sample data for a correlation matrix added, gevp manual updated, exam…

…ple added

R/gevp.R

@@ -72,10 +72,12 @@ gevp <- function(cf, Time, t0 = 1, element.order = 1:cf$nrObs,
 
   ## now the time dependence for t != t0
   ## we need to multiply from the left with t(invL) and from the right with invL
-  for(t in c((t0 + 1):(Thalf), 0:(t0-1))) {
+  for(t in c((t0 + 1):(Thalf), (t0-1):0)) {
     t.sort <- t0+2
-    ## if wanted sort by the previous t, i.e. t.sort <- t + 1 - 1
-    if((t != t0+1) && !sort.t0) t.sort <- t
+    ## if wanted sort by the previous t, i.e. t.sort <- t + 1 - 1 for t > t0
+    if((t > t0+1) && !sort.t0) t.sort <- t
+    ## and t.sort <- t + 1 + 1 for t < t0
+    if((t < t0-1) && !sort.t0) t.sort <- t + 2
     ## matrix at t and symmetrise
     cM <- 0.5*matrix(Cor[ii+t], nrow=matrix.size, ncol=matrix.size)
     cM <- cM + t(cM)

data/datalist

@@ -1,3 +1,4 @@
 plaq.sample
 pscor.sample
 samplecf
+correlatormatrix

man/bootstrap.gevp.Rd

@@ -39,12 +39,17 @@ element.order=c(1,2,3,4), seed=1234, sort.type="vectors", sort.t0=TRUE)
   \item{sort.type}{
     Sort the eigenvalues either in descending order, or by using the
     scalar product of the eigenvectors with the eigenvectors at
-    \eqn{t=t_0+1}{t=t0+1}. Possible values are "values" or "vectors". 
+    \eqn{t=t_0+1}{t=t0+1}. Possible values are "values", "vectors" and
+    "det". The last one represents a time consuming, but in principle
+    better version of sorting by vectors. 
   }
   \item{sort.t0}{
     for \code{sort.type} "vectors" use \eqn{t_0}{t0} as reference or \eqn{t-1}{t-1}.
   }
 }
+\details{
+  Say something on "det" sorting method.
+}
 \value{
   Returns an object of class \code{gevp} with member objects:
 
@@ -70,14 +75,20 @@ element.order=c(1,2,3,4), seed=1234, sort.type="vectors", sort.t0=TRUE)
   \code{gevp}, \code{extract.obs}, \code{bootstrap.cf}
 }
 \examples{
-\dontrun{pion.cor.gevp <- bootstrap.gevp(pion.cor, t0=1)}
-\dontrun{## extract the first principal correlator}
-\dontrun{pion.pc1 <- gevp2cf(pion.cor.gevp, id=1)}
-\dontrun{## which can now be treated like a bootstrapped correlation function}
-\dontrun{pion.pc1.effectivemass <- bootstrap.effectivemass(cf=pion.pc1, type="acosh")}
-\dontrun{pion.pc1.effectivemass <- fit.effectivemass(pion.pc1.effectivemass, t1=12, t2=23, useCov=FALSE)}
-\dontrun{summary(pion.pc1.effectivemass)}
-\dontrun{plot(pion.pc1.effectivemass, xlab=c("t/a"), ylab=c("aM"))}
+data(correlatormatrix)
+## bootstrap the correlator matrix
+correlatormatrix <- bootstrap.cf(correlatormatrix, boot.R=400, boot.l=1, seed=132435)
+## solve the GEVP
+correlatormatrix.gevp <- bootstrap.gevp(cf=correlatormatrix, t0=4, element.order=c(1,2,3,4))
+## extract the ground state and plot
+pc1 <- gevp2cf(gevp=correlatormatrix.gevp, id=1)
+plot(pc1, log="y")
+## determine the corresponding effective masses
+pc1.effectivemass <- bootstrap.effectivemass(cf=pc1)
+pc1.effectivemass <- fit.effectivemass(cf=pc1.effectivemass, t1=5, t2=20)
+## summary and plot
+summary(pc1.effectivemass)
+plot(pc1.effectivemass)
 }
 \author{Carsten Urbach, \email{curbach@gmx.de}}
 \keyword{GEVP}