version 4.8

Author: ac-guidoum

R/MCM.sde.R

@@ -270,19 +270,7 @@ print.MCM.sde <- function(x, digits=NULL, ...)
 		" | t in [",format(x$mod$t0,digits=digits),",",format(x$mod$T,digits=digits),"] with mesh equal to ",format(x$mod$Dt,digits=digits),"\n",
         sep="")}
     }} else {
-    if (x$Class=="snssde1d"){
-    Dr <- deparse(eval(substitute(substitute(e, list(x=quote(X(t)))), list(e = x$mod$drift))))
-    DD <- deparse(eval(substitute(substitute(e, list(x=quote(X(t)))), list(e = x$mod$diffusion))))
-    if(x$mod$type=="ito"){
-    cat(Ito," Sde 1D:","\n",
-        " | dX(t) = ", Dr," * dt + ", DD," * dW(t)","\n",
-		" | t in [",format(x$mod$t0,digits=digits),",",format(x$mod$T,digits=digits),"] with mesh equal to ",format(x$mod$Dt,digits=digits),"\n",
-        sep="")}else{
-    cat("Stratonovich Sde 1D:","\n",
-        " | dX(t) = ", Dr," * dt + ", DD," o dW(t)","\n",
-		" | t in [",format(x$mod$t0,digits=digits),",",format(x$mod$T,digits=digits),"] with mesh equal to ",format(x$mod$Dt,digits=digits),"\n",
-        sep="")}
-    }else if (x$Class=="snssde2d"){
+    if (x$Class=="snssde2d"){
 	Drx <- deparse(eval(substitute(substitute(e, list(x=quote(X(t)),y=quote(Y(t)))), list(e = x$mod$driftx))))
     DDx <- deparse(eval(substitute(substitute(e, list(x=quote(X(t)),y=quote(Y(t)))), list(e = x$mod$diffx))))
 	Dry <- deparse(eval(substitute(substitute(e, list(x=quote(X(t)),y=quote(Y(t)))), list(e = x$mod$drifty))))

tests/MCM_test.R

@@ -20,7 +20,13 @@ sde.fun1d <- function(data, i){
      return(c(mean(d),var(d)))
 }
 # Parallel MOnte Carlo for mod1
+
+mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=1, exact=list(m=E.mod1(1),S=V.mod1(1)))
+mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, names=c("Ex","Vx"))
 mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, exact=list(m=E.mod1(1),S=V.mod1(1)))
+mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, exact=list(m=E.mod1(1),S=V.mod1(1)),names=c("Ex","Vx"))
+mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5)
+mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, exact=list(m=E.mod1(1),S=V.mod1(1)),parallel="multicore",ncpus=2)
 mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, exact=list(m=E.mod1(1),S=V.mod1(1)),parallel="snow",cl= parallel::makeCluster(getOption("cl.cores", 2)),ncpus=2)
 mcm.mod1 = MCM.sde(model=mod1,statistic=sde.fun1d,R=5, exact=list(m=E.mod1(1),S=V.mod1(1)),parallel="snow",ncpus=2)
 print(mcm.mod1)
@@ -33,6 +39,8 @@ print(mcm.mod2)
 plot(mcm.mod1,index = 1)  # mean
 plot(mcm.mod1,index = 2)  # variance
 
+plot(mcm.mod1,index = c(1,2))
+
 ## ----fig.cap=' MC output of mean and variance of `mod2`', fig.env='figure*'----
 # plot(s) of Monte Carlo outputs of mod2
 plot(mcm.mod2,index = 1)  # mean
@@ -58,6 +66,7 @@ sde.fun2d <- function(data, i){
 }
 ## Parallel Monte-Carlo of 'OUI' at time 10
 mcm.mod2d = MCM.sde(OUI,statistic=sde.fun2d,time=10,R=5,exact=tvalue)
+mcm.mod2d = MCM.sde(OUI,statistic=sde.fun2d,time=10,R=5,exact=tvalue,parallel="multicore",ncpus=2)
 mcm.mod2d = MCM.sde(OUI,statistic=sde.fun2d,time=10,R=5,exact=tvalue,parallel="snow",cl= parallel::makeCluster(getOption("cl.cores", 2)),ncpus=2)
 mcm.mod2d = MCM.sde(OUI,statistic=sde.fun2d,time=10,R=5,exact=tvalue,parallel="snow",ncpus=2)
 print(mcm.mod2d)
@@ -139,6 +148,7 @@ sde.fun3d <- function(data, i){
 }
 ## Monte-Carlo at time = 10
 mcm.mod3d = MCM.sde(modtra,statistic=sde.fun3d,R=5)
+mcm.mod3d = MCM.sde(modtra,statistic=sde.fun3d,R=5,parallel="multicore",ncpus=2)
 mcm.mod3d = MCM.sde(modtra,statistic=sde.fun3d,R=5,parallel="snow",cl= parallel::makeCluster(getOption("cl.cores", 2)),ncpus=2)
 mcm.mod3d = MCM.sde(modtra,statistic=sde.fun3d,R=5,parallel="snow",ncpus=2)
 print(mcm.mod3d)