=outa$y,X=X,beta=betai)
+ }
> 
> ## set parms for priors and Z
> Prior1=list(ncomp=ncomp) 
> keep=1
> Mcmc1=list(R=R,keep=keep) 
> Data1=list(list(lgtdata=simlgtdata, p=p, Z=Z))
> 
> N = length(Data1[[1]]$lgtdata)
> 
> s=1
> 
> #Partition data into s shards
> Data2 = partition_data(Data = Data1, s = s)
Data already partitioned into 1 shards> 
> #Run distributed first stage
> timing_result1 = system.time({
+  out_distributed = parallel::mclapply(Data2, FUN = rhierMnlDPParallel, 
+  Prior = Prior1, Mcmc = Mcmc1, mc.cores = s, mc.set.seed = FALSE)
+ })
Table of Y values pooled over all units
ypooled
   1    2    3 
1038  673 3289 
Starting MCMC Inference for Hierarchical Logit:
Dirichlet Process Prior
  3  alternatives;  3  variables in X
For  1000  cross-sectional units
 
Prior Parameters:
G0 ~ N(mubar, Sigma (x) Amu^-1)
mubar = 0
Sigma ~ IW(nu, nu*v*I)
Amu ~ uniform[ 0.01 , 10 ]
nu ~ uniform on log grid [ 3.01005016708417 , 22.0855369231877 ]
v ~ uniform[ 0.1 , 4 ]
 
alpha ~ (1 - (alpha - alphamin) / (alphamax - alphamin))^power
Istarmin =  1
Istarmax =  50
alphamin =  0.07501157823808
alphamax =  6.6133645766977
power =  0.8
 
deltabar
[1] 0 0 0 0 0 0
Ad
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.01 0.00 0.00 0.00 0.00 0.00
[2,] 0.00 0.01 0.00 0.00 0.00 0.00
[3,] 0.00 0.00 0.01 0.00 0.00 0.00
[4,] 0.00 0.00 0.00 0.01 0.00 0.00
[5,] 0.00 0.00 0.00 0.00 0.01 0.00
[6,] 0.00 0.00 0.00 0.00 0.00 0.01
 
MCMC Parameters:
s = 1.374  w = 0.1  R = 1000  keep = 1  nprint = 100  maxuniq = 200  gridsize for lambda hyperparameters = 20
 
Initializing Metropolis candidate densities for 1000 units ...
Error in mnlHess(out$par, lgtdata[[i]]$y, lgtdata[[i]]$X) : 
  could not find function "mnlHess"
Calls: system.time -> <Anonymous> -> lapply -> FUN
Timing stopped at: 0.24 0 0.24
Execution halted
* checking PDF version of manual ... [13s] OK
* checking HTML version of manual ... OK
* DONE
Status: 1 ERROR, 1 NOTE