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kinerrmin <- function(kinfits, kinmodel = "SFO", alpha = 0.05)
{
m = kinfits[[kinmodel]]
kindata <- data.frame(t = kinfits[[kinmodel]]$model$t,
parent = kinfits[[kinmodel]]$model$parent)
kindata.means <- aggregate(kindata, list(kindata$t), mean)
kindata.means.mean <- mean(kindata.means$parent, na.rm=TRUE)
n.parms = length(coef(m))
df = length(kindata.means$parent) - n.parms
kindata.means$est <- predict(m, kindata.means)
f <- function(err)
{
(sum((kindata.means$parent - kindata.means$est)^2/((err*kindata.means.mean)^2)) -
qchisq(1 - alpha,df))^2
}
err.min <- optimize(f, c(0.01,0.9))$minimum
return(err.min)
}
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