print.b1fit <- function(fit) {
summary(fit)
}
summary.b1fit <- function(fit) {
kappa <- fit$kappa
mu <- fit$mu
t1 <- fit$t1
t2 <- fit$t2
ij <- seq(1, fit$no.masses*(fit$matrix.size+1), by=fit$matrix.size+1)
sortindex <- order(abs(fit$fitresult$par[ij+fit$matrix.size]))
ii <- ij[sortindex]
fit.mass <- abs(fit$fitresult$par[ii[1] + fit$matrix.size])
fit.chisqr <- fit$fitresult$value
fit.dof <- length(fit$fitdata$t)-length(fit$fitresult$par)
cat("mu = ", mu, "\n")
cat("kappa = ", kappa, "\n")
cat("No of measurements = ", fit$N, "\n")
cat("No of replica = ", length(fit$nrep), "\n")
cat("no or measurements per replicum: ", fit$nrep, "\n")
cat("fitrange = ", t1, "-", t2, "\n")
cat("chi^2 = ", fit.chisqr, "\n")
cat("dof = ", fit.dof, "\n")
cat("chi^2/dof = ", fit.chisqr/fit.dof, "\n")
if(fit$no.masses == 1) {
cat("mb1 = ", fit.mass, "\n")
}
cat("\nstate =", seq(1,fit$no.masses), "\n", sep="\t")
cat("masses =", abs(fit$fitresult$par[ii+fit$matrix.size]), "\n", sep="\t")
cat("B_L =", fit$fitresult$par[ii], "\n", sep="\t")
cat("B_F =", fit$fitresult$par[ii+1], "\n", sep="\t")
if(!is.null(fit$uwerrresultmb1)) {
cat("\n--- Autocorrelation analysis for mb1 ---\n")
cat("\nS = ", fit$uwerrresultmb1$S, "\n")
cat("mb1 = ", fit$uwerrresultmb1$value, "\n")
cat("dmb1 = ", fit$uwerrresultmb1$dvalue, "\n")
cat("ddmb1 = ", fit$uwerrresultmb1$ddvalue, "\n")
cat("tauint = ", fit$uwerrresultmb1$tauint, "\n")
cat("dtauint = ", fit$uwerrresultmb1$dtauint, "\n")
cat("Wopt = ", fit$uwerrresultmb1$Wopt, "\n")
if(fit$uwerrresultmb1$R>1) {
cat("Qval =", fit$uwerrresultmb1$Qval, "\n")
}
if(fit$no.masses > 1) {
cat("\n--- Autocorrelation analysis for mb1 ---\n")
cat("\nS = ", fit$uwerrresultmb12$S, "\n")
cat("mb12 = ", fit$uwerrresultmb12$value, "\n")
cat("dmb12 = ", fit$uwerrresultmb12$dvalue, "\n")
cat("ddmb12 = ", fit$uwerrresultmb12$ddvalue, "\n")
cat("tauint2 = ", fit$uwerrresultmb12$tauint, "\n")
cat("dtauint2 = ", fit$uwerrresultmb12$dtauint, "\n")
cat("Wopt2 = ", fit$uwerrresultmb12$Wopt, "\n")
if(fit$uwerrresultmb12$R>1) {
cat("Qval =", fit$uwerrresultmb12$Qval, "\n")
}
}
}
if(!is.null(fit$boot)) {
cat("--- Bootstrap analysis ---\n")
cat("---", fit$boot$R, "samples ---\n")
cat(" mean -err +err stderr bias\n")
for(no in 1:fit$no.masses) {
index <- (no-1)*(fit$matrix.size+1)+1
b.ci <- boot.ci(fit$boot, type = c("norm"), index=index)
cat("mb1[",no,"] = ", abs(fit$boot$t0[index]), "(", (b.ci$normal[1,2]-fit$boot$t0[index])/1.96
, ",", -(fit$boot$t0[index]-b.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,index]),
mean(fit$boot$t[,index])-fit$boot$t0[index],"\n")
}
}
if(!is.null(fit$tsboot)) {
cat("\n--- Bootstrap analysis with blocking ---\n")
cat("---", fit$tsboot$R, "samples ---\n")
cat("--- block size", fit$tsboot$l, "---\n")
for(no in 1:fit$no.masses) {
index <- (no-1)*(fit$matrix.size+1)+1
tsb.ci <- boot.ci(fit$tsboot, type = c("norm"), index=index)
cat("mb1[",no,"] = ", fit$tsboot$t0[index], "(", (tsb.ci$normal[1,2]-fit$tsboot$t0[index])/1.96
, ",", -(fit$tsboot$t0[index]-tsb.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,index]),
mean(fit$tsboot$t[,index])-fit$tsboot$t0[index], "\n")
}
}
if(!is.null(fit$variational.masses)) {
cat("\n--- Variational analysis ---\n")
cat("masses:", fit$variational.masses, "\n")
}
}
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