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-rw-r--r--man/mkinfit.Rd18
1 files changed, 8 insertions, 10 deletions
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index f7dd7009..85b742e8 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -31,8 +31,8 @@ mkinfit(mkinmod, observed,
quiet = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
error_model = c("const", "obs", "tc"),
- error_model_algorithm = c("d_3", "direct", "twostep", "threestep", "fourstep", "IRLS",
- "OLS"),
+ error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep",
+ "fourstep", "IRLS", "OLS"),
reweight.tol = 1e-8, reweight.max.iter = 10,
trace_parms = FALSE, ...)
}
@@ -175,12 +175,13 @@ mkinfit(mkinmod, observed,
distribution as assumed by this method.
}
\item{error_model_algorithm}{
- If the error model is "const", the error model algorithm is ignored,
- because no special algorithm is needed and unweighted (also known as
- ordinary) least squares fitting (listed as "OLS" in the summary) can be
- applied.
+ If "auto", the selected algorithm depends on the error model.
+ If the error model is "const", nonlinear least squares fitting ("OLS") is
+ selected. If the error model is "obs", iteratively reweighted least squares
+ fitting ("IRLS") is selected. If the error model is "tc", the "d_3"
+ algorithm is selected.
- The default algorithm "d_3" will directly minimize the negative
+ The algorithm "d_3" will directly minimize the negative
log-likelihood and - independently - also use the three step algorithm
described below. The fit with the higher likelihood is returned.
@@ -206,9 +207,6 @@ mkinfit(mkinmod, observed,
parameters and subsequent
optimization of the degradation model using those error model parameters,
until the error model parameters converge.
-
- The algorithm "OLS" (Ordinary Least Squares) is automatically selected when
- the error model is "const" and results in an unweighted least squares fit.
}
\item{reweight.tol}{
Tolerance for the convergence criterion calculated from the error model

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