Continued
| ### Calculate new estimate of Vartrue, set to zero if negative and |
| ### store in vector v |
| vart <− (n/(n− p))* varo− varm |
| vart <− max(vart, 0) |
| v[i+ 1] <− vart |
| ### Terminate iterations when Vhattrue has converged (at 10−8) and calculate |
| new estimates of lnSIR (newbeta) and var(lnSIR) (newvar) |
| if (Mod(v[i+ 1]− v) < 1e-007) { |
| vart <− v[i+ 1] |
| vstar <− (((n− p− 2)* vhat)/(n− p)) |
| tstar <− vart* diag(n)+ vhat− vstar |
| newbeta <− W %*% ((tstar %*% bhat)+ (vstar %*%mu)) |
| ### The definition of newvar is taken from Ref. 15 |
| H <− z %*% (solve (t(z) %*% W %*% z)) %*% t(z) %*% W |
| bstar <− ((n− p− 2)* W %*% vhat)/(n− p) |
| E <− bstar %*% D* sqrt (varm/diag(vhat)) |
| newvar <− diag(vhat− (t(diag(n)− H) %*% vhat %*% bstar)+ |
| (2* E %*% t (E))/(n− p− 2)) |
| break |
| } |
| } |
| } |
| In an example such as that presented in this article, for which we assume no correlations between the original estimates (and no prior knowledge is incorporated), the above program simplifies. Vhat becomes a vector of variances instead of a variance-covariance matrix, and z is no longer necessary. |
| function (bhat, vhat, niter) |
| { |
| ### Define the number of estimates (n) |
| n <− length (bhat) |
| ### Set initial value for Vhattrue (vart) to 0 |
| vart <− 0 |
| ### Generate vector to store values of Vhattrue from each iteration |
| v <− vector (length = niter+ 1) |
| v [1] <− 0 |
| ### Start iterative loop |
| for (i in 1:niter) { |
| ### Define a vector of weights (w) and calculate lnSIRmean (pi) |
| ### vhat is a vector of the variances of the original estimates |
| w <− 1/(vhat+ vart) |
| pi <− sum (w* bhat)/sum(w) |
| ### Calculate D, Vhatobs (varo) and Vhatmean (varm) |
| D <− bhat− pi |
| varo <− sum(w* D* D)/sum(w) |
| varm <− sum(w* vhat)/sum(w) |
| ### Calculate new estimate of Vartrue, set to zero if negative and |
| ### store in vector v |
| vart <− max((varo− varm), 0) |
| v[i+ 1] <− vart |
| ### Terminate iterations when Vhattrue has converged and calculate new |
| estimates of lnSIR (newbeta) and var(lnSIR) (newvar) |
| if (Mod(v[i+ 1] − v) < 1e-007) { |
| vart <− v[i+ 1] |
| newbeta <− w*((vart* bhat)+ (vhat* pi)) |
| E <− w* vhat* D* sqrt(varm/vhat) |
| newvar <− vhat* (1− vhat * w)+ (2* E* t(E))/n |
| break |
| } |
| } |
| } |
| ### Calculate new estimate of Vartrue, set to zero if negative and |
| ### store in vector v |
| vart <− (n/(n− p))* varo− varm |
| vart <− max(vart, 0) |
| v[i+ 1] <− vart |
| ### Terminate iterations when Vhattrue has converged (at 10−8) and calculate |
| new estimates of lnSIR (newbeta) and var(lnSIR) (newvar) |
| if (Mod(v[i+ 1]− v) < 1e-007) { |
| vart <− v[i+ 1] |
| vstar <− (((n− p− 2)* vhat)/(n− p)) |
| tstar <− vart* diag(n)+ vhat− vstar |
| newbeta <− W %*% ((tstar %*% bhat)+ (vstar %*%mu)) |
| ### The definition of newvar is taken from Ref. 15 |
| H <− z %*% (solve (t(z) %*% W %*% z)) %*% t(z) %*% W |
| bstar <− ((n− p− 2)* W %*% vhat)/(n− p) |
| E <− bstar %*% D* sqrt (varm/diag(vhat)) |
| newvar <− diag(vhat− (t(diag(n)− H) %*% vhat %*% bstar)+ |
| (2* E %*% t (E))/(n− p− 2)) |
| break |
| } |
| } |
| } |
| In an example such as that presented in this article, for which we assume no correlations between the original estimates (and no prior knowledge is incorporated), the above program simplifies. Vhat becomes a vector of variances instead of a variance-covariance matrix, and z is no longer necessary. |
| function (bhat, vhat, niter) |
| { |
| ### Define the number of estimates (n) |
| n <− length (bhat) |
| ### Set initial value for Vhattrue (vart) to 0 |
| vart <− 0 |
| ### Generate vector to store values of Vhattrue from each iteration |
| v <− vector (length = niter+ 1) |
| v [1] <− 0 |
| ### Start iterative loop |
| for (i in 1:niter) { |
| ### Define a vector of weights (w) and calculate lnSIRmean (pi) |
| ### vhat is a vector of the variances of the original estimates |
| w <− 1/(vhat+ vart) |
| pi <− sum (w* bhat)/sum(w) |
| ### Calculate D, Vhatobs (varo) and Vhatmean (varm) |
| D <− bhat− pi |
| varo <− sum(w* D* D)/sum(w) |
| varm <− sum(w* vhat)/sum(w) |
| ### Calculate new estimate of Vartrue, set to zero if negative and |
| ### store in vector v |
| vart <− max((varo− varm), 0) |
| v[i+ 1] <− vart |
| ### Terminate iterations when Vhattrue has converged and calculate new |
| estimates of lnSIR (newbeta) and var(lnSIR) (newvar) |
| if (Mod(v[i+ 1] − v) < 1e-007) { |
| vart <− v[i+ 1] |
| newbeta <− w*((vart* bhat)+ (vhat* pi)) |
| E <− w* vhat* D* sqrt(varm/vhat) |
| newvar <− vhat* (1− vhat * w)+ (2* E* t(E))/n |
| break |
| } |
| } |
| } |