Description Usage Arguments Details Value See Also Examples
Optimizes the size of the grid to try to limit the combined time of the envelope construction and the simulation phase.
1 | EnvelopeOpt(a1, n)
|
a1 |
Diagonal elements of data precision matrix for a model in standard form |
n |
Number of draws to generate |
This function attempts to find a computationally optimal gridsize by using information on the strenght of the prior and the number of iterations desired. Generally, more data (i.e., larger values for the diagonal elements of the precision matrix) will require a larger grid. The same also holds when the number of desired draws is higher (as the setup costs associated with the larger grid is offset by the savings in the number of candidates per sample).
A vector containing information on how many component each dimension should be split into.
rglmb
, EnvelopeBuild
, EnvelopeSort
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | data(menarche2)
summary(menarche2)
plot(Menarche/Total ~ Age, data=menarche2)
Age2=menarche2$Age-13
x<-matrix(as.numeric(1.0),nrow=length(Age2),ncol=2)
x[,2]=Age2
y=menarche2$Menarche/menarche2$Total
wt=menarche2$Total
mu<-matrix(as.numeric(0.0),nrow=2,ncol=1)
mu[2,1]=(log(0.9/0.1)-log(0.5/0.5))/3
V1<-1*diag(as.numeric(2.0))
# 2 standard deviations for prior estimate at age 13 between 0.1 and 0.9
## Specifies uncertainty around the point estimates
V1[1,1]<-((log(0.9/0.1)-log(0.5/0.5))/2)^2
V1[2,2]=(3*mu[2,1]/2)^2 # Allows slope to be up to 1 times as large as point estimate
famfunc<-glmbfamfunc(binomial(logit))
f1<-famfunc$f1
f2<-famfunc$f2
f3<-famfunc$f3
f5<-famfunc$f5
f6<-famfunc$f6
dispersion2<-as.numeric(1.0)
start <- mu
offset2=rep(as.numeric(0.0),length(y))
P=solve(V1)
n=1000
###### Adjust weight for dispersion
wt2=wt/dispersion2
######################### Shift mean vector to offset so that adjusted model has 0 mean
alpha=x%*%as.vector(mu)+offset2
mu2=0*as.vector(mu)
P2=P
x2=x
##### Optimization step to find posterior mode and associated Precision
parin=start-mu
opt_out=optim(parin,f2,f3,y=as.vector(y),x=as.matrix(x),mu=as.vector(mu2),
P=as.matrix(P),alpha=as.vector(alpha),wt=as.vector(wt2),
method="BFGS",hessian=TRUE
)
bstar=opt_out$par ## Posterior mode for adjusted model
bstar
bstar+as.vector(mu) # mode for actual model
A1=opt_out$hessian # Approximate Precision at mode
## Standardize Model
Standard_Mod=glmb_Standardize_Model(y=as.vector(y), x=as.matrix(x),
P=as.matrix(P),bstar=as.matrix(bstar,ncol=1), A1=as.matrix(A1))
bstar2=Standard_Mod$bstar2
A=Standard_Mod$A
x2=Standard_Mod$x2
mu2=Standard_Mod$mu2
P2=Standard_Mod$P2
L2Inv=Standard_Mod$L2Inv
L3Inv=Standard_Mod$L3Inv
Env2=EnvelopeBuild(as.vector(bstar2), as.matrix(A),y, as.matrix(x2),
as.matrix(mu2,ncol=1),as.matrix(P2),as.vector(alpha),as.vector(wt2),
family="binomial",link="logit",Gridtype=as.integer(3),
n=as.integer(n),sortgrid=TRUE)
## These now seem to match
Env2
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.