* Run the naive analysis. This can be done in GLM as well
qvf firstchd age smoke cholest2 LSBP2, family(binomial)

* Input the measurement error variance
matrix U = 0.01260 

* Run regression calibration with analytical standard errors
rcal (firstchd = age smoke cholest2) (wlsbp:LSBP2), family(binomial) suuinit(U)

* Run regression calibration with bootstrap standard errors
rcal (firstchd = age smoke cholest2) (wlsbp:LSBP2), family(binomial) suuinit(U) bstrap seed(10002)

* Run SIMEX with bootstrap standard errors
simex (firstchd = age smoke cholest2) (wlsbp:LSBP2), family(binomial) suuinit(U) bstrap seed(10003)

* Get the SIMEX plot of transformed SBP
simexplot wlsbp


* Now we are going to allow for two variables measured
* with error, namely tranaformed cholesterol and 
* transformed blood pressure. 

* We first transform cholesterol
gen lcholest2 = log(cholest2)

* We next input the estimated error covariance matrix
mat V = (0.01260208144393, 0.00067287539939 \ 0.00067287539939, 0.00845894714763)

* Run regression calibration
rcal (firstchd = age smoke) (wlsbp:LSBP2)  (wcholest:lcholest2), family(binomial) suuinit(V)

* Run simex with bootstrap standard errors
simex (firstchd = age smoke) (wlsbp:LSBP2) (wcholest:lcholest2), family(binomial) suuinit(V) bstrap seed(10008)

* Run the SIMEX plots
simexplot age

simexplot smoke

simexplot wlsbp

simexplot wcholest