Fit example with rminuit2_expr

In [2]:

library(rminuit2, quiet=TRUE)

In [4]:

#
# simulate model y = a*exp(-x/b) + k
#

x = seq(0, 1, length.out=31)
y.func = function(x, norm, tau, const) norm*exp(-x/tau) + const

In [5]:

#
# simulate data with Gaussian errors for specific model
# the model includes norm and tau but not const, which is a fixed parameter
#
model.par = c(norm=2.3, tau=0.47)
model.extra.const = 4.7
y.err = 0.01
y = do.call(y.func, c(list(x), model.par, const=model.extra.const)) + rnorm(sd=y.err, n=length(x))

In [6]:

# fit model on data, ask to compute Minos errors too
fit.rc = rminuit2_expr(y ~ norm*exp(-x/tau) + const, c(norm=1, tau=10),
  data=data.frame(x=x, y=y, y.err=y.err), errors=y.err,
  lh="Gaussian", opt="hm", const=model.extra.const)

In [10]:

# chi square / number of degrees of freedom
cat("chi square and number of degrees of freedom")
cbind(chisq=fit.rc$chisq, ndof=fit.rc$ndof)

# fitted parameters and their estimated uncertainties
cat("fit parameters: model, fit result, uncertainty, minos uncertainties\n")
cbind(model.value=model.par, value=fit.rc$par, error=fit.rc$err,
      minos_pos=fit.rc$err_minos_pos, minos_neg=fit.rc$err_minos_neg)

# parameters' correlation matrix
cat("fit parameter correlation matrix\n")
fit.rc$cor

chi square and number of degrees of freedom

chisq	ndof
21.89387	29

fit parameters: model, fit result, uncertainty, minos uncertainties

	model.value	value	error	minos_pos	minos_neg
norm	2.30	2.2942003	0.005197186	0.007353179	-0.007346653
tau	0.47	0.4701774	0.001727393	0.002452698	-0.002433189

fit parameter correlation matrix

	norm	tau
norm	1.0000000	-0.7102571
tau	-0.7102571	1.0000000