Nothing
print.cfit <- function(fit) {
summary(fit)
}
summary.cfit <- function(fit) {
kappa <- fit$kappa
mu <- fit$mu
t1 <- fit$t1
t2 <- fit$t2
fit.mass <- abs(fit$fitresult$par[fit$matrix.size+1])
fit.fpi <- 2*kappa*2*mu/sqrt(2)*abs(fit$fitresult$par[1])/sqrt(fit.mass^3)
fit.chisqr <- fit$fitresult$value
fit.dof <- length(fit$fitdata$t)-length(fit$fitresult$par)
cat("mu = ", mu, "\n")
cat("kappa = ", kappa, "\n")
cat("Nr of measurements = ", fit$N, "\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(is.null(fit$uwerrresultmv) && is.null(fit$mv.boot)) {
cat("\nmpi = ", fit.mass, "\n")
cat("fpi = ", fit.fpi, "\n")
if(fit$matrix.size > 2) {
cat("mpcac = ", fit.mass*fit$fitresult$par[3]/fit$fitresult$par[1]/2., "\n")
}
cat("\nP_L = ", fit$fitresult$par[1], "\n")
cat("P_F = ", fit$fitresult$par[2], "\n")
if(fit$matrix.size >2) {
cat("A_L = ", fit$fitresult$par[3], "\n")
cat("A_F = ", fit$fitresult$par[4], "\n")
}
if(fit$matrix.size >4) {
cat("4_L = ", fit$fitresult$par[5], "\n")
cat("4_F = ", fit$fitresult$par[6], "\n")
}
}
if(!is.null(fit$uwerrresultmv)) {
cat("mv = ", abs(fit$fitresult$par[fit$matrix.size+1]), "\n")
cat("4_L = ", fit$fitresult$par[1], "\n")
cat("4_F = ", fit$fitresult$par[2], "\n")
if(fit$matrix.size == 2 && fit$no.masses == 2) {
cat("mv2 = ", abs(fit$fitresult$par[2*(fit$matrix.size+1)]), "\n")
cat("4_L2 = ", fit$fitresult$par[(fit$matrix.size+2)], "\n")
cat("4_F2 = ", fit$fitresult$par[(fit$matrix.size+3)], "\n")
}
if(fit$matrix.size == 4) {
cat("A_L = ", fit$fitresult$par[3], "\n")
cat("A_F = ", fit$fitresult$par[4], "\n")
}
if(fit$matrix.size == 6) {
cat("V_L = ", fit$fitresult$par[5], "\n")
cat("V_F = ", fit$fitresult$par[6], "\n")
}
}
if(!is.null(fit$uwerrresultmpcac)) {
cat("\n--- Autocorrelation analysis for m_pcac ---\n")
cat("\nS = ", fit$uwerrresultmpcac$S, "\n")
cat("mpcac = ", fit$uwerrresultmpcac$value, "\n")
cat("dmpcac = ", fit$uwerrresultmpcac$dvalue, "\n")
cat("ddmpcac = ", fit$uwerrresultmpcac$ddvalue, "\n")
cat("tauint = ", fit$uwerrresultmpcac$tauint, "\n")
cat("dtauint = ", fit$uwerrresultmpcac$dtauint, "\n")
cat("Wopt = ", fit$uwerrresultmpcac$Wopt, "\n")
}
if(!is.null(fit$uwerrresultmps)) {
cat("\n--- Autocorrelation analysis for m_ps ---\n")
cat("\nS = ", fit$uwerrresultmps$S, "\n")
cat("mps = ", fit$uwerrresultmps$value, "\n")
cat("dmps = ", fit$uwerrresultmps$dvalue, "\n")
cat("ddmps = ", fit$uwerrresultmps$ddvalue, "\n")
cat("tauint = ", fit$uwerrresultmps$tauint, "\n")
cat("dtauint = ", fit$uwerrresultmps$dtauint, "\n")
cat("Wopt = ", fit$uwerrresultmps$Wopt, "\n")
}
if(!is.null(fit$uwerrresultmv)) {
cat("\n--- Autocorrelation analysis for m_v ---\n")
cat("\nS = ", fit$uwerrresultmv$S, "\n")
cat("mv = ", fit$uwerrresultmv$value, "\n")
cat("dmv = ", fit$uwerrresultmv$dvalue, "\n")
cat("ddmv = ", fit$uwerrresultmv$ddvalue, "\n")
cat("tauint = ", fit$uwerrresultmv$tauint, "\n")
cat("dtauint = ", fit$uwerrresultmv$dtauint, "\n")
cat("Wopt = ", fit$uwerrresultmv$Wopt, "\n")
if(fit$no.masses > 1) {
cat("\n--- Autocorrelation analysis for m_v2 ---\n")
cat("\nS = ", fit$uwerrresultmv2$S, "\n")
cat("mv2 = ", fit$uwerrresultmv2$value, "\n")
cat("dmv2 = ", fit$uwerrresultmv2$dvalue, "\n")
cat("ddmv2 = ", fit$uwerrresultmv2$ddvalue, "\n")
cat("tauint2 = ", fit$uwerrresultmv2$tauint, "\n")
cat("dtauint2 = ", fit$uwerrresultmv2$dtauint, "\n")
cat("Wopt2 = ", fit$uwerrresultmv2$Wopt, "\n")
}
}
if(!is.null(fit$uwerrresultfps)) {
cat("\n--- Autocorrelation analysis for f_ps ---\n")
cat("\nS = ", fit$uwerrresultfps$S, "\n")
cat("fps = ", fit$uwerrresultfps$value*2*kappa*2*mu/sqrt(2), "\n")
cat("dfps = ", fit$uwerrresultfps$dvalue*2*kappa*2*mu/sqrt(2), "\n")
cat("ddfps = ", fit$uwerrresultfps$ddvalue*2*kappa*2*mu/sqrt(2), "\n")
cat("tauint = ", fit$uwerrresultfps$tauint, "\n")
cat("dtauint = ", fit$uwerrresultfps$dtauint, "\n")
cat("Wopt = ", fit$uwerrresultfps$Wopt, "\n")
}
if(!is.null(fit$boot)) {
cat("--- Bootstrap analysis ---\n")
cat("---", fit$boot$R, "samples ---\n")
cat(" mean -err +err stderr bias\n")
fit$boot.ci <- boot.ci(fit$boot, type = c("norm"), index=1)
cat("mpi = ", fit$boot$t0[1], "(", (fit$boot.ci$normal[1,2]-fit$boot$t0[1])/1.96
, ",", -(fit$boot$t0[1]-fit$boot.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,1]),
mean(fit$boot$t[,1])-fit$boot$t0[1],"\n")
fit$boot.ci <- boot.ci(fit$boot, type = c("norm"), index=2)
cat("fpi = ", fit$boot$t0[2], "(", (fit$boot.ci$normal[1,2]-fit$boot$t0[2])/1.96
, ",", -(fit$boot$t0[2]-fit$boot.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,2]),
mean(fit$boot$t[,2])-fit$boot$t0[2], "\n")
if(fit$matrix.size > 2) {
fit$boot.ci <- boot.ci(fit$boot, type = c("norm"), index=fit$matrix.size+3)
cat("mpcac = ", fit$boot$t0[fit$matrix.size+3], "(", (fit$boot.ci$normal[1,2]-fit$boot$t0[fit$matrix.size+3])/1.96
, ",", -(fit$boot$t0[fit$matrix.size+3]-fit$boot.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,(fit$matrix.size+3)]),
mean(fit$boot$t[,(fit$matrix.size+3)])-fit$boot$t0[fit$matrix.size+3], "\n")
}
fit$boot.ci <- boot.ci(fit$boot, type = c("norm"), index=4)
cat("P_L = ", fit$boot$t0[4], "(", (fit$boot.ci$normal[1,2]-fit$boot$t0[4])/1.96
, ",", -(fit$boot$t0[4]-fit$boot.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,4]),
mean(fit$boot$t[,4])-fit$boot$t0[4], "\n")
fit$boot.ci <- boot.ci(fit$boot, type = c("norm"), index=5)
cat("P_F = ", fit$boot$t0[5], "(", (fit$boot.ci$normal[1,2]-fit$boot$t0[5])/1.96
, ",", -(fit$boot$t0[5]-fit$boot.ci$normal[1,3])/1.96, ")", sd(fit$boot$t[,5]),
mean(fit$boot$t[,5])-fit$boot$t0[5],"\n")
}
if(!is.null(fit$tsboot)) {
cat("\n--- Bootstrap analysis with blocking ---\n")
cat("---", fit$boot$R, "samples ---\n")
cat("--- block size", fit$tsboot$l, "---\n")
fit$tsboot.ci <- boot.ci(fit$tsboot, type = c("norm"), index=1)
cat("mpi = ", fit$tsboot$t0[1], "(", (fit$tsboot.ci$normal[1,2]-fit$tsboot$t0[1])/1.96
, ",", -(fit$tsboot$t0[1]-fit$tsboot.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,1]),
mean(fit$tsboot$t[,1])-fit$tsboot$t0[1], "\n")
fit$tsboot.ci <- boot.ci(fit$tsboot, type = c("norm"), index=2)
cat("fpi = ", fit$tsboot$t0[2], "(", (fit$tsboot.ci$normal[1,2]-fit$tsboot$t0[2])/1.96
, ",", -(fit$tsboot$t0[2]-fit$tsboot.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,2]),
mean(fit$tsboot$t[,2])-fit$tsboot$t0[2], "\n")
if(fit$matrix.size > 2) {
fit$tsboot.ci <- boot.ci(fit$tsboot, type = c("norm"), index=fit$matrix.size+3)
cat("mpcac = ", fit$tsboot$t0[fit$matrix.size+3], "(", (fit$tsboot.ci$normal[1,2]-fit$tsboot$t0[fit$matrix.size+3])/1.96
, ",", -(fit$tsboot$t0[fit$matrix.size+3]-fit$tsboot.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,(fit$matrix.size+3)]),
mean(fit$tsboot$t[,(fit$matrix.size+3)])-fit$tsboot$t0[fit$matrix.size+3], "\n")
}
fit$tsboot.ci <- boot.ci(fit$tsboot, type = c("norm"), index=4)
cat("P_L = ", fit$tsboot$t0[4], "(", (fit$tsboot.ci$normal[1,2]-fit$tsboot$t0[4])/1.96
, ",", -(fit$tsboot$t0[4]-fit$tsboot.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,4]),
mean(fit$tsboot$t[,4])-fit$tsboot$t0[4], "\n")
fit$tsboot.ci <- boot.ci(fit$tsboot, type = c("norm"), index=5)
cat("P_F = ", fit$tsboot$t0[5], "(", (fit$tsboot.ci$normal[1,2]-fit$tsboot$t0[5])/1.96
, ",", -(fit$tsboot$t0[5]-fit$tsboot.ci$normal[1,3])/1.96, ")", sd(fit$tsboot$t[,5]),
mean(fit$tsboot$t[,5])-fit$tsboot$t0[5], "\n")
}
if(!is.null(fit$mv.boot)) {
cat("--- Bootstrap analysis ---\n")
cat("---", fit$mv.boot$R, "samples ---\n")
cat(" mean -err +err stderr bias\n")
for(no in 1:fit$no.masses) {
index <- no*(fit$matrix.size+1)
b.ci <- boot.ci(fit$mv.boot, type = c("norm"), index=index)
cat("mv[",no,"] = ", abs(fit$mv.boot$t0[index]), "(", (b.ci$normal[1,2]-fit$mv.boot$t0[index])/1.96
, ",", -(fit$mv.boot$t0[index]-b.ci$normal[1,3])/1.96, ")", sd(fit$mv.boot$t[,index]),
mean(fit$mv.boot$t[,index])-fit$mv.boot$t0[index],"\n")
}
}
if(!is.null(fit$mv.tsboot)) {
cat("\n--- Bootstrap analysis with blocking ---\n")
cat("---", fit$mv.tsboot$R, "samples ---\n")
cat("--- block size", fit$mv.tsboot$l, "---\n")
for(no in 1:fit$no.masses) {
index <- no*(fit$matrix.size+1)
tsb.ci <- boot.ci(fit$mv.tsboot, type = c("norm"), index=index)
cat("mv[",no,"] = ", fit$mv.tsboot$t0[index], "(", (tsb.ci$normal[1,2]-fit$mv.tsboot$t0[index])/1.96
, ",", -(fit$mv.tsboot$t0[index]-tsb.ci$normal[1,3])/1.96, ")", sd(fit$mv.tsboot$t[,index]),
mean(fit$mv.tsboot$t[,index])-fit$mv.tsboot$t0[index], "\n")
}
}
if(!is.null(fit$variational.masses)) {
cat("\n--- Variational analysis ---\n")
cat("masses:", fit$variational.masses, "\n")
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.